A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

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.

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.

On computing the global time-optimal motions of robotic manipulators in the presence of obstacles

NASA Technical Reports Server (NTRS)

Shiller, Zvi; Dubowsky, Steven

1991-01-01

A method for computing the time-optimal motions of robotic manipulators is presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem is reduced to a search for the time-optimal path in the n-dimensional position space. A small set of near-optimal paths is first efficiently selected from a grid, using a branch and bound search and a series of lower bound estimates on the traveling time along a given path. These paths are further optimized with a local path optimization to yield the global optimal solution. Obstacles are considered by eliminating the collision points from the tessellated space and by adding a penalty function to the motion time in the local optimization. The computational efficiency of the method stems from the reduced dimensionality of the searched spaced and from combining the grid search with a local optimization. The method is demonstrated in several examples for two- and six-degree-of-freedom manipulators with obstacles.

Global optimization methods for engineering design

NASA Technical Reports Server (NTRS)

Arora, Jasbir S.

1990-01-01

The problem is to find a global minimum for the Problem P. Necessary and sufficient conditions are available for local optimality. However, global solution can be assured only under the assumption of convexity of the problem. If the constraint set S is compact and the cost function is continuous on it, existence of a global minimum is guaranteed. However, in view of the fact that no global optimality conditions are available, a global solution can be found only by an exhaustive search to satisfy Inequality. The exhaustive search can be organized in such a way that the entire design space need not be searched for the solution. This way the computational burden is reduced somewhat. It is concluded that zooming algorithm for global optimizations appears to be a good alternative to stochastic methods. More testing is needed; a general, robust, and efficient local minimizer is required. IDESIGN was used in all numerical calculations which is based on a sequential quadratic programming algorithm, and since feasible set keeps on shrinking, a good algorithm to find an initial feasible point is required. Such algorithms need to be developed and evaluated.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Particle swarm optimization and its application in MEG source localization using single time sliced data

NASA Astrophysics Data System (ADS)

Lin, Juan; Liu, Chenglian; Guo, Yongning

2014-10-01

The estimation of neural active sources from the magnetoencephalography (MEG) data is a very critical issue for both clinical neurology and brain functions research. A widely accepted source-modeling technique for MEG involves calculating a set of equivalent current dipoles (ECDs). Depth in the brain is one of difficulties in MEG source localization. Particle swarm optimization(PSO) is widely used to solve various optimization problems. In this paper we discuss its ability and robustness to find the global optimum in different depths of the brain when using single equivalent current dipole (sECD) model and single time sliced data. The results show that PSO is an effective global optimization to MEG source localization when given one dipole in different depths.

Globally optimal trial design for local decision making.

PubMed

Eckermann, Simon; Willan, Andrew R

2009-02-01

Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials. Copyright (c) 2008 John Wiley & Sons, Ltd.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins.

PubMed

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-05

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH.

PubMed

Volk, Jochen; Herrmann, Torsten; Wüthrich, Kurt

2008-07-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.

QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.

PubMed

Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon

2012-01-01

Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins

NASA Astrophysics Data System (ADS)

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-01

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

Optimal design of geodesically stiffened composite cylindrical shells

NASA Technical Reports Server (NTRS)

Gendron, G.; Guerdal, Z.

1992-01-01

An optimization system based on the finite element code Computations Structural Mechanics (CSM) Testbed and the optimization program, Automated Design Synthesis (ADS), is described. The optimization system can be used to obtain minimum-weight designs of composite stiffened structures. Ply thickness, ply orientations, and stiffener heights can be used as design variables. Buckling, displacement, and material failure constraints can be imposed on the design. The system is used to conduct a design study of geodesically stiffened shells. For comparison purposes, optimal designs of unstiffened shells and shells stiffened by rings and stingers are also obtained. Trends in the design of geodesically stiffened shells are identified. An approach to include local stress concentrations during the design optimization process is then presented. The method is based on a global/local analysis technique. It employs spline interpolation functions to determine displacements and rotations from a global model which are used as 'boundary conditions' for the local model. The organization of the strategy in the context of an optimization process is described. The method is validated with an example.

Determination of hyporheic travel time distributions and other parameters from concurrent conservative and reactive tracer tests by local-in-global optimization

NASA Astrophysics Data System (ADS)

Knapp, Julia L. A.; Cirpka, Olaf A.

2017-06-01

The complexity of hyporheic flow paths requires reach-scale models of solute transport in streams that are flexible in their representation of the hyporheic passage. We use a model that couples advective-dispersive in-stream transport to hyporheic exchange with a shape-free distribution of hyporheic travel times. The model also accounts for two-site sorption and transformation of reactive solutes. The coefficients of the model are determined by fitting concurrent stream-tracer tests of conservative (fluorescein) and reactive (resazurin/resorufin) compounds. The flexibility of the shape-free models give rise to multiple local minima of the objective function in parameter estimation, thus requiring global-search algorithms, which is hindered by the large number of parameter values to be estimated. We present a local-in-global optimization approach, in which we use a Markov-Chain Monte Carlo method as global-search method to estimate a set of in-stream and hyporheic parameters. Nested therein, we infer the shape-free distribution of hyporheic travel times by a local Gauss-Newton method. The overall approach is independent of the initial guess and provides the joint posterior distribution of all parameters. We apply the described local-in-global optimization method to recorded tracer breakthrough curves of three consecutive stream sections, and infer section-wise hydraulic parameter distributions to analyze how hyporheic exchange processes differ between the stream sections.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

PubMed

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

PubMed Central

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

NASA Astrophysics Data System (ADS)

Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François

2018-04-01

Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.

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)

Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

PubMed

Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J

2009-01-01

This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.

An Evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions

NASA Astrophysics Data System (ADS)

Butler, Roger A. R.; Slaminka, Edward E.

1992-03-01

The performance of Sniffer—a new global optimization algorithm—is compared with that of Simulated Annealing. Using the number of function evaluations as a measure of efficiency, the new algorithm is shown to be significantly better at finding the global minimum of seven standard test functions. Several of the test functions used have many local minima and very steep walls surrounding the global minimum. Such functions are intended to thwart global minimization algorithms.

PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

PubMed Central

Chen, Shuangqing; Wei, Lixin; Guan, Bing

2018-01-01

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. PMID:29675036

Binary Bees Algorithm - bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem

NASA Astrophysics Data System (ADS)

Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan

2011-11-01

The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

Optimal Design of Grid-Stiffened Panels and Shells With Variable Curvature

NASA Technical Reports Server (NTRS)

Ambur, Damodar R.; Jaunky, Navin

2001-01-01

A design strategy for optimal design of composite grid-stiffened structures with variable curvature 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. Stiffening configuration is herein defined as a design variable that indicates the combination of axial, transverse and diagonal stiffeners in the stiffened panel. The design optimization process is adapted to identify the lightest-weight stiffening configuration and stiffener spacing for grid-stiffened composite panels given the overall panel dimensions. in-plane design loads, material properties. and boundary conditions of the grid-stiffened panel or shell.

Adaptation, Growth, and Resilience in Biological Distribution Networks

NASA Astrophysics Data System (ADS)

Ronellenfitsch, Henrik; Katifori, Eleni

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. 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 plant and animal vasculature. In addition, we show how the incorporation of spatially collective fluctuating sources yields a minimal model of realistic reticulation in distribution networks and thus resilience against damage.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

NASA Astrophysics Data System (ADS)

Wang, Zhongfeng; Zhang, Xuyang; Hu, Bo; Liu, Jun; Li, Ligang; Gu, Yongqiang; Zhou, Bowen

2018-02-01

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

Optimal Design of General Stiffened Composite Circular Cylinders for Global Buckling with Strength Constraints

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.

Optimal Design of General Stiffened Composite Circular Cylinders for Global Buckling with Strength Constraints

NASA Technical Reports Server (NTRS)

Jaunky, Navin; Knight, Norman F., Jr.; Ambur, Damodar R.

1998-01-01

A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and, strength constraints is developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory is used for the global analysis. Local buckling of skin segments are assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments are also assessed. Constraints on the axial membrane strain in the skin and stiffener segments are imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study are the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence, and stiffening configuration, where herein 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 is 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 configuration.

Optimal Sensor Layouts in Underwater Locomotory Systems

NASA Astrophysics Data System (ADS)

Colvert, Brendan; Kanso, Eva

2015-11-01

Retrieving and understanding global flow characteristics from local sensory measurements is a challenging but extremely relevant problem in fields such as defense, robotics, and biomimetics. It is an inverse problem in that the goal is to translate local information into global flow properties. In this talk we present techniques for optimization of sensory layouts within the context of an idealized underwater locomotory system. Using techniques from fluid mechanics and control theory, we show that, under certain conditions, local measurements can inform the submerged body about its orientation relative to the ambient flow, and allow it to recognize local properties of shear flows. We conclude by commenting on the relevance of these findings to underwater navigation in engineered systems and live organisms.

SGO: A fast engine for ab initio atomic structure global optimization by differential evolution

NASA Astrophysics Data System (ADS)

Chen, Zhanghui; Jia, Weile; Jiang, Xiangwei; Li, Shu-Shen; Wang, Lin-Wang

2017-10-01

As the high throughout calculations and material genome approaches become more and more popular in material science, the search for optimal ways to predict atomic global minimum structure is a high research priority. This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and a plane-wave density functional theory code running on GPU machines. The purpose is to show what can be achieved by combining the superior algorithms at the different levels of the searching scheme. SGO can search the global-minimum configurations of crystals, two-dimensional materials and quantum clusters without prior symmetry restriction in a relatively short time (half or several hours for systems with less than 25 atoms), thus making such a task a routine calculation. Comparisons with other existing methods such as minima hopping and genetic algorithm are provided. One motivation of our study is to investigate the properties of magnetic systems in different phases. The SGO engine is capable of surveying the local minima surrounding the global minimum, which provides the information for the overall energy landscape of a given system. Using this capability we have found several new configurations for testing systems, explored their energy landscape, and demonstrated that the magnetic moment of metal clusters fluctuates strongly in different local minima.

Optimal global value of information trials: better aligning manufacturer and decision maker interests and enabling feasible risk sharing.

PubMed

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.

Multidisciplinary Optimization and Damage Tolerance of Stiffened Structures

NASA Astrophysics Data System (ADS)

Jrad, Mohamed

THE structural optimization of a cantilever aircraft wing with curvilinear spars and ribs and stiffeners is described. For the optimization of a complex wing, a common strategy is to divide the optimization procedure into two subsystems: the global wing optimization which optimizes the geometry of spars, ribs and wing skins; and the local panel optimization which optimizes the design variables of local panels bordered by spars and ribs. The stiffeners are placed on the local panels to increase the stiffness and buckling resistance. During the local panel optimization, the stress information is taken from the global model as a displacement boundary condition on the panel edges using the so-called "Global-Local Approach". Particle swarm optimization is used in the integration of global/local optimization to optimize the SpaRibs. Parallel computing approach has been developed in the Python programming language to reduce the CPU time. The license cycle-check method and memory self-adjustment method are two approaches that have been applied in the parallel framework in order to optimize the use of the resources by reducing the license and memory limitations and making the code robust. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing, which proves the methodology's application scaling with medium fidelity FEM analysis. The structural weight of the wing has been reduced by 42% and the parallel implementation allowed a reduction in the CPU time by 89%. The aforementioned Global-Local Approach is investigated and applied to a composite panel with crack at its center. Because of composite laminates' heterogeneity, an accurate analysis of these requires very high time and storage space. A possible alternative to reduce the computational complexity is the global-local analysis which involves an approximate analysis of the whole structure followed by a detailed analysis of a significantly smaller region of interest. Buckling analysis of a composite panel with attached longitudinal stiffeners under compressive loads is performed using Ritz method with trigonometric functions. Results are then compared to those from Abaqus FEA for different shell elements. The case of composite panel with one, two, and three stiffeners is investigated. The effect of the distance between the stiffeners on the buckling load is also studied. The variation of the buckling load and buckling modes with the stiffeners' height is investigated. It is shown that there is an optimum value of stiffeners' height beyond which the structural response of the stiffened panel is not improved and the buckling load does not increase. Furthermore, there exist different critical values of stiffener's height at which the buckling mode of the structure changes. Next, buckling analysis of a composite panel with two straight stiffeners and a crack at the center is performed. Finally, buckling analysis of a composite panel with curvilinear stiffeners and a crack at the center is also conducted. Results show that panels with a larger crack have a reduced buckling load and that the buckling load decreases slightly when using higher order 2D shell FEM elements. A damage tolerance framework, EBF3PanelOpt, has been developed to design and analyze curvilinearly stiffened panels. The framework is written with the scripting language Python and it interacts with the commercial software MSC. Patran (for geometry and mesh creation), MSC. Nastran (for finite element analysis), and MSC. Marc (for damage tolerance analysis). The crack location is set to the location of the maximum value of the major principal stress while its orientation is set normal to the major principal axis direction. The effective stress intensity factor is calculated using the Virtual Crack Closure Technique and compared to the fracture toughness of the material in order to decide whether the crack will expand or not. The ratio of these two quantities is used as a constraint, along with the buckling factor, Kreisselmeier and Steinhauser criteria, and crippling factor. The EBF3PanelOpt framework is integrated within a two-step Particle Swarm Optimization in order to minimize the weight of the panel while satisfying the aforementioned constraints and using all the shape and thickness parameters as design variables. The result of the PSO is used then as an initial guess for the Gradient Based Optimization using only the thickness parameters as design variables and employing VisualDOC. Stiffened panel with two curvilinear stiffeners is optimized for two load cases. In both cases, significant reduction has been made for the panel's weight.

GPU color space conversion

NASA Astrophysics Data System (ADS)

Chase, Patrick; Vondran, Gary

2011-01-01

Tetrahedral interpolation is commonly used to implement continuous color space conversions from sparse 3D and 4D lookup tables. We investigate the implementation and optimization of tetrahedral interpolation algorithms for GPUs, and compare to the best known CPU implementations as well as to a well known GPU-based trilinear implementation. We show that a 500 NVIDIA GTX-580 GPU is 3x faster than a 1000 Intel Core i7 980X CPU for 3D interpolation, and 9x faster for 4D interpolation. Performance-relevant GPU attributes are explored including thread scheduling, local memory characteristics, global memory hierarchy, and cache behaviors. We consider existing tetrahedral interpolation algorithms and tune based on the structure and branching capabilities of current GPUs. Global memory performance is improved by reordering and expanding the lookup table to ensure optimal access behaviors. Per multiprocessor local memory is exploited to implement optimally coalesced global memory accesses, and local memory addressing is optimized to minimize bank conflicts. We explore the impacts of lookup table density upon computation and memory access costs. Also presented are CPU-based 3D and 4D interpolators, using SSE vector operations that are faster than any previously published solution.

Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments.

PubMed

Daily, Jeff

2016-02-10

Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations. A faster intra-sequence local pairwise alignment implementation is described and benchmarked, including new global and semi-global variants. Using a 375 residue query sequence a speed of 136 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon E5-2670 24-core processor system, the highest reported for an implementation based on Farrar's 'striped' approach. Rognes's SWIPE optimal database search application is still generally the fastest available at 1.2 to at best 2.4 times faster than Parasail for sequences shorter than 500 amino acids. However, Parasail was faster for longer sequences. For global alignments, Parasail's prefix scan implementation is generally the fastest, faster even than Farrar's 'striped' approach, however the opal library is faster for single-threaded applications. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license. Applications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

A multilevel control system for the large space telescope. [numerical analysis/optimal control

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Sundareshan, S. K.; Vukcevic, M. B.

1975-01-01

A multilevel scheme was proposed for control of Large Space Telescope (LST) modeled by a three-axis-six-order nonlinear equation. Local controllers were used on the subsystem level to stabilize motions corresponding to the three axes. Global controllers were applied to reduce (and sometimes nullify) the interactions among the subsystems. A multilevel optimization method was developed whereby local quadratic optimizations were performed on the subsystem level, and global control was again used to reduce (nullify) the effect of interactions. The multilevel stabilization and optimization methods are presented as general tools for design and then used in the design of the LST Control System. The methods are entirely computerized, so that they can accommodate higher order LST models with both conceptual and numerical advantages over standard straightforward design techniques.

Local Feature Selection for Data Classification.

PubMed

Armanfard, Narges; Reilly, James P; Komeili, Majid

2016-06-01

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

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 carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

NASA Astrophysics Data System (ADS)

Zhang, Shupeng; Yi, Xue; Zheng, Xiaogu; Chen, Zhuoqi; Dan, Bo; Zhang, Xuanze

2014-11-01

In this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie-Ames-Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002-2008 period is 2.97 ± 1.1 PgC yr-1, and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr-1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.

A survey of compiler optimization techniques

NASA Technical Reports Server (NTRS)

Schneck, P. B.

1972-01-01

Major optimization techniques of compilers are described and grouped into three categories: machine dependent, architecture dependent, and architecture independent. Machine-dependent optimizations tend to be local and are performed upon short spans of generated code by using particular properties of an instruction set to reduce the time or space required by a program. Architecture-dependent optimizations are global and are performed while generating code. These optimizations consider the structure of a computer, but not its detailed instruction set. Architecture independent optimizations are also global but are based on analysis of the program flow graph and the dependencies among statements of source program. A conceptual review of a universal optimizer that performs architecture-independent optimizations at source-code level is also presented.

Optimal resource states for local state discrimination

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Somshubhro; Halder, Saronath; Nathanson, Michael

2018-02-01

We study the problem of locally distinguishing pure quantum states using shared entanglement as a resource. For a given set of locally indistinguishable states, we define a resource state to be useful if it can enhance local distinguishability and optimal if it can distinguish the states as well as global measurements and is also minimal with respect to a partial ordering defined by entanglement and dimension. We present examples of useful resources and show that an entangled state need not be useful for distinguishing a given set of states. We obtain optimal resources with explicit local protocols to distinguish multipartite Greenberger-Horne-Zeilinger and graph states and also show that a maximally entangled state is an optimal resource under one-way local operations and classical communication to distinguish any bipartite orthonormal basis which contains at least one entangled state of full Schmidt rank.

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Visualizing and improving the robustness of phase retrieval algorithms

DOE PAGES

Tripathi, Ashish; Leyffer, Sven; Munson, Todd; ...

2015-06-01

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Visualizing and improving the robustness of phase retrieval algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tripathi, Ashish; Leyffer, Sven; Munson, Todd

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Flow of Funds Modeling for Localized Financial Markets: An Application of Spatial Price and Allocation Activity Analysis Models.

DTIC Science & Technology

1981-01-01

on modeling the managerial aspects of the firm. The second has been the application to economic theory led by ...individual portfolio optimization problems which were embedded in a larger global optimization problem. In the global problem, portfolios were linked by market ...demand quantities or be given by linear demand relationships. As in~ the source markets , the model

Optimal Control via Self-Generated Stochasticity

NASA Technical Reports Server (NTRS)

Zak, Michail

2011-01-01

The problem of global maxima of functionals has been examined. Mathematical roots of local maxima are the same as those for a much simpler problem of finding global maximum of a multi-dimensional function. The second problem is instability even if an optimal trajectory is found, there is no guarantee that it is stable. As a result, a fundamentally new approach is introduced to optimal control based upon two new ideas. The first idea is to represent the functional to be maximized as a limit of a probability density governed by the appropriately selected Liouville equation. Then, the corresponding ordinary differential equations (ODEs) become stochastic, and that sample of the solution that has the largest value will have the highest probability to appear in ODE simulation. The main advantages of the stochastic approach are that it is not sensitive to local maxima, the function to be maximized must be only integrable but not necessarily differentiable, and global equality and inequality constraints do not cause any significant obstacles. The second idea is to remove possible instability of the optimal solution by equipping the control system with a self-stabilizing device. The applications of the proposed methodology will optimize the performance of NASA spacecraft, as well as robot performance.

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE PAGES

Graf, Peter A.; Billups, Stephen

2017-07-24

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graf, Peter A.; Billups, Stephen

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

Global Education: From Thought to Action. The 1991 ASCD Yearbook.

ERIC Educational Resources Information Center

Tye, Kenneth A., Ed.

Viewed as a social movement for change, the global education movement calls for the infusion of a global perspective into all curriculum areas. Two assumptions of global education include the view of the individual school as the optimal unit for change efforts, and the importance of local teacher and school action for lasting school improvement.…

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Utility of coupling nonlinear optimization methods with numerical modeling software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murphy, M.J.

1996-08-05

Results of using GLO (Global Local Optimizer), a general purpose nonlinear optimization software package for investigating multi-parameter problems in science and engineering is discussed. The package consists of the modular optimization control system (GLO), a graphical user interface (GLO-GUI), a pre-processor (GLO-PUT), a post-processor (GLO-GET), and nonlinear optimization software modules, GLOBAL & LOCAL. GLO is designed for controlling and easy coupling to any scientific software application. GLO runs the optimization module and scientific software application in an iterative loop. At each iteration, the optimization module defines new values for the set of parameters being optimized. GLO-PUT inserts the new parametermore » values into the input file of the scientific application. GLO runs the application with the new parameter values. GLO-GET determines the value of the objective function by extracting the results of the analysis and comparing to the desired result. GLO continues to run the scientific application over and over until it finds the ``best`` set of parameters by minimizing (or maximizing) the objective function. An example problem showing the optimization of material model is presented (Taylor cylinder impact test).« less

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).

Optimal Network Modularity for Information Diffusion

NASA Astrophysics Data System (ADS)

Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol

2014-08-01

We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.

The Analytic Methods of Operations Research

DTIC Science & Technology

1977-01-01

stock market behavior (Fama, 1970), but few other applications . A 2*1 - --- 41 12. QUEUEING THEORY The study of congestion in service...Behavior," by T. von Neumann and 0. MHrgenstern, and an esoteric j - 2 paperbrtk by Charnes. Cooper, and Henderson on the optimal mixing of peanuKs and...2nd-order conditions, then i X is also globally optimal . This enables one to use local exploration to lead to the global

An efficient and practical approach to obtain a better optimum solution for structural optimization

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu; Huang, Jyun-Hao

2013-08-01

For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.

Rock climbing: A local-global algorithm to compute minimum energy and minimum free energy pathways.

PubMed

Templeton, Clark; Chen, Szu-Hua; Fathizadeh, Arman; Elber, Ron

2017-10-21

The calculation of minimum energy or minimum free energy paths is an important step in the quantitative and qualitative studies of chemical and physical processes. The computations of these coordinates present a significant challenge and have attracted considerable theoretical and computational interest. Here we present a new local-global approach to study reaction coordinates, based on a gradual optimization of an action. Like other global algorithms, it provides a path between known reactants and products, but it uses a local algorithm to extend the current path in small steps. The local-global approach does not require an initial guess to the path, a major challenge for global pathway finders. Finally, it provides an exact answer (the steepest descent path) at the end of the calculations. Numerical examples are provided for the Mueller potential and for a conformational transition in a solvated ring system.

Well-conditioning global-local analysis using stable generalized/extended finite element method for linear elastic fracture mechanics

NASA Astrophysics Data System (ADS)

Malekan, Mohammad; Barros, Felicio Bruzzi

2016-11-01

Using the locally-enriched strategy to enrich a small/local part of the problem by generalized/extended finite element method (G/XFEM) leads to non-optimal convergence rate and ill-conditioning system of equations due to presence of blending elements. The local enrichment can be chosen from polynomial, singular, branch or numerical types. The so-called stable version of G/XFEM method provides a well-conditioning approach when only singular functions are used in the blending elements. This paper combines numeric enrichment functions obtained from global-local G/XFEM method with the polynomial enrichment along with a well-conditioning approach, stable G/XFEM, in order to show the robustness and effectiveness of the approach. In global-local G/XFEM, the enrichment functions are constructed numerically from the solution of a local problem. Furthermore, several enrichment strategies are adopted along with the global-local enrichment. The results obtained with these enrichments strategies are discussed in detail, considering convergence rate in strain energy, growth rate of condition number, and computational processing. Numerical experiments show that using geometrical enrichment along with stable G/XFEM for global-local strategy improves the convergence rate and the conditioning of the problem. In addition, results shows that using polynomial enrichment for global problem simultaneously with global-local enrichments lead to ill-conditioned system matrices and bad convergence rate.

Methodology of Numerical Optimization for Orbital Parameters of Binary Systems

NASA Astrophysics Data System (ADS)

Araya, I.; Curé, M.

2010-02-01

The use of a numerical method of maximization (or minimization) in optimization processes allows us to obtain a great amount of solutions. Therefore, we can find a global maximum or minimum of the problem, but this is only possible if we used a suitable methodology. To obtain the global optimum values, we use the genetic algorithm called PIKAIA (P. Charbonneau) and other four algorithms implemented in Mathematica. We demonstrate that derived orbital parameters of binary systems published in some papers, based on radial velocity measurements, are local minimum instead of global ones.

Methanol clusters (CH3OH)n: putative global minimum-energy structures from model potentials and dispersion-corrected density functional theory.

PubMed

Kazachenko, Sergey; Bulusu, Satya; Thakkar, Ajit J

2013-06-14

Putative global minima are reported for methanol clusters (CH3OH)n with n ≤ 15. The predictions are based on global optimization of three intermolecular potential energy models followed by local optimization and single-point energy calculations using two variants of dispersion-corrected density functional theory. Recurring structural motifs include folded and/or twisted rings, folded rings with a short branch, and stacked rings. Many of the larger structures are stabilized by weak C-H···O bonds.

Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm

NASA Astrophysics Data System (ADS)

Yi, Jin; Li, Xinyu; Xiao, Mi; Xu, Junnan; Zhang, Lin

2017-01-01

Engineering design often involves different types of simulation, which results in expensive computational costs. Variable fidelity approximation-based design optimization approaches can realize effective simulation and efficiency optimization of the design space using approximation models with different levels of fidelity and have been widely used in different fields. As the foundations of variable fidelity approximation models, the selection of sample points of variable-fidelity approximation, called nested designs, is essential. In this article a novel nested maximin Latin hypercube design is constructed based on successive local enumeration and a modified novel global harmony search algorithm. In the proposed nested designs, successive local enumeration is employed to select sample points for a low-fidelity model, whereas the modified novel global harmony search algorithm is employed to select sample points for a high-fidelity model. A comparative study with multiple criteria and an engineering application are employed to verify the efficiency of the proposed nested designs approach.

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.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

Optimal control to modelling motorcycle rider steering: local versus global coordinate systems in rider preview

NASA Astrophysics Data System (ADS)

Rowell, S.; Popov, A. A.; Meijaard, J. P.

2010-04-01

The response of a motorcycle is heavily dependent on the rider's control actions, and consequently a means of replicating the rider's behaviour provides an important extension to motorcycle dynamics. The primary objective here is to develop effective path-following simulations and to understand how riders control motorcycles. Optimal control theory is applied to the tracking of roadway by a motorcycle, using a non-linear motorcycle model operating in free control by steering torque input. A path-following controller with road preview is designed by minimising tracking errors and control effort. Tight controls with high weightings on performance and loose controls with high weightings on control power are defined. Special attention is paid to the modelling of multipoint preview in local and global coordinate systems. The controller model is simulated over a standard single lane-change manoeuvre. It is argued that the local coordinates point of view is more representative of the way that a human rider operates and interprets information. The simulations suggest that for accurate path following, using optimal control, the problem must be solved by the local coordinates approach in order to achieve accurate results with short preview horizons. Furthermore, some weaknesses of the optimal control approach are highlighted here.

Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

NASA Astrophysics Data System (ADS)

Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping

2017-09-01

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

A connectionist model for diagnostic problem solving

NASA Technical Reports Server (NTRS)

Peng, Yun; Reggia, James A.

1989-01-01

A competition-based connectionist model for solving diagnostic problems is described. The problems considered are computationally difficult in that (1) multiple disorders may occur simultaneously and (2) a global optimum in the space exponential to the total number of possible disorders is sought as a solution. The diagnostic problem is treated as a nonlinear optimization problem, and global optimization criteria are decomposed into local criteria governing node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each individual manifestation, yet complement each other to account for all manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested, and the decomposition plus competition plus resettling approach yielded very high accuracy.

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

NASA Astrophysics Data System (ADS)

Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

2018-06-01

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

Analysis and Optimization of Building Energy Consumption

NASA Astrophysics Data System (ADS)

Chuah, Jun Wei

Energy is one of the most important resources required by modern human society. In 2010, energy expenditures represented 10% of global gross domestic product (GDP). By 2035, global energy consumption is expected to increase by more than 50% from current levels. The increased pace of global energy consumption leads to significant environmental and socioeconomic issues: (i) carbon emissions, from the burning of fossil fuels for energy, contribute to global warming, and (ii) increased energy expenditures lead to reduced standard of living. Efficient use of energy, through energy conservation measures, is an important step toward mitigating these effects. Residential and commercial buildings represent a prime target for energy conservation, comprising 21% of global energy consumption and 40% of the total energy consumption in the United States. This thesis describes techniques for the analysis and optimization of building energy consumption. The thesis focuses on building retrofits and building energy simulation as key areas in building energy optimization and analysis. The thesis first discusses and evaluates building-level renewable energy generation as a solution toward building energy optimization. The thesis next describes a novel heating system, called localized heating. Under localized heating, building occupants are heated individually by directed radiant heaters, resulting in a considerably reduced heated space and significant heating energy savings. To support localized heating, a minimally-intrusive indoor occupant positioning system is described. The thesis then discusses occupant-level sensing (OLS) as the next frontier in building energy optimization. OLS captures the exact environmental conditions faced by each building occupant, using sensors that are carried by all building occupants. The information provided by OLS enables fine-grained optimization for unprecedented levels of energy efficiency and occupant comfort. The thesis also describes a retrofit-oriented building energy simulator, ROBESim, that natively supports building retrofits. ROBESim extends existing building energy simulators by providing a platform for the analysis of novel retrofits, in addition to simulating existing retrofits. Using ROBESim, retrofits can be automatically applied to buildings, obviating the need for users to manually update building characteristics for comparisons between different building retrofits. ROBESim also includes several ease-of-use enhancements to support users of all experience levels.

Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

PubMed

Zhao, Meng; Ding, Baocang

2015-03-01

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem

DOE PAGES

Stefanescu, Razvan; Schmidt, Kathleen; Hite, Jason; ...

2016-12-12

In this paper, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 × 180 m block of an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Owing to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms composed of mixed optimization techniques. For global optimization, we consider simulated annealing, particlemore » swarm, and genetic algorithm, which rely solely on objective function evaluations; that is, they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic implicit filtering method, which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques, combining global optimization and implicit filtering address, difficulties associated with the non-smooth response, and their performances, are shown to significantly decrease the computational time over the global optimization methods. To quantify uncertainties associated with the source location and intensity, we employ the delayed rejection adaptive Metropolis and DiffeRential Evolution Adaptive Metropolis algorithms. Finally, marginal densities of the source properties are obtained, and the means of the chains compare accurately with the estimates produced by the hybrid algorithms.« less

Topology Optimization of an Aircraft Wing

DTIC Science & Technology

2015-06-11

Fraction VWT Virtual Wind Tunnel xvi TOPOLOGY OPTIMIZATION OF AN AIRCRAFT WING I. Introduction 1.1 Background Current aircraft wing design , which...ware in order to optimize the design of individual spars and wing-box structures for large commercial aircraft . They considered a hybrid global/local...weight in an aircraft by eliminating unnecessary material. An optimized approach has the potential to streamline the design process by allowing a

The Tunneling Method for Global Optimization in Multidimensional Scaling.

ERIC Educational Resources Information Center

Groenen, Patrick J. F.; Heiser, Willem J.

1996-01-01

A tunneling method for global minimization in multidimensional scaling is introduced and adjusted for multidimensional scaling with general Minkowski distances. The method alternates a local search step with a tunneling step in which a different configuration is sought with the same STRESS implementation. (SLD)

Fundamental resource-allocating model in colleges and universities based on Immune Clone Algorithms

NASA Astrophysics Data System (ADS)

Ye, Mengdie

2017-05-01

In this thesis we will seek the combination of antibodies and antigens converted from the optimal course arrangement and make an analogy with Immune Clone Algorithms. According to the character of the Algorithms, we apply clone, clone gene and clone selection to arrange courses. Clone operator can combine evolutionary search and random search, global search and local search. By cloning and clone mutating candidate solutions, we can find the global optimal solution quickly.

Effect of local minima on adiabatic quantum optimization.

PubMed

Amin, M H S

2008-04-04

We present a perturbative method to estimate the spectral gap for adiabatic quantum optimization, based on the structure of the energy levels in the problem Hamiltonian. We show that, for problems that have an exponentially large number of local minima close to the global minimum, the gap becomes exponentially small making the computation time exponentially long. The quantum advantage of adiabatic quantum computation may then be accessed only via the local adiabatic evolution, which requires phase coherence throughout the evolution and knowledge of the spectrum. Such problems, therefore, are not suitable for adiabatic quantum computation.

Bifurcation Analysis and Optimal Harvesting of a Delayed Predator-Prey Model

NASA Astrophysics Data System (ADS)

Tchinda Mouofo, P.; Djidjou Demasse, R.; Tewa, J. J.; Aziz-Alaoui, M. A.

A delay predator-prey model is formulated with continuous threshold prey harvesting and Holling response function of type III. Global qualitative and bifurcation analyses are combined to determine the global dynamics of the model. The positive invariance of the non-negative orthant is proved and the uniform boundedness of the trajectories. Stability of equilibria is investigated and the existence of some local bifurcations is established: saddle-node bifurcation, Hopf bifurcation. We use optimal control theory to provide the correct approach to natural resource management. Results are also obtained for optimal harvesting. Numerical simulations are given to illustrate the results.

Coastal Adaptation Planning for Sea Level Rise and Extremes: A Global Model for Adaptation Decision-making at the Local Level Given Uncertain Climate Projections

NASA Astrophysics Data System (ADS)

Turner, D.

2014-12-01

Understanding the potential economic and physical impacts of climate change on coastal resources involves evaluating a number of distinct adaptive responses. This paper presents a tool for such analysis, a spatially-disaggregated optimization model for adaptation to sea level rise (SLR) and storm surge, the Coastal Impact and Adaptation Model (CIAM). This decision-making framework fills a gap between very detailed studies of specific locations and overly aggregate global analyses. While CIAM is global in scope, the optimal adaptation strategy is determined at the local level, evaluating over 12,000 coastal segments as described in the DIVA database (Vafeidis et al. 2006). The decision to pursue a given adaptation measure depends on local socioeconomic factors like income, population, and land values and how they develop over time, relative to the magnitude of potential coastal impacts, based on geophysical attributes like inundation zones and storm surge. For example, the model's decision to protect or retreat considers the costs of constructing and maintaining coastal defenses versus those of relocating people and capital to minimize damages from land inundation and coastal storms. Uncertain storm surge events are modeled with a generalized extreme value distribution calibrated to data on local surge extremes. Adaptation is optimized for the near-term outlook, in an "act then learn then act" framework that is repeated over the model time horizon. This framework allows the adaptation strategy to be flexibly updated, reflecting the process of iterative risk management. CIAM provides new estimates of the economic costs of SLR; moreover, these detailed results can be compactly represented in a set of adaptation and damage functions for use in integrated assessment models. Alongside the optimal result, CIAM evaluates suboptimal cases and finds that global costs could increase by an order of magnitude, illustrating the importance of adaptive capacity and coastal policy.

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

PubMed

Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

2015-01-01

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Concurrent optimization of material spatial distribution and material anisotropy repartition for two-dimensional structures

NASA Astrophysics Data System (ADS)

Ranaivomiarana, Narindra; Irisarri, François-Xavier; Bettebghor, Dimitri; Desmorat, Boris

2018-04-01

An optimization methodology to find concurrently material spatial distribution and material anisotropy repartition is proposed for orthotropic, linear and elastic two-dimensional membrane structures. The shape of the structure is parameterized by a density variable that determines the presence or absence of material. The polar method is used to parameterize a general orthotropic material by its elasticity tensor invariants by change of frame. A global structural stiffness maximization problem written as a compliance minimization problem is treated, and a volume constraint is applied. The compliance minimization can be put into a double minimization of complementary energy. An extension of the alternate directions algorithm is proposed to solve the double minimization problem. The algorithm iterates between local minimizations in each element of the structure and global minimizations. Thanks to the polar method, the local minimizations are solved explicitly providing analytical solutions. The global minimizations are performed with finite element calculations. The method is shown to be straightforward and efficient. Concurrent optimization of density and anisotropy distribution of a cantilever beam and a bridge are presented.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks

PubMed Central

Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A.

2012-01-01

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R0, does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only. PMID:21653570

Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks.

PubMed

Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A

2012-01-07

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only.

Optimization of composite box-beam structures including effects of subcomponent interactions

NASA Technical Reports Server (NTRS)

Ragon, Scott A.; Guerdal, Zafer; Starnes, James H., Jr.

1995-01-01

Minimum mass designs are obtained for a simple box beam structure subject to bending, torque and combined bending/torque load cases. These designs are obtained subject to point strain and linear buckling constraints. The present work differs from previous efforts in that special attention is payed to including the effects of subcomponent panel interaction in the optimal design process. Two different approaches are used to impose the buckling constraints. When the global approach is used, buckling constraints are imposed on the global structure via a linear eigenvalue analysis. This approach allows the subcomponent panels to interact in a realistic manner. The results obtained using this approach are compared to results obtained using a traditional, less expensive approach, called the local approach. When the local approach is used, in-plane loads are extracted from the global model and used to impose buckling constraints on each subcomponent panel individually. In the global cases, it is found that there can be significant interaction between skin, spar, and rib design variables. This coupling is weak or nonexistent in the local designs. It is determined that weight savings of up to 7% may be obtained by using the global approach instead of the local approach to design these structures. Several of the designs obtained using the linear buckling analysis are subjected to a geometrically nonlinear analysis. For the designs which were subjected to bending loads, the innermost rib panel begins to collapse at less than half the intended design load and in a mode different from that predicted by linear analysis. The discrepancy between the predicted linear and nonlinear responses is attributed to the effects of the nonlinear rib crushing load, and the parameter which controls this rib collapse failure mode is shown to be the rib thickness. The rib collapse failure mode may be avoided by increasing the rib thickness above the value obtained from the (linear analysis based) optimizer. It is concluded that it would be necessary to include geometric nonlinearities in the design optimization process if the true optimum in this case were to be found.

Inference of Stochastic Nonlinear Oscillators with Applications to Physiological Problems

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry G.

2004-01-01

A new method of inferencing of coupled stochastic nonlinear oscillators is described. The technique does not require extensive global optimization, provides optimal compensation for noise-induced errors and is robust in a broad range of dynamical models. We illustrate the main ideas of the technique by inferencing a model of five globally and locally coupled noisy oscillators. Specific modifications of the technique for inferencing hidden degrees of freedom of coupled nonlinear oscillators is discussed in the context of physiological applications.

Parameterizing sorption isotherms using a hybrid global-local fitting procedure.

PubMed

Matott, L Shawn; Singh, Anshuman; Rabideau, Alan J

2017-05-01

Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

NASA Astrophysics Data System (ADS)

Khogeer, Ahmed Sirag

2005-11-01

Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not follow the same trends as either global or local single objective optimums. Catastrophic failure effects on refinery operations and on local objectives are more significant than environmental objective effects, and changes in the capacity or the local objectives follow a discrete behavioral pattern, in contrast to environmental objective cases in which the effects are smoother. (Abstract shortened by UMI.)

Optimal Foraging in Semantic Memory

ERIC Educational Resources Information Center

Hills, Thomas T.; Jones, Michael N.; Todd, Peter M.

2012-01-01

Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to optimal foraging strategies seen for spatial foraging? Results from a number of fields suggest these possibilities, including the shared…

Optimal Design of a Two-Layered Elastic Strip Subjected to Transient Loading

DTIC Science & Technology

2005-05-01

M.J., 1999. GLO––global local optimizer users manual. Report UCRL -MA-133858, Energetic Materials Center, Lawrence Livermore National Laboratory...3 DARPA L CHRISTODOULOU W COBLENZ S WAX 3701 N FAIRFAX DR ARLINGTON VA 22203-1714 1 DIRECTOR US ARMY RESEARCH LAB AMSRL CS

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.

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

NASA Astrophysics Data System (ADS)

Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

2016-12-01

The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

Multilevel algorithms for nonlinear optimization

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

PubMed Central

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment. PMID:29181020

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.

PubMed

Nie, Xiaohua; Wang, Wei; Nie, Haoyao

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Hybrid region merging method for segmentation of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi; Wang, Jiangeng; Wang, Zuo

2014-12-01

Image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM.

Attending Globally or Locally: Incidental Learning of Optimal Visual Attention Allocation

ERIC Educational Resources Information Center

Beck, Melissa R.; Goldstein, Rebecca R.; van Lamsweerde, Amanda E.; Ericson, Justin M.

2018-01-01

Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF)…

Policy-Relevant Nonconvexities in the Production of Multiple Forest Benefits?

Treesearch

Stephen K. Swallow; Peter J. Parks; David N. Wear

1990-01-01

This paper challenges common assumptions about convexity in forest rotation models which optimize timber plus nontimber benefits. If a local optimum occurs earlier than the globally optimal age, policy based on marginal incentives may achieve suboptimal results. Policy-relevant nonconvexities are more likely if (i) nontimber benefits dominate for young stands while...

New Method of Calibrating IRT Models.

ERIC Educational Resources Information Center

Jiang, Hai; Tang, K. Linda

This discussion of new methods for calibrating item response theory (IRT) models looks into new optimization procedures, such as the Genetic Algorithm (GA) to improve on the use of the Newton-Raphson procedure. The advantages of using a global optimization procedure like GA is that this kind of procedure is not easily affected by local optima and…

Adaptive self-organization of Bali's ancient rice terraces.

PubMed

Lansing, J Stephen; Thurner, Stefan; Chung, Ning Ning; Coudurier-Curveur, Aurélie; Karakaş, Çağil; Fesenmyer, Kurt A; Chew, Lock Yue

2017-06-20

Spatial patterning often occurs in ecosystems as a result of a self-organizing process caused by feedback between organisms and the physical environment. Here, we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmers' decisions and the ecology of the paddies, which triggers a transition from local to global-scale control of water shortages and rice pests. We propose an evolutionary game, based on local farmers' decisions that predicts specific power laws in spatial patterning that are also seen in a multispectral image analysis of Balinese rice terraces. The model shows how feedbacks between human decisions and ecosystem processes can evolve toward an optimal state in which total harvests are maximized and the system approaches Pareto optimality. It helps explain how multiscale cooperation from the community to the watershed scale could persist for centuries, and why the disruption of this self-organizing system by the Green Revolution caused chaos in irrigation and devastating losses from pests. The model shows that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In previous exogenously driven SOC models, adaptation plays no role, and no optimization occurs. In contrast, adaptive SOC is a self-organizing process where local adaptations drive the system toward local and global optima.

On strong homogeneity of a class of global optimization algorithms working with infinite and infinitesimal scales

NASA Astrophysics Data System (ADS)

Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.

2018-06-01

The necessity to find the global optimum of multiextremal functions arises in many applied problems where finding local solutions is insufficient. One of the desirable properties of global optimization methods is strong homogeneity meaning that a method produces the same sequences of points where the objective function is evaluated independently both of multiplication of the function by a scaling constant and of adding a shifting constant. In this paper, several aspects of global optimization using strongly homogeneous methods are considered. First, it is shown that even if a method possesses this property theoretically, numerically very small and large scaling constants can lead to ill-conditioning of the scaled problem. Second, a new class of global optimization problems where the objective function can have not only finite but also infinite or infinitesimal Lipschitz constants is introduced. Third, the strong homogeneity of several Lipschitz global optimization algorithms is studied in the framework of the Infinity Computing paradigm allowing one to work numerically with a variety of infinities and infinitesimals. Fourth, it is proved that a class of efficient univariate methods enjoys this property for finite, infinite and infinitesimal scaling and shifting constants. Finally, it is shown that in certain cases the usage of numerical infinities and infinitesimals can avoid ill-conditioning produced by scaling. Numerical experiments illustrating theoretical results are described.

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

An Automatic Medium to High Fidelity Low-Thrust Global Trajectory Toolchain; EMTG-GMAT

NASA Technical Reports Server (NTRS)

Beeson, Ryne T.; Englander, Jacob A.; Hughes, Steven P.; Schadegg, Maximillian

2015-01-01

Solving the global optimization, low-thrust, multiple-flyby interplanetary trajectory problem with high-fidelity dynamical models requires an unreasonable amount of computational resources. A better approach, and one that is demonstrated in this paper, is a multi-step process whereby the solution of the aforementioned problem is solved at a lower-fidelity and this solution is used as an initial guess for a higher-fidelity solver. The framework presented in this work uses two tools developed by NASA Goddard Space Flight Center: the Evolutionary Mission Trajectory Generator (EMTG) and the General Mission Analysis Tool (GMAT). EMTG is a medium to medium-high fidelity low-thrust interplanetary global optimization solver, which now has the capability to automatically generate GMAT script files for seeding a high-fidelity solution using GMAT's local optimization capabilities. A discussion of the dynamical models as well as thruster and power modeling for both EMTG and GMAT are given in this paper. Current capabilities are demonstrated with examples that highlight the toolchains ability to efficiently solve the difficult low-thrust global optimization problem with little human intervention.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, William E.; Siirola, John Daniel

We describe new capabilities for modeling MPEC problems within the Pyomo modeling software. These capabilities include new modeling components that represent complementar- ity conditions, modeling transformations for re-expressing models with complementarity con- ditions in other forms, and meta-solvers that apply transformations and numeric optimization solvers to optimize MPEC problems. We illustrate the breadth of Pyomo's modeling capabil- ities for MPEC problems, and we describe how Pyomo's meta-solvers can perform local and global optimization of MPEC problems.

Differences in Multitask Resource Reallocation After Change in Task Values.

PubMed

Matton, Nadine; Paubel, Pierre; Cegarra, Julien; Raufaste, Eric

2016-12-01

The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values. When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy. We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices. When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance. In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy. During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment. © 2016, Human Factors and Ergonomics Society.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Dengwang; Wang, Jie; Kapp, Daniel S.

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 weremore » 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 supported by NIH/NIBIB (1R01-EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109)« less

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

NASA Astrophysics Data System (ADS)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Online 3D Ear Recognition by Combining Global and Local Features.

PubMed

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

Online 3D Ear Recognition by Combining Global and Local Features

PubMed Central

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

Computational Characterization of Type I collagen-based Extra-cellular Matrix

NASA Astrophysics Data System (ADS)

Liang, Long; Jones, Christopher Allen Rucksack; Lin, Daniel; Jiao, Yang; Sun, Bo

2015-03-01

A model of extracellular matrix (ECM) of collagen fibers has been built, in which cells could communicate with distant partners via fiber-mediated long-range-transmitted stress states. The ECM is modeled as a spring-like fiber network derived from skeletonized confocal microscopy data. Different local and global perturbations have been performed on the network, each followed by an optimized global Monte-Carlo (MC) energy minimization leading to the deformed network in response to the perturbations. In the optimization, a highly efficient local energy update procedure is employed and force-directed MC moves are used, which results in a convergence to the energy minimum state 20 times faster than the commonly used random displacement trial moves in MC. Further analysis and visualization of the distribution and correlation of the resulting force network reveal that local perturbations can give rise to global impacts: the force chains formed with a linear extent much further than the characteristic length scale associated with the perturbation sites and average fiber length. This behavior provides a strong evidence for our hypothesis of fiber-mediated long-range force transmission in ECM networks and the resulting long-range cell-cell mechanical signaling. ASU Seed Grant.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

Mohsen, Abdulqader M

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Optimizer convergence and local minima errors and their clinical importance

NASA Astrophysics Data System (ADS)

Jeraj, Robert; Wu, Chuan; Mackie, Thomas R.

2003-09-01

Two of the errors common in the inverse treatment planning optimization have been investigated. The first error is the optimizer convergence error, which appears because of non-perfect convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second error is the local minima error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the errors, their relative importance in comparison to other errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer convergence errors are rather small, especially compared to other convergence errors, e.g., convergence errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima errors were also found to be relatively small and typically in the range of the dose calculation convergence errors. Even for the cases where significantly higher objective function scores were obtained the local minima errors were not significantly higher. Clinical evaluation of the optimizer convergence error showed good correlation between the convergence of the clinical TCP or NTCP measures and convergence of the physical dose distribution. On the other hand, the local minima errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.

Optimizer convergence and local minima errors and their clinical importance.

PubMed

Jeraj, Robert; Wu, Chuan; Mackie, Thomas R

2003-09-07

Two of the errors common in the inverse treatment planning optimization have been investigated. The first error is the optimizer convergence error, which appears because of non-perfect convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second error is the local minima error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the errors, their relative importance in comparison to other errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer convergence errors are rather small, especially compared to other convergence errors, e.g., convergence errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima errors were also found to be relatively small and typically in the range of the dose calculation convergence errors. Even for the cases where significantly higher objective function scores were obtained the local minima errors were not significantly higher. Clinical evaluation of the optimizer convergence error showed good correlation between the convergence of the clinical TCP or NTCP measures and convergence of the physical dose distribution. On the other hand, the local minima errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.

An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.

PubMed

Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur

2017-01-01

Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level  leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.

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

NASA Technical Reports Server (NTRS)

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

1995-01-01

This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

Brain tumor segmentation in 3D MRIs using an improved Markov random field model

NASA Astrophysics Data System (ADS)

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2011-10-01

Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.

Coordinated and uncoordinated optimization of networks

NASA Astrophysics Data System (ADS)

Brede, Markus

2010-06-01

In this paper, we consider spatial networks that realize a balance between an infrastructure cost (the cost of wire needed to connect the network in space) and communication efficiency, measured by average shortest path length. A global optimization procedure yields network topologies in which this balance is optimized. These are compared with network topologies generated by a competitive process in which each node strives to optimize its own cost-communication balance. Three phases are observed in globally optimal configurations for different cost-communication trade offs: (i) regular small worlds, (ii) starlike networks, and (iii) trees with a center of interconnected hubs. In the latter regime, i.e., for very expensive wire, power laws in the link length distributions P(w)∝w-α are found, which can be explained by a hierarchical organization of the networks. In contrast, in the local optimization process the presence of sharp transitions between different network regimes depends on the dimension of the underlying space. Whereas for d=∞ sharp transitions between fully connected networks, regular small worlds, and highly cliquish periphery-core networks are found, for d=1 sharp transitions are absent and the power law behavior in the link length distribution persists over a much wider range of link cost parameters. The measured power law exponents are in agreement with the hypothesis that the locally optimized networks consist of multiple overlapping suboptimal hierarchical trees.

A Novel Hybrid Firefly Algorithm for Global Optimization.

PubMed

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

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.

A Novel Hybrid Firefly Algorithm for Global Optimization

PubMed Central

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

Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

NASA Astrophysics Data System (ADS)

Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

2018-06-01

Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

An efficient algorithm for global periodic orbits generation near irregular-shaped asteroids

NASA Astrophysics Data System (ADS)

Shang, Haibin; Wu, Xiaoyu; Ren, Yuan; Shan, Jinjun

2017-07-01

Periodic orbits (POs) play an important role in understanding dynamical behaviors around natural celestial bodies. In this study, an efficient algorithm was presented to generate the global POs around irregular-shaped uniformly rotating asteroids. The algorithm was performed in three steps, namely global search, local refinement, and model continuation. First, a mascon model with a low number of particles and optimized mass distribution was constructed to remodel the exterior gravitational potential of the asteroid. Using this model, a multi-start differential evolution enhanced with a deflection strategy with strong global exploration and bypassing abilities was adopted. This algorithm can be regarded as a search engine to find multiple globally optimal regions in which potential POs were located. This was followed by applying a differential correction to locally refine global search solutions and generate the accurate POs in the mascon model in which an analytical Jacobian matrix was derived to improve convergence. Finally, the concept of numerical model continuation was introduced and used to convert the POs from the mascon model into a high-fidelity polyhedron model by sequentially correcting the initial states. The efficiency of the proposed algorithm was substantiated by computing the global POs around an elongated shoe-shaped asteroid 433 Eros. Various global POs with different topological structures in the configuration space were successfully located. Specifically, the proposed algorithm was generic and could be conveniently extended to explore periodic motions in other gravitational systems.

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.

Alternatives for Jet Engine Control

NASA Technical Reports Server (NTRS)

Leake, R. J.; Sain, M. K.

1976-01-01

Approaches are developed as alternatives to current design methods which rely heavily on linear quadratic and Riccati equation methods. The main alternatives are discussed in two broad categories, local multivariable frequency domain methods and global nonlinear optimal methods.

Multilevel decomposition approach to integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

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

1994-01-01

This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.

Detail and Gestalt Focus in Individuals with Optimal Outcomes from Autism Spectrum Disorders

ERIC Educational Resources Information Center

Fitch, Allison; Fein, Deborah A.; Eigsti, Inge-Marie

2015-01-01

Individuals with high-functioning autism (HFA) have a cognitive style that privileges local over global or gestalt details. While not a core symptom of autism, individuals with HFA seem to reliably show this bias. Our lab has been studying a sample of children who have overcome their early ASD diagnoses, showing "optimal outcomes" (OO).…

Optimization of a hydrodynamic separator using a multiscale computational fluid dynamics approach.

PubMed

Schmitt, Vivien; Dufresne, Matthieu; Vazquez, Jose; Fischer, Martin; Morin, Antoine

2013-01-01

This article deals with the optimization of a hydrodynamic separator working on the tangential separation mechanism along a screen. The aim of this study is to optimize the shape of the device to avoid clogging. A multiscale approach is used. This methodology combines measurements and computational fluid dynamics (CFD). A local model enables us to observe the different phenomena occurring at the orifice scale, which shows the potential of expanded metal screens. A global model is used to simulate the flow within the device using a conceptual model of the screen (porous wall). After validation against the experimental measurements, the global model was used to investigate the influence of deflectors and disk plates in the structure.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 1. Evaluation and Adaptation of GastroPlus To Predict Bioavailability of Medchem Series.

PubMed

Daga, Pankaj R; Bolger, Michael B; Haworth, Ian S; Clark, Robert D; Martin, Eric J

2018-03-05

When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the <2-fold average error needed to guide lead optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CL loc ). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CL loc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.

Local search for optimal global map generation using mid-decadal landsat images

USGS Publications Warehouse

Khatib, L.; Gasch, J.; Morris, Robert; 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.

The q-G method : A q-version of the Steepest Descent method for global optimization.

PubMed

Soterroni, Aline C; Galski, Roberto L; Scarabello, Marluce C; Ramos, Fernando M

2015-01-01

In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems.

Balancing building and maintenance costs in growing transport networks

NASA Astrophysics Data System (ADS)

Bottinelli, Arianna; Louf, Rémi; Gherardi, Marco

2017-09-01

The costs associated to the length of links impose unavoidable constraints to the growth of natural and artificial transport networks. When future network developments cannot be predicted, the costs of building and maintaining connections cannot be minimized simultaneously, requiring competing optimization mechanisms. Here, we study a one-parameter nonequilibrium model driven by an optimization functional, defined as the convex combination of building cost and maintenance cost. By varying the coefficient of the combination, the model interpolates between global and local length minimization, i.e., between minimum spanning trees and a local version known as dynamical minimum spanning trees. We show that cost balance within this ensemble of dynamical networks is a sufficient ingredient for the emergence of tradeoffs between the network's total length and transport efficiency, and of optimal strategies of construction. At the transition between two qualitatively different regimes, the dynamics builds up power-law distributed waiting times between global rearrangements, indicating a point of nonoptimality. Finally, we use our model as a framework to analyze empirical ant trail networks, showing its relevance as a null model for cost-constrained network formation.

Complex dynamics of an SEIR epidemic model with saturated incidence rate and treatment

NASA Astrophysics Data System (ADS)

Khan, Muhammad Altaf; Khan, Yasir; Islam, Saeed

2018-03-01

In this paper, we describe the dynamics of an SEIR epidemic model with saturated incidence, treatment function, and optimal control. Rigorous mathematical results have been established for the model. The stability analysis of the model is investigated and found that the model is locally asymptotically stable when R0 < 1. The model is locally as well as globally asymptotically stable at endemic equilibrium when R0 > 1. The proposed model may possess a backward bifurcation. The optimal control problem is designed and obtained their necessary results. Numerical results have been presented for justification of theoretical results.

Compactness of viral genomes: effect of disperse and localized random mutations

NASA Astrophysics Data System (ADS)

Lošdorfer Božič, Anže; Micheletti, Cristian; Podgornik, Rudolf; Tubiana, Luca

2018-02-01

Genomes of single-stranded RNA viruses have evolved to optimize several concurrent properties. One of them is the architecture of their genomic folds, which must not only feature precise structural elements at specific positions, but also allow for overall spatial compactness. The latter was shown to be disrupted by random synonymous mutations, a disruption which can consequently negatively affect genome encapsidation. In this study, we use three mutation schemes with different degrees of locality to mutate the genomes of phage MS2 and Brome Mosaic virus in order to understand the observed sensitivity of the global compactness of their folds. We find that mutating local stretches of their genomes’ sequence or structure is less disruptive to their compactness compared to inducing randomly-distributed mutations. Our findings are indicative of a mechanism for the conservation of compactness acting on a global scale of the genomes, and have several implications for understanding the interplay between local and global architecture of viral RNA genomes.

Computationally optimized ECoG stimulation with local safety constraints.

PubMed

Guler, Seyhmus; Dannhauer, Moritz; Roig-Solvas, Biel; Gkogkidis, Alexis; Macleod, Rob; Ball, Tonio; Ojemann, Jeffrey G; Brooks, Dana H

2018-06-01

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Taboo Search: An Approach to the Multiple Minima Problem

NASA Astrophysics Data System (ADS)

Cvijovic, Djurdje; Klinowski, Jacek

1995-02-01

Described here is a method, based on Glover's taboo search for discrete functions, of solving the multiple minima problem for continuous functions. As demonstrated by model calculations, the algorithm avoids entrapment in local minima and continues the search to give a near-optimal final solution. Unlike other methods of global optimization, this procedure is generally applicable, easy to implement, derivative-free, and conceptually simple.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Local empathy provides global minimization of congestion in communication networks

NASA Astrophysics Data System (ADS)

Meloni, Sandro; Gómez-Gardeñes, Jesús

2010-11-01

We present a mechanism to avoid congestion in complex networks based on a local knowledge of traffic conditions and the ability of routers to self-coordinate their dynamical behavior. In particular, routers make use of local information about traffic conditions to either reject or accept information packets from their neighbors. We show that when nodes are only aware of their own congestion state they self-organize into a hierarchical configuration that delays remarkably the onset of congestion although leading to a sharp first-order-like congestion transition. We also consider the case when nodes are aware of the congestion state of their neighbors. In this case, we show that empathy between nodes is strongly beneficial to the overall performance of the system and it is possible to achieve larger values for the critical load together with a smooth, second-order-like, transition. Finally, we show how local empathy minimize the impact of congestion as much as global minimization. Therefore, here we present an outstanding example of how local dynamical rules can optimize the system’s functioning up to the levels reached using global knowledge.

Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daily, Jeffrey A.

Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations. As a result, a faster intra-sequence pairwise alignment implementation is described and benchmarked. Using a 375 residue query sequence a speed of 136 billion cell updates permore » second (GCUPS) was achieved on a dual Intel Xeon E5-2670 12-core processor system, the highest reported for an implementation based on Farrar’s ’striped’ approach. When using only a single thread, parasail was 1.7 times faster than Rognes’s SWIPE. For many score matrices, parasail is faster than BLAST. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license. In conclusion, applications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.« less

Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments

DOE PAGES

Daily, Jeffrey A.

2016-02-10

Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations. As a result, a faster intra-sequence pairwise alignment implementation is described and benchmarked. Using a 375 residue query sequence a speed of 136 billion cell updates permore » second (GCUPS) was achieved on a dual Intel Xeon E5-2670 12-core processor system, the highest reported for an implementation based on Farrar’s ’striped’ approach. When using only a single thread, parasail was 1.7 times faster than Rognes’s SWIPE. For many score matrices, parasail is faster than BLAST. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license. In conclusion, applications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.« less

Multi-objective aerodynamic shape optimization of small livestock trailers

NASA Astrophysics Data System (ADS)

Gilkeson, C. A.; Toropov, V. V.; Thompson, H. M.; Wilson, M. C. T.; Foxley, N. A.; Gaskell, P. H.

2013-11-01

This article presents a formal optimization study of the design of small livestock trailers, within which the majority of animals are transported to market in the UK. The benefits of employing a headboard fairing to reduce aerodynamic drag without compromising the ventilation of the animals' microclimate are investigated using a multi-stage process involving computational fluid dynamics (CFD), optimal Latin hypercube (OLH) design of experiments (DoE) and moving least squares (MLS) metamodels. Fairings are parameterized in terms of three design variables and CFD solutions are obtained at 50 permutations of design variables. Both global and local search methods are employed to locate the global minimum from metamodels of the objective functions and a Pareto front is generated. The importance of carefully selecting an objective function is demonstrated and optimal fairing designs, offering drag reductions in excess of 5% without compromising animal ventilation, are presented.

Hybrid Motion Planning with Multiple Destinations

NASA Technical Reports Server (NTRS)

Clouse, Jeffery

1998-01-01

In our initial proposal, we laid plans for developing a hybrid motion planning system that combines the concepts of visibility-based motion planning, artificial potential field based motion planning, evolutionary constrained optimization, and reinforcement learning. Our goal was, and still is, to produce a hybrid motion planning system that outperforms the best traditional motion planning systems on problems with dynamic environments. The proposed hybrid system will be in two parts the first is a global motion planning system and the second is a local motion planning system. The global system will take global information about the environment, such as the placement of the obstacles and goals, and produce feasible paths through those obstacles. We envision a system that combines the evolutionary-based optimization and visibility-based motion planning to achieve this end.

Localization of the lumbar discs using machine learning and exact probabilistic inference.

PubMed

Oktay, Ayse Betul; Akgul, Yusuf Sinan

2011-01-01

We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.

A multi-group firefly algorithm for numerical optimization

NASA Astrophysics Data System (ADS)

Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun

2017-08-01

To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.

Radiofrequency energy deposition and radiofrequency power requirements in parallel transmission with increasing distance from the coil to the sample.

PubMed

Deniz, Cem M; Vaidya, Manushka V; Sodickson, Daniel K; Lattanzi, Riccardo

2016-01-01

We investigated global specific absorption rate (SAR) and radiofrequency (RF) power requirements in parallel transmission as the distance between the transmit coils and the sample was increased. We calculated ultimate intrinsic SAR (UISAR), which depends on object geometry and electrical properties but not on coil design, and we used it as the reference to compare the performance of various transmit arrays. We investigated the case of fixing coil size and increasing the number of coils while moving the array away from the sample, as well as the case of fixing coil number and scaling coil dimensions. We also investigated RF power requirements as a function of lift-off, and tracked local SAR distributions associated with global SAR optima. In all cases, the target excitation profile was achieved and global SAR (as well as associated maximum local SAR) decreased with lift-off, approaching UISAR, which was constant for all lift-offs. We observed a lift-off value that optimizes the balance between global SAR and power losses in coil conductors. We showed that, using parallel transmission, global SAR can decrease at ultra high fields for finite arrays with a sufficient number of transmit elements. For parallel transmission, the distance between coils and object can be optimized to reduce SAR and minimize RF power requirements associated with homogeneous excitation. © 2015 Wiley Periodicals, Inc.

Estimating of aquifer parameters from the single-well water-level measurements in response to advancing longwall mine by using particle swarm optimization

NASA Astrophysics Data System (ADS)

Buyuk, Ersin; Karaman, Abdullah

2017-04-01

We estimated transmissivity and storage coefficient values from the single well water-level measurements positioned ahead of the mining face by using particle swarm optimization (PSO) technique. The water-level response to the advancing mining face contains an semi-analytical function that is not suitable for conventional inversion shemes because the partial derivative is difficult to calculate . Morever, the logaritmic behaviour of the model create difficulty for obtaining an initial model that may lead to a stable convergence. The PSO appears to obtain a reliable solution that produce a reasonable fit between water-level data and model function response. Optimization methods have been used to find optimum conditions consisting either minimum or maximum of a given objective function with regard to some criteria. Unlike PSO, traditional non-linear optimization methods have been used for many hydrogeologic and geophysical engineering problems. These methods indicate some difficulties such as dependencies to initial model, evolution of the partial derivatives that is required while linearizing the model and trapping at local optimum. Recently, Particle swarm optimization (PSO) became the focus of modern global optimization method that is inspired from the social behaviour of birds of swarms, and appears to be a reliable and powerful algorithms for complex engineering applications. PSO that is not dependent on an initial model, and non-derivative stochastic process appears to be capable of searching all possible solutions in the model space either around local or global optimum points.

Bifurcation analysis of eight coupled degenerate optical parametric oscillators

NASA Astrophysics Data System (ADS)

Ito, Daisuke; Ueta, Tetsushi; Aihara, Kazuyuki

2018-06-01

A degenerate optical parametric oscillator (DOPO) network realized as a coherent Ising machine can be used to solve combinatorial optimization problems. Both theoretical and experimental investigations into the performance of DOPO networks have been presented previously. However a problem remains, namely that the dynamics of the DOPO network itself can lower the search success rates of globally optimal solutions for Ising problems. This paper shows that the problem is caused by pitchfork bifurcations due to the symmetry structure of coupled DOPOs. Some two-parameter bifurcation diagrams of equilibrium points express the performance deterioration. It is shown that the emergence of non-ground states regarding local minima hampers the system from reaching the ground states corresponding to the global minimum. We then describe a parametric strategy for leading a system to the ground state by actively utilizing the bifurcation phenomena. By adjusting the parameters to break particular symmetry, we find appropriate parameter sets that allow the coherent Ising machine to obtain the globally optimal solution alone.

Scaling issues in local productivity hotspots in marine ecosystems using remote sensing data: A case study in the Gulf of Maine

NASA Astrophysics Data System (ADS)

Ribera, M.; Gopal, S.

2014-12-01

Productivity hotspots are traditionally defined as concentrations of relatively high biomass compared to global reference values. These hotspots often signal atypical processes occurring in a location, and identifying them is a great first step at understanding the complexity inherent in the system. However, identifying local hotspots can be difficult when an overarching global pattern (i.e. spatial autocorrelation) already exists. This problem is particularly apparent in marine ecosystems because values of productivity in near-shore areas are consistently higher than those of the open ocean due to oceanographic processes such as upwelling. In such cases, if the global reference layer used to detect hotspots is too wide, hotspots may be only identified near the coast while missing known concentrations of organisms in offshore waters. On the other hand, if the global reference layer is too small, every single location may be considered a hotspot. We applied spatial and traditional statistics to remote sensing data to determine the optimal reference global spatial scale for identifying marine productivity hotspots in the Gulf of Maine. Our iterative process measured Getis and Ord's local G* statistic at different global scales until the variance of each hotspot was maximized. We tested this process with different full resolution MERIS chlorophyll layers (300m spatial resolution) for the whole Gulf of Maine. We concluded that the optimal global scale depends on the time of the year the remote sensing data was collected, particularly when coinciding with known seasonal phytoplankton blooms. The hotspots found through this process were also spatially heterogeneous in size, with bigger hotspots in areas offshore than in locations inshore. These results may be instructive for both managers and fisheries researchers as they adapt their fisheries management policies and methods to an ecosystem based approach (EBM).

Optimal correction and design parameter search by modern methods of rigorous global optimization

NASA Astrophysics Data System (ADS)

Makino, K.; Berz, M.

2011-07-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle optics for the computation of aberrations allow the determination of particularly sharp underestimators for large regions. As a consequence, the subsequent progressive pruning of the allowed search space as part of the optimization progresses is carried out particularly effectively. The end result is the rigorous determination of the single or multiple optimal solutions of the parameter optimization, regardless of their location, their number, and the starting values of optimization. The methods are particularly powerful if executed in interplay with genetic optimizers generating their new populations within the currently active unpruned space. Their current best guess provides rigorous upper bounds of the minima, which can then beneficially be used for better pruning. Examples of the method and its performance will be presented, including the determination of all operating points of desired tunes or chromaticities, etc. in storage ring lattices.

Influence of cost functions and optimization methods on solving the inverse problem in spatially resolved diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Rakotomanga, Prisca; Soussen, Charles; Blondel, Walter C. P. M.

2017-03-01

Diffuse reflectance spectroscopy (DRS) has been acknowledged as a valuable optical biopsy tool for in vivo characterizing pathological modifications in epithelial tissues such as cancer. In spatially resolved DRS, accurate and robust estimation of the optical parameters (OP) of biological tissues is a major challenge due to the complexity of the physical models. Solving this inverse problem requires to consider 3 components: the forward model, the cost function, and the optimization algorithm. This paper presents a comparative numerical study of the performances in estimating OP depending on the choice made for each of the latter components. Mono- and bi-layer tissue models are considered. Monowavelength (scalar) absorption and scattering coefficients are estimated. As a forward model, diffusion approximation analytical solutions with and without noise are implemented. Several cost functions are evaluated possibly including normalized data terms. Two local optimization methods, Levenberg-Marquardt and TrustRegion-Reflective, are considered. Because they may be sensitive to the initial setting, a global optimization approach is proposed to improve the estimation accuracy. This algorithm is based on repeated calls to the above-mentioned local methods, with initial parameters randomly sampled. Two global optimization methods, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are also implemented. Estimation performances are evaluated in terms of relative errors between the ground truth and the estimated values for each set of unknown OP. The combination between the number of variables to be estimated, the nature of the forward model, the cost function to be minimized and the optimization method are discussed.

A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation

NASA Astrophysics Data System (ADS)

Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava

2015-12-01

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

JIGSAW: Joint Inhomogeneity estimation via Global Segment Assembly for Water-fat separation.

PubMed

Lu, Wenmiao; Lu, Yi

2011-07-01

Water-fat separation in magnetic resonance imaging (MRI) is of great clinical importance, and the key to uniform water-fat separation lies in field map estimation. This work deals with three-point field map estimation, in which water and fat are modelled as two single-peak spectral lines, and field inhomogeneities shift the spectrum by an unknown amount. Due to the simplified spectrum modelling, there exists inherent ambiguity in forming field maps from multiple locally feasible field map values at each pixel. To resolve such ambiguity, spatial smoothness of field maps has been incorporated as a constraint of an optimization problem. However, there are two issues: the optimization problem is computationally intractable and even when it is solved exactly, it does not always separate water and fat images. Hence, robust field map estimation remains challenging in many clinically important imaging scenarios. This paper proposes a novel field map estimation technique called JIGSAW. It extends a loopy belief propagation (BP) algorithm to obtain an approximate solution to the optimization problem. The solution produces locally smooth segments and avoids error propagation associated with greedy methods. The locally smooth segments are then assembled into a globally consistent field map by exploiting the periodicity of the feasible field map values. In vivo results demonstrate that JIGSAW outperforms existing techniques and produces correct water-fat separation in challenging imaging scenarios.

New evidence favoring multilevel decomposition and optimization

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Polignone, Debra A.

1990-01-01

The issue of the utility of multilevel decomposition and optimization remains controversial. To date, only the structural optimization community has actively developed and promoted multilevel optimization techniques. However, even this community acknowledges that multilevel optimization is ideally suited for a rather limited set of problems. It is warned that decomposition typically requires eliminating local variables by using global variables and that this in turn causes ill-conditioning of the multilevel optimization by adding equality constraints. The purpose is to suggest a new multilevel optimization technique. This technique uses behavior variables, in addition to design variables and constraints, to decompose the problem. The new technique removes the need for equality constraints, simplifies the decomposition of the design problem, simplifies the programming task, and improves the convergence speed of multilevel optimization compared to conventional optimization.

The trust-region self-consistent field method in Kohn-Sham density-functional theory.

PubMed

Thøgersen, Lea; Olsen, Jeppe; Köhn, Andreas; Jørgensen, Poul; Sałek, Paweł; Helgaker, Trygve

2005-08-15

The trust-region self-consistent field (TRSCF) method is extended to the optimization of the Kohn-Sham energy. In the TRSCF method, both the Roothaan-Hall step and the density-subspace minimization step are replaced by trust-region optimizations of local approximations to the Kohn-Sham energy, leading to a controlled, monotonic convergence towards the optimized energy. Previously the TRSCF method has been developed for optimization of the Hartree-Fock energy, which is a simple quadratic function in the density matrix. However, since the Kohn-Sham energy is a nonquadratic function of the density matrix, the local energy functions must be generalized for use with the Kohn-Sham model. Such a generalization, which contains the Hartree-Fock model as a special case, is presented here. For comparison, a rederivation of the popular direct inversion in the iterative subspace (DIIS) algorithm is performed, demonstrating that the DIIS method may be viewed as a quasi-Newton method, explaining its fast local convergence. In the global region the convergence behavior of DIIS is less predictable. The related energy DIIS technique is also discussed and shown to be inappropriate for the optimization of the Kohn-Sham energy.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Multicriteria hierarchical iterative interactive algorithm for organizing operational modes of large heat supply systems

NASA Astrophysics Data System (ADS)

Korotkova, T. I.; Popova, V. I.

2017-11-01

The generalized mathematical model of decision-making in the problem of planning and mode selection providing required heat loads in a large heat supply system is considered. The system is multilevel, decomposed into levels of main and distribution heating networks with intermediate control stages. Evaluation of the effectiveness, reliability and safety of such a complex system is carried out immediately according to several indicators, in particular pressure, flow, temperature. This global multicriteria optimization problem with constraints is decomposed into a number of local optimization problems and the coordination problem. An agreed solution of local problems provides a solution to the global multicriterion problem of decision making in a complex system. The choice of the optimum operational mode of operation of a complex heat supply system is made on the basis of the iterative coordination process, which converges to the coordinated solution of local optimization tasks. The interactive principle of multicriteria task decision-making includes, in particular, periodic adjustment adjustments, if necessary, guaranteeing optimal safety, reliability and efficiency of the system as a whole in the process of operation. The degree of accuracy of the solution, for example, the degree of deviation of the internal air temperature from the required value, can also be changed interactively. This allows to carry out adjustment activities in the best way and to improve the quality of heat supply to consumers. At the same time, an energy-saving task is being solved to determine the minimum required values of heads at sources and pumping stations.

Singularities in Optimal Structural Design

NASA Technical Reports Server (NTRS)

Patnaik, S. N.; Guptill, J. D.; Berke, L.

1992-01-01

Singularity conditions that arise during structural optimization can seriously degrade the performance of the optimizer. The singularities are intrinsic to the formulation of the structural optimization problem and are not associated with the method of analysis. Certain conditions that give rise to singularities have been identified in earlier papers, encompassing the entire structure. Further examination revealed more complex sets of conditions in which singularities occur. Some of these singularities are local in nature, being associated with only a segment of the structure. Moreover, the likelihood that one of these local singularities may arise during an optimization procedure can be much greater than that of the global singularity identified earlier. Examples are provided of these additional forms of singularities. A framework is also given in which these singularities can be recognized. In particular, the singularities can be identified by examination of the stress displacement relations along with the compatibility conditions and/or the displacement stress relations derived in the integrated force method of structural analysis.

Singularities in optimal structural design

NASA Technical Reports Server (NTRS)

Patnaik, S. N.; Guptill, J. D.; Berke, L.

1992-01-01

Singularity conditions that arise during structural optimization can seriously degrade the performance of the optimizer. The singularities are intrinsic to the formulation of the structural optimization problem and are not associated with the method of analysis. Certain conditions that give rise to singularities have been identified in earlier papers, encompassing the entire structure. Further examination revealed more complex sets of conditions in which singularities occur. Some of these singularities are local in nature, being associated with only a segment of the structure. Moreover, the likelihood that one of these local singularities may arise during an optimization procedure can be much greater than that of the global singularity identified earlier. Examples are provided of these additional forms of singularities. A framework is also given in which these singularities can be recognized. In particular, the singularities can be identified by examination of the stress displacement relations along with the compatibility conditions and/or the displacement stress relations derived in the integrated force method of structural analysis.

GALAXY: A new hybrid MOEA for the optimal design of Water Distribution Systems

NASA Astrophysics Data System (ADS)

Wang, Q.; Savić, D. A.; Kapelan, Z.

2017-03-01

A new hybrid optimizer, called genetically adaptive leaping algorithm for approximation and diversity (GALAXY), is proposed for dealing with the discrete, combinatorial, multiobjective design of Water Distribution Systems (WDSs), which is NP-hard and computationally intensive. The merit of GALAXY is its ability to alleviate to a great extent the parameterization issue and the high computational overhead. It follows the generational framework of Multiobjective Evolutionary Algorithms (MOEAs) and includes six search operators and several important strategies. These operators are selected based on their leaping ability in the objective space from the global and local search perspectives. These strategies steer the optimization and balance the exploration and exploitation aspects simultaneously. A highlighted feature of GALAXY lies in the fact that it eliminates majority of parameters, thus being robust and easy-to-use. The comparative studies between GALAXY and three representative MOEAs on five benchmark WDS design problems confirm its competitiveness. GALAXY can identify better converged and distributed boundary solutions efficiently and consistently, indicating a much more balanced capability between the global and local search. Moreover, its advantages over other MOEAs become more substantial as the complexity of the design problem increases.

Generating Multi-Destination Maps.

PubMed

Zhang, Junsong; Fan, Jiepeng; Luo, Zhenshan

2017-08-01

Multi-destination maps are a kind of navigation maps aimed to guide visitors to multiple destinations within a region, which can be of great help to urban visitors. However, they have not been developed in the current online map service. To address this issue, we introduce a novel layout model designed especially for generating multi-destination maps, which considers the global and local layout of a multi-destination map. We model the layout problem as a graph drawing that satisfies a set of hard and soft constraints. In the global layout phase, we balance the scale factor between ROIs. In the local layout phase, we make all edges have good visibility and optimize the map layout to preserve the relative length and angle of roads. We also propose a perturbation-based optimization method to find an optimal layout in the complex solution space. The multi-destination maps generated by our system are potential feasible on the modern mobile devices and our result can show an overview and a detail view of the whole map at the same time. In addition, we perform a user study to evaluate the effectiveness of our method, and the results prove that the multi-destination maps achieve our goals well.

Interbody fusion cage design using integrated global layout and local microstructure topology optimization.

PubMed

Lin, Chia-Ying; Hsiao, Chun-Ching; Chen, Po-Quan; Hollister, Scott J

2004-08-15

An approach combining global layout and local microstructure topology optimization was used to create a new interbody fusion cage design that concurrently enhanced stability, biofactor delivery, and mechanical tissue stimulation for improved arthrodesis. To develop a new interbody fusion cage design by topology optimization with porous internal architecture. To compare the performance of this new design to conventional threaded cage designs regarding early stability and long-term stress shielding effects on ingrown bone. Conventional interbody cage designs mainly fall into categories of cylindrical or rectangular shell shapes. The designs contribute to rigid stability and maintain disc height for successful arthrodesis but may also suffer mechanically mediated failures of dislocation or subsidence, as well as the possibility of bone resorption. The new optimization approach created a cage having designed microstructure that achieved desired mechanical performance while providing interconnected channels for biofactor delivery. The topology optimization algorithm determines the material layout under desirable volume fraction (50%) and displacement constraints favorable to bone formation. A local microstructural topology optimization method was used to generate periodic microstructures for porous isotropic materials. Final topology was generated by the integration of the two-scaled structures according to segmented regions and the corresponding material density. Image-base finite element analysis was used to compare the mechanical performance of the topology-optimized cage and conventional threaded cage. The final design can be fabricated by a variety of Solid Free-Form systems directly from the image output. The new design exhibited a narrower, more uniform displacement range than the threaded cage design and lower stress at the cage-vertebra interface, suggesting a reduced risk of subsidence. Strain energy density analysis also indicated that a higher portion of total strain energy density was transferred into the new bone region inside the new designed cage, indicating a reduced risk of stress shielding. The new design approach using integrated topology optimization demonstrated comparable or better stability by limited displacement and reduced localized deformation related to the risk of subsidence. Less shielding of newly formed bone was predicted inside the new designed cage. Using the present approach, it is also possible to tailor cage design for specific materials, either titanium or polymer, that can attain the desired balance between stability, reduced stress shielding, and porosity for biofactor delivery.

Combining pressure and temperature control in dynamics on energy landscapes

NASA Astrophysics Data System (ADS)

Hoffmann, Karl Heinz; Christian Schön, J.

2017-05-01

Complex systems from science, technology or mathematics usually appear to be very different in their specific dynamical evolution. However, the concept of an energy landscape with its basins corresponding to locally ergodic regions separated by energy barriers provides a unifying approach to the description of complex systems dynamics. In such systems one is often confronted with the task to control the dynamics such that a certain basin is reached with the highest possible probability. Typically one aims for the global minimum, e.g. when dealing with global optimization problems, but frequently other local minima such as the metastable compounds in materials science are of primary interest. Here we show how this task can be solved by applying control theory using magnesium fluoride as an example system, where different modifications of MgF2 are considered as targets. In particular, we generalize previous work restricted to temperature controls only and present controls which simultaneously adjust temperature and pressure in an optimal fashion.

A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.

Global solutions of restricted open-shell Hartree-Fock theory from semidefinite programming with applications to strongly correlated quantum systems.

PubMed

Veeraraghavan, Srikant; Mazziotti, David A

2014-03-28

We present a density matrix approach for computing global solutions of restricted open-shell Hartree-Fock theory, based on semidefinite programming (SDP), that gives upper and lower bounds on the Hartree-Fock energy of quantum systems. While wave function approaches to Hartree-Fock theory yield an upper bound to the Hartree-Fock energy, we derive a semidefinite relaxation of Hartree-Fock theory that yields a rigorous lower bound on the Hartree-Fock energy. We also develop an upper-bound algorithm in which Hartree-Fock theory is cast as a SDP with a nonconvex constraint on the rank of the matrix variable. Equality of the upper- and lower-bound energies guarantees that the computed solution is the globally optimal solution of Hartree-Fock theory. The work extends a previously presented method for closed-shell systems [S. Veeraraghavan and D. A. Mazziotti, Phys. Rev. A 89, 010502-R (2014)]. For strongly correlated systems the SDP approach provides an alternative to the locally optimized Hartree-Fock energies and densities with a certificate of global optimality. Applications are made to the potential energy curves of C2, CN, Cr2, and NO2.

Coordinate alignment of combined measurement systems using a modified common points method

NASA Astrophysics Data System (ADS)

Zhao, G.; Zhang, P.; Xiao, W.

2018-03-01

The co-ordinate metrology has been extensively researched for its outstanding advantages in measurement range and accuracy. The alignment of different measurement systems is usually achieved by integrating local coordinates via common points before measurement. The alignment errors would accumulate and significantly reduce the global accuracy, thus need to be minimized. In this thesis, a modified common points method (MCPM) is proposed to combine different traceable system errors of the cooperating machines, and optimize the global accuracy by introducing mutual geometric constraints. The geometric constraints, obtained by measuring the common points in individual local coordinate systems, provide the possibility to reduce the local measuring uncertainty whereby enhance the global measuring certainty. A simulation system is developed in Matlab to analyze the feature of MCPM using the Monto-Carlo method. An exemplary setup is constructed to verify the feasibility and efficiency of the proposed method associated with laser tracker and indoor iGPS systems. Experimental results show that MCPM could significantly improve the alignment accuracy.

Applications of New Surrogate Global Optimization Algorithms including Efficient Synchronous and Asynchronous Parallelism for Calibration of Expensive Nonlinear Geophysical Simulation Models.

NASA Astrophysics Data System (ADS)

Shoemaker, C. A.; Pang, M.; Akhtar, T.; Bindel, D.

2016-12-01

New parallel surrogate global optimization algorithms are developed and applied to objective functions that are expensive simulations (possibly with multiple local minima). The algorithms can be applied to most geophysical simulations, including those with nonlinear partial differential equations. The optimization does not require simulations be parallelized. Asynchronous (and synchronous) parallel execution is available in the optimization toolbox "pySOT". The parallel algorithms are modified from serial to eliminate fine grained parallelism. The optimization is computed with open source software pySOT, a Surrogate Global Optimization Toolbox that allows user to pick the type of surrogate (or ensembles), the search procedure on surrogate, and the type of parallelism (synchronous or asynchronous). pySOT also allows the user to develop new algorithms by modifying parts of the code. In the applications here, the objective function takes up to 30 minutes for one simulation, and serial optimization can take over 200 hours. Results from Yellowstone (NSF) and NCSS (Singapore) supercomputers are given for groundwater contaminant hydrology simulations with applications to model parameter estimation and decontamination management. All results are compared with alternatives. The first results are for optimization of pumping at many wells to reduce cost for decontamination of groundwater at a superfund site. The optimization runs with up to 128 processors. Superlinear speed up is obtained for up to 16 processors, and efficiency with 64 processors is over 80%. Each evaluation of the objective function requires the solution of nonlinear partial differential equations to describe the impact of spatially distributed pumping and model parameters on model predictions for the spatial and temporal distribution of groundwater contaminants. The second application uses an asynchronous parallel global optimization for groundwater quality model calibration. The time for a single objective function evaluation varies unpredictably, so efficiency is improved with asynchronous parallel calculations to improve load balancing. The third application (done at NCSS) incorporates new global surrogate multi-objective parallel search algorithms into pySOT and applies it to a large watershed calibration problem.

Aeroelastic Wingbox Stiffener Topology Optimization

NASA Technical Reports Server (NTRS)

Stanford, Bret K.

2017-01-01

This work considers an aeroelastic wingbox model seeded with run-out blade stiffeners along the skins. Topology optimization is conducted within the shell webs of the stiffeners, in order to add cutouts and holes for mass reduction. This optimization is done with a global-local approach in order to moderate the computational cost: aeroelastic loads are computed at the wing-level, but the topology and sizing optimization is conducted at the panel-level. Each panel is optimized separately under stress, buckling, and adjacency constraints, and periodically reassembled to update the trimmed aeroelastic loads. The resulting topology is baselined against a design with standard full-depth solid stiffener blades, and found to weigh 7.43% less.

Optimization of Composite Structures with Curved Fiber Trajectories

NASA Astrophysics Data System (ADS)

Lemaire, Etienne; Zein, Samih; Bruyneel, Michael

2014-06-01

This paper studies the problem of optimizing composites shells manufactured using Automated Tape Layup (ATL) or Automated Fiber Placement (AFP) processes. The optimization procedure relies on a new approach to generate equidistant fiber trajectories based on Fast Marching Method. Starting with a (possibly curved) reference fiber direction defined on a (possibly curved) meshed surface, the new method allows determining fibers orientation resulting from a uniform thickness layup. The design variables are the parameters defining the position and the shape of the reference curve which results in very few design variables. Thanks to this efficient parameterization, maximum stiffness optimization numerical applications are proposed. The shape of the design space is discussed, regarding local and global optimal solutions.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

PubMed

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

PubMed Central

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization.

PubMed

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.

Singularity in structural optimization

NASA Technical Reports Server (NTRS)

Patnaik, S. N.; Guptill, J. D.; Berke, L.

1993-01-01

The conditions under which global and local singularities may arise in structural optimization are examined. Examples of these singularities are presented, and a framework is given within which the singularities can be recognized. It is shown, in particular, that singularities can be identified through the analysis of stress-displacement relations together with compatibility conditions or the displacement-stress relations derived by the integrated force method of structural analysis. Methods of eliminating the effects of singularities are suggested and illustrated numerically.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kimmel, Gregory; Sadovskyy, Ivan A.; Glatz, Andreas

For many technological applications of superconductors the performance of a material is determined by the highest current it can carry losslessly-the critical current. In turn, the critical current can be controlled by adding nonsuperconducting defects in the superconductor matrix. Here we report on systematic comparison of different local and global optimization strategies to predict optimal structures of pinning centers leading to the highest possible critical currents. We demonstrate performance of these methods for a superconductor with randomly placed spherical, elliptical, and columnar defects.

A statistical-based scheduling algorithm in automated data path synthesis

NASA Technical Reports Server (NTRS)

Jeon, Byung Wook; Lursinsap, Chidchanok

1992-01-01

In this paper, we propose a new heuristic scheduling algorithm based on the statistical analysis of the cumulative frequency distribution of operations among control steps. It has a tendency of escaping from local minima and therefore reaching a globally optimal solution. The presented algorithm considers the real world constraints such as chained operations, multicycle operations, and pipelined data paths. The result of the experiment shows that it gives optimal solutions, even though it is greedy in nature.

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Optimal multiguidance integration in insect navigation.

PubMed

Hoinville, Thierry; Wehner, Rüdiger

2018-03-13

In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with "optimal" meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator's optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network.

PubMed

Cheng, Jing; Xia, Linyuan

2016-08-31

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm.

An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network

PubMed Central

Cheng, Jing; Xia, Linyuan

2016-01-01

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm. PMID:27589756

Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.; Martin, P.

2008-12-01

Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond layouts, and water-supply constraints, indicate that the number of new wells is insensitive to water-supply constraints; however, pumping rates and patterns of the existing wells are sensitive. The locations of new wells are mildly sensitive to the pond layout.

Mid-sagittal plane and mid-sagittal surface optimization in brain MRI using a local symmetry measure

NASA Astrophysics Data System (ADS)

Stegmann, Mikkel B.; Skoglund, Karl; Ryberg, Charlotte

2005-04-01

This paper describes methods for automatic localization of the mid-sagittal plane (MSP) and mid-sagittal surface (MSS). The data used is a subset of the Leukoaraiosis And DISability (LADIS) study consisting of three-dimensional magnetic resonance brain data from 62 elderly subjects (age 66 to 84 years). Traditionally, the mid-sagittal plane is localized by global measures. However, this approach fails when the partitioning plane between the brain hemispheres does not coincide with the symmetry plane of the head. We instead propose to use a sparse set of profiles in the plane normal direction and maximize the local symmetry around these using a general-purpose optimizer. The plane is parameterized by azimuth and elevation angles along with the distance to the origin in the normal direction. This approach leads to solutions confirmed as the optimal MSP in 98 percent of the subjects. Despite the name, the mid-sagittal plane is not always planar, but a curved surface resulting in poor partitioning of the brain hemispheres. To account for this, this paper also investigates an optimization strategy which fits a thin-plate spline surface to the brain data using a robust least median of squares estimator. Albeit computationally more expensive, mid-sagittal surface fitting demonstrated convincingly better partitioning of curved brains into cerebral hemispheres.

Using a derivative-free optimization method for multiple solutions of inverse transport problems

DOE PAGES

Armstrong, Jerawan C.; Favorite, Jeffrey A.

2016-01-14

Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a meshmore » adaptive direct search (MADS) algorithm is incorporated into the local phase. Furthermore, numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.« less

Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals.

PubMed

Shao, Feng; Jiang, Qiuping; Fu, Randi; Yu, Mei; Jiang, Gangyi

2016-05-30

Visual comfort is a long-facing problem in stereoscopic 3D (S3D) display. In this paper, targeting to produce S3D content based on color-plus-depth signals, a general framework for depth mapping to optimize visual comfort for S3D display is proposed. The main motivation of this work is to remap the depth range of color-plus-depth signals to a new depth range that is suitable to comfortable S3D display. Towards this end, we first remap the depth range globally based on the adjusted zero disparity plane, and then present a two-stage global and local depth optimization solution to solve the visual comfort problem. The remapped depth map is used to generate the S3D output. We demonstrate the power of our approach on perceptually uncomfortable and comfortable stereoscopic images.

Sustainability for the Americas Initiative: Land Design Institute, Ball State University

Treesearch

J. L. Motloch; Pedro Pacheco; Eloy F. Jr. Casagrande

2006-01-01

The Ball State University Land Design Institute (LDI) pursues ecologically and culturally sustainable land design through education, research, outreach, and demonstration. LDI seeks to lead communities (local, regional, global) to sustainable futures. It connects communities and sustainability experts to optimize education about land management, planning, and design...

Glassy nature and glass-to-crystal transition in the binary metallic glass CuZr

NASA Astrophysics Data System (ADS)

Wei, Zi-Yang; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-06-01

The prediction for the stability of glassy material is a key challenge in physical science. Here, we report a theoretical framework to predict the glass stability based on stochastic surface walking global optimization and reaction pathway sampling. This is demonstrated by revealing for the first time the global potential energy surface (PES) of two systems, CuZr binary metallic glass and nonglassy pure Cu systems, and establishing the lowest energy pathways linking glassy/amorphous structures with crystalline structures. The CuZr system has a significant number of glassy structures on PES that are ˜0.045 eV /atom above the crystal structure. Two clear trends are identified from global PES in the glass-to-crystal transition of the CuZr system: (i) the local Zr-Cu coordination (nearest neighbor) increases, and (ii) the local Zr bonding environment becomes homogeneous. This allows us to introduce quantitative structural and energetics conditions to distinguish the glassy structures from the crystalline structures. Because of the local Zr-Cu exchange in the glass-to-crystal transition, a high reaction barrier (>0.048 eV /atom ) is present to separate the glassy structures and the crystals in CuZr. By contrast, the Cu system, although it does possess amorphous structures that appear at much higher energy (˜0.075 eV /atom ) with respect to the crystal structure, has very low reaction barriers for the crystallization of amorphous structures, i.e. <0.011 eV /atom . The quantitative data on PES now available from global optimization techniques deepens our understanding on the microscopic nature of glassy material and might eventually facilitate the design of stable glassy materials.

Searching for quantum optimal controls under severe constraints

DOE PAGES

Riviello, Gregory; Tibbetts, Katharine Moore; Brif, Constantin; ...

2015-04-06

The success of quantum optimal control for both experimental and theoretical objectives is connected to the topology of the corresponding control landscapes, which are free from local traps if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is of full rank, and (3) there are no constraints on the control field. This paper investigates how the violation of assumption (3) affects gradient searches for globally optimal control fields. The satisfaction of assumptions (1) and (2) ensures that the control landscape lacks fundamental traps, butmore » certain control constraints can still prevent successful optimization of the objective. Using optimal control simulations, we show that the most severe field constraints are those that limit essential control resources, such as the number of control variables, the control duration, and the field strength. Proper management of these resources is an issue of great practical importance for optimization in the laboratory. For each resource, we show that constraints exceeding quantifiable limits can introduce artificial traps to the control landscape and prevent gradient searches from reaching a globally optimal solution. These results demonstrate that careful choice of relevant control parameters helps to eliminate artificial traps and facilitate successful optimization.« less

Shape optimization techniques for musical instrument design

NASA Astrophysics Data System (ADS)

Henrique, Luis; Antunes, Jose; Carvalho, Joao S.

2002-11-01

The design of musical instruments is still mostly based on empirical knowledge and costly experimentation. One interesting improvement is the shape optimization of resonating components, given a number of constraints (allowed parameter ranges, shape smoothness, etc.), so that vibrations occur at specified modal frequencies. Each admissible geometrical configuration generates an error between computed eigenfrequencies and the target set. Typically, error surfaces present many local minima, corresponding to suboptimal designs. This difficulty can be overcome using global optimization techniques, such as simulated annealing. However these methods are greedy, concerning the number of function evaluations required. Thus, the computational effort can be unacceptable if complex problems, such as bell optimization, are tackled. Those issues are addressed in this paper, and a method for improving optimization procedures is proposed. Instead of using the local geometric parameters as searched variables, the system geometry is modeled in terms of truncated series of orthogonal space-funcitons, and optimization is performed on their amplitude coefficients. Fourier series and orthogonal polynomials are typical such functions. This technique reduces considerably the number of searched variables, and has a potential for significant computational savings in complex problems. It is illustrated by optimizing the shapes of both current and uncommon marimba bars.

SpaRibs Geometry Parameterization for Wings with Multiple Sections using Single Design

NASA Technical Reports Server (NTRS)

De, Shuvodeep; Jrad, Mohamed; Locatelli, Davide; Kapania, Rakesh K.; Baker, Myles; Pak, Chan-Gi

2017-01-01

The SpaRibs topology of an aircraft wing has a significant effect on its structural behavior and stability as well as the flutter performance. The development of additive manufacturing techniques like Electron Beam Free Form Fabrication (EBF3) has made it feasible to manufacture aircraft wings with curvilinear spars, ribs (SpaRibs) and stiffeners. In this article a new global-local optimization framework for wing with multiple sections using curvilinear SpaRibs is described. A single design space is used to parameterize the SpaRibs geometry. This method has been implemented using MSC-PATRAN to create a broad range of SpaRibs topologies using limited number of parameters. It ensures C0 and C1 continuities in SpaRibs geometry at the junction of two wing sections with airfoil thickness gradient discontinuity as well as mesh continuity between all structural components. This method is advantageous in complex multi-disciplinary optimization due to its potential to reduce the number of design variables. For the global-local optimization the local panels are generated by an algorithm which is totally based on a set algebra on the connectivity matrix data. The great advantage of this method is that it is completely independent of the coordinates of the nodes of the finite element model. It is also independent of the order in which the elements are distributed in the FEM. The code is verified by optimizing of the CRM Baseline model at trim condition at Mach number equal to 0.85 for five different angle of attack (-2deg, 0deg,2deg,4deg and 6deg). The final weight of the wing is 19,090.61 lb. This value is comparable to that obtained by Qiang et al. 6 (19,269 lb).

Inferring neural activity from BOLD signals through nonlinear optimization.

PubMed

Vakorin, Vasily A; Krakovska, Olga O; Borowsky, Ron; Sarty, Gordon E

2007-11-01

The blood oxygen level-dependent (BOLD) fMRI signal does not measure neuronal activity directly. This fact is a key concern for interpreting functional imaging data based on BOLD. Mathematical models describing the path from neural activity to the BOLD response allow us to numerically solve the inverse problem of estimating the timing and amplitude of the neuronal activity underlying the BOLD signal. In fact, these models can be viewed as an advanced substitute for the impulse response function. In this work, the issue of estimating the dynamics of neuronal activity from the observed BOLD signal is considered within the framework of optimization problems. The model is based on the extended "balloon" model and describes the conversion of neuronal signals into the BOLD response through the transitional dynamics of the blood flow-inducing signal, cerebral blood flow, cerebral blood volume and deoxyhemoglobin concentration. Global optimization techniques are applied to find a control input (the neuronal activity and/or the biophysical parameters in the model) that causes the system to follow an admissible solution to minimize discrepancy between model and experimental data. As an alternative to a local linearization (LL) filtering scheme, the optimization method escapes the linearization of the transition system and provides a possibility to search for the global optimum, avoiding spurious local minima. We have found that the dynamics of the neural signals and the physiological variables as well as the biophysical parameters can be robustly reconstructed from the BOLD responses. Furthermore, it is shown that spiking off/on dynamics of the neural activity is the natural mathematical solution of the model. Incorporating, in addition, the expansion of the neural input by smooth basis functions, representing a low-pass filtering, allows us to model local field potential (LFP) solutions instead of spiking solutions.

Reliability Sensitivity Analysis and Design Optimization of Composite Structures Based on Response Surface Methodology

NASA Technical Reports Server (NTRS)

Rais-Rohani, Masoud

2003-01-01

This report discusses the development and application of two alternative strategies in the form of global and sequential local response surface (RS) techniques for the solution of reliability-based optimization (RBO) problems. The problem of a thin-walled composite circular cylinder under axial buckling instability is used as a demonstrative example. In this case, the global technique uses a single second-order RS model to estimate the axial buckling load over the entire feasible design space (FDS) whereas the local technique uses multiple first-order RS models with each applied to a small subregion of FDS. Alternative methods for the calculation of unknown coefficients in each RS model are explored prior to the solution of the optimization problem. The example RBO problem is formulated as a function of 23 uncorrelated random variables that include material properties, thickness and orientation angle of each ply, cylinder diameter and length, as well as the applied load. The mean values of the 8 ply thicknesses are treated as independent design variables. While the coefficients of variation of all random variables are held fixed, the standard deviations of ply thicknesses can vary during the optimization process as a result of changes in the design variables. The structural reliability analysis is based on the first-order reliability method with reliability index treated as the design constraint. In addition to the probabilistic sensitivity analysis of reliability index, the results of the RBO problem are presented for different combinations of cylinder length and diameter and laminate ply patterns. The two strategies are found to produce similar results in terms of accuracy with the sequential local RS technique having a considerably better computational efficiency.

Trust regions in Kriging-based optimization with expected improvement

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2016-06-01

The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.doi.org/10.1080/0305215X.2015.1082350. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

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.

A dynamic routing strategy with limited buffer on scale-free network

NASA Astrophysics Data System (ADS)

Wang, Yufei; Liu, Feng

2016-04-01

In this paper, we propose an integrated routing strategy based on global static topology information and local dynamic data packet queue lengths to improve the transmission efficiency of scale-free networks. The proposed routing strategy is a combination of a global static routing strategy (based on the shortest path algorithm) and local dynamic queue length management, in which, instead of using an infinite buffer, the queue length of each node i in the proposed routing strategy is limited by a critical queue length Qic. When the network traffic is lower and the queue length of each node i is shorter than its critical queue length Qic, it forwards packets according to the global routing table. With increasing network traffic, when the buffers of the nodes with higher degree are full, they do not receive packets due to their limited buffers and the packets have to be delivered to the nodes with lower degree. The global static routing strategy can shorten the transmission time that it takes a packet to reach its destination, and the local limited queue length can balance the network traffic. The optimal critical queue lengths of nodes have been analysed. Simulation results show that the proposed routing strategy can get better performance than that of the global static strategy based on topology, and almost the same performance as that of the global dynamic routing strategy with less complexity.

A Method of Trajectory Design for Manned Asteroids Exploration

NASA Astrophysics Data System (ADS)

Gan, Q. B.; Zhang, Y.; Zhu, Z. F.; Han, W. H.; Dong, X.

2014-11-01

A trajectory optimization method of the nuclear propulsion manned asteroids exploration is presented. In the case of launching between 2035 and 2065, based on the Lambert transfer orbit, the phases of departure from and return to the Earth are searched at first. Then the optimal flight trajectory in the feasible regions is selected by pruning the flight sequences. Setting the nuclear propulsion flight plan as propel-coast-propel, and taking the minimal mass of aircraft departure as the index, the nuclear propulsion flight trajectory is separately optimized using a hybrid method. With the initial value of the optimized local parameters of each three phases, the global parameters are jointedly optimized. At last, the minimal departure mass trajectory design result is given.

Optimized graph-based mosaicking for virtual microscopy

NASA Astrophysics Data System (ADS)

Steckhan, Dirk G.; Wittenberg, Thomas

2009-02-01

Virtual microscopy has the potential to partially replace traditional microscopy. For virtualization, the slide is scanned once by a fully automatized robotic microscope and saved digitally. Typically, such a scan results in several hundreds to thousands of fields of view. Since robotic stages have positioning errors, these fields of view have to be registered locally and globally in an additional step. In this work we propose a new global mosaicking method for the creation of virtual slides based on sub-pixel exact phase correlation for local alignment in combination with Prim's minimum spanning tree algorithm for global alignment. Our algorithm allows for a robust reproduction of the original slide even in the presence of views with little to no information content. This makes it especially suitable for the mosaicking of cervical smears. These smears often exhibit large empty areas, which do not contain enough information for common stitching approaches.

Modeling intraspecific adaptation of Abies sachalinensis to local altitude and responses to global warming, based on a 36-year reciprocal transplant experiment.

PubMed

Ishizuka, Wataru; Goto, Susumu

2012-04-01

Intraspecific adaptation in Abies sachalinensis was examined using models based on long-term monitoring data gathered during a reciprocal transplant experiment with eight seed source populations and six transplantation sites along an altitudinal gradient. The consequence of local adaptation was evaluated by testing the home-site advantage for upslope and downslope transplants at five ages. The populations' fitness-linked trait was set as their productivity (tree height × survival rate) at each age. The effects of global warming were evaluated on the basis of the 36-year performance of downslope transplants. Evidence was found for adaptive genetic variation affecting both height and survival from an early age. Increasing the distance between seed source and planting site significantly reduced productivity for both upslope and downslope transplantation, demonstrating the existence of a significant home-site advantage. The decrease in productivity was most distinct for upslope transplantations, indicating strong local adaptation to high altitudes. Global warming is predicted to increase the productivity of high-altitude populations. However, owing to their existing local adaptation, all tested populations exhibited lower productivity under warming than demes that were optimal for the new climate. These negative predictions should be considered when planning the management of locally adapted plant species such as A. sachalinensis.

Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices.

PubMed

Qiao, Wei; Venayagamoorthy, Ganesh K; Harley, Ronald G

2008-01-01

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

Fast global image smoothing based on weighted least squares.

PubMed

Min, Dongbo; Choi, Sunghwan; Lu, Jiangbo; Ham, Bumsub; Sohn, Kwanghoon; Do, Minh N

2014-12-01

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

Non-linear Multidimensional Optimization for use in Wire Scanner Fitting

NASA Astrophysics Data System (ADS)

Henderson, Alyssa; Terzic, Balsa; Hofler, Alicia; Center Advanced Studies of Accelerators Collaboration

2014-03-01

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator at Jefferson Lab, beam energy, size, and position must be measured. Wire scanners are devices inserted into the beamline to produce measurements which are used to obtain beam properties. Extracting physical information from the wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches: genetic algorithm, differential evolution, and particle-swarm. In this Python-implemented approach, augmenting the locally-convergent NCG with one of the globally-convergent methods ensures the quality, robustness, and automation of curve-fitting. After comparing the methods, we establish that given an initial data-derived guess, each finds a solution with the same chi-square- a measurement of the agreement of the fit to the data. NCG is the fastest method, so it is the first to attempt data-fitting. The curve-fitting procedure escalates to one of the globally-convergent NI methods only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems.

TRANSCULTURALIZATION RECOMMENDATIONS FOR DEVELOPING LATIN AMERICAN CLINICAL PRACTICE ALGORITHMS IN ENDOCRINOLOGY--PROCEEDINGS OF THE 2015 PAN-AMERICAN WORKSHOP BY THE AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY.

PubMed

Mechanick, Jeffrey I; Harrell, R Mack; Allende-Vigo, Myriam Z; Alvayero, Carlos; Arita-Melzer, Onix; Aschner, Pablo; Camacho, Pauline M; Castillo, Rogelio Zacarias; Cerdas, Sonia; Coutinho, Walmir F; Davidson, Jaime A; Garber, Jeffrey R; Garvey, W Timothy; González, Fernando Javier Lavalle; Granados, Denis O; Hamdy, Osama; Handelsman, Yehuda; Jiménez-Navarrete, Manuel Francisco; Lupo, Mark A; Mendoza, Enrique J; Jiménez-Montero, José G; Zangeneh, Farhad

2016-04-01

The American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) convened their first Workshop for recommendations to optimize Clinical Practice Algorithm (CPA) development for Latin America (LA) in diabetes (focusing on glycemic control), obesity (focusing on weight loss), thyroid (focusing on thyroid nodule diagnostics), and bone (focusing on postmenopausal osteoporosis) on February 28, 2015, in San Jose, Costa Rica. A standardized methodology is presented incorporating various transculturalization factors: resource availability (including imaging equipment and approved pharmaceuticals), health care professional and patient preferences, lifestyle variables, socio-economic parameters, web-based global accessibility, electronic implementation, and need for validation protocols. A standardized CPA template with node-specific recommendations to assist the local transculturalization process is provided. Participants unanimously agreed on the following five overarching principles for LA: (1) there is only one level of optimal endocrine care, (2) hemoglobin A1C should be utilized at every level of diabetes care, (3) nutrition education and increased pharmaceutical options are necessary to optimize the obesity care model, (4) quality neck ultrasound must be part of an optimal thyroid nodule care model, and (5) more scientific evidence is needed on osteoporosis prevalence and cost to justify intervention by governmental health care authorities. This 2015 AACE/ACE Workshop marks the beginning of a structured activity that assists local experts in creating culturally sensitive, evidence-based, and easy-to-implement tools for optimizing endocrine care on a global scale.

Enabling Controlling Complex Networks with Local Topological Information.

PubMed

Li, Guoqi; Deng, Lei; Xiao, Gaoxi; Tang, Pei; Wen, Changyun; Hu, Wuhua; Pei, Jing; Shi, Luping; Stanley, H Eugene

2018-03-15

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.

Detail and gestalt focus in individuals with optimal outcomes from Autism Spectrum Disorders

PubMed Central

Fitch, Allison; Fein, Deborah A.; Eigsti, Inge-Marie

2015-01-01

Individuals with high-functioning autism (HFA) have a cognitive style that privileges local over global or gestalt details. While not a core symptom of autism, individuals with HFA seem to reliably show this bias. Our lab has been studying a sample of children who have overcome their early ASD diagnoses, showing “optimal outcomes” (OO). This study characterizes performance by OO, HFA, and typically developing (TD) adolescents as they describe paintings under cognitive load. Analyses of detail focus in painting descriptions indicated that the HFA group displayed significantly more local focus than both OO and TD groups, while the OO and TD groups did not differ. We discuss implications for the centrality of detail focus to the autism diagnosis. PMID:25563455

Detail and gestalt focus in individuals with optimal outcomes from autism spectrum disorders.

PubMed

Fitch, Allison; Fein, Deborah A; Eigsti, Inge-Marie

2015-06-01

Individuals with high-functioning autism (HFA) have a cognitive style that privileges local over global or gestalt details. While not a core symptom of autism, individuals with HFA seem to reliably show this bias. Our lab has been studying a sample of children who have overcome their early ASD diagnoses, showing "optimal outcomes" (OO). This study characterizes performance by OO, HFA, and typically developing (TD) adolescents as they describe paintings under cognitive load. Analyses of detail focus in painting descriptions indicated that the HFA group displayed significantly more local focus than both OO and TD groups, while the OO and TD groups did not differ. We discuss implications for the centrality of detail focus to the autism diagnosis.

Perspectives on global health amongst obstetrician gynecologists: A national survey.

PubMed

Easter, Sarah Rae; Raglan, Greta B; Little, Sarah E; Schulkin, Jay; Robinson, Julian N

2018-02-01

Objective To characterize contemporary attitudes toward global health amongst board-certified obstetricians-gynecologists (Ob-Gyns) in the US. Methods A questionnaire was mailed to members of the American College of Obstetricians and Gynecologists. Respondents were stratified by interest and experience in global health and group differences were reported. Results A total of 202 of 400 (50.5%) surveys were completed; and 67.3% ( n = 136) of respondents expressed an interest in global health while 25.2% ( n = 51) had experience providing healthcare abroad. Personal safety was the primary concern of respondents (88 of 185, 47.6%), with 44.5% (57 of 128) identifying 2 weeks as an optimal period of time to spend abroad. The majority (113 of 186, 60.8%) cited hosting of local physicians in the US as the most valuable service to developing a nation's healthcare provision. Conclusion Despite high interest in global health, willingness to spend significant time abroad was limited. Concerns surrounding personal safety dovetailed with the belief that training local physicians in the US provides the most valuable service to international efforts. These attitudes and concerns suggest novel solutions will be required to increase involvement of Ob-Gyns in global women's health.

Management of a Single Species Fishery with Stage Structure

ERIC Educational Resources Information Center

Kar, T. K.; Pahari, U. K.; Chaudhuri, K. S.

2004-01-01

A dynamic model for a single species fishery with stage structure is proposed using taxation as a control instrument to protect the fish population from overexploitation. Criteria for local stability and global stability of the system are derived. The optimal tax policy is established by using Pontryagin's maximal principle. By numerical…

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Tracking Virus Particles in Fluorescence Microscopy Images Using Multi-Scale Detection and Multi-Frame Association.

PubMed

Jaiswal, Astha; Godinez, William J; Eils, Roland; Lehmann, Maik Jorg; Rohr, Karl

2015-11-01

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Accelerating atomic structure search with cluster regularization

NASA Astrophysics Data System (ADS)

Sørensen, K. H.; Jørgensen, M. S.; Bruix, A.; Hammer, B.

2018-06-01

We present a method for accelerating the global structure optimization of atomic compounds. The method is demonstrated to speed up the finding of the anatase TiO2(001)-(1 × 4) surface reconstruction within a density functional tight-binding theory framework using an evolutionary algorithm. As a key element of the method, we use unsupervised machine learning techniques to categorize atoms present in a diverse set of partially disordered surface structures into clusters of atoms having similar local atomic environments. Analysis of more than 1000 different structures shows that the total energy of the structures correlates with the summed distances of the atomic environments to their respective cluster centers in feature space, where the sum runs over all atoms in each structure. Our method is formulated as a gradient based minimization of this summed cluster distance for a given structure and alternates with a standard gradient based energy minimization. While the latter minimization ensures local relaxation within a given energy basin, the former enables escapes from meta-stable basins and hence increases the overall performance of the global optimization.

On the utilization of engineering knowledge in design optimization

NASA Technical Reports Server (NTRS)

Papalambros, P.

1984-01-01

Some current research work conducted at the University of Michigan is described to illustrate efforts for incorporating knowledge in optimization in a nontraditional way. The incorporation of available knowledge in a logic structure is examined in two circumstances. The first examines the possibility of introducing global design information in a local active set strategy implemented during the iterations of projection-type algorithms for nonlinearly constrained problems. The technique used algorithms for nonlinearly constrained problems. The technique used combines global and local monotinicity analysis of the objective and constraint functions. The second examines a knowledge-based program which aids the user to create condigurations that are most desirable from the manufacturing assembly viewpoint. The data bank used is the classification scheme suggested by Boothroyd. The important aspect of this program is that it is an aid for synthesis intended for use in the design concept phase in a way similar to the so-called idea-triggers in creativity-enhancement techniques like brain-storming. The idea generation, however, is not random but it is driven by the goal of achieving the best acceptable configuration.

Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?

PubMed Central

Newberry, Mitchell G.; Savage, Van M.

2016-01-01

Modern models that derive allometric relationships between metabolic rate and body mass are based on the architectural design of the cardiovascular system and presume sibling vessels are symmetric in terms of radius, length, flow rate, and pressure. Here, we study the cardiovascular structure of the human head and torso and of a mouse lung based on three-dimensional images processed via our software Angicart. In contrast to modern allometric theories, we find systematic patterns of asymmetry in vascular branching, potentially explaining previously documented mismatches between predictions (power-law or concave curvature) and observed empirical data (convex curvature) for the allometric scaling of metabolic rate. To examine why these systematic asymmetries in vascular branching might arise, we construct a mathematical framework to derive predictions based on local, junction-level optimality principles that have been proposed to be favored in the course of natural selection and development. The two most commonly used principles are material-cost optimizations (construction materials or blood volume) and optimization of efficient flow via minimization of power loss. We show that material-cost optimization solutions match with distributions for asymmetric branching across the whole network but do not match well for individual junctions. Consequently, we also explore random branching that is constrained at scales that range from local (junction-level) to global (whole network). We find that material-cost optimizations are the strongest predictor of vascular branching in the human head and torso, whereas locally or intermediately constrained random branching is comparable to material-cost optimizations for the mouse lung. These differences could be attributable to developmentally-programmed local branching for larger vessels and constrained random branching for smaller vessels. PMID:27902691

Porous biodegradable lumbar interbody fusion cage design and fabrication using integrated global-local topology optimization with laser sintering.

PubMed

Kang, Heesuk; Hollister, Scott J; La Marca, Frank; Park, Paul; Lin, Chia-Ying

2013-10-01

Biodegradable cages have received increasing attention for their use in spinal procedures involving interbody fusion to resolve complications associated with the use of nondegradable cages, such as stress shielding and long-term foreign body reaction. However, the relatively weak initial material strength compared to permanent materials and subsequent reduction due to degradation may be problematic. To design a porous biodegradable interbody fusion cage for a preclinical large animal study that can withstand physiological loads while possessing sufficient interconnected porosity for bony bridging and fusion, we developed a multiscale topology optimization technique. Topology optimization at the macroscopic scale provides optimal structural layout that ensures mechanical strength, while optimally designed microstructures, which replace the macroscopic material layout, ensure maximum permeability. Optimally designed cages were fabricated using solid, freeform fabrication of poly(ε-caprolactone) mixed with hydroxyapatite. Compression tests revealed that the yield strength of optimized fusion cages was two times that of typical human lumbar spine loads. Computational analysis further confirmed the mechanical integrity within the human lumbar spine, although the pore structure locally underwent higher stress than yield stress. This optimization technique may be utilized to balance the complex requirements of load-bearing, stress shielding, and interconnected porosity when using biodegradable materials for fusion cages.

Effort in Multitasking: Local and Global Assessment of Effort.

PubMed

Kiesel, Andrea; Dignath, David

2017-01-01

When performing multiple tasks in succession, self-organization of task order might be superior compared to external-controlled task schedules, because self-organization allows optimizing processing modes and thus reduces switch costs, and it increases commitment to task goals. However, self-organization is an additional executive control process that is not required if task order is externally specified and as such it is considered as time-consuming and effortful. To compare self-organized and externally controlled task scheduling, we suggest assessing global subjective and objectives measures of effort in addition to local performance measures. In our new experimental approach, we combined characteristics of dual tasking settings and task switching settings and compared local and global measures of effort in a condition with free choice of task sequence and a condition with cued task sequence. In a multi-tasking environment, participants chose the task order while the task requirement of the not-yet-performed task remained the same. This task preview allowed participants to work on the previously non-chosen items in parallel and resulted in faster responses and fewer errors in task switch trials than in task repetition trials. The free-choice group profited more from this task preview than the cued group when considering local performance measures. Nevertheless, the free-choice group invested more effort than the cued group when considering global measures. Thus, self-organization in task scheduling seems to be effortful even in conditions in which it is beneficiary for task processing. In a second experiment, we reduced the possibility of task preview for the not-yet-performed tasks in order to hinder efficient self-organization. Here neither local nor global measures revealed substantial differences between the free-choice and a cued task sequence condition. Based on the results of both experiments, we suggest that global assessment of effort in addition to local performance measures might be a useful tool for multitasking research.

Stability of mechanical joints in launching vehicles: Local and global stationary values of energy density

NASA Astrophysics Data System (ADS)

Chue, Ching-Hwei

A method was developed for predicting the behavior of mechanical joints in launch vehicles with particular emphasis placed on how the combined effects of loading, geometry, and materials could be optimized in terms of structure instability and/or integrity. What was considered to be essential is the fluctuation of the volume energy density with time in the structure. The peaks and valleys of the volume energy density function will be associated with failure by fracture and/or yielding while the distance between their local and global stationary values govern the structure instability. The Solid Rocket Booster (SRB) of the space shuttle was analyzed under axisymmetric and non-axisymmetric loadings. A semi-analytical finite element program was developed for solving the case of non-axisymmetric loading. Following a dynamic stress analysis, contours of the volume energy density in the structure were obtained as a function of time. The magnitudes and locations of these stationary values were then calculated locally and globally and related to possible failure by fracture. In the case of axisymmetric flight, the local and global instability behavior do not change appreciably. Fluctuations in the energy density and the dynamic stability length parameter become appreciable when the non-axisymmetric loads are considered. The magnitude of the energy in the shell structure is sensitive to alterations in the gas pressure induced by the solid propellant.

Clustering methods for the optimization of atomic cluster structure

NASA Astrophysics Data System (ADS)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

Constructing IGA-suitable planar parameterization from complex CAD boundary by domain partition and global/local optimization

NASA Astrophysics Data System (ADS)

Xu, Gang; Li, Ming; Mourrain, Bernard; Rabczuk, Timon; Xu, Jinlan; Bordas, Stéphane P. A.

2018-01-01

In this paper, we propose a general framework for constructing IGA-suitable planar B-spline parameterizations from given complex CAD boundaries consisting of a set of B-spline curves. Instead of forming the computational domain by a simple boundary, planar domains with high genus and more complex boundary curves are considered. Firstly, some pre-processing operations including B\\'ezier extraction and subdivision are performed on each boundary curve in order to generate a high-quality planar parameterization; then a robust planar domain partition framework is proposed to construct high-quality patch-meshing results with few singularities from the discrete boundary formed by connecting the end points of the resulting boundary segments. After the topology information generation of quadrilateral decomposition, the optimal placement of interior B\\'ezier curves corresponding to the interior edges of the quadrangulation is constructed by a global optimization method to achieve a patch-partition with high quality. Finally, after the imposition of C1=G1-continuity constraints on the interface of neighboring B\\'ezier patches with respect to each quad in the quadrangulation, the high-quality B\\'ezier patch parameterization is obtained by a C1-constrained local optimization method to achieve uniform and orthogonal iso-parametric structures while keeping the continuity conditions between patches. The efficiency and robustness of the proposed method are demonstrated by several examples which are compared to results obtained by the skeleton-based parameterization approach.

Global Search Capabilities of Indirect Methods for Impulsive Transfers

NASA Astrophysics Data System (ADS)

Shen, Hong-Xin; Casalino, Lorenzo; Luo, Ya-Zhong

2015-09-01

An optimization method which combines an indirect method with homotopic approach is proposed and applied to impulsive trajectories. Minimum-fuel, multiple-impulse solutions, with either fixed or open time are obtained. The homotopic approach at hand is relatively straightforward to implement and does not require an initial guess of adjoints, unlike previous adjoints estimation methods. A multiple-revolution Lambert solver is used to find multiple starting solutions for the homotopic procedure; this approach can guarantee to obtain multiple local solutions without relying on the user's intuition, thus efficiently exploring the solution space to find the global optimum. The indirect/homotopic approach proves to be quite effective and efficient in finding optimal solutions, and outperforms the joint use of evolutionary algorithms and deterministic methods in the test cases.

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

PubMed

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.

Fast optimization of binary clusters using a novel dynamic lattice searching method.

PubMed

Wu, Xia; Cheng, Wen

2014-09-28

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.

A Grouping Particle Swarm Optimizer with Personal-Best-Position Guidance for Large Scale Optimization.

PubMed

Guo, Weian; Si, Chengyong; Xue, Yu; Mao, Yanfen; Wang, Lei; Wu, Qidi

2017-05-04

Particle Swarm Optimization (PSO) is a popular algorithm which is widely investigated and well implemented in many areas. However, the canonical PSO does not perform well in population diversity maintenance so that usually leads to a premature convergence or local optima. To address this issue, we propose a variant of PSO named Grouping PSO with Personal- Best-Position (Pbest) Guidance (GPSO-PG) which maintains the population diversity by preserving the diversity of exemplars. On one hand, we adopt uniform random allocation strategy to assign particles into different groups and in each group the losers will learn from the winner. On the other hand, we employ personal historical best position of each particle in social learning rather than the current global best particle. In this way, the exemplars diversity increases and the effect from the global best particle is eliminated. We test the proposed algorithm to the benchmarks in CEC 2008 and CEC 2010, which concern the large scale optimization problems (LSOPs). By comparing several current peer algorithms, GPSO-PG exhibits a competitive performance to maintain population diversity and obtains a satisfactory performance to the problems.

Online optimal obstacle avoidance for rotary-wing autonomous unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Kang, Keeryun

This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic. The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle. The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. The flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.

Truss systems and shape optimization

NASA Astrophysics Data System (ADS)

Pricop, Mihai Victor; Bunea, Marian; Nedelcu, Roxana

2017-07-01

Structure optimization is an important topic because of its benefits and wide applicability range, from civil engineering to aerospace and automotive industries, contributing to a more green industry and life. Truss finite elements are still in use in many research/industrial codesfor their simple stiffness matrixand are naturally matching the requirements for cellular materials especially considering various 3D printing technologies. Optimality Criteria combined with Solid Isotropic Material with Penalization is the optimization method of choice, particularized for truss systems. Global locked structures areobtainedusinglocally locked lattice local organization, corresponding to structured or unstructured meshes. Post processing is important for downstream application of the method, to make a faster link to the CAD systems. To export the optimal structure in CATIA, a CATScript file is automatically generated. Results, findings and conclusions are given for two and three-dimensional cases.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Tractable Pareto Optimization of Temporal Preferences

NASA Technical Reports Server (NTRS)

Morris, Robert; Morris, Paul; Khatib, Lina; Venable, Brent

2003-01-01

This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.

Lighting design for globally illuminated volume rendering.

PubMed

Zhang, Yubo; Ma, Kwan-Liu

2013-12-01

With the evolution of graphics hardware, high quality global illumination becomes available for real-time volume rendering. Compared to local illumination, global illumination can produce realistic shading effects which are closer to real world scenes, and has proven useful for enhancing volume data visualization to enable better depth and shape perception. However, setting up optimal lighting could be a nontrivial task for average users. There were lighting design works for volume visualization but they did not consider global light transportation. In this paper, we present a lighting design method for volume visualization employing global illumination. The resulting system takes into account view and transfer-function dependent content of the volume data to automatically generate an optimized three-point lighting environment. Our method fully exploits the back light which is not used by previous volume visualization systems. By also including global shadow and multiple scattering, our lighting system can effectively enhance the depth and shape perception of volumetric features of interest. In addition, we propose an automatic tone mapping operator which recovers visual details from overexposed areas while maintaining sufficient contrast in the dark areas. We show that our method is effective for visualizing volume datasets with complex structures. The structural information is more clearly and correctly presented under the automatically generated light sources.

Optimal phase estimation with arbitrary a priori knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demkowicz-Dobrzanski, Rafal

2011-06-15

The optimal-phase estimation strategy is derived when partial a priori knowledge on the estimated phase is available. The solution is found with the help of the most famous result from the entanglement theory: the positive partial transpose criterion. The structure of the optimal measurements, estimators, and the optimal probe states is analyzed. This Rapid Communication provides a unified framework bridging the gap in the literature on the subject which until now dealt almost exclusively with two extreme cases: almost perfect knowledge (local approach based on Fisher information) and no a priori knowledge (global approach based on covariant measurements). Special attentionmore » is paid to a natural a priori probability distribution arising from a diffusion process.« less

It looks easy! Heuristics for combinatorial optimization problems.

PubMed

Chronicle, Edward P; MacGregor, James N; Ormerod, Thomas C; Burr, Alistair

2006-04-01

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

PubMed

Zhang, Jun; Dolg, Michael

2015-10-07

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.

Different types of maximum power point tracking techniques for renewable energy systems: A survey

NASA Astrophysics Data System (ADS)

Khan, Mohammad Junaid; Shukla, Praveen; Mustafa, Rashid; Chatterji, S.; Mathew, Lini

2016-03-01

Global demand for electricity is increasing while production of energy from fossil fuels is declining and therefore the obvious choice of the clean energy source that is abundant and could provide security for development future is energy from the sun. In this paper, the characteristic of the supply voltage of the photovoltaic generator is nonlinear and exhibits multiple peaks, including many local peaks and a global peak in non-uniform irradiance. To keep global peak, MPPT is the important component of photovoltaic systems. Although many review articles discussed conventional techniques such as P & O, incremental conductance, the correlation ripple control and very few attempts have been made with intelligent MPPT techniques. This document also discusses different algorithms based on fuzzy logic, Ant Colony Optimization, Genetic Algorithm, artificial neural networks, Particle Swarm Optimization Algorithm Firefly, Extremum seeking control method and hybrid methods applied to the monitoring of maximum value of power at point in systems of photovoltaic under changing conditions of irradiance.

A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

PubMed Central

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

Changing Education for a Changing World: Internationalizing Education

ERIC Educational Resources Information Center

Idogho, Philipa Omamhe; Eshiotse, Sunday Gabriel

2012-01-01

That change is permanent has never been an issue, but how to anticipate, adopt, equip, and re-position to optimize benefits of change is the heart of research. Thus, true to Marshall McLuhan's prophetic foresight, the world, indeed, has become a village where otherwise local events now exert profound global consequences. In this paper, the…

Fast and Accurate Construction of Ultra-Dense Consensus Genetic Maps Using Evolution Strategy Optimization

PubMed Central

Mester, David; Ronin, Yefim; Schnable, Patrick; Aluru, Srinivas; Korol, Abraham

2015-01-01

Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time. PMID:25867943

A voting-based star identification algorithm utilizing local and global distribution

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang; Sun, Junhua

2018-03-01

A novel star identification algorithm based on voting scheme is presented in this paper. In the proposed algorithm, the global distribution and local distribution of sensor stars are fully utilized, and the stratified voting scheme is adopted to obtain the candidates for sensor stars. The database optimization is employed to reduce its memory requirement and improve the robustness of the proposed algorithm. The simulation shows that the proposed algorithm exhibits 99.81% identification rate with 2-pixel standard deviations of positional noises and 0.322-Mv magnitude noises. Compared with two similar algorithms, the proposed algorithm is more robust towards noise, and the average identification time and required memory is less. Furthermore, the real sky test shows that the proposed algorithm performs well on the real star images.

Optimal perturbations for nonlinear systems using graph-based optimal transport

NASA Astrophysics Data System (ADS)

Grover, Piyush; Elamvazhuthi, Karthik

2018-06-01

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models.

PubMed

Maghrabi, Ali H A; McGuffin, Liam J

2017-07-03

Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The regional approach and regional studies method in the process of geography teaching

NASA Astrophysics Data System (ADS)

Dermendzhieva, Stela; Doikov, Martin

2017-03-01

We define the regional approach as a manner of relations among the global trends of development of the "Society-man-nature" system and the local differentiating level of knowledge. Conditionally, interactions interlace under the influence of the character of Geography as a science, education, approaches, goals and teaching methods. Global, national and local development differentiates in three concentric circles at the level of knowledge. It is determined as a conception of modern, complex and effective mechanism for young people, through which knowledge develops in regional historical and cultural perspective; self-consciousness for socio-economic and cultural integration is formed as a part of the. historical-geographical image of the native land. This way an attitude to the. native land is formed as a connecting construct between patriotism to the motherland and the same in global aspect. The possibility for integration and cooperation of the educative geographical content with all the local historical-geographical, regional, profession orientating, artistic, municipal and district institutions, is outlined. Contemporary geographical education appears to be a powerful and indispensable mechanism for organization of human sciences, while the regional approach and the application of the regional studies method stimulate and motivate the development and realization of optimal capacities for direct connection with the local structures and environments.

Nurse Scheduling by Cooperative GA with Effective Mutation Operator

NASA Astrophysics Data System (ADS)

Ohki, Makoto

In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

Structural Analysis of the Redesigned Ice/Frost Ramp Bracket

NASA Technical Reports Server (NTRS)

Phillips, D. R.; Dawicke, D. S.; Gentz, S. J.; Roberts, P. W.; Raju, I. S.

2007-01-01

This paper describes the interim structural analysis of a redesigned Ice/Frost Ramp bracket for the Space Shuttle External Tank (ET). The proposed redesigned bracket consists of mounts for attachment to the ET wall, supports for the electronic/instrument cables and propellant repressurization lines that run along the ET, an upper plate, a lower plate, and complex bolted connections. The eight nominal bolted connections are considered critical in the summarized structural analysis. Each bolted connection contains a bolt, a nut, four washers, and a non-metallic spacer and block that are designed for thermal insulation. A three-dimensional (3D) finite element model of the bracket is developed using solid 10-node tetrahedral elements. The loading provided by the ET Project is used in the analysis. Because of the complexities associated with accurately modeling the bolted connections in the bracket, the analysis is performed using a global/local analysis procedure. The finite element analysis of the bracket identifies one of the eight bolted connections as having high stress concentrations. A local area of the bracket surrounding this bolted connection is extracted from the global model and used as a local model. Within the local model, the various components of the bolted connection are refined, and contact is introduced along the appropriate interfaces determined by the analysts. The deformations from the global model are applied as boundary conditions to the local model. The results from the global/local analysis show that while the stresses in the bolts are well within yield, the spacers fail due to compression. The primary objective of the interim structural analysis is to show concept viability for static thermal testing. The proposed design concept would undergo continued design optimization to address the identified analytical assumptions and concept shortcomings, assuming successful thermal testing.

A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

PubMed

Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza

2013-02-07

Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

An ITK framework for deterministic global optimization for medical image registration

NASA Astrophysics Data System (ADS)

Dru, Florence; Wachowiak, Mark P.; Peters, Terry M.

2006-03-01

Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.

Structural design of composite rotor blades with consideration of manufacturability, durability, and manufacturing uncertainties

NASA Astrophysics Data System (ADS)

Li, Leihong

A modular structural design methodology for composite blades is developed. This design method can be used to design composite rotor blades with sophisticate geometric cross-sections. This design method hierarchically decomposed the highly-coupled interdisciplinary rotor analysis into global and local levels. In the global level, aeroelastic response analysis and rotor trim are conduced based on multi-body dynamic models. In the local level, variational asymptotic beam sectional analysis methods are used for the equivalent one-dimensional beam properties. Compared with traditional design methodology, the proposed method is more efficient and accurate. Then, the proposed method is used to study three different design problems that have not been investigated before. The first is to add manufacturing constraints into design optimization. The introduction of manufacturing constraints complicates the optimization process. However, the design with manufacturing constraints benefits the manufacturing process and reduces the risk of violating major performance constraints. Next, a new design procedure for structural design against fatigue failure is proposed. This procedure combines the fatigue analysis with the optimization process. The durability or fatigue analysis employs a strength-based model. The design is subject to stiffness, frequency, and durability constraints. Finally, the manufacturing uncertainty impacts on rotor blade aeroelastic behavior are investigated, and a probabilistic design method is proposed to control the impacts of uncertainty on blade structural performance. The uncertainty factors include dimensions, shapes, material properties, and service loads.

Non-linear Multidimensional Optimization for use in Wire Scanner Fitting

NASA Astrophysics Data System (ADS)

Henderson, Alyssa; Terzic, Balsa; Hofler, Alicia; CASA and Accelerator Ops Collaboration

2013-10-01

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator at Jefferson Lab, beam energy, size, and position must be measured. Wire scanners are devices inserted into the beamline to produce measurements which are used to obtain beam properties. Extracting physical information from the wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches: genetic algorithm, differential evolution, and particle-swarm. In this Python-implemented approach, augmenting the locally-convergent NCG with one of the globally-convergent methods ensures the quality, robustness, and automation of curve-fitting. After comparing the methods, we establish that given an initial data-derived guess, each finds a solution with the same chi-square- a measurement of the agreement of the fit to the data. NCG is the fastest method, so it is the first to attempt data-fitting. The curve-fitting procedure escalates to one of the globally-convergent NI methods only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems. Financial support from DoE, NSF, ODU, DoD, and Jefferson Lab.

Cuckoo Search with Lévy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting

PubMed Central

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

Cuckoo search with Lévy flights for weighted Bayesian energy functional optimization in global-support curve data fitting.

PubMed

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.

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…

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

Study on UKF based federal integrated navigation for high dynamic aviation

NASA Astrophysics Data System (ADS)

Zhao, Gang; Shao, Wei; Chen, Kai; Yan, Jie

2011-08-01

High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter (UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation. The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence rate.

The Hartree product and the description of local and global quantities in atomic systems: A study within Kohn-Sham theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garza, Jorge; Nichols, Jeffrey A.; Dixon, David A.

2000-01-15

The Hartree product is analyzed in the context of Kohn-Sham theory. The differential equations that emerge from this theory are solved with the optimized effective potential using the Krieger, Li, and Iafrate approximation, in order to get a local potential as required by the ordinary Kohn-Sham procedure. Because the diagonal terms of the exact exchange energy are included in Hartree theory, it is self-interaction free and the exchange potential has the proper asymptotic behavior. We have examined the impact of this correct asymptotic behavior on local and global properties using this simple model to approximate the exchange energy. Local quantities,more » such as the exchange potential and the average local electrostatic potential are used to examine whether the shell structure in an atom is revealed by this theory. Global quantities, such as the highest occupied orbital energy (related to the ionization potential) and the exchange energy are also calculated. These quantities are contrasted with those obtained from calculations with the local density approximation, the generalized gradient approximation, and the self-interaction correction approach proposed by Perdew and Zunger. We conclude that the main characteristics in an atomic system are preserved with the Hartree theory. In particular, the behavior of the exchange potential obtained in this theory is similar to those obtained within other Kohn-Sham approximations. (c) 2000 American Institute of Physics.« less

Cost effective simulation-based multiobjective optimization in the performance of an internal combustion engine

NASA Astrophysics Data System (ADS)

Aittokoski, Timo; Miettinen, Kaisa

2008-07-01

Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.

Generalized bipartite quantum state discrimination problems with sequential measurements

NASA Astrophysics Data System (ADS)

Nakahira, Kenji; Kato, Kentaro; Usuda, Tsuyoshi Sasaki

2018-02-01

We investigate an optimization problem of finding quantum sequential measurements, which forms a wide class of state discrimination problems with the restriction that only local operations and one-way classical communication are allowed. Sequential measurements from Alice to Bob on a bipartite system are considered. Using the fact that the optimization problem can be formulated as a problem with only Alice's measurement and is convex programming, we derive its dual problem and necessary and sufficient conditions for an optimal solution. Our results are applicable to various practical optimization criteria, including the Bayes criterion, the Neyman-Pearson criterion, and the minimax criterion. In the setting of the problem of finding an optimal global measurement, its dual problem and necessary and sufficient conditions for an optimal solution have been widely used to obtain analytical and numerical expressions for optimal solutions. Similarly, our results are useful to obtain analytical and numerical expressions for optimal sequential measurements. Examples in which our results can be used to obtain an analytical expression for an optimal sequential measurement are provided.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

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. © 2015 Wiley Periodicals, Inc.

Turbomachinery Airfoil Design Optimization Using Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

The perception of minimal structures: performance on open and closed versions of visually presented Euclidean travelling salesperson problems.

PubMed

Vickers, Douglas; Bovet, Pierre; Lee, Michael D; Hughes, Peter

2003-01-01

The planar Euclidean version of the travelling salesperson problem (TSP) requires finding a tour of minimal length through a two-dimensional set of nodes. Despite the computational intractability of the TSP, people can produce rapid, near-optimal solutions to visually presented versions of such problems. To explain this, MacGregor et al (1999, Perception 28 1417-1428) have suggested that people use a global-to-local process, based on a perceptual tendency to organise stimuli into convex figures. We review the evidence for this idea and propose an alternative, local-to-global hypothesis, based on the detection of least distances between the nodes in an array. We present the results of an experiment in which we examined the relationships between three objective measures and performance measures of optimality and response uncertainty in tasks requiring participants to construct a closed tour or an open path. The data are not well accounted for by a process based on the convex hull. In contrast, results are generally consistent with a locally focused process based initially on the detection of nearest-neighbour clusters. Individual differences are interpreted in terms of a hierarchical process of constructing solutions, and the findings are related to a more general analysis of the role of nearest neighbours in the perception of structure and motion.

A hybrid binary particle swarm optimization for large capacitated multi item multi level lot sizing (CMIMLLS) problem

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Sahithi, V. V. D.; Rao, C. S. P.

2016-09-01

The lot sizing problem deals with finding optimal order quantities which minimizes the ordering and holding cost of product mix. when multiple items at multiple levels with all capacity restrictions are considered, the lot sizing problem become NP hard. Many heuristics were developed in the past have inevitably failed due to size, computational complexity and time. However the authors were successful in the development of PSO based technique namely iterative improvement binary particles swarm technique to address very large capacitated multi-item multi level lot sizing (CMIMLLS) problem. First binary particle Swarm Optimization algorithm is used to find a solution in a reasonable time and iterative improvement local search mechanism is employed to improvise the solution obtained by BPSO algorithm. This hybrid mechanism of using local search on the global solution is found to improve the quality of solutions with respect to time thus IIBPSO method is found best and show excellent results.

The global Minmax k-means algorithm.

PubMed

Wang, Xiaoyan; Bai, Yanping

2016-01-01

The global k -means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k -means to minimize the sum of the intra-cluster variances. However the global k -means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k -means algorithm. In this paper, we modified the global k -means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k -means clustering error method to global k -means algorithm to overcome the effect of bad initialization, proposed the global Minmax k -means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k -means algorithm, the global k -means algorithm and the MinMax k -means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper.

Global Health Diplomacy, "San Francisco Values," and HIV/AIDS: From the Local to the Global.

PubMed

Kevany, Sebastian

2015-01-01

San Francisco has a distinguished history as a cosmopolitan, progressive, and international city, including extensive associations with global health. These circumstances have contributed to new, interdisciplinary scholarship in the field of global health diplomacy (GHD). In the present review, we describe the evolution and history of GHD at the practical and theoretical levels within the San Francisco medical community, trace related associations between the local and the global, and propose a range of potential opportunities for further development of this dynamic field. We provide a historical overview of the development of the "San Francisco Model" of collaborative, community-owned HIV/AIDS treatment and care programs as pioneered under the "Ward 86" paradigm of the 1980s. We traced the expansion and evolution of this model to the national level under the Ryan White Care Act, and internationally via the President's Emergency Plan for AIDS Relief. In parallel, we describe the evolution of global health diplomacy practices, from the local to the global, including the integration of GHD principles into intervention design to ensure social, political, and cultural acceptability and sensitivity. Global health programs, as informed by lessons learned from the San Francisco Model, are increasingly aligned with diplomatic principles and practices. This awareness has aided implementation, allowed policymakers to pursue related and progressive social and humanitarian issues in conjunction with medical responses, and elevated global health to the realm of "high politics." In the 21st century, the integration between diplomatic, medical, and global health practices will continue under "smart global health" and GHD paradigms. These approaches will enhance intervention cost-effectiveness by addressing and optimizing, in tandem with each other, a wide range of (health and non-health) foreign policy, diplomatic, security, and economic priorities in a synergistic manner--without sacrificing health outcomes. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

Impacts of climate extremes on gross primary production under global warming

DOE PAGES

Williams, I. N.; Torn, M. S.; Riley, W. J.; ...

2014-09-24

The impacts of historical droughts and heat-waves on ecosystems are often considered indicative of future global warming impacts, under the assumption that water stress sets in above a fixed high temperature threshold. Historical and future (RCP8.5) Earth system model (ESM) climate projections were analyzed in this study to illustrate changes in the temperatures for onset of water stress under global warming. The ESMs examined here predict sharp declines in gross primary production (GPP) at warm temperature extremes in historical climates, similar to the observed correlations between GPP and temperature during historical heat-waves and droughts. However, soil moisture increases at themore » warm end of the temperature range, and the temperature at which soil moisture declines with temperature shifts to a higher temperature. The temperature for onset of water stress thus increases under global warming and is associated with a shift in the temperature for maximum GPP to warmer temperatures. Despite the shift in this local temperature optimum, the impacts of warm extremes on GPP are approximately invariant when extremes are defined relative to the optimal temperature within each climate period. The GPP sensitivity to these relative temperature extremes therefore remains similar between future and present climates, suggesting that the heat- and drought-induced GPP reductions seen recently can be expected to be similar in the future, and may be underestimates of future impacts given model projections of increased frequency and persistence of heat-waves and droughts. The local temperature optimum can be understood as the temperature at which the combination of water stress and light limitations is minimized, and this concept gives insights into how GPP responds to climate extremes in both historical and future climate periods. Both cold (temperature and light-limited) and warm (water-limited) relative temperature extremes become more persistent in future climate projections, and the time taken to return to locally optimal climates for GPP following climate extremes increases by more than 25% over many land regions.« less

Impacts of climate extremes on gross primary production under global warming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, I. N.; Torn, M. S.; Riley, W. J.

The impacts of historical droughts and heat-waves on ecosystems are often considered indicative of future global warming impacts, under the assumption that water stress sets in above a fixed high temperature threshold. Historical and future (RCP8.5) Earth system model (ESM) climate projections were analyzed in this study to illustrate changes in the temperatures for onset of water stress under global warming. The ESMs examined here predict sharp declines in gross primary production (GPP) at warm temperature extremes in historical climates, similar to the observed correlations between GPP and temperature during historical heat-waves and droughts. However, soil moisture increases at themore » warm end of the temperature range, and the temperature at which soil moisture declines with temperature shifts to a higher temperature. The temperature for onset of water stress thus increases under global warming and is associated with a shift in the temperature for maximum GPP to warmer temperatures. Despite the shift in this local temperature optimum, the impacts of warm extremes on GPP are approximately invariant when extremes are defined relative to the optimal temperature within each climate period. The GPP sensitivity to these relative temperature extremes therefore remains similar between future and present climates, suggesting that the heat- and drought-induced GPP reductions seen recently can be expected to be similar in the future, and may be underestimates of future impacts given model projections of increased frequency and persistence of heat-waves and droughts. The local temperature optimum can be understood as the temperature at which the combination of water stress and light limitations is minimized, and this concept gives insights into how GPP responds to climate extremes in both historical and future climate periods. Both cold (temperature and light-limited) and warm (water-limited) relative temperature extremes become more persistent in future climate projections, and the time taken to return to locally optimal climates for GPP following climate extremes increases by more than 25% over many land regions.« less

Tensor voting for image correction by global and local intensity alignment.

PubMed

Jia, Jiaya; Tang, Chi-Keung

2005-01-01

This paper presents a voting method to perform image correction by global and local intensity alignment. The key to our modeless approach is the estimation of global and local replacement functions by reducing the complex estimation problem to the robust 2D tensor voting in the corresponding voting spaces. No complicated model for replacement function (curve) is assumed. Subject to the monotonic constraint only, we vote for an optimal replacement function by propagating the curve smoothness constraint using a dense tensor field. Our method effectively infers missing curve segments and rejects image outliers. Applications using our tensor voting approach are proposed and described. The first application consists of image mosaicking of static scenes, where the voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix. In the presence of occlusion, our replacement function can be employed to construct a visually acceptable mosaic by detecting occlusion which has large and piecewise constant color. Furthermore, by the simultaneous consideration of color matches and spatial constraints in the voting space, we perform image intensity compensation and high contrast image correction using our voting framework, when only two defective input images are given.

A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints.

PubMed

Xiao, Hu; Cui, Rongxin; Xu, Demin

2018-06-01

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

Multifidelity Analysis and Optimization for Supersonic Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Willcox, Karen; March, Andrew; Haas, Alex; Rajnarayan, Dev; Kays, Cory

2010-01-01

Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.

An artifact caused by undersampling optimal trees in supermatrix analyses of locally sampled characters.

PubMed

Simmons, Mark P; Goloboff, Pablo A

2013-10-01

Empirical and simulated examples are used to demonstrate an artifact caused by undersampling optimal trees in data matrices that consist mostly or entirely of locally sampled (as opposed to globally, for most or all terminals) characters. The artifact is that unsupported clades consisting entirely of terminals scored for the same locally sampled partition may be resolved and assigned high resampling support-despite their being properly unsupported (i.e., not resolved in the strict consensus of all optimal trees). This artifact occurs despite application of random-addition sequences for stepwise terminal addition. The artifact is not necessarily obviated with thorough conventional branch swapping methods (even tree-bisection-reconnection) when just a single tree is held, as is sometimes implemented in parsimony bootstrap pseudoreplicates, and in every GARLI, PhyML, and RAxML pseudoreplicate and search for the most likely tree for the matrix as a whole. Hence GARLI, RAxML, and PhyML-based likelihood results require extra scrutiny, particularly when they provide high resolution and support for clades that are entirely unsupported by methods that perform more thorough searches, as in most parsimony analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Network placement optimization for large-scale distributed system

NASA Astrophysics Data System (ADS)

Ren, Yu; Liu, Fangfang; Fu, Yunxia; Zhou, Zheng

2018-01-01

The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.

Assimilation of global versus local data sets into a regional model of the Gulf Stream system. 1. Data effectiveness

NASA Astrophysics Data System (ADS)

Malanotte-Rizzoli, Paola; Young, Roberta E.

1995-12-01

The primary objective of this paper is to assess the relative effectiveness of data sets with different space coverage and time resolution when they are assimilated into an ocean circulation model. We focus on obtaining realistic numerical simulations of the Gulf Stream system typically of the order of 3-month duration by constructing a "synthetic" ocean simultaneously consistent with the model dynamics and the observations. The model used is the Semispectral Primitive Equation Model. The data sets are the "global" Optimal Thermal Interpolation Scheme (OTIS) 3 of the Fleet Numerical Oceanography Center providing temperature and salinity fields with global coverage and with bi-weekly frequency, and the localized measurements, mostly of current velocities, from the central and eastern array moorings of the Synoptic Ocean Prediction (SYNOP) program, with daily frequency but with a very small spatial coverage. We use a suboptimal assimilation technique ("nudging"). Even though this technique has already been used in idealized data assimilation studies, to our knowledge this is the first study in which the effectiveness of nudging is tested by assimilating real observations of the interior temperature and salinity fields. This is also the first work in which a systematic assimilation is carried out of the localized, high-quality SYNOP data sets in numerical experiments longer than 1-2 weeks, that is, not aimed to forecasting. We assimilate (1) the global OTIS 3 alone, (2) the local SYNOP observations alone, and (3) both OTIS 3 and SYNOP observations. We assess the success of the assimilations with quantitative measures of performance, both on the global and local scale. The results can be summarized as follows. The intermittent assimilation of the global OTIS 3 is necessary to keep the model "on track" over 3-month simulations on the global scale. As OTIS 3 is assimilated at every model grid point, a "gentle" weight must be prescribed to it so as not to overconstrain the model. However, in these assimilations the predicted velocity fields over the SYNOP arrays are greatly in error. The continuous assimilation of the localized SYNOP data sets with a strong weight is necessary to obtain local realistic evolutions. Then assimilation of velocity measurements alone recovers the density structure over the array area. However, the spatial coverage of the SYNOP measurements is too small to constrain the model on the global scale. Thus the blending of both types of datasets is necessary in the assimilation as they constrain different time and space scales. Our choice of "gentle" nudging weight for the global OTIS 3 and "strong" weight for the local SYNOP data provides for realistic simulations of the Gulf Stream system, both globally and locally, on the 3- to 4-month-long timescale, the one governed by the Gulf Stream jet internal dynamics.

Evaluating localism in the management of post-consumer plastic bottles in Honolulu, Hawai'i: perspectives from industrial ecology and political ecology.

PubMed

Park, Joo Young; Gupta, Clare

2015-05-01

Localism or regionalization has become a popular topic in urban design, but recent critics raise the question of whether the local or regional scale is most desirable for industrial ecosystems. As a way to explore the claim that localized metabolism is more sustainable, this study examines the costs and benefits of two differentially scaled strategies for the management of post-consumer polyethylene terephthalate (PET) bottles originating in the city of Honolulu, Hawai'i: local incineration and trans-continental recycling. We first estimate total environmental impacts of two options using life cycle assessment, and then disaggregate them into local versus non-local impacts to examine the spatial distribution of costs and benefits. We further assess the environmental justification for localized waste management in relation to the broader socio-economic motivations that underlie the way that plastics are managed in Honolulu. In doing so we assess the scale at which waste management is optimized from an environmental standpoint as well as the non-environmental considerations such as security and safety that influence the politics of scale involved in urban metabolic design. By illustrating the trade-offs between a local versus global metabolic pathway for plastic waste, the results from our Honolulu case study are globally relevant for communities interested in sustainable urban design and in particular urban waste management. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit

NASA Astrophysics Data System (ADS)

Gaddy, Melissa R.; Yıldız, Sercan; Unkelbach, Jan; Papp, Dávid

2018-01-01

Spatiotemporal fractionation schemes, that is, treatments delivering different dose distributions in different fractions, can potentially lower treatment side effects without compromising tumor control. This can be achieved by hypofractionating parts of the tumor while delivering approximately uniformly fractionated doses to the surrounding tissue. Plan optimization for such treatments is based on biologically effective dose (BED); however, this leads to computationally challenging nonconvex optimization problems. Optimization methods that are in current use yield only locally optimal solutions, and it has hitherto been unclear whether these plans are close to the global optimum. We present an optimization framework to compute rigorous bounds on the maximum achievable normal tissue BED reduction for spatiotemporal plans. The approach is demonstrated on liver tumors, where the primary goal is to reduce mean liver BED without compromising any other treatment objective. The BED-based treatment plan optimization problems are formulated as quadratically constrained quadratic programming (QCQP) problems. First, a conventional, uniformly fractionated reference plan is computed using convex optimization. Then, a second, nonconvex, QCQP model is solved to local optimality to compute a spatiotemporally fractionated plan that minimizes mean liver BED, subject to the constraints that the plan is no worse than the reference plan with respect to all other planning goals. Finally, we derive a convex relaxation of the second model in the form of a semidefinite programming problem, which provides a rigorous lower bound on the lowest achievable mean liver BED. The method is presented on five cases with distinct geometries. The computed spatiotemporal plans achieve 12-35% mean liver BED reduction over the optimal uniformly fractionated plans. This reduction corresponds to 79-97% of the gap between the mean liver BED of the uniform reference plans and our lower bounds on the lowest achievable mean liver BED. The results indicate that spatiotemporal treatments can achieve substantial reductions in normal tissue dose and BED, and that local optimization techniques provide high-quality plans that are close to realizing the maximum potential normal tissue dose reduction.

Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis

NASA Astrophysics Data System (ADS)

Kasaiezadeh, Alireza; Khajepour, Amir; Waslander, Steven L.

2014-04-01

A biologically-inspired algorithm called Spiral Bacterial Foraging Optimization (SBFO) is investigated in this article. SBFO, previously proposed by the same authors, is a multi-agent, gradient-based algorithm that minimizes both the main objective function (local cost) and the distance between each agent and a temporary central point (global cost). A random jump is included normal to the connecting line of each agent to the central point, which produces a vortex around the temporary central point. This random jump is also suitable to cope with premature convergence, which is a feature of swarm-based optimization methods. The most important advantages of this algorithm are as follows: First, this algorithm involves a stochastic type of search with a deterministic convergence. Second, as gradient-based methods are employed, faster convergence is demonstrated over GA, DE, BFO, etc. Third, the algorithm can be implemented in a parallel fashion in order to decentralize large-scale computation. Fourth, the algorithm has a limited number of tunable parameters, and finally SBFO has a strong certainty of convergence which is rare in existing global optimization algorithms. A detailed convergence analysis of SBFO for continuously differentiable objective functions has also been investigated in this article.

Hybrid preconditioning for iterative diagonalization of ill-conditioned generalized eigenvalue problems in electronic structure calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Yunfeng, E-mail: yfcai@math.pku.edu.cn; Department of Computer Science, University of California, Davis 95616; Bai, Zhaojun, E-mail: bai@cs.ucdavis.edu

2013-12-15

The iterative diagonalization of a sequence of large ill-conditioned generalized eigenvalue problems is a computational bottleneck in quantum mechanical methods employing a nonorthogonal basis for ab initio electronic structure calculations. We propose a hybrid preconditioning scheme to effectively combine global and locally accelerated preconditioners for rapid iterative diagonalization of such eigenvalue problems. In partition-of-unity finite-element (PUFE) pseudopotential density-functional calculations, employing a nonorthogonal basis, we show that the hybrid preconditioned block steepest descent method is a cost-effective eigensolver, outperforming current state-of-the-art global preconditioning schemes, and comparably efficient for the ill-conditioned generalized eigenvalue problems produced by PUFE as the locally optimal blockmore » preconditioned conjugate-gradient method for the well-conditioned standard eigenvalue problems produced by planewave methods.« less

Local and Global Bifurcations of Flow Fields During Physical Vapor Transport: Application to a Microgravity Experiment

NASA Technical Reports Server (NTRS)

Duval, W. M. B.; Singh, N. B.; Glicksman, M. E.

1996-01-01

The local bifurcation of the flow field, during physical vapor transport for a parametric range of experimental interest, shows that its dynamical state ranges from steady to aperiodic. Comparison of computationally predicted velocity profiles with laser doppler velocimetry measurements shows reasonable agreement in both magnitude and planform. Correlation of experimentally measured crystal quality with the predicted dynamical state of the flow field shows a degradation of quality with an increase in Rayleigh number. The global bifurcation of the flow field corresponding to low crystal quality indicates the presence of a traveling wave for Ra = 1.09 x 10(exp 5). For this Rayleigh number threshold a chaotic transport state occurs. However, a microgravity environment for this case effectively stabilizes the flow to diffusive-advective and provides the setting to grow crystals with optimal quality.

A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu

2015-01-01

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908

Nonlinear Rayleigh wave inversion based on the shuffled frog-leaping algorithm

NASA Astrophysics Data System (ADS)

Sun, Cheng-Yu; Wang, Yan-Yan; Wu, Dun-Shi; Qin, Xiao-Jun

2017-12-01

At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm (SFLA), into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.

Experimental test of an online ion-optics optimizer

NASA Astrophysics Data System (ADS)

Amthor, A. M.; Schillaci, Z. M.; Morrissey, D. J.; Portillo, M.; Schwarz, S.; Steiner, M.; Sumithrarachchi, Ch.

2018-07-01

A technique has been developed and tested to automatically adjust multiple electrostatic or magnetic multipoles on an ion optical beam line - according to a defined optimization algorithm - until an optimal tune is found. This approach simplifies the process of determining high-performance optical tunes, satisfying a given set of optical properties, for an ion optical system. The optimization approach is based on the particle swarm method and is entirely model independent, thus the success of the optimization does not depend on the accuracy of an extant ion optical model of the system to be optimized. Initial test runs of a first order optimization of a low-energy (<60 keV) all-electrostatic beamline at the NSCL show reliable convergence of nine quadrupole degrees of freedom to well-performing tunes within a reasonable number of trial solutions, roughly 500, with full beam optimization run times of roughly two hours. Improved tunes were found both for quasi-local optimizations and for quasi-global optimizations, indicating a good ability of the optimizer to find a solution with or without a well defined set of initial multipole settings.

A new design approach based on differential evolution algorithm for geometric optimization of magnetorheological brakes

NASA Astrophysics Data System (ADS)

Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung

2016-12-01

In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.

Optimal positions and parameters of translational and rotational mass dampers in beams subjected to random excitation

NASA Astrophysics Data System (ADS)

Łatas, Waldemar

2018-01-01

The problem of vibrations of the beam with the attached system of translational and rotational dynamic mass dampers subjected to random excitations with peaked power spectral densities, is presented in the hereby paper. The Euler-Bernoulli beam model is applied, while for solving the equation of motion the Galerkin method and the Laplace time transform are used. The obtained transfer functions allow to determine power spectral densities of the beam deflection and other dependent variables. Numerical examples present simple optimization problems of mass dampers parameters for local and global objective functions.

Nonlinear structural joint model updating based on instantaneous characteristics of dynamic responses

NASA Astrophysics Data System (ADS)

Wang, Zuo-Cai; Xin, Yu; Ren, Wei-Xin

2016-08-01

This paper proposes a new nonlinear joint model updating method for shear type structures based on the instantaneous characteristics of the decomposed structural dynamic responses. To obtain an accurate representation of a nonlinear system's dynamics, the nonlinear joint model is described as the nonlinear spring element with bilinear stiffness. The instantaneous frequencies and amplitudes of the decomposed mono-component are first extracted by the analytical mode decomposition (AMD) method. Then, an objective function based on the residuals of the instantaneous frequencies and amplitudes between the experimental structure and the nonlinear model is created for the nonlinear joint model updating. The optimal values of the nonlinear joint model parameters are obtained by minimizing the objective function using the simulated annealing global optimization method. To validate the effectiveness of the proposed method, a single-story shear type structure subjected to earthquake and harmonic excitations is simulated as a numerical example. Then, a beam structure with multiple local nonlinear elements subjected to earthquake excitation is also simulated. The nonlinear beam structure is updated based on the global and local model using the proposed method. The results show that the proposed local nonlinear model updating method is more effective for structures with multiple local nonlinear elements. Finally, the proposed method is verified by the shake table test of a real high voltage switch structure. The accuracy of the proposed method is quantified both in numerical and experimental applications using the defined error indices. Both the numerical and experimental results have shown that the proposed method can effectively update the nonlinear joint model.

Image-optimized Coronal Magnetic Field Models

NASA Astrophysics Data System (ADS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

Optimizing Land and Water Resources for Agriculture in the Krishna River Basin, India

NASA Astrophysics Data System (ADS)

Jain Figueroa, A.; McLaughlin, D.

2017-12-01

Many estimates suggest that the world needs a 50% increase in food production to meet the demands of the 2050 global population. Cropland expansion and yield improvements are unlikely to be sufficient and could have adverse environmental impacts. This work focuses on reallocating limited land and water resources to improve efficiency and increase benefits. We accomplish this by combining optimization methods, global data sources, and hydrologic modeling to identify opportunities for increasing crop production of subsistence and/or cash crops, subject to sustainability contraints. Our approach identifies the tradeoffs between the population that can be fed with local resources, revenue from crop exports, and environmental benefit from riparian flows. We focus our case study on India's Krishna river basin, a semi-arid region with a high proportion of subsistence farmers, a diverse crop mix, and increasing stress on water resources.

Alien Genetic Algorithm for Exploration of Search Space

NASA Astrophysics Data System (ADS)

Patel, Narendra; Padhiyar, Nitin

2010-10-01

Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.

Image-Optimized Coronal Magnetic Field Models

NASA Technical Reports Server (NTRS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

Aerodynamic Shape Optimization Using Hybridized Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2003-01-01

An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.

A generalized optimization principle for asymmetric branching in fluidic networks

PubMed Central

Stephenson, David

2016-01-01

When applied to a branching network, Murray’s law states that the optimal branching of vascular networks is achieved when the cube of the parent channel radius is equal to the sum of the cubes of the daughter channel radii. It is considered integral to understanding biological networks and for the biomimetic design of artificial fluidic systems. However, despite its ubiquity, we demonstrate that Murray’s law is only optimal (i.e. maximizes flow conductance per unit volume) for symmetric branching, where the local optimization of each individual channel corresponds to the global optimum of the network as a whole. In this paper, we present a generalized law that is valid for asymmetric branching, for any cross-sectional shape, and for a range of fluidic models. We verify our analytical solutions with the numerical optimization of a bifurcating fluidic network for the examples of laminar, turbulent and non-Newtonian fluid flows. PMID:27493583

Optimization of wireless sensor networks based on chicken swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Wang, Qingxi; Zhu, Lihua

2017-05-01

In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

Performance optimization of an MHD generator with physical constraints

NASA Technical Reports Server (NTRS)

Pian, C. C. P.; Seikel, G. R.; Smith, J. M.

1979-01-01

A technique has been described which optimizes the power out of a Faraday MHD generator operating under a prescribed set of electrical and magnetic constraints. The method does not rely on complicated numerical optimization techniques. Instead the magnetic field and the electrical loading are adjusted at each streamwise location such that the resultant generator design operates at the most limiting of the cited stress levels. The simplicity of the procedure makes it ideal for optimizing generator designs for system analysis studies of power plants. The resultant locally optimum channel designs are, however, not necessarily the global optimum designs. The results of generator performance calculations are presented for an approximately 2000 MWe size plant. The difference between the maximum power generator design and the optimal design which maximizes net MHD power are described. The sensitivity of the generator performance to the various operational parameters are also presented.

A quasi-dense matching approach and its calibration application with Internet photos.

PubMed

Wan, Yanli; Miao, Zhenjiang; Wu, Q M Jonathan; Wang, Xifu; Tang, Zhen; Wang, Zhifei

2015-03-01

This paper proposes a quasi-dense matching approach to the automatic acquisition of camera parameters, which is required for recovering 3-D information from 2-D images. An affine transformation-based optimization model and a new matching cost function are used to acquire quasi-dense correspondences with high accuracy in each pair of views. These correspondences can be effectively detected and tracked at the sub-pixel level in multiviews with our neighboring view selection strategy. A two-layer iteration algorithm is proposed to optimize 3-D quasi-dense points and camera parameters. In the inner layer, different optimization strategies based on local photometric consistency and a global objective function are employed to optimize the 3-D quasi-dense points and camera parameters, respectively. In the outer layer, quasi-dense correspondences are resampled to guide a new estimation and optimization process of the camera parameters. We demonstrate the effectiveness of our algorithm with several experiments.

Design of parallel transmission pulses for simultaneous multislice with explicit control for peak power and local specific absorption rate.

PubMed

Guérin, Bastien; Setsompop, Kawin; Ye, Huihui; Poser, Benedikt A; Stenger, Andrew V; Wald, Lawrence L

2015-05-01

To design parallel transmit (pTx) simultaneous multislice (SMS) spokes pulses with explicit control for peak power and local and global specific absorption rate (SAR). We design SMS pTx least-squares and magnitude least squares spokes pulses while constraining local SAR using the virtual observation points (VOPs) compression of SAR matrices. We evaluate our approach in simulations of a head (7T) and a body (3T) coil with eight channels arranged in two z-rows. For many of our simulations, control of average power by Tikhonov regularization of the SMS pTx spokes pulse design yielded pulses that violated hardware and SAR safety limits. On the other hand, control of peak power alone yielded pulses that violated local SAR limits. Pulses optimized with control of both local SAR and peak power satisfied all constraints and therefore had the best excitation performance under limited power and SAR constraints. These results extend our previous results for single slice pTx excitations but are more pronounced because of the large power demands and SAR of SMS pulses. Explicit control of local SAR and peak power is required to generate optimal SMS pTx excitations satisfying both the system's hardware limits and regulatory safety limits. © 2014 Wiley Periodicals, Inc.

Using Intel's Knight Landing Processor to Accelerate Global Nested Air Quality Prediction Modeling System (GNAQPMS) Model

NASA Astrophysics Data System (ADS)

Wang, H.; Chen, H.; Chen, X.; Wu, Q.; Wang, Z.

2016-12-01

The Global Nested Air Quality Prediction Modeling System for Hg (GNAQPMS-Hg) is a global chemical transport model coupled Hg transport module to investigate the mercury pollution. In this study, we present our work of transplanting the GNAQPMS model on Intel Xeon Phi processor, Knights Landing (KNL) to accelerate the model. KNL is the second-generation product adopting Many Integrated Core Architecture (MIC) architecture. Compared with the first generation Knight Corner (KNC), KNL has more new hardware features, that it can be used as unique processor as well as coprocessor with other CPU. According to the Vtune tool, the high overhead modules in GNAQPMS model have been addressed, including CBMZ gas chemistry, advection and convection module, and wet deposition module. These high overhead modules were accelerated by optimizing code and using new techniques of KNL. The following optimized measures was done: 1) Changing the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP; 2.Vectorizing the code to using the 512-bit wide vector computation unit. 3. Reducing unnecessary memory access and calculation. 4. Reducing Thread Local Storage (TLS) for common variables with each OpenMP thread in CBMZ. 5. Changing the way of global communication from files writing and reading to MPI functions. After optimization, the performance of GNAQPMS is greatly increased both on CPU and KNL platform, the single-node test showed that optimized version has 2.6x speedup on two sockets CPU platform and 3.3x speedup on one socket KNL platform compared with the baseline version code, which means the KNL has 1.29x speedup when compared with 2 sockets CPU platform.

Task allocation among multiple intelligent robots

NASA Technical Reports Server (NTRS)

Gasser, L.; Bekey, G.

1987-01-01

Researchers describe the design of a decentralized mechanism for allocating assembly tasks in a multiple robot assembly workstation. Currently, the approach focuses on distributed allocation to explore its feasibility and its potential for adaptability to changing circumstances, rather than for optimizing throughput. Individual greedy robots make their own local allocation decisions using both dynamic allocation policies which propagate through a network of allocation goals, and local static and dynamic constraints describing which robots are elibible for which assembly tasks. Global coherence is achieved by proper weighting of allocation pressures propagating through the assembly plan. Deadlock avoidance and synchronization is achieved using periodic reassessments of local allocation decisions, ageing of allocation goals, and short-term allocation locks on goals.

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

Final Report from The University of Texas at Austin for DEGAS: Dynamic Global Address Space programming environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erez, Mattan; Yelick, Katherine; Sarkar, Vivek

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. Our approach is to provide an efficient and scalable programming model that can be adapted to application needs through the use of dynamic runtime features and domain-specific languages for computational kernels. We address the following technical challenges: Programmability: Rich set of programming constructs based on a Hierarchical Partitioned Global Address Space (HPGAS) model, demonstrated in UPC++. Scalability: Hierarchical locality control, lightweight communication (extended GASNet), and ef- ficient synchronization mechanisms (Phasers). Performance Portability:more » Just-in-time specialization (SEJITS) for generating hardware-specific code and scheduling libraries for domain-specific adaptive runtimes (Habanero). Energy Efficiency: Communication-optimal code generation to optimize energy efficiency by re- ducing data movement. Resilience: Containment Domains for flexible, domain-specific resilience, using state capture mechanisms and lightweight, asynchronous recovery mechanisms. Interoperability: Runtime and language interoperability with MPI and OpenMP to encourage broad adoption.« less

Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

PubMed

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

PubMed

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

2016-10-01

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

Pricing of swing options: A Monte Carlo simulation approach

NASA Astrophysics Data System (ADS)

Leow, Kai-Siong

We study the problem of pricing swing options, a class of multiple early exercise options that are traded in energy market, particularly in the electricity and natural gas markets. These contracts permit the option holder to periodically exercise the right to trade a variable amount of energy with a counterparty, subject to local volumetric constraints. In addition, the total amount of energy traded from settlement to expiration with the counterparty is restricted by a global volumetric constraint. Violation of this global volumetric constraint is allowed but would lead to penalty settled at expiration. The pricing problem is formulated as a stochastic optimal control problem in discrete time and state space. We present a stochastic dynamic programming algorithm which is based on piecewise linear concave approximation of value functions. This algorithm yields the value of the swing option under the assumption that the optimal exercise policy is applied by the option holder. We present a proof of an almost sure convergence that the algorithm generates the optimal exercise strategy as the number of iterations approaches to infinity. Finally, we provide a numerical example for pricing a natural gas swing call option.

Critical phenomena in communication/computation networks with various topologies and suboptimal to optimal resource allocation

NASA Astrophysics Data System (ADS)

Cogoni, Marco; Busonera, Giovanni; Anedda, Paolo; Zanetti, Gianluigi

2015-01-01

We generalize previous studies on critical phenomena in communication networks [1,2] by adding computational capabilities to the nodes. In our model, a set of tasks with random origin, destination and computational structure is distributed on a computational network, modeled as a graph. By varying the temperature of a Metropolis Montecarlo, we explore the global latency for an optimal to suboptimal resource assignment at a given time instant. By computing the two-point correlation function for the local overload, we study the behavior of the correlation distance (both for links and nodes) while approaching the congested phase: a transition from peaked to spread g(r) is seen above a critical (Montecarlo) temperature Tc. The average latency trend of the system is predicted by averaging over several network traffic realizations while maintaining a spatially detailed information for each node: a sharp decrease of performance is found over Tc independently of the workload. The globally optimized computational resource allocation and network routing defines a baseline for a future comparison of the transition behavior with respect to existing routing strategies [3,4] for different network topologies.

Alternative difference analysis scheme combining R -space EXAFS fit with global optimization XANES fit for X-ray transient absorption spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhan, Fei; Tao, Ye; Zhao, Haifeng

Time-resolved X-ray absorption spectroscopy (TR-XAS), based on the laser-pump/X-ray-probe method, is powerful in capturing the change of the geometrical and electronic structure of the absorbing atom upon excitation. TR-XAS data analysis is generally performed on the laser-on minus laser-off difference spectrum. Here, a new analysis scheme is presented for the TR-XAS difference fitting in both the extended X-ray absorption fine-structure (EXAFS) and the X-ray absorption near-edge structure (XANES) regions.R-space EXAFS difference fitting could quickly provide the main quantitative structure change of the first shell. The XANES fitting part introduces a global non-derivative optimization algorithm and optimizes the local structure changemore » in a flexible way where both the core XAS calculation package and the search method in the fitting shell are changeable. The scheme was applied to the TR-XAS difference analysis of Fe(phen) 3spin crossover complex and yielded reliable distance change and excitation population.« less

Branch and bound algorithm for accurate estimation of analytical isotropic bidirectional reflectance distribution function models.

PubMed

Yu, Chanki; Lee, Sang Wook

2016-05-20

We present a reliable and accurate global optimization framework for estimating parameters of isotropic analytical bidirectional reflectance distribution function (BRDF) models. This approach is based on a branch and bound strategy with linear programming and interval analysis. Conventional local optimization is often very inefficient for BRDF estimation since its fitting quality is highly dependent on initial guesses due to the nonlinearity of analytical BRDF models. The algorithm presented in this paper employs L₁-norm error minimization to estimate BRDF parameters in a globally optimal way and interval arithmetic to derive our feasibility problem and lower bounding function. Our method is developed for the Cook-Torrance model but with several normal distribution functions such as the Beckmann, Berry, and GGX functions. Experiments have been carried out to validate the presented method using 100 isotropic materials from the MERL BRDF database, and our experimental results demonstrate that the L₁-norm minimization provides a more accurate and reliable solution than the L₂-norm minimization.

Alternative difference analysis scheme combining R-space EXAFS fit with global optimization XANES fit for X-ray transient absorption spectroscopy.

PubMed

Zhan, Fei; Tao, Ye; Zhao, Haifeng

2017-07-01

Time-resolved X-ray absorption spectroscopy (TR-XAS), based on the laser-pump/X-ray-probe method, is powerful in capturing the change of the geometrical and electronic structure of the absorbing atom upon excitation. TR-XAS data analysis is generally performed on the laser-on minus laser-off difference spectrum. Here, a new analysis scheme is presented for the TR-XAS difference fitting in both the extended X-ray absorption fine-structure (EXAFS) and the X-ray absorption near-edge structure (XANES) regions. R-space EXAFS difference fitting could quickly provide the main quantitative structure change of the first shell. The XANES fitting part introduces a global non-derivative optimization algorithm and optimizes the local structure change in a flexible way where both the core XAS calculation package and the search method in the fitting shell are changeable. The scheme was applied to the TR-XAS difference analysis of Fe(phen) 3 spin crossover complex and yielded reliable distance change and excitation population.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

iTOUGH2: A multiphysics simulation-optimization framework for analyzing subsurface systems

NASA Astrophysics Data System (ADS)

Finsterle, S.; Commer, M.; Edmiston, J. K.; Jung, Y.; Kowalsky, M. B.; Pau, G. S. H.; Wainwright, H. M.; Zhang, Y.

2017-11-01

iTOUGH2 is a simulation-optimization framework for the TOUGH suite of nonisothermal multiphase flow models and related simulators of geophysical, geochemical, and geomechanical processes. After appropriate parameterization of subsurface structures and their properties, iTOUGH2 runs simulations for multiple parameter sets and analyzes the resulting output for parameter estimation through automatic model calibration, local and global sensitivity analyses, data-worth analyses, and uncertainty propagation analyses. Development of iTOUGH2 is driven by scientific challenges and user needs, with new capabilities continually added to both the forward simulator and the optimization framework. This review article provides a summary description of methods and features implemented in iTOUGH2, and discusses the usefulness and limitations of an integrated simulation-optimization workflow in support of the characterization and analysis of complex multiphysics subsurface systems.

Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

PubMed Central

2017-01-01

Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms. PMID:28761438

Extended shortest path selection for package routing of complex networks

NASA Astrophysics Data System (ADS)

Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

NASA Astrophysics Data System (ADS)

Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B.

2018-01-01

Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2 (110 )-(4 ×1 ) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

Experimental Evaluation of UWB Indoor Positioning for Sport Postures

PubMed Central

Defraye, Jense; Steendam, Heidi; Gerlo, Joeri; De Clercq, Dirk; De Poorter, Eli

2018-01-01

Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities. PMID:29315267

Global curriculum in research literacy for the surgical oncologist.

PubMed

Are, C; Yanala, U; Malhotra, G; Hall, B; Smith, L; Cummings, C; Lecoq, C; Wyld, L; Audisio, R A; Berman, R S

2018-01-01

The ability to provide optimal care to cancer patients depends on awareness of current evidence-based practices emanating from research or involvement in research where circumstances permit. The significant global variations in cancer-related research activity and its correlation to cancer-specific outcomes may have an influence on the care provided to cancer patients and their outcomes. The aim of this project is to develop a global curriculum in research literacy for the surgical oncologist. The leadership of the Society of Surgical Oncology and European Society of Surgical Oncology convened a global curriculum committee to develop a global curriculum in research literacy for the Surgical Oncologist. A global curriculum in research literacy is developed to incorporate the required domains considered to be essential to interpret the published research or become involved in research activity where circumstances permit. The purpose of this curriculum is to promote research literacy for the surgical oncologist, wherever they are based. It does not mandate direct research participation which may not be feasible due to restrictions within the local health-care delivery environment, socio-economic priorities and the educational environment of the individual institution where they work. A global curriculum in research literacy is proposed which may promote research literacy or encourage involvement in research activity where circumstances permit. It is hoped that this will enhance cancer-related research activity, promote awareness of optimal evidence-based practices and improve outcomes for cancer patients globally. Copyright © 2017 Society of Surgical Oncology, European Society of Surgical Oncology. Published by Elsevier Ltd.. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodríguez-Cantano, Rocío; Pérez de Tudela, Ricardo; Bartolomei, Massimiliano

Coronene-doped helium clusters have been studied by means of classical and quantum mechanical (QM) methods using a recently developed He–C{sub 24}H{sub 12} global potential based on the use of optimized atom-bond improved Lennard-Jones functions. Equilibrium energies and geometries at global and local minima for systems with up to 69 He atoms were calculated by means of an evolutive algorithm and a basin-hopping approach and compared with results from path integral Monte Carlo (PIMC) calculations at 2 K. A detailed analysis performed for the smallest sizes shows that the precise localization of the He atoms forming the first solvation layer overmore » the molecular substrate is affected by differences between relative potential minima. The comparison of the PIMC results with the predictions from the classical approaches and with diffusion Monte Carlo results allows to examine the importance of both the QM and thermal effects.« less

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep

PubMed Central

Tagliazucchi, Enzo; Sanjuán, Ana

2017-01-01

Abstract A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states. PMID:28966977

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep.

PubMed

Deco, Gustavo; Tagliazucchi, Enzo; Laufs, Helmut; Sanjuán, Ana; Kringelbach, Morten L

2017-01-01

A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

PubMed

Ismail, Ahmad Muhaimin; Mohamad, Mohd Saberi; Abdul Majid, Hairudin; Abas, Khairul Hamimah; Deris, Safaai; Zaki, Nazar; Mohd Hashim, Siti Zaiton; Ibrahim, Zuwairie; Remli, Muhammad Akmal

2017-12-01

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

PubMed Central

Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Yan, Jia; Wang, Lidan

2016-01-01

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas. PMID:27529247

Computational modeling of chemo-electro-mechanical coupling: A novel implicit monolithic finite element approach

PubMed Central

Wong, J.; Göktepe, S.; Kuhl, E.

2014-01-01

Summary Computational modeling of the human heart allows us to predict how chemical, electrical, and mechanical fields interact throughout a cardiac cycle. Pharmacological treatment of cardiac disease has advanced significantly over the past decades, yet it remains unclear how the local biochemistry of an individual heart cell translates into global cardiac function. Here we propose a novel, unified strategy to simulate excitable biological systems across three biological scales. To discretize the governing chemical, electrical, and mechanical equations in space, we propose a monolithic finite element scheme. We apply a highly efficient and inherently modular global-local split, in which the deformation and the transmembrane potential are introduced globally as nodal degrees of freedom, while the chemical state variables are treated locally as internal variables. To ensure unconditional algorithmic stability, we apply an implicit backward Euler finite difference scheme to discretize the resulting system in time. To increase algorithmic robustness and guarantee optimal quadratic convergence, we suggest an incremental iterative Newton-Raphson scheme. The proposed algorithm allows us to simulate the interaction of chemical, electrical, and mechanical fields during a representative cardiac cycle on a patient-specific geometry, robust and stable, with calculation times on the order of four days on a standard desktop computer. PMID:23798328

Beam angle optimization for intensity-modulated radiation therapy using a guided pattern search method

NASA Astrophysics Data System (ADS)

Rocha, Humberto; Dias, Joana M.; Ferreira, Brígida C.; Lopes, Maria C.

2013-05-01

Generally, the inverse planning of radiation therapy consists mainly of the fluence optimization. The beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) consists of selecting appropriate radiation incidence directions and may influence the quality of the IMRT plans, both to enhance better organ sparing and to improve tumor coverage. However, in clinical practice, most of the time, beam directions continue to be manually selected by the treatment planner without objective and rigorous criteria. The goal of this paper is to introduce a novel approach that uses beam’s-eye-view dose ray tracing metrics within a pattern search method framework in the optimization of the highly non-convex BAO problem. Pattern search methods are derivative-free optimization methods that require a few function evaluations to progress and converge and have the ability to better avoid local entrapment. The pattern search method framework is composed of a search step and a poll step at each iteration. The poll step performs a local search in a mesh neighborhood and ensures the convergence to a local minimizer or stationary point. The search step provides the flexibility for a global search since it allows searches away from the neighborhood of the current iterate. Beam’s-eye-view dose metrics assign a score to each radiation beam direction and can be used within the pattern search framework furnishing a priori knowledge of the problem so that directions with larger dosimetric scores are tested first. A set of clinical cases of head-and-neck tumors treated at the Portuguese Institute of Oncology of Coimbra is used to discuss the potential of this approach in the optimization of the BAO problem.

Citizen Science Seismic Stations for Monitoring Regional and Local Events

NASA Astrophysics Data System (ADS)

Zucca, J. J.; Myers, S.; Srikrishna, D.

2016-12-01

The earth has tens of thousands of seismometers installed on its surface or in boreholes that are operated by many organizations for many purposes including the study of earthquakes, volcanos, and nuclear explosions. Although global networks such as the Global Seismic Network and the International Monitoring System do an excellent job of monitoring nuclear test explosions and other seismic events, their thresholds could be lowered with the addition of more stations. In recent years there has been interest in citizen-science approaches to augment government-sponsored monitoring networks (see, for example, Stubbs and Drell, 2013). A modestly-priced seismic station that could be purchased by citizen scientists could enhance regional and local coverage of the GSN, IMS, and other networks if those stations are of high enough quality and distributed optimally. In this paper we present a minimum set of hardware and software specifications that a citizen seismograph station would need in order to add value to global networks. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Multiparameter Estimation in Networked Quantum Sensors

NASA Astrophysics Data System (ADS)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-01

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.

Global Search Methods for Stellarator Design

NASA Astrophysics Data System (ADS)

Mynick, H. E.; Pomphrey, N.

2001-10-01

We have implemented a new variant Stellopt-DE of the stellarator optimizer Stellopt used by the NCSX team.(A. Reiman, G. Fu, S. Hirshman, D. Monticello, et al., EPS Meeting on Controlled Fusion and Plasma Physics Research, Maastricht, the Netherlands, June 14-18, 1999, (European Physical Society, Petit-Lancy, 1999).) It is based on the ``differential evolution'' (DE) algorithm,(R. Storn, K. Price, U.C. Berkeley Technical Report TR-95-012, ICSI (March, 1995).) a global search method which is far less prone than local algorithms such as the Levenberg-Marquardt method presently used in Stellopt to become trapped in local suboptimal minima of the cost function \\chi. Explorations of stellarator configuration space z to which the DE method has been applied will be presented. Additionally, an accompanying effort to understand the results of this more global exploration has found that a wide range of Quasi-Axisymmetric Stellarators (QAS) previously studied fall into a small number of classes, and we obtain maps of \\chi(z) from which one can see the relative positions of these QAS, and the reasons for the classes into which they fall.

Current trends in satellite based emergency mapping - the need for harmonisation

NASA Astrophysics Data System (ADS)

Voigt, Stefan

2013-04-01

During the past years, the availability and use of satellite image data to support disaster management and humanitarian relief organisations has largely increased. The automation and data processing techniques are greatly improving as well as the capacity in accessing and processing satellite imagery in getting better globally. More and more global activities via the internet and through global organisations like the United Nations or the International Charter Space and Major Disaster engage in the topic, while at the same time, more and more national or local centres engage rapid mapping operations and activities. In order to make even more effective use of this very positive increase of capacity, for the sake of operational provision of analysis results, for fast validation of satellite derived damage assessments, for better cooperation in the joint inter agency generation of rapid mapping products and for general scientific use, rapid mapping results in general need to be better harmonized, if not even standardized. In this presentation, experiences from various years of rapid mapping gained by the DLR Center for satellite based Crisis Information (ZKI) within the context of the national activities, the International Charter Space and Major Disasters, GMES/Copernicus etc. are reported. Furthermore, an overview on how automation, quality assurance and optimization can be achieved through standard operation procedures within a rapid mapping workflow is given. Building on this long term rapid mapping experience, and building on the DLR initiative to set in pace an "International Working Group on Satellite Based Emergency Mapping" current trends in rapid mapping are discussed and thoughts on how the sharing of rapid mapping information can be optimized by harmonizing analysis results and data structures are presented. Such an harmonization of analysis procedures, nomenclatures and representations of data as well as meta data are the basis to better cooperate within the global rapid mapping community throughout local/national, regional/supranational and global scales

Comparison of empirical strategies to maximize GENEHUNTER lod scores.

PubMed

Chen, C H; Finch, S J; Mendell, N R; Gordon, D

1999-01-01

We compare four strategies for finding the settings of genetic parameters that maximize the lod scores reported in GENEHUNTER 1.2. The four strategies are iterated complete factorial designs, iterated orthogonal Latin hypercubes, evolutionary operation, and numerical optimization. The genetic parameters that are set are the phenocopy rate, penetrance, and disease allele frequency; both recessive and dominant models are considered. We selected the optimization of a recessive model on the Collaborative Study on the Genetics of Alcoholism (COGA) data of chromosome 1 for complete analysis. Convergence to a setting producing a local maximum required the evaluation of over 100 settings (for a time budget of 800 minutes on a Pentium II 300 MHz PC). Two notable local maxima were detected, suggesting the need for a more extensive search before claiming that a global maximum had been found. The orthogonal Latin hypercube design was the best strategy for finding areas that produced high lod scores with small numbers of evaluations. Numerical optimization starting from a region producing high lod scores was the strategy that found the highest maximum observed.

Collectives for Multiple Resource Job Scheduling Across Heterogeneous Servers

NASA Technical Reports Server (NTRS)

Tumer, K.; Lawson, J.

2003-01-01

Efficient management of large-scale, distributed data storage and processing systems is a major challenge for many computational applications. Many of these systems are characterized by multi-resource tasks processed across a heterogeneous network. Conventional approaches, such as load balancing, work well for centralized, single resource problems, but breakdown in the more general case. In addition, most approaches are often based on heuristics which do not directly attempt to optimize the world utility. In this paper, we propose an agent based control system using the theory of collectives. We configure the servers of our network with agents who make local job scheduling decisions. These decisions are based on local goals which are constructed to be aligned with the objective of optimizing the overall efficiency of the system. We demonstrate that multi-agent systems in which all the agents attempt to optimize the same global utility function (team game) only marginally outperform conventional load balancing. On the other hand, agents configured using collectives outperform both team games and load balancing (by up to four times for the latter), despite their distributed nature and their limited access to information.

Global issues and opportunities for optimized retinoblastoma care.

PubMed

Gallie, Brenda L; Zhao, Junyang; Vandezande, Kirk; White, Abigail; Chan, Helen S L

2007-12-01

The RB1 gene is important in all human cancers. Studies of human retinoblastoma point to a rare retinal cell with extreme dependency on RB1 for initiation but not progression to full malignancy. In developed countries, genetic testing within affected families can predict children at high risk of retinoblastoma before birth; chemotherapy with local therapy often saves eyes and vision; and mortality is 4%. In less developed countries where 92% of children with retinoblastoma are born, mortality reaches 90%. Global collaboration is building for the dramatic change in mortality that awareness, simple expertise and therapies could achieve in less developed countries. Copyright 2007 Wiley-Liss, Inc.

Planning minimum-energy paths in an off-road environment with anisotropic traversal costs and motion constraints. Doctoral thesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ross, R.S.

1989-06-01

For a vehicle operating across arbitrarily-contoured terrain, finding the most fuel-efficient route between two points can be viewed as a high-level global path-planning problem with traversal costs and stability dependent on the direction of travel (anisotropic). The problem assumes a two-dimensional polygonal map of homogeneous cost regions for terrain representation constructed from elevation information. The anisotropic energy cost of vehicle motion has a non-braking component dependent on horizontal distance, a braking component dependent on vertical distance, and a constant path-independent component. The behavior of minimum-energy paths is then proved to be restricted to a small, but optimal set of traversalmore » types. An optimal-path-planning algorithm, using a heuristic search technique, reduces the infinite number of paths between the start and goal points to a finite number by generating sequences of goal-feasible window lists from analyzing the polygonal map and applying pruning criteria. The pruning criteria consist of visibility analysis, heading analysis, and region-boundary constraints. Each goal-feasible window lists specifies an associated convex optimization problem, and the best of all locally-optimal paths through the goal-feasible window lists is the globally-optimal path. These ideas have been implemented in a computer program, with results showing considerably better performance than the exponential average-case behavior predicted.« less

Efficiency of extracting stereo-driven object motions

PubMed Central

Jain, Anshul; Zaidi, Qasim

2013-01-01

Most living things and many nonliving things deform as they move, requiring observers to separate object motions from object deformations. When the object is partially occluded, the task becomes more difficult because it is not possible to use two-dimensional (2-D) contour correlations (Cohen, Jain, & Zaidi, 2010). That leaves dynamic depth matching across the unoccluded views as the main possibility. We examined the role of stereo cues in extracting motion of partially occluded and deforming three-dimensional (3-D) objects, simulated by disk-shaped random-dot stereograms set at randomly assigned depths and placed uniformly around a circle. The stereo-disparities of the disks were temporally oscillated to simulate clockwise or counterclockwise rotation of the global shape. To dynamically deform the global shape, random disparity perturbation was added to each disk's depth on each stimulus frame. At low perturbation, observers reported rotation directions consistent with the global shape, even against local motion cues, but performance deteriorated at high perturbation. Using 3-D global shape correlations, we formulated an optimal Bayesian discriminator for rotation direction. Based on rotation discrimination thresholds, human observers were 75% as efficient as the optimal model, demonstrating that global shapes derived from stereo cues facilitate inferences of object motions. To complement reports of stereo and motion integration in extrastriate cortex, our results suggest the possibilities that disparity selectivity and feature tracking are linked, or that global motion selective neurons can be driven purely from disparity cues. PMID:23325345

MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy.

PubMed

Han, Dianwei; Zhang, Jun; Tang, Guiliang

2012-01-01

An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.

A general framework for multicharacter segmentation and its application in recognizing multilingual Asian documents

NASA Astrophysics Data System (ADS)

Wen, Di; Ding, Xiaoqing

2003-12-01

In this paper we propose a general framework for character segmentation in complex multilingual documents, which is an endeavor to combine the traditionally separated segmentation and recognition processes into a cooperative system. The framework contains three basic steps: Dissection, Local Optimization and Global Optimization, which are designed to fuse various properties of the segmentation hypotheses hierarchically into a composite evaluation to decide the final recognition results. Experimental results show that this framework is general enough to be applied in variety of documents. A sample system based on this framework to recognize Chinese, Japanese and Korean documents and experimental performance is reported finally.

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Evolutionary Agent-based Models to design distributed water management strategies

NASA Astrophysics Data System (ADS)

Giuliani, M.; Castelletti, A.; Reed, P. M.

2012-12-01

There is growing awareness in the scientific community that the traditional centralized approach to water resources management, as described in much of the water resources literature, provides an ideal optimal solution, which is certainly useful to quantify the best physically achievable performance, but is generally inapplicable. Most real world water resources management problems are indeed characterized by the presence of multiple, distributed and institutionally-independent decision-makers. Multi-Agent Systems provide a potentially more realistic alternative framework to model multiple and self-interested decision-makers in a credible context. Each decision-maker can be represented by an agent who, being self-interested, acts according to local objective functions and produces negative externalities on system level objectives. Different levels of coordination can potentially be included in the framework by designing coordination mechanisms to drive the current decision-making structure toward the global system efficiency. Yet, the identification of effective coordination strategies can be particularly complex in modern institutional contexts and current practice is dependent on largely ad-hoc coordination strategies. In this work we propose a novel Evolutionary Agent-based Modeling (EAM) framework that enables a mapping of fully uncoordinated and centrally coordinated solutions into their relative "many-objective" tradeoffs using multiobjective evolutionary algorithms. Then, by analysing the conflicts between local individual agent and global system level objectives it is possible to more fully understand the causes, consequences, and potential solution strategies for coordination failures. Game-theoretic criteria have value for identifying the most interesting alternatives from a policy making point of view as well as the coordination mechanisms that can be applied to obtain these interesting solutions. The proposed approach is numerically tested on a synthetic case study, representing a Y-shaped system composed by two regulated lakes, whose releases merge just upstream of a city. Each reservoir is operated by an agent in order to prevent floods along the lake shores (local objective). However, the optimal operation of the reservoirs with respect to the local objectives is conflicting with the minimization of floods in the city (global objective). The evolution of the Agent-based Model from individualistic management strategies of the reservoirs toward a global compromise that reduces the costs for the city is analysed.

A global optimization algorithm for protein surface alignment

PubMed Central

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

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

Collective intelligence for control of distributed dynamical systems

NASA Astrophysics Data System (ADS)

Wolpert, D. H.; Wheeler, K. R.; Tumer, K.

2000-03-01

We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, The American Economic Review, 84 (1994) 406; D. Challet and Y. C. Zhang, Physica A, 256 (1998) 514). We view it as an instance of the general problem of how to configure the nodal elements of a distributed dynamical system so that they do not "work at cross purposes", in that their collective dynamics avoids frustration and thereby achieves a provided global goal. We summarize a mathematical theory for such configuration applicable when (as in the bar problem) the global goal can be expressed as minimizing a global energy function and the nodes can be expressed as minimizers of local free energy functions. We show that a system designed with that theory performs nearly optimally for the bar problem.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

A Framework for Optimizing the Placement of Tidal Turbines

NASA Astrophysics Data System (ADS)

Nelson, K. S.; Roberts, J.; Jones, C.; James, S. C.

2013-12-01

Power generation with marine hydrokinetic (MHK) current energy converters (CECs), often in the form of underwater turbines, is receiving growing global interest. Because of reasonable investment, maintenance, reliability, and environmental friendliness, this technology can contribute to national (and global) energy markets and is worthy of research investment. Furthermore, in remote areas, small-scale MHK energy from river, tidal, or ocean currents can provide a local power supply. However, little is known about the potential environmental effects of CEC operation in coastal embayments, estuaries, or rivers, or of the cumulative impacts of these devices on aquatic ecosystems over years or decades of operation. There is an urgent need for practical, accessible tools and peer-reviewed publications to help industry and regulators evaluate environmental impacts and mitigation measures, while establishing best sitting and design practices. Sandia National Laboratories (SNL) and Sea Engineering, Inc. (SEI) have investigated the potential environmental impacts and performance of individual tidal energy converters (TECs) in Cobscook Bay, ME; TECs are a subset of CECs that are specifically deployed in tidal channels. Cobscook Bay is the first deployment location of Ocean Renewable Power Company's (ORPC) TidGenTM unit. One unit is currently in place with four more to follow. Together, SNL and SEI built a coarse-grid, regional-scale model that included Cobscook Bay and all other landward embayments using the modeling platform SNL-EFDC. Within SNL-EFDC tidal turbines are represented using a unique set of momentum extraction, turbulence generation, and turbulence dissipation equations at TEC locations. The global model was then coupled to a local-scale model that was centered on the proposed TEC deployment locations. An optimization frame work was developed that used the refined model to determine optimal device placement locations that maximized array performance. Within the framework, environmental effects are considered to minimize the possibility of altering flows to an extent that would affect fish-swimming behavior and sediment-transport trends. Simulation results were compared between model runs with the optimized array configuration, and the originally purposed deployment locations; the optimized array showed a 17% increase in power generation. The developed framework can provide regulators and developers with a tool for assessing environmental impacts and device-performance parameters for the deployment of MHK devices. The more thoroughly understood this promising technology, the more likely it will become a viable source of alternative energy.

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

PubMed

Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck

2016-07-16

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

System Risk Assessment and Allocation in Conceptual Design

NASA Technical Reports Server (NTRS)

Mahadevan, Sankaran; Smith, Natasha L.; Zang, Thomas A. (Technical Monitor)

2003-01-01

As aerospace systems continue to evolve in addressing newer challenges in air and space transportation, there exists a heightened priority for significant improvement in system performance, cost effectiveness, reliability, and safety. Tools, which synthesize multidisciplinary integration, probabilistic analysis, and optimization, are needed to facilitate design decisions allowing trade-offs between cost and reliability. This study investigates tools for probabilistic analysis and probabilistic optimization in the multidisciplinary design of aerospace systems. A probabilistic optimization methodology is demonstrated for the low-fidelity design of a reusable launch vehicle at two levels, a global geometry design and a local tank design. Probabilistic analysis is performed on a high fidelity analysis of a Navy missile system. Furthermore, decoupling strategies are introduced to reduce the computational effort required for multidisciplinary systems with feedback coupling.

Broken symmetry in a two-qubit quantum control landscape

NASA Astrophysics Data System (ADS)

Bukov, Marin; Day, Alexandre G. R.; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj; Sels, Dries

2018-05-01

We analyze the physics of optimal protocols to prepare a target state with high fidelity in a symmetrically coupled two-qubit system. By varying the protocol duration, we find a discontinuous phase transition, which is characterized by a spontaneous breaking of a Z2 symmetry in the functional form of the optimal protocol, and occurs below the quantum speed limit. We study in detail this phase and demonstrate that even though high-fidelity protocols come degenerate with respect to their fidelity, they lead to final states of different entanglement entropy shared between the qubits. Consequently, while globally both optimal protocols are equally far away from the target state, one is locally closer than the other. An approximate variational mean-field theory which captures the physics of the different phases is developed.

Cost-effectiveness on a local level: whether and when to adopt a new technology.

PubMed

Woertman, Willem H; Van De Wetering, Gijs; Adang, Eddy M M

2014-04-01

Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

PubMed Central

Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

2017-01-01

Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

Particle swarm optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Yan; Mohanty, Soumya D.; Center for Gravitational Wave Astronomy, Department of Physics and Astronomy, University of Texas at Brownsville, 80 Fort Brown, Brownsville, Texas 78520

2010-03-15

The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method ismore » applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.« less

Adaptive Particle Swarm Optimizer with Varying Acceleration Coefficients for Finding the Most Stable Conformer of Small Molecules.

PubMed

Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra

2015-11-01

A novel parameter automation strategy for Particle Swarm Optimization called APSO (Adaptive PSO) is proposed. The algorithm is designed to efficiently control the local search and convergence to the global optimum solution. Parameters c1 controls the impact of the cognitive component on the particle trajectory and c2 controls the impact of the social component. Instead of fixing the value of c1 and c2 , this paper updates the value of these acceleration coefficients by considering time variation of evaluation function along with varying inertia weight factor in PSO. Here the maximum and minimum value of evaluation function is use to gradually decrease and increase the value of c1 and c2 respectively. Molecular energy minimization is one of the most challenging unsolved problems and it can be formulated as a global optimization problem. The aim of the present paper is to investigate the effect of newly developed APSO on the highly complex molecular potential energy function and to check the efficiency of the proposed algorithm to find the global minimum of the function under consideration. The proposed algorithm APSO is therefore applied in two cases: Firstly, for the minimization of a potential energy of small molecules with up to 100 degrees of freedom and finally for finding the global minimum energy conformation of 1,2,3-trichloro-1-flouro-propane molecule based on a realistic potential energy function. The computational results of all the cases show that the proposed method performs significantly better than the other algorithms. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nelson, Andrea; Aftosmis, Michael J.; Nemec, Marian; Pulliam, Thomas H.

2004-01-01

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

A small perturbation based optimization approach for the frequency placement of high aspect ratio wings

NASA Astrophysics Data System (ADS)

Goltsch, Mandy

Design denotes the transformation of an identified need to its physical embodiment in a traditionally iterative approach of trial and error. Conceptual design plays a prominent role but an almost infinite number of possible solutions at the outset of design necessitates fast evaluations. The corresponding practice of empirical equations and low fidelity analyses becomes obsolete in the light of novel concepts. Ever increasing system complexity and resource scarcity mandate new approaches to adequately capture system characteristics. Contemporary concerns in atmospheric science and homeland security created an operational need for unconventional configurations. Unmanned long endurance flight at high altitudes offers a unique showcase for the exploration of new design spaces and the incidental deficit of conceptual modeling and simulation capabilities. Structural and aerodynamic performance requirements necessitate light weight materials and high aspect ratio wings resulting in distinct structural and aeroelastic response characteristics that stand in close correlation with natural vibration modes. The present research effort evolves around the development of an efficient and accurate optimization algorithm for high aspect ratio wings subject to natural frequency constraints. Foundational corner stones are beam dimensional reduction and modal perturbation redesign. Local and global analyses inherent to the former suggest corresponding levels of local and global optimization. The present approach departs from this suggestion. It introduces local level surrogate models to capacitate a methodology that consists of multi level analyses feeding into a single level optimization. The innovative heart of the new algorithm originates in small perturbation theory. A sequence of small perturbation solutions allows the optimizer to make incremental movements within the design space. It enables a directed search that is free of costly gradients. System matrices are decomposed based on a Timoshenko stiffness effect separation. The formulation of respective linear changes falls back on surrogate models that approximate cross sectional properties. Corresponding functional responses are readily available. Their direct use by the small perturbation based optimizer ensures constitutive laws and eliminates a previously necessary optimization at the local level. The scope of the present work is derived from an existing configuration such as a conceptual baseline or a prototype that experiences aeroelastic instabilities. Due to the lack of respective design studies in the traditional design process it is not uncommon for an initial wing design to have such stability problems. The developed optimization scheme allows the effective redesign of high aspect ratio wings subject to natural frequency objectives. Its successful application is demonstrated by three separate optimization studies. The implementation results of all three studies confirm that the gradient liberation of the new methodology brings about great computational savings. A generic wing study is used to indicate the connection between the proposed methodology and the aeroelastic stability problems outlined in the motivation. It is also used to illustrate an important practical aspect of structural redesign, i.e., a minimum departure from the existing baseline configuration. The proposed optimization scheme is naturally conducive to this practical aspect by using a minimum change optimization criterion. However, only an elemental formulation truly enables a minimum change solution. It accounts for the spanwise significance of a structural modification to the mode of interest. This idea of localized reinforcement greatly benefits the practical realization of structural redesign efforts. The implementation results also highlight the fundamental limitation of the proposed methodology. The exclusive consideration of mass and stiffness effects on modal response characteristics disregards other disciplinary problems such as allowable stresses or buckling loads. Both are of central importance to the structural integrity of an aircraft but are currently not accounted for in the proposed optimization scheme. The concluding discussion thus outlines the need for respective constraints and/or additional analyses to capture all requirements necessary for a comprehensive structural redesign study.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

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 optimality of the found solutions can be made. Identification of the leading particle traditionally requires a costly combination of ranking and niching techniques. In our approach, we use a decision rule under uncertainty to identify the currently leading particle of the swarm. In doing so, we consider the different objectives of our optimization problem as competing agents with partially conflicting interests. Analysis of the maximin fitness function allows for robust and cheap identification of the currently leading particle. The final optimization result comprises a set of possible models spread along the Pareto front. For convex Pareto fronts, solution density is expected to be maximal in the region ideally compromising all objectives, i.e. the region of highest curvature.

A new mathematical modeling approach for the energy of threonine molecule

NASA Astrophysics Data System (ADS)

Sahiner, Ahmet; Kapusuz, Gulden; Yilmaz, Nurullah

2017-07-01

In this paper, we propose an improved new methodology in energy conformation problems for finding optimum energy values. First, we construct the Bezier surfaces near local minimizers based on the data obtained from Density Functional Theory (DFT) calculations. Second, we blend the constructed surfaces in order to obtain a single smooth model. Finally, we apply the global optimization algorithm to find two torsion angles those make the energy of the molecule minimum.

Bypass Ratio: The US Air Force and Light-Attack Aviation

DTIC Science & Technology

2013-06-01

for making recommendations which optimize base activity and its impact on the environment. Local and state politics can keep a base open even if it is...for the region, and this conduct can affect global commerce. Such disruption and destabilization in turn can have large impacts on the US diplomatic... IFR ) operations, emergency procedures, low-level flight and two-ship formation flight by this stage. Once track selection occurs, the light-attack

Energy Based Topology Optimization of Morphing Wings a Multidisciplinary Global/Local Design Approach

DTIC Science & Technology

2006-12-01

subsystem that drives the active materials to achieve the desired shape changes. As opposed to fixed wing structures in which the aerodynamic and...structures and aerodynamics occur in conjunction with the active material and electronic subsystem interactions that involve transfer of energy from a source...which the aerodynamic and structure integration for the entire wing is the most important interaction mechanism, in the case of a morphing wing

Improved Ant Algorithms for Software Testing Cases Generation

PubMed Central

Yang, Shunkun; Xu, Jiaqi

2014-01-01

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. PMID:24883391

An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

NASA Astrophysics Data System (ADS)

Li, Si-Yu; Wang, Shu-Ming; Wang, Peng-Fei; Su, Xiao-Lu; Zhang, Xin-Song; Dong, Zhi-Hui

2018-05-01

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

On convergence of differential evolution over a class of continuous functions with unique global optimum.

PubMed

Ghosh, Sayan; Das, Swagatam; Vasilakos, Athanasios V; Suresh, Kaushik

2012-02-01

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

Global optimization of ionic Mg(n)F(2n) (n=1-30) clusters.

PubMed

Francisco, E; Martín Pendás, A; Blanco, M A

2005-12-15

The global optimization basin-hopping (BH) method has been used to locate the global minima (GM) of Mg(n)F(2n) (n=1-30) clusters using a Born-Mayer-type potential. Some of the GM were particularly difficult to find, requiring more than 1.5 x 10(4) BH steps. We have found that both the binding energy per MgF2 unit and the effective volume of the GM isomers increase almost linearly with n, and that cluster symmetry decreases with cluster size. The data derived from the BH runs reveal a growing density of local minima just above the GM as n increases. Despite this, the attraction basin around each GM is relatively large, since after all their atomic coordinates are randomly displaced by values as high as 2.0 bohrs, the perturbed structures, upon reoptimization, relax back to the GM in more than 50% of the cases (except for n=10 and 11). The relative stabilities derived from energy second differences suggest that n=8,10,13,15, and 20 are probably the magic numbers for these systems. Mass spectrum experiments would be very useful to clarify this issue.

Applications of hybrid genetic algorithms in seismic tomography

NASA Astrophysics Data System (ADS)

Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet T.; Papazachos, Constantinos

2011-11-01

Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems. In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets that employ the same experimental geometry, wavelength and geometrical characteristics of the model anomalies. Moreover, real data from a crosswell tomographic project for the subsurface mapping of an ancient wall foundation are used for testing the efficiency of the proposed algorithm. The results show that the combined use of both methods can exploit the benefits of each approach, leading to improved final models and producing realistic velocity models, without significantly increasing the required computation time.

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 often achieves better GLF than \\ell \\text{EUD} model optimization does, the GLF model directly optimizes a more clinically relevant metric and can further improve fEUD plan quality.

Simultaneous Helmert transformations among multiple frames considering all relevant measurements

NASA Astrophysics Data System (ADS)

Chang, Guobin; Lin, Peng; Bian, Hefang; Gao, Jingxiang

2018-03-01

Helmert or similarity models are widely employed to relate different coordinate frames. It is often encountered in practice to transform coordinates from more than one old frame into a new one. One may perform separate Helmert transformations for each old frame. However, although each transformation is locally optimal, this is not globally optimal. Transformations among three frames, namely one new and two old, are studied as an example. Simultaneous Helmert transformations among all frames are also studied. Least-squares estimation of the transformation parameters and the coordinates in the new frame of all stations involved is performed. A functional model for the transformations among multiple frames is developed. A realistic stochastic model is followed, in which not only non-common stations are taken into consideration, but also errors in all measurements are addressed. An algorithm of iterative linearizations and estimations is derived in detail. The proposed method is globally optimal, and, perhaps more importantly, it produces a unified network of the new frame providing coordinate estimates for all involved stations and the associated covariance matrix, with the latter being consistent with the true errors of the former. Simulations are conducted, and the results validate the superiority of the proposed combined method over separate approaches.

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

NASA Astrophysics Data System (ADS)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various large heterogeneous spatial-temporal datasets provide evidence that the benefits of the proposed methodology for efficient and accurate learning exist beyond the area of retrieval of geophysical parameters.

Local setup errors in image-guided radiotherapy for head and neck cancer patients immobilized with a custom-made device.

PubMed

Giske, Kristina; Stoiber, Eva M; Schwarz, Michael; Stoll, Armin; Muenter, Marc W; Timke, Carmen; Roeder, Falk; Debus, Juergen; Huber, Peter E; Thieke, Christian; Bendl, Rolf

2011-06-01

To evaluate the local positioning uncertainties during fractionated radiotherapy of head-and-neck cancer patients immobilized using a custom-made fixation device and discuss the effect of possible patient correction strategies for these uncertainties. A total of 45 head-and-neck patients underwent regular control computed tomography scanning using an in-room computed tomography scanner. The local and global positioning variations of all patients were evaluated by applying a rigid registration algorithm. One bounding box around the complete target volume and nine local registration boxes containing relevant anatomic structures were introduced. The resulting uncertainties for a stereotactic setup and the deformations referenced to one anatomic local registration box were determined. Local deformations of the patients immobilized using our custom-made device were compared with previously published results. Several patient positioning correction strategies were simulated, and the residual local uncertainties were calculated. The patient anatomy in the stereotactic setup showed local systematic positioning deviations of 1-4 mm. The deformations referenced to a particular anatomic local registration box were similar to the reported deformations assessed from patients immobilized with commercially available Aquaplast masks. A global correction, including the rotational error compensation, decreased the remaining local translational errors. Depending on the chosen patient positioning strategy, the remaining local uncertainties varied considerably. Local deformations in head-and-neck patients occur even if an elaborate, custom-made patient fixation method is used. A rotational error correction decreased the required margins considerably. None of the considered correction strategies achieved perfect alignment. Therefore, weighting of anatomic subregions to obtain the optimal correction vector should be investigated in the future. Copyright © 2011 Elsevier Inc. All rights reserved.

Global cost and weight evaluation of fuselage keel design concepts

NASA Technical Reports Server (NTRS)

Flynn, B. W.; Morris, M. R.; Metschan, S. L.; Swanson, G. D.; Smith, P. J.; Griess, K. H.; Schramm, M. R.; Humphrey, R. J.

1993-01-01

The Boeing program entitled Advanced Technology Composite Aircraft Structure (ATCAS) is focused on the application of affordable composite technology to pressurized fuselage structure of future aircraft. As part of this effort, a design study was conducted on the keel section of the aft fuselage. A design build team (DBT) approach was used to identify and evaluate several design concepts which incorporated different material systems, fabrication processes, structural configurations, and subassembly details. The design concepts were developed in sufficient detail to accurately assess their potential for cost and weight savings as compared with a metal baseline representing current wide body technology. The cost and weight results, along with an appraisal of performance and producibility risks, are used to identify a globally optimized keel design; one which offers the most promising cost and weight advantages over metal construction. Lastly, an assessment is given of the potential for further cost and weight reductions of the selected keel design during local optimization.

Image-optimized Coronal Magnetic Field Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M., E-mail: shaela.i.jones-mecholsky@nasa.gov, E-mail: shaela.i.jonesmecholsky@nasa.gov

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outsidemore » of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.« less

Incremental triangulation by way of edge swapping and local optimization

NASA Technical Reports Server (NTRS)

Wiltberger, N. Lyn

1994-01-01

This document is intended to serve as an installation, usage, and basic theory guide for the two dimensional triangulation software 'HARLEY' written for the Silicon Graphics IRIS workstation. This code consists of an incremental triangulation algorithm based on point insertion and local edge swapping. Using this basic strategy, several types of triangulations can be produced depending on user selected options. For example, local edge swapping criteria can be chosen which minimizes the maximum interior angle (a MinMax triangulation) or which maximizes the minimum interior angle (a MaxMin or Delaunay triangulation). It should be noted that the MinMax triangulation is generally only locally optical (not globally optimal) in this measure. The MaxMin triangulation, however, is both locally and globally optical. In addition, Steiner triangulations can be constructed by inserting new sites at triangle circumcenters followed by edge swapping based on the MaxMin criteria. Incremental insertion of sites also provides flexibility in choosing cell refinement criteria. A dynamic heap structure has been implemented in the code so that once a refinement measure is specified (i.e., maximum aspect ratio or some measure of a solution gradient for the solution adaptive grid generation) the cell with the largest value of this measure is continually removed from the top of the heap and refined. The heap refinement strategy allows the user to specify either the number of cells desired or refine the mesh until all cell refinement measures satisfy a user specified tolerance level. Since the dynamic heap structure is constantly updated, the algorithm always refines the particular cell in the mesh with the largest refinement criteria value. The code allows the user to: triangulate a cloud of prespecified points (sites), triangulate a set of prespecified interior points constrained by prespecified boundary curve(s), Steiner triangulate the interior/exterior of prespecified boundary curve(s), refine existing triangulations based on solution error measures, and partition meshes based on the Cuthill-McKee, spectral, and coordinate bisection strategies.

Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions.

PubMed

Liu, Hongcheng; Yao, Tao; Li, Runze; Ye, Yinyu

2017-11-01

This paper concerns the folded concave penalized sparse linear regression (FCPSLR), a class of popular sparse recovery methods. Although FCPSLR yields desirable recovery performance when solved globally, computing a global solution is NP-complete. Despite some existing statistical performance analyses on local minimizers or on specific FCPSLR-based learning algorithms, it still remains open questions whether local solutions that are known to admit fully polynomial-time approximation schemes (FPTAS) may already be sufficient to ensure the statistical performance, and whether that statistical performance can be non-contingent on the specific designs of computing procedures. To address the questions, this paper presents the following threefold results: (i) Any local solution (stationary point) is a sparse estimator, under some conditions on the parameters of the folded concave penalties. (ii) Perhaps more importantly, any local solution satisfying a significant subspace second-order necessary condition (S 3 ONC), which is weaker than the second-order KKT condition, yields a bounded error in approximating the true parameter with high probability. In addition, if the minimal signal strength is sufficient, the S 3 ONC solution likely recovers the oracle solution. This result also explicates that the goal of improving the statistical performance is consistent with the optimization criteria of minimizing the suboptimality gap in solving the non-convex programming formulation of FCPSLR. (iii) We apply (ii) to the special case of FCPSLR with minimax concave penalty (MCP) and show that under the restricted eigenvalue condition, any S 3 ONC solution with a better objective value than the Lasso solution entails the strong oracle property. In addition, such a solution generates a model error (ME) comparable to the optimal but exponential-time sparse estimator given a sufficient sample size, while the worst-case ME is comparable to the Lasso in general. Furthermore, to guarantee the S 3 ONC admits FPTAS.

Modeling Hydrodynamic Changes Due to Marine Hydrokinetic Power Production: Community Outreach and Education

NASA Astrophysics Data System (ADS)

James, S. C.; Jones, C.; Roberts, J.

2013-12-01

Power generation with marine hydrokinetic (MHK) turbines is receiving growing global interest. Because of reasonable investment, maintenance, reliability, and environmental friendliness, this technology can contribute to national (and global) energy markets and is worthy of research investment. Furthermore, in remote areas, small-scale MHK energy from river, tidal, or ocean currents can provide a local power supply. The power-generating capacity of MHK turbines will depend, among other factors, upon the turbine type and number and the local flow velocities. There is an urgent need for deployment of practical, accessible tools and techniques to help the industry optimize MHK array layouts while establishing best sitting and design practices that minimize environmental impacts. Sandia National Laboratories (SNL) has modified the open-source flow and transport Environmental Fluid Dynamics Code (EFDC) to include the capability of simulating the effects of MHK power production. Upon removing energy (momentum) from the system, changes to the local and far-field flow dynamics can be estimated (e.g., flow speeds, tidal ranges, flushing rates, etc.). The effects of these changes on sediment dynamics and water quality can also be simulated using this model. Moreover, the model can be used to optimize MHK array layout to maximize power capture and minimize environmental impacts. Both a self-paced tutorial and in-depth training course have been developed as part of an outreach program to train academics, technology developers, and regulators in the use and application of this software. This work outlines SNL's outreach efforts using this modeling framework as applied to two specific sites where MHK turbines have been deployed.

Fast globally optimal segmentation of cells in fluorescence microscopy images.

PubMed

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.

Parameter optimization for reproducible cardiac 1 H-MR spectroscopy at 3 Tesla.

PubMed

de Heer, Paul; Bizino, Maurice B; Lamb, Hildo J; Webb, Andrew G

2016-11-01

To optimize data acquisition parameters in cardiac proton MR spectroscopy, and to evaluate the intra- and intersession variability in myocardial triglyceride content. Data acquisition parameters at 3 Tesla (T) were optimized and reproducibility measured using, in total, 49 healthy subjects. The signal-to-noise-ratio (SNR) and the variance in metabolite amplitude between averages were measured for: (i) global versus local power optimization; (ii) static magnetic field (B 0 ) shimming performed during free-breathing or within breathholds; (iii) post R-wave peak measurement times between 50 and 900 ms; (iv) without respiratory compensation, with breathholds and with navigator triggering; and (v) frequency selective excitation, Chemical Shift Selective (CHESS) and Multiply Optimized Insensitive Suppression Train (MOIST) water suppression techniques. Using the optimized parameters intra- and intersession myocardial triglyceride content reproducibility was measured. Two cardiac proton spectra were acquired with the same parameters and compared (intrasession reproducibility) after which the subject was removed from the scanner and placed back in the scanner and a third spectrum was acquired which was compared with the first measurement (intersession reproducibility). Local power optimization increased SNR on average by 22% compared with global power optimization (P = 0.0002). The average linewidth was not significantly different for pencil beam B 0 shimming using free-breathing or breathholds (19.1 Hz versus 17.5 Hz; P = 0.15). The highest signal stability occurred at a cardiac trigger delay around 240 ms. The mean amplitude variation was significantly lower for breathholds versus free-breathing (P = 0.03) and for navigator triggering versus free-breathing (P = 0.03) as well as for navigator triggering versus breathhold (P = 0.02). The mean residual water signal using CHESS (1.1%, P = 0.01) or MOIST (0.7%, P = 0.01) water suppression was significantly lower than using frequency selective excitation water suppression (7.0%). Using the optimized parameters an intrasession limits of agreement of the myocardial triglyceride content of -0.11% to +0.04%, and an intersession of -0.15% to +0.9%, were achieved. The coefficient of variation was 5% for the intrasession reproducibility and 6.5% for the intersession reproducibility. Using approaches designed to optimize SNR and minimize the variation in inter-average signal intensities and frequencies/phases, a protocol was developed to perform cardiac MR spectroscopy on a clinical 3T system with high reproducibility. J. Magn. Reson. Imaging 2016;44:1151-1158. © 2016 International Society for Magnetic Resonance in Medicine.

The storage capacity of Potts models for semantic memory retrieval

NASA Astrophysics Data System (ADS)

Kropff, Emilio; Treves, Alessandro

2005-08-01

We introduce and analyse a minimal network model of semantic memory in the human brain. The model is a global associative memory structured as a collection of N local modules, each coding a feature, which can take S possible values, with a global sparseness a (the average fraction of features describing a concept). We show that, under optimal conditions, the number cM of modules connected on average to a module can range widely between very sparse connectivity (high dilution, c_{M}/N\\to 0 ) and full connectivity (c_{M}\\to N ), maintaining a global network storage capacity (the maximum number pc of stored and retrievable concepts) that scales like pc~cMS2/a, with logarithmic corrections consistent with the constraint that each synapse may store up to a fraction of a bit.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

PubMed

Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

2017-01-01

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Nark, Douglas M.; Jones, Michael G.

2011-01-01

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

Adaptive local linear regression with application to printer color management.

PubMed

Gupta, Maya R; Garcia, Eric K; Chin, Erika

2008-06-01

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

Structural modeling and optimization of a joined-wing configuration of a High-Altitude Long-Endurance (HALE) aircraft

NASA Astrophysics Data System (ADS)

Kaloyanova, Valentina B.

Recent research trends have indicated an interest in High-Altitude, Long-Endurance (HALE) aircraft as a low-cost alternative to certain space missions, such as telecommunication relay, environmental sensing and military reconnaissance. HALE missions require a light vehicle flying at low speed in the stratosphere at altitudes of 60,000-80,000 ft, with a continuous loiter time of up to several days. To provide high lift and low drag at these high altitudes, where the air density is low, the wing area should be increased, i.e., high-aspect-ratio wings are necessary. Due to its large span and lightweight, the wing structure is very flexible. To reduce the structural deformation, and increase the total lift in a long-spanned wing, a sensorcraft model with a joined-wing configuration, proposed by AFRL, is employed. The joined-wing encompasses a forward wing, which is swept back with a positive dihedral angle, and connected with an aft wing, which is swept forward. The joined-wing design combines structural strength, high aerodynamic performance and efficiency. As a first step to study the joined-wing structural behavior an 1-D approximation model is developed. The 1-D approximation is a simple structural model created using ANSYS BEAM4 elements to present a possible approach for the aerodynamics-structure coupling. The pressure loads from the aerodynamic analysis are integrated numerically to obtain the resultant aerodynamic forces and moments (spanwise lift and pitching moment distributions, acting at the aerodynamic center). These are applied on the 1-D structural model. A linear static analysis is performed under this equivalent load, and the deformed shape of the 1-D model is used to obtain the deformed shape of the actual 3-D joined wing, i.e. deformed aerodynamic surface grid. To date in the existing studies, only simplified structural models have been examined. In the present work, in addition to the simple 1-D beam model, a semi-monocoque structural model is developed. All stringers, skin panels, ribs and spars are represented by appropriate elements in a finite-element model. Also, the model accounts for the fuel weight and sensorcraft antennae housed within the wings. Linear and nonlinear static analyses under the aerodynamic load are performed. The stress distribution in the wing as well as deformation is explored. Starting with a structural model with uniform mass distribution, a design optimization is performed to achieve a fully stressed design. As the joined-wing structure is prone to buckling, after the design optimization is complete linear and nonlinear bucking analyses are performed to study the global joined-wing structural instability, the load magnitude at which it is expected to occur, and the buckling mode. The buckled shape of the aft wing (which is subjected to compression) is found to resemble that of a fixed-pinned column. The linear buckling analysis overestimates the buckling load. However, even the nonlinear buckling analysis results in a load factor higher than 3, i.e. the wing structure is buckling safe under its current loading conditions. As the region of the joint has a very complicated geometry that has adverse effects in the flow and stress behavior an independent, more finely meshed model (submodel) of the joint region is generated and analyzed. A detailed discussion of the stress distribution obtained in the joint region via the submodeling technique is presented in this study as well. It is found out that compared to its structural response, the joint adverse effects are much more pronounced in its aerodynamic response, so it is suggested for future studies the geometry of the joint to be optimized based on its aerodynamic performance. As this design and analysis study is aimed towards developing a realistic structural representation of the innovative joined-wing configuration, in addition to the "global", or upper-level optimization, a local level design optimization is performed as well. At the lower (local) level detailed models of wing structural panels are used to compute more complex failure modes and to design the details that are not included in the upper (global) level model. Proper coordination between local skin-stringer panel models and the global joined-wing model prevents inconsistency between the upper- (global) and lower- (local) level design models. (Abstract shortened by UMI.)

Developing a Hydrologic Assessment Tool for Designing Bioretention in a watershed

NASA Astrophysics Data System (ADS)

Baek, Sangsoo; Ligaray, Mayzonee; Park, Jeong-Pyo; Kwon, Yongsung; Cho, Kyung Hwa

2017-04-01

Continuous urbanization has negatively impacted the ecological and hydrological environments at the global, regional, and local scales. This issue was addressed by developing Low Impact Development (LID) practices to deliver better hydrologic function and improve the environmental, economic, social and cultural outcomes. This study developed a modeling software to simulate and optimize bioretentions among LID in a given watershed. The model calculated a detailed soil infiltration process in bioretention with hydrological conditions and hydraulic facilities (e.g. riser and underdrain) and also generated an optimized plan using Flow Duration Curve (FDC). The optimization result from the simulation demonstrated that the location and size of bioretention, as well as the soil texture, are important elements for an efficient bioretention. We hope that the developed software in this study could be useful for establishing an appropriate scheme of LID installment

Bayesian Optimization Under Mixed Constraints with A Slack-Variable Augmented Lagrangian

DOE Office of Scientific and Technical Information (OSTI.GOV)

Picheny, Victor; Gramacy, Robert B.; Wild, Stefan M.

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof.more » We show our new slack “ALBO” compares favorably to the original. Its superiority over conventional alternatives is reinforced on several mixed constraint examples.« less

Challenges and solutions in medically managed ACS in the Asia-Pacific region: expert recommendations from the Asia-Pacific ACS Medical Management Working Group.

PubMed

Huo, Yong; Thompson, Peter; Buddhari, Wacin; Ge, Junbo; Harding, Scott; Ramanathan, Letchuman; Reyes, Eugenio; Santoso, Anwar; Tam, Li-Wah; Vijayaraghavan, Govindan; Yeh, Hung-I

2015-03-15

Acute coronary syndromes (ACS) remain a leading cause of mortality and morbidity in the Asia-Pacific (APAC) region. International guidelines advocate invasive procedures in all but low-risk ACS patients; however, a high proportion of ACS patients in the APAC region receive solely medical management due to a combination of unique geographical, socioeconomic, and population-specific barriers. The APAC ACS Medical Management Working Group recently convened to discuss the ACS medical management landscape in the APAC region. Local and international ACS guidelines and the global and APAC clinical evidence-base for medical management of ACS were reviewed. Challenges in the provision of optimal care for these patients were identified and broadly categorized into issues related to (1) accessibility/systems of care, (2) risk stratification, (3) education, (4) optimization of pharmacotherapy, and (5) cost/affordability. While ACS guidelines clearly represent a valuable standard of care, the group concluded that these challenges can be best met by establishing cardiac networks and individual hospital models/clinical pathways taking into account local risk factors (including socioeconomic status), affordability and availability of pharmacotherapies/invasive facilities, and the nature of local healthcare systems. Potential solutions central to the optimization of ACS medical management in the APAC region are outlined with specific recommendations. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Modeling, simulation and optimization approaches for design of lightweight car body structures

NASA Astrophysics Data System (ADS)

Kiani, Morteza

Simulation-based design optimization and finite element method are used in this research to investigate weight reduction of car body structures made of metallic and composite materials under different design criteria. Besides crashworthiness in full frontal, offset frontal, and side impact scenarios, vibration frequencies, static stiffness, and joint rigidity are also considered. Energy absorption at the component level is used to study the effectiveness of carbon fiber reinforced polymer (CFRP) composite material with consideration of different failure criteria. A global-local design strategy is introduced and applied to multi-objective optimization of car body structures with CFRP components. Multiple example problems involving the analysis of full-vehicle crash and body-in-white models are used to examine the effect of material substitution and the choice of design criteria on weight reduction. The results of this study show that car body structures that are optimized for crashworthiness alone may not meet the vibration criterion. Moreover, optimized car body structures with CFRP components can be lighter with superior crashworthiness than the baseline and optimized metallic structures.

Computational methods for global/local analysis

NASA Technical Reports Server (NTRS)

Ransom, Jonathan B.; Mccleary, Susan L.; Aminpour, Mohammad A.; Knight, Norman F., Jr.

1992-01-01

Computational methods for global/local analysis of structures which include both uncoupled and coupled methods are described. In addition, global/local analysis methodology for automatic refinement of incompatible global and local finite element models is developed. Representative structural analysis problems are presented to demonstrate the global/local analysis methods.

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots.

PubMed

Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

2015-09-16

One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

PubMed Central

Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

2015-01-01

One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914

Global optimization of multicomponent distillation configurations: 2. Enumeration based global minimization algorithm

DOE PAGES

Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; ...

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

Global Binary Optimization on Graphs for Classification of High Dimensional Data

DTIC Science & Technology

2014-09-01

Buades et al . in [10] introduce a new non-local means algorithm for image denoising and compare it to some of the best methods. In [28], Grady de...scribes a random walk algorithm for image seg- mentation using the solution to a Dirichlet prob- lem. Elmoataz et al . present generalizations of the...graph Laplacian [19] for image denoising and man- ifold smoothing. Couprie et al . in [16] propose a parameterized graph-based energy function that unifies

Multiparameter Estimation in Networked Quantum Sensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Multiparameter Estimation in Networked Quantum Sensors

DOE PAGES

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-21

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Effect of Mesoscale and Multiscale Modeling on the Performance of Kevlar Woven Fabric Subjected to Ballistic Impact: A Numerical Study

NASA Astrophysics Data System (ADS)

Jia, Xin; Huang, Zhengxiang; Zu, Xudong; Gu, Xiaohui; Xiao, Qiangqiang

2013-12-01

In this study, an optimal finite element model of Kevlar woven fabric that is more computational efficient compared with existing models was developed to simulate ballistic impact onto fabric. Kevlar woven fabric was modeled to yarn level architecture by using the hybrid elements analysis (HEA), which uses solid elements in modeling the yarns at the impact region and uses shell elements in modeling the yarns away from the impact region. Three HEA configurations were constructed, in which the solid element region was set as about one, two, and three times that of the projectile's diameter with impact velocities of 30 m/s (non-perforation case) and 200 m/s (perforation case) to determine the optimal ratio between the solid element region and the shell element region. To further reduce computational time and to maintain the necessary accuracy, three multiscale models were presented also. These multiscale models combine the local region with the yarn level architecture by using the HEA approach and the global region with homogenous level architecture. The effect of the varying ratios of the local and global area on the ballistic performance of fabric was discussed. The deformation and damage mechanisms of fabric were analyzed and compared among numerical models. Simulation results indicate that the multiscale model based on HEA accurately reproduces the baseline results and obviously decreases computational time.

Second-order optimality conditions for problems with C1 data

NASA Astrophysics Data System (ADS)

Ginchev, Ivan; Ivanov, Vsevolod I.

2008-04-01

In this paper we obtain second-order optimality conditions of Karush-Kuhn-Tucker type and Fritz John one for a problem with inequality constraints and a set constraint in nonsmooth settings using second-order directional derivatives. In the necessary conditions we suppose that the objective function and the active constraints are continuously differentiable, but their gradients are not necessarily locally Lipschitz. In the sufficient conditions for a global minimum we assume that the objective function is differentiable at and second-order pseudoconvex at , a notion introduced by the authors [I. Ginchev, V.I. Ivanov, Higher-order pseudoconvex functions, in: I.V. Konnov, D.T. Luc, A.M. Rubinov (Eds.), Generalized Convexity and Related Topics, in: Lecture Notes in Econom. and Math. Systems, vol. 583, Springer, 2007, pp. 247-264], the constraints are both differentiable and quasiconvex at . In the sufficient conditions for an isolated local minimum of order two we suppose that the problem belongs to the class C1,1. We show that they do not hold for C1 problems, which are not C1,1 ones. At last a new notion parabolic local minimum is defined and it is applied to extend the sufficient conditions for an isolated local minimum from problems with C1,1 data to problems with C1 one.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Advanced fitness landscape analysis and the performance of memetic algorithms.

PubMed

Merz, Peter

2004-01-01

Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search. It is shown that the efficiency of MAs can be increased drastically with the use of domain knowledge. However, effectiveness highly depends on the structure of the problem. As is well-known, identifying this structure is made easier with the notion of fitness landscapes: the local properties of the fitness landscape strongly influence the effectiveness of the local search while the global properties strongly influence the effectiveness of the evolutionary meta-search. This paper also introduces new techniques for analyzing the fitness landscapes of combinatorial problems; these techniques focus on the investigation of random walks in the fitness landscape starting at locally optimal solutions as well as on the escape from the basins of attractions of current local optima. It is shown for NK-landscapes and landscapes of the unconstrained binary quadratic programming problem (BQP) that a random walk to another local optimum can be used to explain the efficiency of recombination in comparison to mutation. Moreover, the paper shows that other aspects like the size of the basins of attractions of local optima are important for the efficiency of MAs and a local search escape analysis is proposed. These simple analysis techniques have several advantages over previously proposed statistical measures and provide valuable insight into the behaviour of MAs on different kinds of landscapes.

Illumination system development using design and analysis of computer experiments

NASA Astrophysics Data System (ADS)

Keresztes, Janos C.; De Ketelaere, Bart; Audenaert, Jan; Koshel, R. J.; Saeys, Wouter

2015-09-01

Computer assisted optimal illumination design is crucial when developing cost-effective machine vision systems. Standard local optimization methods, such as downhill simplex optimization (DHSO), often result in an optimal solution that is influenced by the starting point by converging to a local minimum, especially when dealing with high dimensional illumination designs or nonlinear merit spaces. This work presents a novel nonlinear optimization approach, based on design and analysis of computer experiments (DACE). The methodology is first illustrated with a 2D case study of four light sources symmetrically positioned along a fixed arc in order to obtain optimal irradiance uniformity on a flat Lambertian reflecting target at the arc center. The first step consists of choosing angular positions with no overlap between sources using a fast, flexible space filling design. Ray-tracing simulations are then performed at the design points and a merit function is used for each configuration to quantify the homogeneity of the irradiance at the target. The obtained homogeneities at the design points are further used as input to a Gaussian Process (GP), which develops a preliminary distribution for the expected merit space. Global optimization is then performed on the GP more likely providing optimal parameters. Next, the light positioning case study is further investigated by varying the radius of the arc, and by adding two spots symmetrically positioned along an arc diametrically opposed to the first one. The added value of using DACE with regard to the performance in convergence is 6 times faster than the standard simplex method for equal uniformity of 97%. The obtained results were successfully validated experimentally using a short-wavelength infrared (SWIR) hyperspectral imager monitoring a Spectralon panel illuminated by tungsten halogen sources with 10% of relative error.

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

NASA Astrophysics Data System (ADS)

Grosso, Juan M.; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-10-01

This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

PubMed Central

Yu, Yi; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

PubMed

Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

Evaluation and Optimization of China's Anthropogenic CO2 Emissions using Observations from Northern China (2005-2009).

NASA Astrophysics Data System (ADS)

Dayalu, A.; Munger, J. W.; Wang, Y.; Wofsy, S.; Zhao, Y.; Nielsen, C. P.; Nehrkorn, T.; McElroy, M. B.; Chang, R.

2017-12-01

China has pledged to peak carbon emissions by 2030, but there continues to be significant uncertainty in estimates of its anthropogenic carbon dioxide (CO2) emissions. In this study, we evaluate the performance of three anthropogenic CO2 inventories, two global and one regional, using five years of continuous hourly observations from a site in Northern China. We model five years of continuous hourly observations (2005 to 2009) using the Stochastic Time-Inverted Lagrangian Transport Model (STILT) run in backward time mode driven by high resolution meteorology from the Weather Research and Forecasting Model version 3.6.1 (WRF) with vegetation fluxes prescribed by a simple biosphere model. We calculate regional enhancements to advected background CO2 derived from NOAA CarbonTracker on seasonal and annual bases and use observations to optimize emissions inventories within the site's influence region at these timescales. Finally, we use annual enhancements to examine carbon intensity of provinces in and adjacent to Northern China as CO2 per unit of the region's GDP to evaluate the effects of local and global economic events on CO2 emissions. With the exception of peak growing season where discrepancies are confounded by errors in the vegetation model, we find the regional inventory agrees significantly better with observations than the global inventories at all timescales. Here we use a single measurement site; significant improvements in inventory optimizations can be achieved with a network of measurements stations. This study highlights the importance of China-specific data over global averages in emissions evaluation and demonstrates the value of top-down studies in independently evaluating inventory performance. We demonstrate the framework's ability to resolve differences of at least 20% among inventories, establishing a benchmark for ongoing efforts to decrease uncertainty in China's reported CO2 emissions estimates.

Mathematical modeling of zika virus disease with nonlinear incidence and optimal control

NASA Astrophysics Data System (ADS)

Goswami, Naba Kumar; Srivastav, Akhil Kumar; Ghosh, Mini; Shanmukha, B.

2018-04-01

The Zika virus was first discovered in a rhesus monkey in the Zika Forest of Uganda in 1947, and it was isolated from humans in Nigeria in 1952. Zika virus disease is primarily a mosquito-borne disease, which is transmitted to human primarily through the bite of an infected Aedes species mosquito. However, there is documented evidence of sexual transmission of this disease too. In this paper, a nonlinear mathematical model for Zika virus by considering nonlinear incidence is formulated and analyzed. The equilibria and the basic reproduction number (R0) of the model are found. The stability of the different equilibria of the model is discussed in detail. When the basic reproduction number R0 < 1, the disease-free equilibrium is locally and globally stable i.e. in this case disease dies out. For R0 > 1, we have endemic equilibrium which is locally stable under some restriction on parameters. Further this model is extended to optimal control model and is analyzed by using Pontryagin’s Maximum Principle. It has been observed that optimal control plays a significant role in reducing the number of zika infectives. Finally, numerical simulation is performed to illustrate the analytical findings.

The Multiple-Minima Problem in Protein Folding

NASA Astrophysics Data System (ADS)

Scheraga, Harold A.

1991-10-01

The conformational energy surface of a polypeptide or protein has many local minima, and conventional energy minimization procedures reach only a local minimum (near the starting point of the optimization algorithm) instead of the global minimum (the multiple-minima problem). Several procedures have been developed to surmount this problem, the most promising of which are: (a) build up procedure, (b) optimization of electrostatics, (c) Monte Carlo-plus-energy minimization, (d) electrostatically-driven Monte Carlo, (e) inclusion of distance restraints, (f) adaptive importance-sampling Monte Carlo, (g) relaxation of dimensionality, (h) pattern-recognition, and (i) diffusion equation method. These procedures have been applied to a variety of polypeptide structural problems, and the results of such computations are presented. These include the computation of the structures of open-chain and cyclic peptides, fibrous proteins and globular proteins. Present efforts are being devoted to scaling up these procedures from small polypeptides to proteins, to try to compute the three-dimensional structure of a protein from its amino sequence.

LDRD Final Report: Global Optimization for Engineering Science Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.

Optimization of wall thickness and lay-up for the shell-like composite structure loaded by non-uniform pressure field

NASA Astrophysics Data System (ADS)

Shevtsov, S.; Zhilyaev, I.; Oganesyan, P.; Axenov, V.

2017-01-01

The glass/carbon fiber composites are widely used in the design of various aircraft and rotorcraft components such as fairings and cowlings, which have predominantly a shell-like geometry and are made of quasi-isotropic laminates. The main requirements to such the composite parts are the specified mechanical stiffness to withstand the non-uniform air pressure at the different flight conditions and reduce a level of noise caused by the airflow-induced vibrations at the constrained weight of the part. The main objective of present study is the optimization of wall thickness and lay-up of composite shell-like cowling. The present approach assumes conversion of the CAD model of the cowling surface to finite element (FE) representation, then its wind tunnel testing simulation at the different orientation of airflow to find the most stressed mode of flight. Numerical solutions of the Reynolds averaged Navier-Stokes (RANS) equations supplemented by k-w turbulence model provide the spatial distributions of air pressure applied to the shell surface. At the formulation of optimization problem the global strain energy calculated within the optimized shell was assumed as the objective. A wall thickness of the shell had to change over its surface to minimize the objective at the constrained weight. We used a parameterization of the problem that assumes an initiation of auxiliary sphere with varied radius and coordinates of the center, which were the design variables. Curve that formed by the intersection of the shell with sphere defined boundary of area, which should be reinforced by local thickening the shell wall. To eliminate a local stress concentration this increment was defined as the smooth function defined on the shell surface. As a result of structural optimization we obtained the thickness of shell's wall distribution, which then was used to design the draping and lay-up of composite prepreg layers. The global strain energy in the optimized cowling was reduced in2.5 times at the weight growth up to 15%, whereas the eigenfrequencies at the 6 first natural vibration modes have been increased by 5-15%. The present approach and developed programming tools that demonstrated a good efficiency and stability at the acceptable computational costs can be used to optimize a wide range of shell-like structures made of quasi-isotropic laminates.

Learning to predict where human gaze is using quaternion DCT based regional saliency detection

NASA Astrophysics Data System (ADS)

Li, Ting; Xu, Yi; Zhang, Chongyang

2014-09-01

Many current visual attention approaches used semantic features to accurately capture human gaze. However, these approaches demand high computational cost and can hardly be applied to daily use. Recently, some quaternion-based saliency detection models, such as PQFT (phase spectrum of Quaternion Fourier Transform), QDCT (Quaternion Discrete Cosine Transform), have been proposed to meet real-time requirement of human gaze tracking tasks. However, current saliency detection methods used global PQFT and QDCT to locate jump edges of the input, which can hardly detect the object boundaries accurately. To address the problem, we improved QDCT-based saliency detection model by introducing superpixel-wised regional saliency detection mechanism. The local smoothness of saliency value distribution is emphasized to distinguish noises of background from salient regions. Our algorithm called saliency confidence can distinguish the patches belonging to the salient object and those of the background. It decides whether the image patches belong to the same region. When an image patch belongs to a region consisting of other salient patches, this patch should be salient as well. Therefore, we use saliency confidence map to get background weight and foreground weight to do the optimization on saliency map obtained by QDCT. The optimization is accomplished by least square method. The optimization approach we proposed unifies local and global saliency by combination of QDCT and measuring the similarity between each image superpixel. We evaluate our model on four commonly-used datasets (Toronto, MIT, OSIE and ASD) using standard precision-recall curves (PR curves), the mean absolute error (MAE) and area under curve (AUC) measures. In comparison with most state-of-art models, our approach can achieve higher consistency with human perception without training. It can get accurate human gaze even in cluttered background. Furthermore, it achieves better compromise between speed and accuracy.

EEG functional connectivity, axon delays and white matter disease.

PubMed

Nunez, Paul L; Srinivasan, Ramesh; Fields, R Douglas

2015-01-01

Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.

Recent advances in approximation concepts for optimum structural design

NASA Technical Reports Server (NTRS)

Barthelemy, Jean-Francois M.; Haftka, Raphael T.

1991-01-01

The basic approximation concepts used in structural optimization are reviewed. Some of the most recent developments in that area since the introduction of the concept in the mid-seventies are discussed. The paper distinguishes between local, medium-range, and global approximations; it covers functions approximations and problem approximations. It shows that, although the lack of comparative data established on reference test cases prevents an accurate assessment, there have been significant improvements. The largest number of developments have been in the areas of local function approximations and use of intermediate variable and response quantities. It also appears that some new methodologies are emerging which could greatly benefit from the introduction of new computer architecture.

Intelligent control and cooperation for mobile robots

NASA Astrophysics Data System (ADS)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation of robots subjected to dynamic communication constraints, game theory is used in addition to reinforcement learning. The nodes maintain an extra set of state variables about all the other nodes that they can communicate to. The more important are trust and predictability. They are a way to incorporate knowledge acquired in the past into the control decisions taken by each node. The trust variable provides a simple mechanism for the implementation of reinforcement learning. For robot formations, potential field based control algorithms are used to generate the control commands. The formation structure changes due to the environment and due to the decisions of the nodes. It is a problem of building a graph and coalitions by having distributed decisions but still reaching an optimal behavior globally.

Higher levels of depression are associated with reduced global bias in visual processing.

PubMed

de Fockert, Jan W; Cooper, Andrew

2014-04-01

Negative moods have been associated with a tendency to prioritise local details in visual processing. The current study investigated the relation between depression and visual processing using the Navon task, a standard task of local and global processing. In the Navon task, global stimuli are presented that are made up of many local parts, and the participants are instructed to report the identity of either a global or a local target shape. Participants with a low self-reported level of depression showed evidence of the expected global processing bias, and were significantly faster at responding to the global, compared with the local level. By contrast, no such difference was observed in participants with high levels of depression. The reduction of the global bias associated with high levels of depression was only observed in the overall speed of responses to global (versus local) targets, and not in the level of interference produced by the global (versus local) distractors. These results are in line with recent findings of a dissociation between local/global processing bias and interference from local/global distractors, and support the claim that depression is associated with a reduction in the tendency to prioritise global-level processing.

Solving the infeasible trust-region problem using approximations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Renaud, John E.; Perez, Victor M.; Eldred, Michael Scott

2004-07-01

The use of optimization in engineering design has fueled the development of algorithms for specific engineering needs. When the simulations are expensive to evaluate or the outputs present some noise, the direct use of nonlinear optimizers is not advisable, since the optimization process will be expensive and may result in premature convergence. The use of approximations for both cases is an alternative investigated by many researchers including the authors. When approximations are present, a model management is required for proper convergence of the algorithm. In nonlinear programming, the use of trust-regions for globalization of a local algorithm has been provenmore » effective. The same approach has been used to manage the local move limits in sequential approximate optimization frameworks as in Alexandrov et al., Giunta and Eldred, Perez et al. , Rodriguez et al., etc. The experience in the mathematical community has shown that more effective algorithms can be obtained by the specific inclusion of the constraints (SQP type of algorithms) rather than by using a penalty function as in the augmented Lagrangian formulation. The presence of explicit constraints in the local problem bounded by the trust region, however, may have no feasible solution. In order to remedy this problem the mathematical community has developed different versions of a composite steps approach. This approach consists of a normal step to reduce the amount of constraint violation and a tangential step to minimize the objective function maintaining the level of constraint violation attained at the normal step. Two of the authors have developed a different approach for a sequential approximate optimization framework using homotopy ideas to relax the constraints. This algorithm called interior-point trust-region sequential approximate optimization (IPTRSAO) presents some similarities to the two normal-tangential steps algorithms. In this paper, a description of the similarities is presented and an expansion of the two steps algorithm is presented for the case of approximations.« less

A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

PubMed Central

Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen

2018-01-01

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635

Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients for Economic Dispatch with Valve Point Effects and Multifuel Options

NASA Astrophysics Data System (ADS)

Polprasert, Jirawadee; Ongsakul, Weerakorn; Dieu, Vo Ngoc

2011-06-01

This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.

Combined control-structure optimization

NASA Technical Reports Server (NTRS)

Salama, M.; Milman, M.; Bruno, R.; Scheid, R.; Gibson, S.

1989-01-01

An approach for combined control-structure optimization keyed to enhancing early design trade-offs is outlined and illustrated by numerical examples. The approach employs a homotopic strategy and appears to be effective for generating families of designs that can be used in these early trade studies. Analytical results were obtained for classes of structure/control objectives with linear quadratic Gaussian (LQG) and linear quadratic regulator (LQR) costs. For these, researchers demonstrated that global optima can be computed for small values of the homotopy parameter. Conditions for local optima along the homotopy path were also given. Details of two numerical examples employing the LQR control cost were given showing variations of the optimal design variables along the homotopy path. The results of the second example suggest that introducing a second homotopy parameter relating the two parts of the control index in the LQG/LQR formulation might serve to enlarge the family of Pareto optima, but its effect on modifying the optimal structural shapes may be analogous to the original parameter lambda.

A quantum network of clocks

NASA Astrophysics Data System (ADS)

Komar, Peter; Kessler, Eric; Bishof, Michael; Jiang, Liang; Sorensen, Anders; Ye, Jun; Lukin, Mikhail

2014-05-01

Shared timing information constitutes a key resource for positioning and navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System (GPS). By combining precision metrology and quantum networks, we propose here a quantum, cooperative protocol for the operation of a network consisting of geographically remote optical atomic clocks. Using non-local entangled states, we demonstrate an optimal utilization of the global network resources, and show that such a network can be operated near the fundamental limit set by quantum theory yielding an ultra-precise clock signal. Furthermore, the internal structure of the network, combined with basic techniques from quantum communication, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy. See also: Komar et al. arXiv:1310.6045 (2013) and Kessler et al. arXiv:1310.6043 (2013).

Capacity Building in Global Mental Health: Professional Training

PubMed Central

Fricchione, Gregory L; Borba, Christina P C; Alem, Atalay; Shibre, Teshome; Carney, Julia R; Henderson, David C

2012-01-01

We suggest that the optimal approach to building capacity in global mental health care will require partnerships between professional resources in high-income countries and promising health-related institutions in low- and middle-income countries. The result of these partnerships will be sustainable academic relationships that can educate a new generation of in-country primary care physicians and, eventually, specialized health professionals. Research capabilities will be an essential educational component to inform policy and practice, and to ensure careful outcome measurements of training and of intervention, prevention, and promotion strategies. The goal of these academic centers of excellence will be to develop quality, in-country clinical and research professionals, and to build a productive environment for these professionals to advance their careers locally. In sum, this article discusses human capacity building in global mental health, provides recommendations for training, and offers examples of recent initiatives. (Harv Rev Psychiatry 2012;20:47–57.) PMID:22335182

Assimilative modeling of low latitude ionosphere

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Wang, Chunining; Hajj, George A.; Rosen, I. Gary; Wilson, Brian D.; Mannucci, Anthony J.

2004-01-01

In this paper we present an observation system simulation experiment for modeling low-latitude ionosphere using a 3-dimensional (3-D) global assimilative ionospheric model (GAIM). The experiment is conducted to test the effectiveness of GAIM with a 4-D variational approach (4DVAR) in estimation of the ExB drift and thermospheric wind in the magnetic meridional planes simultaneously for all longitude or local time sectors. The operational Global Positioning System (GPS) satellites and the ground-based global GPS receiver network of the International GPS Service are used in the experiment as the data assimilation source. 'The optimization of the ionospheric state (electron density) modeling is performed through a nonlinear least-squares minimization process that adjusts the dynamical forces to reduce the difference between the modeled and observed slant total electron content in the entire modeled region. The present experiment for multiple force estimations reinforces our previous assessment made through single driver estimations conducted for the ExB drift only.

The Physiological Basis for Thermal Comfort in Different Climates; a Preliminary Study (De fysiologische basis voor thermisch comfort onder diverse klimatologische omstandigheden; een voorstudie),

DTIC Science & Technology

1996-08-07

Thermal comfort is very important for optimal functioning of humans. It gives information about the thermal state of the body, by which the human...receptors and sending efferent information to the effectors by which the body controls its temperature. Thermal comfort is determined by the temperature...global thermal comfort are core temperature, temperature of the extremities and temperature of the environment. In local thermal comfort and pain

Adaptive Implicit Non-Equilibrium Radiation Diffusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Philip, Bobby; Wang, Zhen; Berrill, Mark A

2013-01-01

We describe methods for accurate and efficient long term time integra- tion of non-equilibrium radiation diffusion systems: implicit time integration for effi- cient long term time integration of stiff multiphysics systems, local control theory based step size control to minimize the required global number of time steps while control- ling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton-Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence.

Formulating Spatially Varying Performance in the Statistical Fusion Framework

PubMed Central

Landman, Bennett A.

2012-01-01

To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. PMID:22438513

Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

PubMed

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.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

PubMed Central

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

A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

PubMed Central

Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor

2012-01-01

A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method. PMID:22919371

Tempest - Efficient Computation of Atmospheric Flows Using High-Order Local Discretization Methods

NASA Astrophysics Data System (ADS)

Ullrich, P. A.; Guerra, J. E.

2014-12-01

The Tempest Framework composes several compact numerical methods to easily facilitate intercomparison of atmospheric flow calculations on the sphere and in rectangular domains. This framework includes the implementations of Spectral Elements, Discontinuous Galerkin, Flux Reconstruction, and Hybrid Finite Element methods with the goal of achieving optimal accuracy in the solution of atmospheric problems. Several advantages of this approach are discussed such as: improved pressure gradient calculation, numerical stability by vertical/horizontal splitting, arbitrary order of accuracy, etc. The local numerical discretization allows for high performance parallel computation and efficient inclusion of parameterizations. These techniques are used in conjunction with a non-conformal, locally refined, cubed-sphere grid for global simulations and standard Cartesian grids for simulations at the mesoscale. A complete implementation of the methods described is demonstrated in a non-hydrostatic setting.

Preliminary Analysis of Low-Thrust Gravity Assist Trajectories by An Inverse Method and a Global Optimization Technique.

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 algebraic computation. By this method a low-thrust transfer satisfying the boundary conditions on position and velocity can be quickly assessed, with low computational effort since no numerical propagation is required. The hybrid global optimization algorithm is made of a double step. Through the evolutionary search a large number of optima, and eventually the global one, are located, while the deterministic step consists of a branching process that exhaustively partitions the domain in order to have an extensive characterization of such a complex space of solutions. Furthermore, the approach implements a novel direct constraint-handling technique allowing the treatment of mixed-integer nonlinear programming problems (MINLP) typical of multiple swingby trajectories. A low-thrust transfer to Mars is studied as a test bed for the low-thrust model, thus presenting the main characteristics of the different shapes proposed and the features of the possible sub-arcs segmentations between two planets with respect to different objective functions: minimum time and minimum fuel consumption transfers. Other various test cases are also shown and further optimized, proving the effective capability of the proposed tool.

A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks

PubMed Central

Chen, Jianhui; Tang, Lei; Liu, Jun; Ye, Jieping

2013-01-01

In this paper, we consider the problem of learning from multiple related tasks for improved generalization performance by extracting their shared structures. The alternating structure optimization (ASO) algorithm, which couples all tasks using a shared feature representation, has been successfully applied in various multitask learning problems. However, ASO is nonconvex and the alternating algorithm only finds a local solution. We first present an improved ASO formulation (iASO) for multitask learning based on a new regularizer. We then convert iASO, a nonconvex formulation, into a relaxed convex one (rASO). Interestingly, our theoretical analysis reveals that rASO finds a globally optimal solution to its nonconvex counterpart iASO under certain conditions. rASO can be equivalently reformulated as a semidefinite program (SDP), which is, however, not scalable to large datasets. We propose to employ the block coordinate descent (BCD) method and the accelerated projected gradient (APG) algorithm separately to find the globally optimal solution to rASO; we also develop efficient algorithms for solving the key subproblems involved in BCD and APG. The experiments on the Yahoo webpages datasets and the Drosophila gene expression pattern images datasets demonstrate the effectiveness and efficiency of the proposed algorithms and confirm our theoretical analysis. PMID:23520249

Constraint Optimization Problem For The Cutting Of A Cobalt Chrome Refractory Material

NASA Astrophysics Data System (ADS)

Lebaal, Nadhir; Schlegel, Daniel; Folea, Milena

2011-05-01

This paper shows a complete approach to solve a given problem, from the experimentation to the optimization of different cutting parameters. In response to an industrial problem of slotting FSX 414, a Cobalt-based refractory material, we have implemented a design of experiment to determine the most influent parameters on the tool life, the surface roughness and the cutting forces. After theses trials, an optimization approach has been implemented to find the lowest manufacturing cost while respecting the roughness constraints and cutting force limitation constraints. The optimization approach is based on the Response Surface Method (RSM) using the Sequential Quadratic programming algorithm (SQP) for a constrained problem. To avoid a local optimum and to obtain an accurate solution at low cost, an efficient strategy, which allows improving the RSM accuracy in the vicinity of the global optimum, is presented. With these models and these trials, we could apply and compare our optimization methods in order to get the lowest cost for the best quality, i.e. a satisfying surface roughness and limited cutting forces.

Phase transitions in Pareto optimal complex networks

NASA Astrophysics Data System (ADS)

Seoane, Luís F.; Solé, Ricard

2015-09-01

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

Optimization of Residual Stresses in MMC's Using Compensating/Compliant Interfacial Layers. Part 2: OPTCOMP User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.; Williams, Todd O.

1994-01-01

A user's guide for the computer program OPTCOMP is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in uni-directional metal matrix composites subjected to combined thermo-mechanical axisymmetric loading using compensating or compliant layers at the fiber/matrix interface. The user specifies the architecture and the initial material parameters of the interfacial region, which can be either elastic or elastoplastic, and defines the design variables, together with the objective function, the associated constraints and the loading history through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the elastoplastic response of an arbitrarily layered multiple concentric cylinder model that is coupled to the commercial optimization package DOT. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

PubMed

Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

2014-10-01

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

Mars Mission Optimization Based on Collocation of Resources

NASA Technical Reports Server (NTRS)

Chamitoff, G. E.; James, G. H.; Barker, D. C.; Dershowitz, A. L.

2003-01-01

This paper presents a powerful approach for analyzing Martian data and for optimizing mission site selection based on resource collocation. This approach is implemented in a program called PROMT (Planetary Resource Optimization and Mapping Tool), which provides a wide range of analysis and display functions that can be applied to raw data or imagery. Thresholds, contours, custom algorithms, and graphical editing are some of the various methods that can be used to process data. Output maps can be created to identify surface regions on Mars that meet any specific criteria. The use of this tool for analyzing data, generating maps, and collocating features is demonstrated using data from the Mars Global Surveyor and the Odyssey spacecraft. The overall mission design objective is to maximize a combination of scientific return and self-sufficiency based on utilization of local materials. Landing site optimization involves maximizing accessibility to collocated science and resource features within a given mission radius. Mission types are categorized according to duration, energy resources, and in-situ resource utilization. Optimization results are shown for a number of mission scenarios.

Price competition in the Chinese pharmaceutical market.

PubMed

Wang, Y Richard

2006-06-01

We study price competition between high-quality global products and low-quality local products in a developing country, i.e., China, Nearly all previous studies on pharmaceutical price competition focused on developed countries with bioequivalent generics. In China, local generic products are not bioequivalent and are deemed of lower quality, while global products in the same class are considered similar in quality and better substitutes. We hypothesize that local generic competition drives down local product price but not global product price. In addition, we hypothesize that therapeutic competition among similar global products lowers global product price. Our empirical results support both hypotheses. Number of local generic competitors has a significantly negative effect on local product price but no effect on global product price, while number of global therapeutic competitors has a significantly negative effect on global product price. Policy changes that encourage bioequivalent local products and accelerate global product approvals will enhance price competition in China.

Local and Global Auditory Processing: Behavioral and ERP Evidence

PubMed Central

Sanders, Lisa D.; Poeppel, David

2007-01-01

Differential processing of local and global visual features is well established. Global precedence effects, differences in event-related potentials (ERPs) elicited when attention is focused on local versus global levels, and hemispheric specialization for local and global features all indicate that relative scale of detail is an important distinction in visual processing. Observing analogous differential processing of local and global auditory information would suggest that scale of detail is a general organizational principle of the brain. However, to date the research on auditory local and global processing has primarily focused on music perception or on the perceptual analysis of relatively higher and lower frequencies. The study described here suggests that temporal aspects of auditory stimuli better capture the local-global distinction. By combining short (40 ms) frequency modulated tones in series to create global auditory patterns (500 ms), we independently varied whether pitch increased or decreased over short time spans (local) and longer time spans (global). Accuracy and reaction time measures revealed better performance for global judgments and asymmetric interference that were modulated by amount of pitch change. ERPs recorded while participants listened to identical sounds and indicated the direction of pitch change at the local or global levels provided evidence for differential processing similar to that found in ERP studies employing hierarchical visual stimuli. ERP measures failed to provide evidence for lateralization of local and global auditory perception, but differences in distributions suggest preferential processing in more ventral and dorsal areas respectively. PMID:17113115

Children inhibit global information when the forest is dense and local information when the forest is sparse.

PubMed

Krakowski, Claire-Sara; Borst, Grégoire; Vidal, Julie; Houdé, Olivier; Poirel, Nicolas

2018-09-01

Visual environments are composed of global shapes and local details that compete for attentional resources. In adults, the global level is processed more rapidly than the local level, and global information must be inhibited in order to process local information when the local information and global information are in conflict. Compared with adults, children present less of a bias toward global visual information and appear to be more sensitive to the density of local elements that constitute the global level. The current study aimed, for the first time, to investigate the key role of inhibition during global/local processing in children. By including two different conditions of global saliency during a negative priming procedure, the results showed that when the global level was salient (dense hierarchical figures), 7-year-old children and adults needed to inhibit the global level to process the local information. However, when the global level was less salient (sparse hierarchical figures), only children needed to inhibit the local level to process the global information. These results confirm a weaker global bias and the greater impact of saliency in children than in adults. Moreover, the results indicate that, regardless of age, inhibition of the most salient hierarchical level is systematically required to select the less salient but more relevant level. These findings have important implications for future research in this area. Copyright © 2018 Elsevier Inc. All rights reserved.

Spanish as a World Language: The Interplay of Globalized Localization and Localized Globalization

ERIC Educational Resources Information Center

Nino-Murcia, Mercedes; Godenzzi, Juan Carlos; Rothman, Jason

2008-01-01

This article argues that two movements in constant interplay operate within the historical trajectory of the Spanish language: the localization that becomes globalized and the globalization that becomes localized. Equally, this article illustrates how, at the same time that Spanish is expanding in the world, new idiosyncratic and localized forms…

A vision for an ultra-high resolution integrated water cycle observation and prediction system

NASA Astrophysics Data System (ADS)

Houser, P. R.

2013-05-01

Society's welfare, progress, and sustainable economic growth—and life itself—depend on the abundance and vigorous cycling and replenishing of water throughout the global environment. The water cycle operates on a continuum of time and space scales and exchanges large amounts of energy as water undergoes phase changes and is moved from one part of the Earth system to another. We must move toward an integrated observation and prediction paradigm that addresses broad local-to-global science and application issues by realizing synergies associated with multiple, coordinated observations and prediction systems. A central challenge of a future water and energy cycle observation strategy is to progress from single variable water-cycle instruments to multivariable integrated instruments in electromagnetic-band families. The microwave range in the electromagnetic spectrum is ideally suited for sensing the state and abundance of water because of water's dielectric properties. Eventually, a dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel active and passive microwave measurements. A partial demonstration of these ideas can even be realized with existing microwave satellite observations to support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information. The simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievals that are not possible with isolated measurements. Furthermore, the simultaneous monitoring of several of the land, atmospheric, oceanic, and cryospheric states brings synergies that will substantially enhance understanding of the global water and energy cycle as a system. The multichannel approach also affords advantages to some constituent retrievals—for instance, simultaneous retrieval of vegetation biomass would improve soil-moisture retrieval by avoiding the need for auxiliary vegetation information. This multivariable water-cycle observation system must be integrated with high-resolution, application relevant prediction systems to optimize their information content and utility is addressing critical water cycle issues. One such vision is a real-time ultra-high resolution locally-moasiced global land modeling and assimilation system, that overlays regional high-fidelity information over a baseline global land prediction system. Such a system would provide the best possible local information for use in applications, while integrating and sharing information globally for diagnosing larger water cycle variability. In a sense, this would constitute a hydrologic telecommunication system, where the best local in-situ gage, Doppler radar, and weather station can be shared internationally, and integrated in a consistent manner with global observation platforms like the multivariable water cycle mission. To realize such a vision, large issues must be addressed, such as international data sharing policy, model-observation integration approaches that maintain local extremes while achieving global consistency, and methods for establishing error estimates and uncertainty.

Autism: Hard to Switch from Details to the Whole.

PubMed

Soriano, María Felipa; Ibáñez-Molina, Antonio J; Paredes, Natalia; Macizo, Pedro

2017-12-18

It has long been proposed that individuals with autism exhibit a superior processing of details at the expense of an impaired global processing. This theory has received some empirical support, but results are mixed. In this research we have studied local and global processing in ASD and Typically Developing children, with an adaptation of the Navon task, designed to measure congruency effects between local and global stimuli and switching cost between local and global tasks. ASD children showed preserved global processing; however, compared to Typically Developing children, they exhibited more facilitation from congruent local stimuli when they performed the global task. In addition, children with ASD had more switching cost than Typically Developing children only when they switched from the local to the global task, reflecting a specific difficulty to disengage from local stimuli. Together, results suggest that ASD is characterized by a tendency to process local details, they benefit from the processing of local stimuli at the expense of increasing cost to disengage from local stimuli when global processing is needed. Thus, this work demonstrates experimentally the advantages and disadvantages of the increased local processing in children with ASD.

Implications for local and global climate of alternative forest management strategies in Norway

NASA Astrophysics Data System (ADS)

Bright, Ryan M.; Antón-Fernández, Clara; Astrup, Rasmus; Cherubini, Francesco; Kvalevåg, Maria; Hammer Strømman, Anders

2014-05-01

We applied a mix of observation and empirical models to evaluate both local and global climate effects of three realistic alternative forest management scenarios in the boreal forests of Norway's largest logging region. The alternative management scenarios embraced strategies aimed at increasing harvest intensities and allowing harvested conifer sites to regenerate naturally with broadleaved species. Stand-level analysis was firstly executed to attribute differences in daily, seasonal, and annual mean surface temperatures to differences in surface intrinsic biophysical properties across coniferous, deciduous, and clear-cut sites. Relative to a coniferous site, we observed a slight local cooling of 0.13 °C at a deciduous site and 0.25 °C at a clear-cut site over a 6-year period which was mostly attributed to a higher albedo throughout the year. When monthly mean albedo trajectories over the entire managed forest landscape were taken into consideration, we found that strategies promoting natural regeneration of coniferous sites with native deciduous species led to substantial global direct climate cooling benefits relative to those maintaining current silviculture regimes - despite predicted long-term regional warming feedbacks and a reduced albedo in spring and autumn months. The magnitude and duration of the cooling benefit depended largely on whether management strategies simultaneously promoted an enhanced material supply over business-as-usual levels. While additional climate impact linked to changes in life-cycle emissions and to changes in the global supply and demand of timber products ought to be factored into any mitigation-oriented climate policy involving the forestry sector, our analysis demonstrates that - within the boundaries of the managed forest ecosystem - excluding important biogeophysical considerations like surface albedo change may lead to sub-optimal climate policy.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

PubMed

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-08-14

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Delineation and geometric modeling of road networks

NASA Astrophysics Data System (ADS)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

Bilinear modeling and nonlinear estimation

NASA Technical Reports Server (NTRS)

Dwyer, Thomas A. W., III; Karray, Fakhreddine; Bennett, William H.

1989-01-01

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Multiple-level stakeholder engagement in malaria clinical trials: addressing the challenges of conducting clinical research in resource-limited settings.

PubMed

Mtove, George; Kimani, Joshua; Kisinza, William; Makenga, Geofrey; Mangesho, Peter; Duparc, Stephan; Nakalembe, Miriam; Phiri, Kamija S; Orrico, Russell; Rojo, Ricardo; Vandenbroucke, Pol

2018-03-22

Multinational clinical trials are logistically complex and require close coordination between various stakeholders. They must comply with global clinical standards and are accountable to multiple regulatory and ethical bodies. In resource-limited settings, it is challenging to understand how to apply global clinical standards to international, national, and local factors in clinical trials, making multiple-level stakeholder engagement an important element in the successful conduct of these clinical trials. During the planning and implementation of a large multinational clinical trial for intermittent preventive treatment of malaria in pregnancy in resource-limited areas of sub-Saharan Africa, we encountered numerous challenges, which required implementation of a range of engagement measures to ensure compliance with global clinical and regulatory standards. These challenges included coordination with ongoing global malaria efforts, heterogeneity in national regulatory structures, sub-optimal healthcare infrastructure, local practices and beliefs, and perspectives that view healthcare providers with undue trust or suspicion. In addition to engagement with international bodies, such as the World Health Organization, the Malaria in Pregnancy Consortium, the Steve Biko Centre for Bioethics, and the London School of Hygiene and Tropical Medicine, in order to address the challenges just described, Pfizer Inc. and Medicines for Malaria Venture (the "Sponsoring Entities" for these studies) and investigators liaised with national- and district-level stakeholders such as health ministers and regional/local community health workers. Community engagement measures undertaken by investigators included local meetings with community leaders to explain the research aims and answer questions and concerns voiced by the community. The investigators also engaged with family members of prospective trial participants in order to be sensitive to local practices and beliefs. Engagement with key stakeholders at international and national levels enabled the Sponsoring Entities to address challenges by aligning the study design with the requirements of health and regulatory agencies and to understand and address healthcare infrastructure needs prior to trial initiation. Local stakeholder engagement, including community members, study participants, and family enabled the investigators to address challenges by ensuring that study design and conduct were adapted to local considerations and ensuring accurate information about the study aims was shared with the public. ClinicalTrials.gov, ID: NCT01103063 . Registered on 7 April 2010.

Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field

NASA Astrophysics Data System (ADS)

Chow, J. C. K.

2017-09-01

In the absence of external reference position information (e.g. surveyed targets or Global Navigation Satellite Systems) Simultaneous Localization and Mapping (SLAM) has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetometers. Instead of matching features such as walls and furniture using some variation of the Iterative Closest Point algorithm, the local magnetic field can be matched to provide loop-closure and global trajectory updates in a Gaussian Process (GP) SLAM framework. With a MEMS-based inertial measurement unit providing a continuous trajectory, and the matching of locally distinct magnetic field maps, experimental results in this paper show that a drift-free navigation solution in an indoor environment with millimetre-level accuracy can be achieved. The GP-SLAM approach presented can be formulated as a maximum a posteriori estimation problem and it can naturally perform loop-detection, feature-to-feature distance minimization, global trajectory optimization, and magnetic field map estimation simultaneously. Spatially continuous features (i.e. smooth magnetic field signatures) are used instead of discrete feature correspondences (e.g. point-to-point) as in conventional vision-based SLAM. These position updates from the ambient magnetic field also provide enough information for calibrating the accelerometer bias and gyroscope bias in-use. The only restriction for this method is the need for magnetic disturbances (which is typically not an issue for indoor environments); however, no assumptions are required for the general motion of the sensor (e.g. static periods).

High-resolution modeling of thermal thresholds and environmental influences on coral bleaching for local and regional reef management.

PubMed

Kumagai, Naoki H; Yamano, Hiroya

2018-01-01

Coral reefs are one of the world's most threatened ecosystems, with global and local stressors contributing to their decline. Excessive sea-surface temperatures (SSTs) can cause coral bleaching, resulting in coral death and decreases in coral cover. A SST threshold of 1 °C over the climatological maximum is widely used to predict coral bleaching. In this study, we refined thermal indices predicting coral bleaching at high-spatial resolution (1 km) by statistically optimizing thermal thresholds, as well as considering other environmental influences on bleaching such as ultraviolet (UV) radiation, water turbidity, and cooling effects. We used a coral bleaching dataset derived from the web-based monitoring system Sango Map Project, at scales appropriate for the local and regional conservation of Japanese coral reefs. We recorded coral bleaching events in the years 2004-2016 in Japan. We revealed the influence of multiple factors on the ability to predict coral bleaching, including selection of thermal indices, statistical optimization of thermal thresholds, quantification of multiple environmental influences, and use of multiple modeling methods (generalized linear models and random forests). After optimization, differences in predictive ability among thermal indices were negligible. Thermal index, UV radiation, water turbidity, and cooling effects were important predictors of the occurrence of coral bleaching. Predictions based on the best model revealed that coral reefs in Japan have experienced recent and widespread bleaching. A practical method to reduce bleaching frequency by screening UV radiation was also demonstrated in this paper.

High-resolution modeling of thermal thresholds and environmental influences on coral bleaching for local and regional reef management

PubMed Central

Yamano, Hiroya

2018-01-01

Coral reefs are one of the world’s most threatened ecosystems, with global and local stressors contributing to their decline. Excessive sea-surface temperatures (SSTs) can cause coral bleaching, resulting in coral death and decreases in coral cover. A SST threshold of 1 °C over the climatological maximum is widely used to predict coral bleaching. In this study, we refined thermal indices predicting coral bleaching at high-spatial resolution (1 km) by statistically optimizing thermal thresholds, as well as considering other environmental influences on bleaching such as ultraviolet (UV) radiation, water turbidity, and cooling effects. We used a coral bleaching dataset derived from the web-based monitoring system Sango Map Project, at scales appropriate for the local and regional conservation of Japanese coral reefs. We recorded coral bleaching events in the years 2004–2016 in Japan. We revealed the influence of multiple factors on the ability to predict coral bleaching, including selection of thermal indices, statistical optimization of thermal thresholds, quantification of multiple environmental influences, and use of multiple modeling methods (generalized linear models and random forests). After optimization, differences in predictive ability among thermal indices were negligible. Thermal index, UV radiation, water turbidity, and cooling effects were important predictors of the occurrence of coral bleaching. Predictions based on the best model revealed that coral reefs in Japan have experienced recent and widespread bleaching. A practical method to reduce bleaching frequency by screening UV radiation was also demonstrated in this paper. PMID:29473007

Gender differences in global-local perception? Evidence from orientation and shape judgments.

PubMed

Kimchi, Ruth; Amishav, Rama; Sulitzeanu-Kenan, Anat

2009-01-01

Direct examinations of gender differences in global-local processing are sparse, and the results are inconsistent. We examined this issue with a visuospatial judgment task and with a shape judgment task. Women and men were presented with hierarchical stimuli that varied in closure (open or closed shape) or in line orientation (oblique or horizontal/vertical) at the global or local level. The task was to classify the stimuli on the basis of the variation at the global level (global classification) or at the local level (local classification). Women's classification by closure (global or local) was more accurate than men's for stimuli that varied in closure on both levels, suggesting a female advantage in discriminating shape properties. No gender differences were observed in global-local processing bias. Women and men exhibited a global advantage, and they did not differ in their speed of global or local classification, with only one exception. Women were slower than men in local classification by orientation when the to-be-classified lines were embedded in a global line with a different orientation. This finding suggests that women are more distracted than men by misleading global oriented context when performing local orientation judgments, perhaps because women and men differ in their ability to use cognitive schemes to compensate for the distracting effects of the global context. Our findings further suggest that whether or not gender differences arise depends not only on the nature of the visual task but also on the visual context.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

PubMed

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

PubMed Central

2011-01-01

Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology. PMID:21989196

Global-local methodologies and their application to nonlinear analysis

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1989-01-01

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Parameter estimation of a pulp digester model with derivative-free optimization strategies

NASA Astrophysics Data System (ADS)

Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.

2017-07-01

The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.

Evidence of Reduced Global Processing in Autism Spectrum Disorder.

PubMed

Booth, Rhonda D L; Happé, Francesca G E

2018-04-01

Frith's original notion of 'weak central coherence' suggested that increased local processing in autism spectrum disorder (ASD) resulted from reduced global processing. More recent accounts have emphasised superior local perception and suggested intact global integration. However, tasks often place local and global processing in direct trade-off, making it difficult to determine whether group differences reflect reduced global processing, increased local processing, or both. We present two measures of global integration in which poor performance could not reflect increased local processing. ASD participants were slower to identify fragmented figures and less sensitive to global geometric impossibility than IQ-matched controls. These findings suggest that reduced global integration comprises one important facet of weak central coherence in ASD.

Effects of global and local contexts on chord processing: An ERP study.

PubMed

Zhang, Jingjing; Zhou, Xuefeng; Chang, Ruohan; Yang, Yufang

2018-01-31

In real life, the processing of an incoming event is continuously influenced by prior information at multiple timescales. The present study investigated how harmonic contexts at both local and global levels influence the processing of an incoming chord in an event-related potentials experiment. Chord sequences containing two phrases were presented to musically trained listeners, with the last critical chord either harmonically related or less related to its preceding context at local and/or global levels. ERPs data showed an ERAN-like effect for local context in early time window and a N5-like component for later interaction between the local context and global context. These results suggest that both the local and global contexts influence the processing of an incoming music event, and the local effect happens earlier than the global. Moreover, the interaction between the local context and global context in N5 may suggest that music syntactic integration at local level takes place prior to the integration at global level. Copyright © 2017 Elsevier Ltd. All rights reserved.

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Hemispheric specialization for global and local processing: A direct comparison of linguistic and non-linguistic stimuli.

PubMed

Brederoo, Sanne G; Nieuwenstein, Mark R; Lorist, Monicque M; Cornelissen, Frans W

2017-12-01

It is often assumed that the human brain processes the global and local properties of visual stimuli in a lateralized fashion, with a left hemisphere (LH) specialization for local detail, and a right hemisphere (RH) specialization for global form. However, the evidence for such global-local lateralization stems predominantly from studies using linguistic stimuli, the processing of which has shown to be LH lateralized in itself. In addition, some studies have reported a reversal of global-local lateralization when using non-linguistic stimuli. Accordingly, it remains unclear whether global-local lateralization may in fact be stimulus-specific. To address this issue, we asked participants to respond to linguistic and non-linguistic stimuli that were presented in the right and left visual fields, allowing for first access by the LH and RH, respectively. The results showed global-RH and local-LH advantages for both stimulus types, but the global lateralization effect was larger for linguistic stimuli. Furthermore, this pattern of results was found to be robust, as it was observed regardless of two other task manipulations. We conclude that the instantiation and direction of global and local lateralization is not stimulus-specific. However, the magnitude of global,-but not local-, lateralization is dependent on stimulus type. Copyright © 2017 Elsevier Inc. All rights reserved.

Pollen-tube tip growth requires a balance of lateral propagation and global inhibition of Rho-family GTPase activity

PubMed Central

Hwang, Jae-Ung; Wu, Guang; Yan, An; Lee, Yong-Jik; Grierson, Claire S.; Yang, Zhenbiao

2010-01-01

Rapid tip growth allows for efficient development of highly elongated cells (e.g. neuronal axons, fungal hyphae and pollen tubes) and requires an elaborate spatiotemporal regulation of the growing region. Here, we use the pollen tube as a model to investigate the mechanism regulating the growing region. ROPs (Rho-related GTPases from plants) are essential for pollen tip growth and display oscillatory activity changes in the apical plasma membrane (PM). By manipulating the ROP activity level, we showed that the PM distribution of ROP activity as an apical cap determines the tip growth region and that efficient tip growth requires an optimum level of the apical ROP1 activity. Excessive ROP activation induced the enlargement of the tip growth region, causing growth depolarization and reduced tube elongation. Time-lapse analysis suggests that the apical ROP1 cap is generated by lateral propagation of a localized ROP activity. Subcellular localization and gain- and loss-of-function analyses suggest that RhoGDI- and RhoGAP-mediated global inhibition limits the lateral propagation of apical ROP1 activity. We propose that the balance between the lateral propagation and the global inhibition maintains an optimal apical ROP1 cap and generates the apical ROP1 activity oscillation required for efficient pollen-tube elongation. PMID:20053639

WS-BP: An efficient wolf search based back-propagation algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd; Rehman, M. Z.; Khan, Abdullah

2015-05-01

Wolf Search (WS) is a heuristic based optimization algorithm. Inspired by the preying and survival capabilities of the wolves, this algorithm is highly capable to search large spaces in the candidate solutions. This paper investigates the use of WS algorithm in combination with back-propagation neural network (BPNN) algorithm to overcome the local minima problem and to improve convergence in gradient descent. The performance of the proposed Wolf Search based Back-Propagation (WS-BP) algorithm is compared with Artificial Bee Colony Back-Propagation (ABC-BP), Bat Based Back-Propagation (Bat-BP), and conventional BPNN algorithms. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the WS-BP algorithm effectively avoids the local minima and converge to global minima.

Global-local methodologies and their application to nonlinear analysis. [for structural postbuckling study

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1986-01-01

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified.

Travel time tomography with local image regularization by sparsity constrained dictionary learning

NASA Astrophysics Data System (ADS)

Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

CFD simulation of local and global mixing time in an agitated tank

NASA Astrophysics Data System (ADS)

Li, Liangchao; Xu, Bin

2017-01-01

The Issue of mixing efficiency in agitated tanks has drawn serious concern in many industrial processes. The turbulence model is very critical to predicting mixing process in agitated tanks. On the basis of computational fluid dynamics(CFD) software package Fluent 6.2, the mixing characteristics in a tank agitated by dual six-blade-Rushton-turbines(6-DT) are predicted using the detached eddy simulation(DES) method. A sliding mesh(SM) approach is adopted to solve the rotation of the impeller. The simulated flow patterns and liquid velocities in the agitated tank are verified by experimental data in the literature. The simulation results indicate that the DES method can obtain more flow details than Reynolds-averaged Navier-Stokes(RANS) model. Local and global mixing time in the agitated tank is predicted by solving a tracer concentration scalar transport equation. The simulated results show that feeding points have great influence on mixing process and mixing time. Mixing efficiency is the highest for the feeding point at location of midway of the two impellers. Two methods are used to determine global mixing time and get close result. Dimensionless global mixing time remains unchanged with increasing of impeller speed. Parallel, merging and diverging flow pattern form in the agitated tank, respectively, by changing the impeller spacing and clearance of lower impeller from the bottom of the tank. The global mixing time is the shortest for the merging flow, followed by diverging flow, and the longest for parallel flow. The research presents helpful references for design, optimization and scale-up of agitated tanks with multi-impeller.

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Strongly-motivated positive affects induce faster responses to local than global information of visual stimuli: an approach using large-size Navon letters.

PubMed

Noguchi, Yasuki; Tomoike, Kouta

2016-01-12

Recent studies argue that strongly-motivated positive emotions (e.g. desire) narrow a scope of attention. This argument is mainly based on an observation that, while humans normally respond faster to global than local information of a visual stimulus (global advantage), positive affects eliminated the global advantage by selectively speeding responses to local (but not global) information. In other words, narrowing of attentional scope was indirectly evidenced by the elimination of global advantage (the same speed of processing between global and local information). No study has directly shown that strongly-motivated positive affects induce faster responses to local than global information while excluding a bias for global information (global advantage) in a baseline (emotionally-neutral) condition. In the present study, we addressed this issue by eliminating the global advantage in a baseline (neutral) state. Induction of positive affects under this state resulted in faster responses to local than global information. Our results provided direct evidence that positive affects in high motivational intensity narrow a scope of attention.

Localization/Globalization and the Midwife State: Strategic Dilemmas for State Feminism in Education?

ERIC Educational Resources Information Center

Blackmore, Jill

1999-01-01

Explores implications of the globalization/localization process for state feminism, focusing on Australia. Localization is one response to globalization, exemplified by devolution to self-managing schools. However, global/local relations have gendered effects that resonate cross-nationally. Problems will emerge as the state withdraws from its…

Auditory global-local processing: effects of attention and musical experience.

PubMed

Ouimet, Tia; Foster, Nicholas E V; Hyde, Krista L

2012-10-01

In vision, global (whole) features are typically processed before local (detail) features ("global precedence effect"). However, the distinction between global and local processing is less clear in the auditory domain. The aims of the present study were to investigate: (i) the effects of directed versus divided attention, and (ii) the effect musical training on auditory global-local processing in 16 adult musicians and 16 non-musicians. Participants were presented with short nine-tone melodies, each comprised of three triplet sequences (three-tone units). In a "directed attention" task, participants were asked to focus on either the global or local pitch pattern and had to determine if the pitch pattern went up or down. In a "divided attention" task, participants judged whether the target pattern (up or down) was present or absent. Overall, global structure was perceived faster and more accurately than local structure. The global precedence effect was observed regardless of whether attention was directed to a specific level or divided between levels. Musicians performed more accurately than non-musicians overall, but non-musicians showed a more pronounced global advantage. This study provides evidence for an auditory global precedence effect across attention tasks, and for differences in auditory global-local processing associated with musical experience.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

PubMed

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Physical limits to biomechanical sensing in disordered fibre networks

NASA Astrophysics Data System (ADS)

Beroz, Farzan; Jawerth, Louise M.; Münster, Stefan; Weitz, David A.; Broedersz, Chase P.; Wingreen, Ned S.

2017-07-01

Cells actively probe and respond to the stiffness of their surroundings. Since mechanosensory cells in connective tissue are surrounded by a disordered network of biopolymers, their in vivo mechanical environment can be extremely heterogeneous. Here we investigate how this heterogeneity impacts mechanosensing by modelling the cell as an idealized local stiffness sensor inside a disordered fibre network. For all types of networks we study, including experimentally-imaged collagen and fibrin architectures, we find that measurements applied at different points yield a strikingly broad range of local stiffnesses, spanning roughly two decades. We verify via simulations and scaling arguments that this broad range of local stiffnesses is a generic property of disordered fibre networks. Finally, we show that to obtain optimal, reliable estimates of global tissue stiffness, a cell must adjust its size, shape, and position to integrate multiple stiffness measurements over extended regions of space.

Global/local processing of hierarchical visual stimuli in a conflict-choice task by capuchin monkeys (Sapajus spp.).

PubMed

Truppa, Valentina; Carducci, Paola; De Simone, Diego Antonio; Bisazza, Angelo; De Lillo, Carlo

2017-03-01

In the last two decades, comparative research has addressed the issue of how the global and local levels of structure of visual stimuli are processed by different species, using Navon-type hierarchical figures, i.e. smaller local elements that form larger global configurations. Determining whether or not the variety of procedures adopted to test different species with hierarchical figures are equivalent is of crucial importance to ensure comparability of results. Among non-human species, global/local processing has been extensively studied in tufted capuchin monkeys using matching-to-sample tasks with hierarchical patterns. Local dominance has emerged consistently in these New World primates. In the present study, we assessed capuchins' processing of hierarchical stimuli with a method frequently adopted in studies of global/local processing in non-primate species: the conflict-choice task. Different from the matching-to-sample procedure, this task involved processing local and global information retained in long-term memory. Capuchins were trained to discriminate between consistent hierarchical stimuli (similar global and local shape) and then tested with inconsistent hierarchical stimuli (different global and local shapes). We found that capuchins preferred the hierarchical stimuli featuring the correct local elements rather than those with the correct global configuration. This finding confirms that capuchins' local dominance, typically observed using matching-to-sample procedures, is also expressed as a local preference in the conflict-choice task. Our study adds to the growing body of comparative studies on visual grouping functions by demonstrating that the methods most frequently used in the literature on global/local processing produce analogous results irrespective of extent of the involvement of memory processes.

A New Parallel Boundary Condition for Turbulence Simulations in Stellarators

NASA Astrophysics Data System (ADS)

Martin, Mike F.; Landreman, Matt; Dorland, William; Xanthopoulos, Pavlos

2017-10-01

For gyrokinetic simulations of core turbulence, the ``twist-and-shift'' parallel boundary condition (Beer et al., PoP, 1995), which involves a shift in radial wavenumber proportional to the global shear and a quantization of the simulation domain's aspect ratio, is the standard choice. But as this condition was derived under the assumption of axisymmetry, ``twist-and-shift'' as it stands is formally incorrect for turbulence simulations in stellarators. Moreover, for low-shear stellarators like W7X and HSX, the use of a global shear in the traditional boundary condition places an inflexible constraint on the aspect ratio of the domain, requiring more grid points to fully resolve its extent. Here, we present a parallel boundary condition for ``stellarator-symmetric'' simulations that relies on the local shear along a field line. This boundary condition is similar to ``twist-and-shift'', but has an added flexibility in choosing the parallel length of the domain based on local shear consideration in order to optimize certain parameters such as the aspect ratio of the simulation domain.

Sensor-Web Operations Explorer

NASA Technical Reports Server (NTRS)

Meemong, Lee; Miller, Charles; Bowman, Kevin; Weidner, Richard

2008-01-01

Understanding the atmospheric state and its impact on air quality requires observations of trace gases, aerosols, clouds, and physical parameters across temporal and spatial scales that range from minutes to days and from meters to more than 10,000 kilometers. Observations include continuous local monitoring for particle formation; field campaigns for emissions, local transport, and chemistry; and periodic global measurements for continental transport and chemistry. Understanding includes global data assimilation framework capable of hierarchical coupling, dynamic integration of chemical data and atmospheric models, and feedback loops between models and observations. The objective of the sensor-web system is to observe trace gases, aerosols, clouds, and physical parameters, an integrated observation infrastructure composed of space-borne, air-borne, and in-situ sensors will be simulated based on their measurement physics properties. The objective of the sensor-web operation is to optimally plan for heterogeneous multiple sensors, the sampling strategies will be explored and science impact will be analyzed based on comprehensive modeling of atmospheric phenomena including convection, transport, and chemical process. Topics include system architecture, software architecture, hardware architecture, process flow, technology infusion, challenges, and future direction.

Overset meshing coupled with hybridizable discontinuous Galerkin finite elements

DOE PAGES

Kauffman, Justin A.; Sheldon, Jason P.; Miller, Scott T.

2017-03-01

We introduce the use of hybridizable discontinuous Galerkin (HDG) finite element methods on overlapping (overset) meshes. Overset mesh methods are advantageous for solving problems on complex geometrical domains. We also combine geometric flexibility of overset methods with the advantages of HDG methods: arbitrarily high-order accuracy, reduced size of the global discrete problem, and the ability to solve elliptic, parabolic, and/or hyperbolic problems with a unified form of discretization. This approach to developing the ‘overset HDG’ method is to couple the global solution from one mesh to the local solution on the overset mesh. We present numerical examples for steady convection–diffusionmore » and static elasticity problems. The examples demonstrate optimal order convergence in all primal fields for an arbitrary amount of overlap of the underlying meshes.« less

Parallel Directionally Split Solver Based on Reformulation of Pipelined Thomas Algorithm

NASA Technical Reports Server (NTRS)

Povitsky, A.

1998-01-01

In this research an efficient parallel algorithm for 3-D directionally split problems is developed. The proposed algorithm is based on a reformulated version of the pipelined Thomas algorithm that starts the backward step computations immediately after the completion of the forward step computations for the first portion of lines This algorithm has data available for other computational tasks while processors are idle from the Thomas algorithm. The proposed 3-D directionally split solver is based on the static scheduling of processors where local and non-local, data-dependent and data-independent computations are scheduled while processors are idle. A theoretical model of parallelization efficiency is used to define optimal parameters of the algorithm, to show an asymptotic parallelization penalty and to obtain an optimal cover of a global domain with subdomains. It is shown by computational experiments and by the theoretical model that the proposed algorithm reduces the parallelization penalty about two times over the basic algorithm for the range of the number of processors (subdomains) considered and the number of grid nodes per subdomain.

Sustained Attention to Local and Global Target Features Is Different: Performance and Tympanic Membrane Temperature

ERIC Educational Resources Information Center

Helton, William S.; Hayrynen, Lauren; Schaeffer, David

2009-01-01

Vision researchers have investigated the differences between global and local feature perception. No one has, however, examined the role of global and local feature discrimination in sustained attention tasks. In this experiment participants performed a sustained attention task requiring either global or local letter target discriminations or…

Network-Forming Nanoclusters in Binary As-S/Se Glasses: From Ab Initio Quantum Chemical Modeling to Experimental Evidences.

PubMed

Hyla, M

2017-12-01

Network-forming As 2 (S/Se) m nanoclusters are employed to recognize expected variations in a vicinity of some remarkable compositions in binary As-Se/S glassy systems accepted as signatures of optimally constrained intermediate topological phases in earlier temperature-modulated differential scanning calorimetry experiments. The ab initio quantum chemical calculations performed using the cation-interlinking network cluster approach show similar oscillating character in tendency to local chemical decomposition but obvious step-like behavior in preference to global phase separation on boundary chemical compounds (pure chalcogen and stoichiometric arsenic chalcogenides). The onsets of stability are defined for chalcogen-rich glasses, these being connected with As 2 Se 5 (Z = 2.29) and As 2 S 6 (Z = 2.25) nanoclusters for As-Se and As-S glasses, respectively. The physical aging effects result preferentially from global phase separation in As-S glass system due to high localization of covalent bonding and local demixing on neighboring As 2 Se m+1 and As 2 Se m-1 nanoclusters in As-Se system. These nanoclusters well explain the lower limits of reversibility windows in temperature-modulated differential scanning calorimetry, but they cannot be accepted as signatures of topological phase transitions in respect to the rigidity theory.

Graph Design via Convex Optimization: Online and Distributed Perspectives

NASA Astrophysics Data System (ADS)

Meng, De

Network and graph have long been natural abstraction of relations in a variety of applications, e.g. transportation, power system, social network, communication, electrical circuit, etc. As a large number of computation and optimization problems are naturally defined on graphs, graph structures not only enable important properties of these problems, but also leads to highly efficient distributed and online algorithms. For example, graph separability enables the parallelism for computation and operation as well as limits the size of local problems. More interestingly, graphs can be defined and constructed in order to take best advantage of those problem properties. This dissertation focuses on graph structure and design in newly proposed optimization problems, which establish a bridge between graph properties and optimization problem properties. We first study a new optimization problem called Geodesic Distance Maximization Problem (GDMP). Given a graph with fixed edge weights, finding the shortest path, also known as the geodesic, between two nodes is a well-studied network flow problem. We introduce the Geodesic Distance Maximization Problem (GDMP): the problem of finding the edge weights that maximize the length of the geodesic subject to convex constraints on the weights. We show that GDMP is a convex optimization problem for a wide class of flow costs, and provide a physical interpretation using the dual. We present applications of the GDMP in various fields, including optical lens design, network interdiction, and resource allocation in the control of forest fires. We develop an Alternating Direction Method of Multipliers (ADMM) by exploiting specific problem structures to solve large-scale GDMP, and demonstrate its effectiveness in numerical examples. We then turn our attention to distributed optimization on graph with only local communication. Distributed optimization arises in a variety of applications, e.g. distributed tracking and localization, estimation problems in sensor networks, multi-agent coordination. Distributed optimization aims to optimize a global objective function formed by summation of coupled local functions over a graph via only local communication and computation. We developed a weighted proximal ADMM for distributed optimization using graph structure. This fully distributed, single-loop algorithm allows simultaneous updates and can be viewed as a generalization of existing algorithms. More importantly, we achieve faster convergence by jointly designing graph weights and algorithm parameters. Finally, we propose a new problem on networks called Online Network Formation Problem: starting with a base graph and a set of candidate edges, at each round of the game, player one first chooses a candidate edge and reveals it to player two, then player two decides whether to accept it; player two can only accept limited number of edges and make online decisions with the goal to achieve the best properties of the synthesized network. The network properties considered include the number of spanning trees, algebraic connectivity and total effective resistance. These network formation games arise in a variety of cooperative multiagent systems. We propose a primal-dual algorithm framework for the general online network formation game, and analyze the algorithm performance by the competitive ratio and regret.

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Numerical algebraic geometry for model selection and its application to the life sciences

PubMed Central

Gross, Elizabeth; Davis, Brent; Ho, Kenneth L.; Bates, Daniel J.

2016-01-01

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation and model selection. These are all optimization problems, well known to be challenging due to nonlinearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data are available. Here, we consider polynomial models (e.g. mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometrical structures relating models and data, and we demonstrate its utility on examples from cell signalling, synthetic biology and epidemiology. PMID:27733697

An efficient interior-point algorithm with new non-monotone line search filter method for nonlinear constrained programming

NASA Astrophysics Data System (ADS)

Wang, Liwei; Liu, Xinggao; Zhang, Zeyin

2017-02-01

An efficient primal-dual interior-point algorithm using a new non-monotone line search filter method is presented for nonlinear constrained programming, which is widely applied in engineering optimization. The new non-monotone line search technique is introduced to lead to relaxed step acceptance conditions and improved convergence performance. It can also avoid the choice of the upper bound on the memory, which brings obvious disadvantages to traditional techniques. Under mild assumptions, the global convergence of the new non-monotone line search filter method is analysed, and fast local convergence is ensured by second order corrections. The proposed algorithm is applied to the classical alkylation process optimization problem and the results illustrate its effectiveness. Some comprehensive comparisons to existing methods are also presented.

Optimal design of piezoelectric transformers: a rational approach based on an analytical model and a deterministic global optimization.

PubMed

Pigache, Francois; Messine, Frédéric; Nogarede, Bertrand

2007-07-01

This paper deals with a deterministic and rational way to design piezoelectric transformers in radial mode. The proposed approach is based on the study of the inverse problem of design and on its reformulation as a mixed constrained global optimization problem. The methodology relies on the association of the analytical models for describing the corresponding optimization problem and on an exact global optimization software, named IBBA and developed by the second author to solve it. Numerical experiments are presented and compared in order to validate the proposed approach.

An efficient algorithm for building locally refined hp - adaptive H-PCFE: Application to uncertainty quantification

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2017-12-01

Hybrid polynomial correlated function expansion (H-PCFE) is a novel metamodel formulated by coupling polynomial correlated function expansion (PCFE) and Kriging. Unlike commonly available metamodels, H-PCFE performs a bi-level approximation and hence, yields more accurate results. However, till date, it is only applicable to medium scaled problems. In order to address this apparent void, this paper presents an improved H-PCFE, referred to as locally refined hp - adaptive H-PCFE. The proposed framework computes the optimal polynomial order and important component functions of PCFE, which is an integral part of H-PCFE, by using global variance based sensitivity analysis. Optimal number of training points are selected by using distribution adaptive sequential experimental design. Additionally, the formulated model is locally refined by utilizing the prediction error, which is inherently obtained in H-PCFE. Applicability of the proposed approach has been illustrated with two academic and two industrial problems. To illustrate the superior performance of the proposed approach, results obtained have been compared with those obtained using hp - adaptive PCFE. It is observed that the proposed approach yields highly accurate results. Furthermore, as compared to hp - adaptive PCFE, significantly less number of actual function evaluations are required for obtaining results of similar accuracy.

Role of collaborative academic partnerships in surgical training, education, and provision.

PubMed

Riviello, Robert; Ozgediz, Doruk; Hsia, Renee Y; Azzie, Georges; Newton, Mark; Tarpley, John

2010-03-01

The global disparities in both surgical disease burden and access to delivery of surgical care are gaining prominence in the medical literature and media. Concurrently, there is an unprecedented groundswell in idealism and interest in global health among North American medical students and trainees in anesthesia and surgical disciplines. Many academic medical centers (AMCs) are seeking to respond by creating partnerships with teaching hospitals overseas. In this article we describe six such partnerships, as follows: (1) University of California San Francisco (UCSF) with the Bellagio Essential Surgery Group; (2) USCF with Makerere University, Uganda; (3) Vanderbilt with Baptist Medical Center, Ogbomoso, Nigeria; (4) Vanderbilt with Kijabe Hospital, Kenya; (5) University of Toronto, Hospital for Sick Children with the Ministry of Health in Botswana; and (6) Harvard (Brigham and Women's Hospital and Children's Hospital Boston) with Partners in Health in Haiti and Rwanda. Reflection on these experiences offers valuable lessons, and we make recommendations of critical components leading to success. These include the importance of relationships, emphasis on mutual learning, the need for "champions," affirming that local training needs to supersede expatriate training needs, the value of collaboration in research, adapting the mission to locally expressed needs, the need for a multidisciplinary approach, and the need to measure outcomes. We conclude that this is an era of cautious optimism and that AMCs have a critical opportunity to both shape future leaders in global surgery and address the current global disparities.

Cooperative global optimal preview tracking control of linear multi-agent systems: an internal model approach

NASA Astrophysics Data System (ADS)

Lu, Yanrong; Liao, Fucheng; Deng, Jiamei; Liu, Huiyang

2017-09-01

This paper investigates the cooperative global optimal preview tracking problem of linear multi-agent systems under the assumption that the output of a leader is a previewable periodic signal and the topology graph contains a directed spanning tree. First, a type of distributed internal model is introduced, and the cooperative preview tracking problem is converted to a global optimal regulation problem of an augmented system. Second, an optimal controller, which can guarantee the asymptotic stability of the augmented system, is obtained by means of the standard linear quadratic optimal preview control theory. Third, on the basis of proving the existence conditions of the controller, sufficient conditions are given for the original problem to be solvable, meanwhile a cooperative global optimal controller with error integral and preview compensation is derived. Finally, the validity of theoretical results is demonstrated by a numerical simulation.

Dispositional optimism and sleep quality: a test of mediating pathways

PubMed Central

Cribbet, Matthew; Kent de Grey, Robert G.; Cronan, Sierra; Trettevik, Ryan; Smith, Timothy W.

2016-01-01

Dispositional optimism has been related to beneficial influences on physical health outcomes. However, its links to global sleep quality and the psychological mediators responsible for such associations are less studied. This study thus examined if trait optimism predicted global sleep quality, and if measures of subjective well-being were statistical mediators of such links. A community sample of 175 participants (93 men, 82 women) completed measures of trait optimism, depression, and life satisfaction. Global sleep quality was assessed using the Pittsburgh Sleep Quality Index. Results indicated that trait optimism was a strong predictor of better PSQI global sleep quality. Moreover, this association was mediated by depression and life satisfaction in both single and multiple mediator models. These results highlight the importance of optimism for the restorative process of sleep, as well as the utility of multiple mediator models in testing distinct psychological pathways. PMID:27592128

Dispositional optimism and sleep quality: a test of mediating pathways.

PubMed

Uchino, Bert N; Cribbet, Matthew; de Grey, Robert G Kent; Cronan, Sierra; Trettevik, Ryan; Smith, Timothy W

2017-04-01

Dispositional optimism has been related to beneficial influences on physical health outcomes. However, its links to global sleep quality and the psychological mediators responsible for such associations are less studied. This study thus examined if trait optimism predicted global sleep quality, and if measures of subjective well-being were statistical mediators of such links. A community sample of 175 participants (93 men, 82 women) completed measures of trait optimism, depression, and life satisfaction. Global sleep quality was assessed using the Pittsburgh Sleep Quality Index. Results indicated that trait optimism was a strong predictor of better PSQI global sleep quality. Moreover, this association was mediated by depression and life satisfaction in both single and multiple mediator models. These results highlight the importance of optimism for the restorative process of sleep, as well as the utility of multiple mediator models in testing distinct psychological pathways.

Global/Local: What Does It Mean for Global Health Educators and How Do We Do It?

PubMed

Rowthorn, Virginia

2015-01-01

There has been dramatic growth in the number of innovative university programs that focus on social justice and teach community-based strategies that are applicable both domestically in North America and internationally. These programs often are referred to as global/local and reflect an effort to link global health and campus community engagement efforts to acknowledge that a common set of transferable skills can be adapted to work with vulnerable populations wherever they may be. However, the concepts underlying global/local education are undertheorized and universities struggle to make the global/local link without a conceptual framework to guide them in this pursuit. This study reports on the outcomes of a 2015 national meeting of 120 global health educators convened to discuss the concepts underlying global/local education, to share models of global/local programs, and to draft a preliminary list of critical elements of a meaningful and didactically sound global/local educational program. A qualitative analysis was conducted of the discussions that took place at the national meeting. The analysis was supported by videorecordings made of full-group discussions. Results were categorized into a preliminary list of global/local program elements. Additionally, a synthesis was developed of critical issues raised at the meeting that warrant future discussion and study. A preliminary list was developed of 7 program components that global health educators consider essential to categorize a program as global/local and to ensure that such a program includes specific critical elements. Interest is great among global health educators to understand and teach the conceptual link between learning on both the global and community levels. Emphasis on this link has high potential to unite the siloed fields of global health and domestic community public health and the institutions, funding options, and career pathways that flow from them. Future research should focus on implementation of global/local programming and evaluation of student learning and community health outcomes related to such programs. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.