Maximally localized Wannier functions in LaMnO3 within PBE + U, hybrid functionals and partially self-consistent GW: an efficient route to construct ab initio tight-binding parameters for eg perovskites.

Science.gov (United States)

Franchini, C; Kováčik, R; Marsman, M; Murthy, S Sathyanarayana; He, J; Ederer, C; Kresse, G

2012-06-13

Using the newly developed VASP2WANNIER90 interface we have constructed maximally localized Wannier functions (MLWFs) for the e(g) states of the prototypical Jahn-Teller magnetic perovskite LaMnO(3) at different levels of approximation for the exchange-correlation kernel. These include conventional density functional theory (DFT) with and without the additional on-site Hubbard U term, hybrid DFT and partially self-consistent GW. By suitably mapping the MLWFs onto an effective e(g) tight-binding (TB) Hamiltonian we have computed a complete set of TB parameters which should serve as guidance for more elaborate treatments of correlation effects in effective Hamiltonian-based approaches. The method-dependent changes of the calculated TB parameters and their interplay with the electron-electron (el-el) interaction term are discussed and interpreted. We discuss two alternative model parameterizations: one in which the effects of the el-el interaction are implicitly incorporated in the otherwise 'noninteracting' TB parameters and a second where we include an explicit mean-field el-el interaction term in the TB Hamiltonian. Both models yield a set of tabulated TB parameters which provide the band dispersion in excellent agreement with the underlying ab initio and MLWF bands.

Surface Acoustic Bloch Oscillations, the Wannier-Stark Ladder, and Landau-Zener Tunneling in a Solid

Science.gov (United States)

de Lima, M. M., Jr.; Kosevich, Yu. A.; Santos, P. V.; Cantarero, A.

2010-04-01

We present the experimental observation of Bloch oscillations, the Wannier-Stark ladder, and Landau-Zener tunneling of surface acoustic waves in perturbed grating structures on a solid substrate. A model providing a quantitative description of our experimental observations, including multiple Landau-Zener transitions of the anticrossed surface acoustic Wannier-Stark states, is developed. The use of a planar geometry for the realization of the Bloch oscillations and Landau-Zener tunneling allows a direct access to the elastic field distribution. The vertical surface displacement has been measured by interferometry.

Adiabatic theory of Wannier threshold laws and ionization cross sections

International Nuclear Information System (INIS)

Macek, J.H.; Ovchinnikov, S.Yu.

1994-01-01

The Wannier threshold law for three-particle fragmentation is reviewed. By integrating the Schroedinger equation along a path where the reaction coordinate R is complex, anharmonic corrections to the simple power law are obtained. These corrections are found to be non-analytic in the energy E, in contrast to the expected analytic dependence upon E

Adiabatic theory of Wannier threshold laws and ionization cross sections

International Nuclear Information System (INIS)

Macek, J.H.; Ovchinnikov, S.Y.

1994-01-01

Adiabatic energy eigenvalues of H 2 + are computed for complex values of the internuclear distance R. The infinite number of bound-state eigenenergies are represented by a function ε(R) that is single valued on a multisheeted Riemann surface. A region is found where ε(R) and the corresponding eigenfunctions exhibit harmonic-oscillator structure characteristic of electron motion on a potential saddle. The Schroedinger equation is solved in the adiabatic approximation along a path in the complex R plane to compute ionization cross sections. The cross section thus obtained joins the Wannier threshold region with the keV energy region, but the exponent near the ionization threshold disagrees with well-accepted values. Accepted values are obtained when a lowest-order diabatic correction is employed, indicating that adiabatic approximations do not give the correct zero velocity limit for ionization cross sections. Semiclassical eigenvalues for general top-of-barrier motion are given and the theory is applied to the ionization of atomic hydrogen by electron impact. The theory with a first diabatic correction gives the Wannier threshold law even for this case

Extension of the radiative lifetime of Wannier-Mott excitons in semiconductor nanoclusters

International Nuclear Information System (INIS)

Kukushkin, V. A.

2015-01-01

The purpose of the study is to calculate the radiative lifetime of Wannier-Mott excitons in three-dimensional potential wells formed of direct-gap narrow-gap semiconductor nanoclusters in wide-gap semiconductors and assumed to be large compared to the exciton radius. Calculations are carried out for the InAs/GaAs heterosystem. It is shown that, as the nanocluster dimensions are reduced to values on the order of the exciton radius, the exciton radiative lifetime becomes several times longer compared to that in a homogeneous semiconductor. The increase in the radiative lifetime is more pronounced at low temperatures. Thus, it is established that the placement of Wannier-Mott excitons into direct-gap semiconductor nanoclusters, whose dimensions are of the order of the exciton radius, can be used for considerable extension of the exciton radiative lifetime

Surface Acoustic Analog of Bloch Oscillations, Wannier-Stark Ladders and Landau-Zener Tunneling

Science.gov (United States)

de Lima, M. M.; Kosevich, Yu. A.; Santos, P. V.; Cantarero, A.

2011-12-01

In this contribution, we discuss the recent experimental demonstration of Wannier-Stark ladders, Bloch Oscillations and Landau Zener tunneling in a solid by means of surface acoustic waves propagating through perturbed grating structures.

Adiabatic theory of Wannier threshold laws and ionization cross sections

International Nuclear Information System (INIS)

Macek, J.H.; Ovchinnikov, S.Y.

1995-01-01

The Wannier threshold law for three-particle fragmentation is reviewed. By integrating the Schroedinger equation along a path where the reaction coordinate R is complex, anharmonic corrections to the simple power law are obtained. These corrections are found to be non-analytic in the energy E, in contrast to the expected analytic dependence upon E. copyright 1995 American Institute of Physics

Crystal field parameters with Wannier functions: application to rare-earth aluminates

Czech Academy of Sciences Publication Activity Database

Novák, Pavel; Knížek, Karel; Kuneš, Jan

2013-01-01

Roč. 87, č. 20 (2013), "205139-1"-"205139-7" ISSN 1098-0121 R&D Projects: GA ČR(CZ) GAP204/11/0713 Institutional support: RVO:68378271 Keywords : crystal-field * rare earths * Wannier functions Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 3.664, year: 2013 http://link.aps.org/doi/10.1103/PhysRevB.87.205139

Raman-laser spectroscopy of Wannier-Stark states

International Nuclear Information System (INIS)

Tackmann, G.; Pelle, B.; Hilico, A.; Beaufils, Q.; Pereira dos Santos, F.

2011-01-01

Raman lasers are used as a spectroscopic probe of the state of atoms confined in a shallow one-dimensional (1D) vertical lattice. For sufficiently long laser pulses, resolved transitions in the bottom band of the lattice between Wannier Stark states corresponding to neighboring wells are observed. Couplings between such states are measured as a function of the lattice laser intensity and compared to theoretical predictions, from which the lattice depth can be extracted. Limits to the linewidth of these transitions are investigated. Transitions to higher bands can also be induced, as well as between transverse states for tilted Raman beams. All these features allow for a precise characterization of the trapping potential and for an efficient control of the atomic external degrees of freedom.

Magnon localization and Bloch oscillations in finite Heisenberg spin chains in an inhomogeneous magnetic field.

Science.gov (United States)

Kosevich, Yuriy A; Gann, Vladimir V

2013-06-19

We study the localization of magnon states in finite defect-free Heisenberg spin-1/2 ferromagnetic chains placed in an inhomogeneous magnetic field with a constant spatial gradient. Continuous transformation from the extended magnon states to the localized Wannier-Zeeman states in a finite spin chain placed in an inhomogeneous field is described both analytically and numerically. We describe for the first time the non-monotonic dependence of the energy levels of magnons, both long and short wavelength, on the magnetic field gradient, which is a consequence of magnon localization in a finite spin chain. We show that, in contrast to the destruction of the magnon band and the establishment of the Wannier-Stark ladder in a vanishingly small field gradient in an infinite chain, the localization of magnon states at the chain ends preserves the memory of the magnon band. Essentially, the localization at the lower- or higher-field chain end resembles the localization of the positive- or negative-effective-mass band quasiparticles. We also show how the beat dynamics of coherent superposition of extended spin waves in a finite chain in a homogeneous or weakly inhomogeneous field transforms into magnon Bloch oscillations of the superposition of localized Wannier-Zeeman states in a strongly inhomogeneous field. We provide a semiclassical description of the magnon Bloch oscillations and show that the correspondence between the quantum and semiclassical descriptions is most accurate for Bloch oscillations of the magnon coherent states, which are built from a coherent superposition of a large number of the nearest-neighbour Wannier-Zeeman states.

Higher-dimensional Wannier Interpolation for the Modern Theory of the Dzyaloshinskii-Moriya Interaction: Application to Co-based Trilayers

Science.gov (United States)

Hanke, Jan-Philipp; Freimuth, Frank; Blügel, Stefan; Mokrousov, Yuriy

2018-04-01

We present an advanced first-principles formalism to evaluate the Dzyaloshinskii-Moriya interaction (DMI) in its modern theory as well as Berry curvatures in complex spaces based on a higher-dimensional Wannier interpolation. Our method is applied to the Co-based trilayer systems IrδPt1-δ/Co/Pt and AuγPt1-γ/Co/Pt, where we gain insights into the correlations between the electronic structure and the DMI, and we uncover prominent sign changes of the chiral interaction with the overlayer composition. Beyond the discussed phenomena, the scope of applications of our Wannier-based scheme is particularly broad as it is ideally suited to study efficiently the Hamiltonian evolution under the slow variation of very general parameters.

Wannier-Frenkel hybrid exciton in organic-semiconductor quantum dot heterostructures

International Nuclear Information System (INIS)

Birman, Joseph L.; Huong, Nguyen Que

2007-01-01

The formation of a hybridization state of Wannier Mott exciton and Frenkel exciton in different hetero-structure configurations involving quantum dots is investigated. The hybrid excitons exist at the interfaces of the semiconductors quantum dots and the organic medium, having unique properties and a large optical non-linearity. The coupling at resonance is very strong and tunable by changing the parameters of the systems (dot radius, dot-dot distance, generation of the organic dendrites and the materials of the system etc...). Different semiconductor quantum dot-organic material combination systems have been considered such as a semiconductor quantum dot lattice embedded in an organic host, a semiconductor quantum dot at the center of an organic dendrite, a semiconductor quantum dot coated by an organic shell

(Non-)Abelian Kramers-Wannier duality and topological field theory

CERN Document Server

Severa, Pavol

2002-01-01

We study a connection between duality and topological field theories. First, 2d Kramers-Wannier duality is formulated as a simple 3d topological claim (more or less Poincare duality), and a similar formulation is given for higher-dimensional cases. In this form they lead to simple TFTs with boundary coloured in two colours. The statistical models live on the boundary of these TFTs, as in the CS/WZW or AdS/CFT correspondence. Classical models (Poisson-Lie T-duality) suggest a non-abelian generalization in the 2dcase, with abelian groups replaced by quantum groups. Amazingly, the TFT formulation solves the problem without computation: quantum groups appear in pictures, independently of the classical motivation. Connection with Chern-Simons theory appears at the symplectic level, and also in the pictures of the Drinfeld double: Reshetikhin-Turaev invariants of links in 3-manifolds, computed from the double, are included in these TFTs. All this suggests nice phenomena in higher dimensions.

Field theoretic approach to dynamical orbital localization in ab initio molecular dynamics

International Nuclear Information System (INIS)

Thomas, Jordan W.; Iftimie, Radu; Tuckerman, Mark E.

2004-01-01

Techniques from gauge-field theory are employed to derive an alternative formulation of the Car-Parrinello ab initio molecular-dynamics method that allows maximally localized Wannier orbitals to be generated dynamically as the calculation proceeds. In particular, the Car-Parrinello Lagrangian is mapped onto an SU(n) non-Abelian gauge-field theory and the fictitious kinetic energy in the Car-Parrinello Lagrangian is modified to yield a fully gauge-invariant form. The Dirac gauge-fixing method is then employed to derive a set of equations of motion that automatically maintain orbital locality by restricting the orbitals to remain in the 'Wannier gauge'. An approximate algorithm for integrating the equations of motion that is stable and maintains orbital locality is then developed based on the exact equations of motion. It is shown in a realistic application (64 water molecules plus one hydrogen-chloride molecule in a periodic box) that orbital locality can be maintained with only a modest increase in CPU time. The ability to keep orbitals localized in an ab initio molecular-dynamics calculation is a crucial ingredient in the development of emerging linear scaling approaches

Interband optical absorption in the Wannier-Stark ladder under the electron-LO-phonon resonance condition

International Nuclear Information System (INIS)

Govorov, A.O.

1993-08-01

Interband optical absorption in the Wannier-Stark ladder in the presence of the electron-LO-phonon resonance is investigated theoretically. The electron-LO-phonon resonance occurs when the energy spacing between adjacent Stark-ladder levels coincides with the LO-phonon energy. We propose a model describing the polaron effect in a superlattice. Calculations show that the absorption line shape is strongly modified due to the polaron effect under the electron-LO-phonon resonance condition. We consider optical phenomena in a normal magnetic field that leads to enhancement of polaron effects. (author). 17 refs, 5 figs

Efficient Iris Localization via Optimization Model

Directory of Open Access Journals (Sweden)

Qi Wang

2017-01-01

Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Non-perturbative embedding of local defects in crystalline materials

International Nuclear Information System (INIS)

Cances, Eric; Deleurence, Amelie; Lewin, Mathieu

2008-01-01

We present a new variational model for computing the electronic first-order density matrix of a crystalline material in the presence of a local defect. A natural way to obtain variational discretizations of this model is to expand the difference Q between the density matrix of the defective crystal and the density matrix of the perfect crystal, in a basis of precomputed maximally localized Wannier functions of the reference perfect crystal. This approach can be used within any semi-empirical or density functional theory framework

Optimal resource states for local state discrimination

Science.gov (United States)

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.

Nearly Perfect Triplet-Triplet Energy Transfer from Wannier Excitons to Naphthalene in Organic-Inorganic Hybrid Quantum-Well Materials

Science.gov (United States)

Ema, K.; Inomata, M.; Kato, Y.; Kunugita, H.; Era, M.

2008-06-01

We report the observation of extremely efficient energy transfer (greater than 99%) in an organic-inorganic hybrid quantum-well structure consisting of perovskite-type lead bromide well layers and naphthalene-linked ammonium barrier layers. Time-resolved photoluminescence measurements confirm that the transfer is triplet-triplet Dexter-type energy transfer from Wannier excitons in the inorganic well to the triplet state of naphthalene molecules in the organic barrier. Using measurements in the 10 300 K temperature range, we also investigated the temperature dependence of the energy transfer.

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

Directory of Open Access Journals (Sweden)

P. SrideviPonmalar

2017-01-01

Full Text Available Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA, Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of firefliesr requirements, variation in time complexity and number of iteration requirements. Keywords: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization

Intrinsic localized modes in arrays of atomic-molecular Bose-Einstein condensates

International Nuclear Information System (INIS)

Abdullaev, F.Kh.; Konotop, V.V.

2003-01-01

The existence of strongly localized matter solitons, intrinsic localized modes (ILM's), in an array of atomic-molecular Bose-Einstein condensates (AMBEC's) is shown. The theory is based on the Wannier function expansion of the system order parameter and predicts the possibility of strong localization of the atomic and molecular components whose relative populations are determined by the Raman detuning parameter and by the atom-molecule conversion rate. ILM's can possess different symmetries and spatial distributions of the components. In this context AMBEC arrays can be viewed as potential compressors and separators of atomic and molecular condensates

Wannier-function-based constrained DFT with nonorthogonality-correcting Pulay forces in application to the reorganization effects in graphene-adsorbed pentacene

Science.gov (United States)

Roychoudhury, Subhayan; O'Regan, David D.; Sanvito, Stefano

2018-05-01

Pulay terms arise in the Hellmann-Feynman forces in electronic-structure calculations when one employs a basis set made of localized orbitals that move with their host atoms. If the total energy of the system depends on a subspace population defined in terms of the localized orbitals across multiple atoms, then unconventional Pulay terms will emerge due to the variation of the orbital nonorthogonality with ionic translation. Here, we derive the required exact expressions for such terms, which cannot be eliminated by orbital orthonormalization. We have implemented these corrected ionic forces within the linear-scaling density functional theory (DFT) package onetep, and we have used constrained DFT to calculate the reorganization energy of a pentacene molecule adsorbed on a graphene flake. The calculations are performed by including ensemble DFT, corrections for periodic boundary conditions, and empirical Van der Waals interactions. For this system we find that tensorially invariant population analysis yields an adsorbate subspace population that is very close to integer-valued when based upon nonorthogonal Wannier functions, and also but less precisely so when using pseudoatomic functions. Thus, orbitals can provide a very effective population analysis for constrained DFT. Our calculations show that the reorganization energy of the adsorbed pentacene is typically lower than that of pentacene in the gas phase. We attribute this effect to steric hindrance.

Improved quantum-behaved particle swarm optimization with local search strategy

Directory of Open Access Journals (Sweden)

Maolong Xi

2017-03-01

Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Accelerating GW calculations with optimal polarizability basis

Energy Technology Data Exchange (ETDEWEB)

Umari, P.; Stenuit, G. [CNR-IOM DEMOCRITOS Theory Elettra Group, Basovizza (Trieste) (Italy); Qian, X.; Marzari, N. [Department of Materials Science and Engineering, MIT, Cambridge, MA (United States); Giacomazzi, L.; Baroni, S. [CNR-IOM DEMOCRITOS Theory Elettra Group, Basovizza (Trieste) (Italy); SISSA - Scuola Internazionale Superiore di Studi Avanzati, Trieste (Italy)

2011-03-15

We present a method for accelerating GW quasi-particle (QP) calculations. This is achieved through the introduction of optimal basis sets for representing polarizability matrices. First the real-space products of Wannier like orbitals are constructed and then optimal basis sets are obtained through singular value decomposition. Our method is validated by calculating the vertical ionization energies of the benzene molecule and the band structure of crystalline silicon. Its potentialities are illustrated by calculating the QP spectrum of a model structure of vitreous silica. Finally, we apply our method for studying the electronic structure properties of a model of quasi-stoichiometric amorphous silicon nitride and of its point defects. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Optimizer convergence and local minima errors and their clinical importance

International Nuclear Information System (INIS)

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

2003-01-01

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

A STRONG OPTIMIZATION THEOREM IN LOCALLY CONVEX SPACES

Institute of Scientific and Technical Information of China (English)

程立新; 腾岩梅

2003-01-01

This paper presents a geometric characterization of convex sets in locally convex spaces onwhich a strong optimization theorem of the Stegall-type holds, and gives Collier's theorem ofw* Asplund spaces a localized setting.

Geometrical optimization of a local ballistic magnetic sensor

Energy Technology Data Exchange (ETDEWEB)

Kanda, Yuhsuke; Hara, Masahiro [Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555 (Japan); Nomura, Tatsuya [Advanced Electronics Research Division, INAMORI Frontier Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395 (Japan); Kimura, Takashi [Advanced Electronics Research Division, INAMORI Frontier Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395 (Japan); Department of Physics, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581 (Japan)

2014-04-07

We have developed a highly sensitive local magnetic sensor by using a ballistic transport property in a two-dimensional conductor. A semiclassical simulation reveals that the sensitivity increases when the geometry of the sensor and the spatial distribution of the local field are optimized. We have also experimentally demonstrated a clear observation of a magnetization process in a permalloy dot whose size is much smaller than the size of an optimized ballistic magnetic sensor fabricated from a GaAs/AlGaAs two-dimensional electron gas.

Optimizing radiation exposure for CT localizer radiographs

Energy Technology Data Exchange (ETDEWEB)

Bohrer, Evelyn; Maeder, Ulf; Fiebich, Martin [Univ. of Applied Sciences, Giessen (Germany). Inst. of Medical Physics and Radiation Protection-IMPS; Schaefer, Stefan; Krombach, Gabriele A. [Univ. Hospital Giessen (Germany). Dept. of Radiology; Noel, Peter B. [Technische Univ. Muenchen (Germany). Dept. of Diagnostic and Interventional Radiology

2017-08-01

The trend towards submillisievert CT scans leads to a higher dose fraction of localizer radiographs in CT examinations. The already existing technical capabilities make dose optimization of localizer radiographs worthwhile. Modern CT scanners apply automatic exposure control (AEC) based on attenuation data in such a localizer. Therefore not only this aspect but also the detectability of anatomical landmarks in the localizer for the desired CT scan range adjustment needs to be considered. The effective dose of a head, chest, and abdomen-pelvis localizer radiograph with standard factory settings and user-optimized settings was determined using Monte Carlo simulations. CT examinations of an anthropomorphic phantom were performed using multiple sets of acquisition parameters for the localizer radiograph and the AEC for the subsequent helical CT scan. Anatomical landmarks were defined to assess the image quality of the localizer. CTDI{sub vol} and effective mAs per slice of the helical CT scan were recorded to examine the impact of localizer settings on a helical CT scan. The dose of the localizer radiograph could be decreased by more than 90% while the image quality remained sufficient when selecting the lowest available settings (80 kVp, 20 mA, pa tube position). The tube position during localizer acquisition had a greater impact on the AEC than the reduction of tube voltage and tube current. Except for the use of a pa tube position, all changes of acquisition parameters for the localizer resulted in a decreased total radiation exposure. A dose reduction of CT localizer radiograph is necessary and possible. In the examined CT system there was no negative impact on the modulated helical CT scan when the lowest tube voltage and tube current were used for the localizer.

ARPES and NMTO Wannier Orbital Theory of Li{sub 0.9}Mo{sub 6}O{sub 17}

Energy Technology Data Exchange (ETDEWEB)

Dudy, L. [Physikalisches Institut, Universitaet Wuerzburg, D- 97074 Wuerzburg (Germany); Allen, J.W. [University of Michigan, Ann Arbor, MI (United States); Denlinger, J.D. [Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); He, J. [Clemson University, Clemson, SC (United States); Greenblatt, M. [Rutgers University, Piscataway, NJ (United States); Haverkort, M.W. [Max-Planck-Institut fuer Chemische Physik fester Stoffe, Dresden (Germany); Andersen, O.K.; Nohara, Y. [Max-Planck-Institut fuer Festkoerperphysik, Stuttgart (Germany)

2015-07-01

Li{sub 0.9}Mo{sub 6}O{sub 17} displays theoretically interesting metallic quasi-one dimensional (1D) behavior that is unusually robust against 3D crossover with decreasing temperature, and is characterized by a large value of anomalous exponent α∼ 0.6. We present very high resolution, low temperature (T=6K-30K) angle resolved photoemission spectroscopy (ARPES) of its band structure and Fermi surface (FS), analyzed with N-th order muffin tin orbital (NMTO) Wannier function band theory. We confirm a previous conclusion that LDA band theory is unusually successful, implying a small Hubbard U, and find in ARPES the dispersion and FS warping and splitting expected for predicted small and long range hoppings (t {sub perpendicular} {sub to} ∼ 10-15 meV) between chains.

Local Approximation and Hierarchical Methods for Stochastic Optimization

Science.gov (United States)

Cheng, Bolong

In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the

Local electric dipole moments for periodic systems via density functional theory embedding.

Science.gov (United States)

Luber, Sandra

2014-12-21

We describe a novel approach for the calculation of local electric dipole moments for periodic systems. Since the position operator is ill-defined in periodic systems, maximally localized Wannier functions based on the Berry-phase approach are usually employed for the evaluation of local contributions to the total electric dipole moment of the system. We propose an alternative approach: within a subsystem-density functional theory based embedding scheme, subset electric dipole moments are derived without any additional localization procedure, both for hybrid and non-hybrid exchange-correlation functionals. This opens the way to a computationally efficient evaluation of local electric dipole moments in (molecular) periodic systems as well as their rigorous splitting into atomic electric dipole moments. As examples, Infrared spectra of liquid ethylene carbonate and dimethyl carbonate are presented, which are commonly employed as solvents in Lithium ion batteries.

Local electric dipole moments for periodic systems via density functional theory embedding

Energy Technology Data Exchange (ETDEWEB)

Luber, Sandra, E-mail: sandra.luber@chem.uzh.ch [Institut für Chemie, Universität Zürich, Winterthurerstrasse 190, 8057 Zürich (Switzerland)

2014-12-21

We describe a novel approach for the calculation of local electric dipole moments for periodic systems. Since the position operator is ill-defined in periodic systems, maximally localized Wannier functions based on the Berry-phase approach are usually employed for the evaluation of local contributions to the total electric dipole moment of the system. We propose an alternative approach: within a subsystem-density functional theory based embedding scheme, subset electric dipole moments are derived without any additional localization procedure, both for hybrid and non-hybrid exchange–correlation functionals. This opens the way to a computationally efficient evaluation of local electric dipole moments in (molecular) periodic systems as well as their rigorous splitting into atomic electric dipole moments. As examples, Infrared spectra of liquid ethylene carbonate and dimethyl carbonate are presented, which are commonly employed as solvents in Lithium ion batteries.

Geometry optimization of molecules within an LCGTO local-density functional approach

International Nuclear Information System (INIS)

Mintmire, J.W.

1990-01-01

We describe our implementation of geometry optimization techniques within the linear combination of Gaussian-type orbitals (LCGTO) approach to local-density functional theory. The algorithm for geometry optimization is based on the evaluation of the gradient of the total energy with respect to internal coordinates within the local-density functional scheme. We present optimization results for a range of small molecules which serve as test cases for our approach

Localization of multilayer networks by optimized single-layer rewiring.

Science.gov (United States)

Jalan, Sarika; Pradhan, Priodyuti

2018-04-01

We study localization properties of principal eigenvectors (PEVs) of multilayer networks (MNs). Starting with a multilayer network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multilayer network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that rewiring only one layer is enough to attain a MN having a highly localized PEV. Our investigation reveals that a single edge rewiring of the optimized MN can lead to the complete delocalization of a highly localized PEV. This sensitivity in the localization behavior of PEVs is accompanied with the second largest eigenvalue lying very close to the largest one. This observation opens an avenue to gain a deeper insight into the origin of PEV localization of networks. Furthermore, analysis of multilayer networks constructed using real-world social and biological data shows that the localization properties of these real-world multilayer networks are in good agreement with the simulation results for the model multilayer network. This paper is relevant to applications that require understanding propagation of perturbation in multilayer networks.

Wannier threshold theory for the description of the two-electron cusp in the ion-induced double ionization of atoms

Energy Technology Data Exchange (ETDEWEB)

Barrachina, R.O., E-mail: barra@cab.cnea.gov.ar [Centro Atómico Bariloche and Instituto Balseiro, Comisíon Nacional de Energía Atómica and Universidad Nacional de Cuyo, 8400 Bariloche, Río Negro (Argentina); Gulyás, L.; Sarkadi, L. [Institute for Nuclear Research of the Hungarian Academy of Sciences (ATOMKI), Pf. 51, H-4001 Debrecen (Hungary)

2016-02-15

The double electron capture into the continuum states of the projectile (double ECC) is investigated theoretically in collisions of 100 keV He{sup 2+} ions with He atoms. The process is described within the framework of the impact parameter and frozen-correlation approximations where the single-electron events are treated by the continuum distorted wave method. On the other hand, the Wannier theory is employed for describing the angular correlation between both ejected electrons. This treatment substantially improved the agreement between the theory and experiment as compared to the previous version of the theory (Gulyás et al., 2010) in which the correlation between the ejected electrons was taken into account by the Coulomb density of states approximation.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Science.gov (United States)

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Directory of Open Access Journals (Sweden)

Huanqing Cui

2017-03-01

Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Is local participation always optimal for sustainable action?

DEFF Research Database (Denmark)

Brandt, Urs Steiner; Svendsen, Gert Tinggaard

2013-01-01

Is local participation always optimal for sustainable action? Here, Local Agenda 21 is a relevant case as it broadly calls for consensus-building among stakeholders. Consensus-building is, however, costly. We show that the costs of making local decisions are likely to rapidly exceed the benefits......-solutions, or not making any choices at all. Even though the informational value of meetings may be helpful to policy makers, the model shows that it also decreases as the number of participants increase. Overall, the result is a thought provoking scenario for Local Agenda 21 as it highlights the risk of less sustainable...

Local beam angle optimization with linear programming and gradient search

International Nuclear Information System (INIS)

Craft, David

2007-01-01

The optimization of beam angles in IMRT planning is still an open problem, with literature focusing on heuristic strategies and exhaustive searches on discrete angle grids. We show how a beam angle set can be locally refined in a continuous manner using gradient-based optimization in the beam angle space. The gradient is derived using linear programming duality theory. Applying this local search to 100 random initial angle sets of a phantom pancreatic case demonstrates the method, and highlights the many-local-minima aspect of the BAO problem. Due to this function structure, we recommend a search strategy of a thorough global search followed by local refinement at promising beam angle sets. Extensions to nonlinear IMRT formulations are discussed. (note)

Theory and Algorithms for Global/Local Design Optimization

National Research Council Canada - National Science Library

Watson, Layne T; Guerdal, Zafer; Haftka, Raphael T

2005-01-01

The motivating application for this research is the global/local optimal design of composite aircraft structures such as wings and fuselages, but the theory and algorithms are more widely applicable...

Phase-Field Relaxation of Topology Optimization with Local Stress Constraints

DEFF Research Database (Denmark)

Stainko, Roman; Burger, Martin

2006-01-01

inequality constraints. We discretize the problem by finite elements and solve the arising finite-dimensional programming problems by a primal-dual interior point method. Numerical experiments for problems with local stress constraints based on different criteria indicate the success and robustness......We introduce a new relaxation scheme for structural topology optimization problems with local stress constraints based on a phase-field method. In the basic formulation we have a PDE-constrained optimization problem, where the finite element and design analysis are solved simultaneously...

A dynamic global and local combined particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Chen Qunxian

2009-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

Directory of Open Access Journals (Sweden)

Sen Zhang

2015-01-01

Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Multiple local minima in IMRT optimization based on dose-volume criteria

International Nuclear Information System (INIS)

Wu Qiuwen; Mohan, Radhe

2002-01-01

Multiple local minima traps are known to exist in dose-volume and dose-response objective functions. Nevertheless, their presence and consequences are not considered impediments in finding satisfactory solutions in routine optimization of IMRT plans using gradient methods. However, there is often a concern that a significantly superior solution may exist unbeknownst to the planner and that the optimization process may not be able to reach it. We have investigated the soundness of the assumption that the presence of multiple minima traps can be ignored. To find local minima, we start the optimization process a large number of times with random initial intensities. We investigated whether the occurrence of local minima depends upon the choice of the objective function parameters and the number of variables and whether their existence is an impediment in finding a satisfactory solution. To learn about the behavior of multiple minima, we first used a symmetric cubic phantom containing a cubic target and an organ-at-risk surrounding it to optimize the beam weights of two pairs of parallel-opposed beams using a gradient technique. The phantom studies also served to test our software. Objective function parameters were chosen to ensure that multiple minima would exist. Data for 500 plans, optimized with random initial beam weights, were analyzed. The search process did succeed in finding the local minima and showed that the number of minima depends on the parameters of the objective functions. It was also found that the consequences of local minima depended on the number of beams. We further searched for the multiple minima in intensity-modulated treatment plans for a head-and-neck case and a lung case. In addition to the treatment plan scores and the dose-volume histograms, we examined the dose distributions and intensity patterns. We did not find any evidence that multiple local minima affect the outcome of optimization using gradient techniques in any clinically

Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

Science.gov (United States)

Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

2014-01-01

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. PMID:24723827

Many-Body Theory of Pyrochlore Iridates and Related Materials

Science.gov (United States)

Wang, Runzhi

In this thesis we focus on two problems. First we propose a numerical method for generating optimized Wannier functions with desired properties. Second we perform the state of the art density functional plus dynamical mean-field calculations in pyrochlore iridates, to investigate the physics induced by the cooperation of spin-orbit coupling and electron correlation. We begin with the introduction for maximally localized Wannier functions and other related extensions. Then we describe the current research in the field of spin-orbit coupling and its interplay with correlation effects, followed by a brief introduction of the `hot' materials of iridates. Before the end of the introduction, we discuss the numerical methods employed in our work, including the density functional theory; dynamical mean-field theory and its combination with the exact diagonalization impurity solver. Then we propose our approach for constructing an optimized set of Wannier functions, which is a generalization of the functionality of the classic maximal localization method put forward by Marzari and Vanderbilt. Our work is motivated by the requirement of the effective description of the local subspace of the Hamiltonian by the beyond density functional theory methods. In extensions of density functional theory such as dynamical mean-field theory, one may want highly accurate description of particular local orbitals, including correct centers and symmetries; while the basis for the remaining degrees of freedom is unimportant. Therefore, we develop the selectively localized Wannier function approach which allows for a greater localization in the selected subset of Wannier functions and at the same time allows us to fix the centers and ensure the point symmetries. Applications in real materials are presented to demonstrate the power of our approach. Next we move to the investigation of pyrochlore iridates, focussing on the metal-insulator transition and material dependence in these compounds. We

Evolutionary multimodal optimization using the principle of locality

KAUST Repository

Wong, Kachun; Wu, Chunho; Mok, Ricky; Peng, Chengbin; Zhang, Zhaolei

2012-01-01

The principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed. © 2012 Elsevier Inc. All rights reserved.

Evolutionary multimodal optimization using the principle of locality

KAUST Repository

Wong, Kachun

2012-07-01

The principle of locality is one of the most widely used concepts in designing computing systems. To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization. Instead of generating trial vectors randomly, the first method proposed takes advantage of spatial locality to generate trial vectors. Temporal locality is also adopted to help generate offspring in the second method proposed. Temporal and spatial locality are then applied together in the third method proposed. Numerical experiments are conducted to compare the proposed methods with the state-of-the-art methods on benchmark functions. Experimental analysis is undertaken to observe the effect of locality and the synergy between temporal locality and spatial locality. Further experiments are also conducted on two application problems. One is the varied-line-spacing holographic grating design problem, while the other is the protein structure prediction problem. The numerical results demonstrate the effectiveness of the methods proposed. © 2012 Elsevier Inc. All rights reserved.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

2014-01-01

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Spatial planning via extremal optimization enhanced by cell-based local search

International Nuclear Information System (INIS)

Sidiropoulos, Epaminondas

2014-01-01

A new treatment is presented for land use planning problems by means of extremal optimization in conjunction to cell-based neighborhood local search. Extremal optimization, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper it complements extremal optimization in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific characteristic problem. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning. The present treatment yields significant compactness values as emergent results

Interactive Cosegmentation Using Global and Local Energy Optimization

OpenAIRE

Xingping Dong,; Jianbing Shen,; Shao, Ling; Yang, Ming-Hsuan

2015-01-01

We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint, which attempts to match the histograms of common objects. To minimize the local energy, we apply the spline regression to learn the smoothne...

Structural Distortion Stabilizing the Antiferromagnetic and Semiconducting Ground State of BaMn2As2

Directory of Open Access Journals (Sweden)

Ekkehard Krüger

2016-09-01

Full Text Available We report evidence that the experimentally found antiferromagnetic structure as well as the semiconducting ground state of BaMn 2 As 2 are caused by optimally-localized Wannier states of special symmetry existing at the Fermi level of BaMn 2 As 2 . In addition, we find that a (small tetragonal distortion of the crystal is required to stabilize the antiferromagnetic semiconducting state. To our knowledge, this distortion has not yet been established experimentally.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

Science.gov (United States)

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Context-Aware Local Optimization of Sensor Network Deployment

Directory of Open Access Journals (Sweden)

Meysam Argany

2015-07-01

Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.

Engineering local optimality in quantum Monte Carlo algorithms

Science.gov (United States)

Pollet, Lode; Van Houcke, Kris; Rombouts, Stefan M. A.

2007-08-01

Quantum Monte Carlo algorithms based on a world-line representation such as the worm algorithm and the directed loop algorithm are among the most powerful numerical techniques for the simulation of non-frustrated spin models and of bosonic models. Both algorithms work in the grand-canonical ensemble and can have a winding number larger than zero. However, they retain a lot of intrinsic degrees of freedom which can be used to optimize the algorithm. We let us guide by the rigorous statements on the globally optimal form of Markov chain Monte Carlo simulations in order to devise a locally optimal formulation of the worm algorithm while incorporating ideas from the directed loop algorithm. We provide numerical examples for the soft-core Bose-Hubbard model and various spin- S models.

Genetic local search algorithm for optimization design of diffractive optical elements.

Science.gov (United States)

Zhou, G; Chen, Y; Wang, Z; Song, H

1999-07-10

We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE's). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA's whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE's that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.

Global-local optimization of flapping kinematics in hovering flight

KAUST Repository

Ghommem, Mehdi; Hajj, M. R.; Mook, Dean T.; Stanford, Bret K.; Bé ran, Philip S.; Watson, Layne T.

2013-01-01

The kinematics of a hovering wing are optimized by combining the 2-d unsteady vortex lattice method with a hybrid of global and local optimization algorithms. The objective is to minimize the required aerodynamic power under a lift constraint. The hybrid optimization is used to efficiently navigate the complex design space due to wing-wake interference present in hovering aerodynamics. The flapping wing is chosen so that its chord length and flapping frequency match the morphological and flight properties of two insects with different masses. The results suggest that imposing a delay between the different oscillatory motions defining the flapping kinematics, and controlling the way through which the wing rotates at the end of each half stroke can improve aerodynamic power under a lift constraint. Furthermore, our optimization analysis identified optimal kinematics that agree fairly well with observed insect kinematics, as well as previously published numerical results.

Global-local optimization of flapping kinematics in hovering flight

KAUST Repository

Ghommem, Mehdi

2013-06-01

The kinematics of a hovering wing are optimized by combining the 2-d unsteady vortex lattice method with a hybrid of global and local optimization algorithms. The objective is to minimize the required aerodynamic power under a lift constraint. The hybrid optimization is used to efficiently navigate the complex design space due to wing-wake interference present in hovering aerodynamics. The flapping wing is chosen so that its chord length and flapping frequency match the morphological and flight properties of two insects with different masses. The results suggest that imposing a delay between the different oscillatory motions defining the flapping kinematics, and controlling the way through which the wing rotates at the end of each half stroke can improve aerodynamic power under a lift constraint. Furthermore, our optimization analysis identified optimal kinematics that agree fairly well with observed insect kinematics, as well as previously published numerical results.

Edge-entanglement spectrum correspondence in a nonchiral topological phase and Kramers-Wannier duality

Science.gov (United States)

Ho, Wen Wei; Cincio, Lukasz; Moradi, Heidar; Gaiotto, Davide; Vidal, Guifre

2015-03-01

In a system with chiral topological order, there is a remarkable correspondence between the edge and entanglement spectra: the low-energy spectrum of the system in the presence of a physical edge coincides with the lowest part of the entanglement spectrum (ES) across a virtual cut of the system into two parts, up to rescaling and shifting. This correspondence is believed to be due to the existence of protected gapless edge modes. In this paper, we explore whether the edge-entanglement spectrum correspondence extends to nonchiral topological phases, where there are no protected gapless edge modes. Specifically, we consider the Wen-plaquette model, which is equivalent to the Kitaev toric code model and has Z2 topological order (quantum double of Z2) . The unperturbed Wen-plaquette model displays an exact correspondence: both the edge and entanglement spectra within each topological sector a (a =1 ,⋯,4 ) are flat and equally degenerate. Here, we show, through a detailed microscopic calculation, that in the presence of generic local perturbations: (i) the effective degrees of freedom for both the physical edge and the entanglement cut consist of a (spin-1 /2 ) spin chain, with effective Hamiltonians Hedgea and Henta, respectively, both of which have a Z2 symmetry enforced by the bulk topological order; (ii) there is in general no match between the low-energy spectra of Hedgea and Henta, that is, there is no edge-ES correspondence. However, if supplement the Z2 topological order with a global symmetry (translational invariance along the edge/entanglement cut), i.e., by considering the Wen-plaquette model as a symmetry-enriched topological phase (SET), then there is a finite domain in Hamiltonian space in which both Hedgea and Henta realize the critical Ising model, whose low-energy effective theory is the c =1 /2 Ising CFT. This is achieved because the presence of the global symmetry implies that the effective degrees of freedom of both the edge and entanglement

First-principle optimal local pseudopotentials construction via optimized effective potential method

International Nuclear Information System (INIS)

Mi, Wenhui; Zhang, Shoutao; Wang, Yanchao; Ma, Yanming; Miao, Maosheng

2016-01-01

The local pseudopotential (LPP) is an important component of orbital-free density functional theory, a promising large-scale simulation method that can maintain information on a material’s electron state. The LPP is usually extracted from solid-state density functional theory calculations, thereby it is difficult to assess its transferability to cases involving very different chemical environments. Here, we reveal a fundamental relation between the first-principles norm-conserving pseudopotential (NCPP) and the LPP. On the basis of this relationship, we demonstrate that the LPP can be constructed optimally from the NCPP for a large number of elements using the optimized effective potential method. Specially, our method provides a unified scheme for constructing and assessing the LPP within the framework of first-principles pseudopotentials. Our practice reveals that the existence of a valid LPP with high transferability may strongly depend on the element.

Topology optimization of nanoparticles for localized electromagnetic field enhancement

DEFF Research Database (Denmark)

Christiansen, Rasmus Ellebæk; Vester-Petersen, Joakim; Madsen, Søren Peder

2017-01-01

We consider the design of individual and periodic arrangements of metal or semiconductor nanoparticles for localized electromagnetic field enhancement utilizing a topology optimization based numerical framework as the design tool. We aim at maximizing a function of the electromagnetic field...

Development and optimization of power plant concepts for local wet fuels

Energy Technology Data Exchange (ETDEWEB)

Raiko, M.O.; Gronfors, T.H.A. [Fortum Energy Solutions, Fortum (Finland); Haukka, P. [Tampere University of Technology (Finland)

2003-01-01

Many changes in business drivers are now affecting power-producing companies. The power market has been opened up and the number of locally operating companies has increased. At the same time the need to utilize locally produced biofuels is increasing because of environmental benefits and regulations. In this situation, power-producing companies have on focus their in-house skills for generating a competitive edge over their rivals, such as the skills needed for developing the most economical energy investments for the best-paying customer for the local biomass producers. This paper explores the role of optimization in the development of small-sized energy investments. The paper provides an overview on a new design process for power companies for improved use of in-house technical and business expertise. As an example, illustrative design and optimization of local wet peat-based power investment is presented. Three concept alternatives are generated. Only power plant production capacity and peat moisture content are optimized for all alternatives. Long commercial experience of using peat as a power plant fuel in Finland can be transferred to bioenergy investments. In this paper, it is shown that conventional technology can be feasible for bioenergy production even in quite small size (below 10 MW). It is important to optimize simultaneously both the technology and the two businesses, power production and fuel production. Further, such high moisture content biomass as sludge, seaweed, grass, etc. can be economical fuels, if advanced drying systems are adopted in a power plant. (author)

Topology Optimization of Continuum Structures with Local Stress Constraints

DEFF Research Database (Denmark)

Duysinx, Pierre; Bendsøe, Martin P

1998-01-01

We introduce an extension of current technologies for topology optimization of continuum structures which allows for treating local stress criteria. We first consider relevant stress criteria for porous composite materials, initially by studying the stress states of the so-called rank 2 layered m...

Effect of Local Junction Losses in the Optimization of T-shaped Flow Channels

Science.gov (United States)

Kosaraju, Srinivas

2015-11-01

T-shaped channels are extensively used in flow distribution applications such as irrigation, chemical dispersion, gas pipelines and space heating and cooling. The geometry of T-shaped channels can be optimized to reduce the overall pressure drop in stem and branch sections. Results of such optimizations are in the form of geometric parameters such as the length and diameter ratios of the stem and branch sections. The traditional approach of this optimization accounts for the pressure drop across the stem and branch sections, however, ignores the pressure drop in the T-junction. In this paper, we conduct geometry optimization while including the effect of local junction losses in laminar flows. From the results, we are able to identify a non-dimensional parameter that can be used to predict the optimal geometric configurations. This parameter can also be used to identify the conditions in which the local junction losses can be ignored during the optimization.

Optimal dietary patterns designed from local foods to achieve maternal nutritional goals.

Science.gov (United States)

Raymond, Jofrey; Kassim, Neema; Rose, Jerman W; Agaba, Morris

2018-04-04

Achieving nutritional requirements for pregnant and lactating mothers in rural households while maintaining the intake of local and culture-specific foods can be a difficult task. Deploying a linear goal programming approach can effectively generate optimal dietary patterns that incorporate local and culturally acceptable diets. The primary objective of this study was to determine whether a realistic and affordable diet that achieves nutritional goals for rural pregnant and lactating women can be formulated from locally available foods in Tanzania. A cross sectional study was conducted to assess dietary intakes of 150 pregnant and lactating women using a weighed dietary record (WDR), 24 h dietary recalls and a 7-days food record. A market survey was also carried out to estimate the cost per 100 g of edible portion of foods that are frequently consumed in the study population. Dietary survey and market data were then used to define linear programming (LP) model parameters for diet optimisation. All LP analyses were done using linear program solver to generate optimal dietary patterns. Our findings showed that optimal dietary patterns designed from locally available foods would improve dietary adequacy for 15 and 19 selected nutrients in pregnant and lactating women, respectively, but inadequacies remained for iron, zinc, folate, pantothenic acid, and vitamin E, indicating that these are problem nutrients (nutrients that did not achieve 100% of their RNIs in optimised diets) in the study population. These findings suggest that optimal use of local foods can improve dietary adequacy for rural pregnant and lactating women aged 19-50 years. However, additional cost-effective interventions are needed to ensure adequate intakes for the identified problem nutrients.

Reinforcement active learning in the vibrissae system: optimal object localization.

Science.gov (United States)

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimalization of Beacon Selection for Localization in Wireless AD-HOC Networks

Directory of Open Access Journals (Sweden)

Martin Matula

2008-01-01

Full Text Available In this paper we engage in optimalization of convenient beacons for localization position of a node in the ad-hoc network. An algorithm designed by us localizes position of moving or static node by RSS (Received Signal Strength method and trilateration. At first, localization of unknown node runs by combination of all beacons. Than optimalizating algorithmreduces the number of beacons (and repeats localization, while only three left. Its reduction is based on highest levels of received signal strength. It is only when signals are from the nearest beacons. Position localizating exactness is statistically interpreted from all localization by beacons combination and its repeating.

Globally-Optimized Local Pseudopotentials for (Orbital-Free) Density Functional Theory Simulations of Liquids and Solids.

Science.gov (United States)

Del Rio, Beatriz G; Dieterich, Johannes M; Carter, Emily A

2017-08-08

The accuracy of local pseudopotentials (LPSs) is one of two major determinants of the fidelity of orbital-free density functional theory (OFDFT) simulations. We present a global optimization strategy for LPSs that enables OFDFT to reproduce solid and liquid properties obtained from Kohn-Sham DFT. Our optimization strategy can fit arbitrary properties from both solid and liquid phases, so the resulting globally optimized local pseudopotentials (goLPSs) can be used in solid and/or liquid-phase simulations depending on the fitting process. We show three test cases proving that we can (1) improve solid properties compared to our previous bulk-derived local pseudopotential generation scheme; (2) refine predicted liquid and solid properties by adding force matching data; and (3) generate a from-scratch, accurate goLPS from the local channel of a non-local pseudopotential. The proposed scheme therefore serves as a full and improved LPS construction protocol.

Locally optimized separability enhancement indices for urban land cover mapping

DEFF Research Database (Denmark)

Feyisa, Gudina L.; Meilby, Henrik; Darrel Jenerette, G.

2016-01-01

data in LULC classification. To more accurately quantify landscape patterns and their changes, we applied new locally optimized separability enhancement indices and decision rules (SEI–DR approach) to address commonly observed classification accuracy problems in urban environments. We tested the SEI...

Locally optimal extracellular stimulation for chaotic desynchronization of neural populations.

Science.gov (United States)

Wilson, Dan; Moehlis, Jeff

2014-10-01

We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

Health-related quality of life, optimism, and coping strategies in persons suffering from localized scleroderma.

Science.gov (United States)

Szramka-Pawlak, B; Dańczak-Pazdrowska, A; Rzepa, T; Szewczyk, A; Sadowska-Przytocka, A; Żaba, R

2013-01-01

The clinical course of localized scleroderma may consist of bodily deformations, and bodily functions may also be affected. Additionally, the secondary lesions, such as discoloration, contractures, and atrophy, are unlikely to regress. The aforementioned symptoms and functional disturbances may decrease one's quality of life (QoL). Although much has been mentioned in the medical literature regarding QoL in persons suffering from dermatologic diseases, no data specifically describing patients with localized scleroderma exist. The aim of the study was to explore QoL in localized scleroderma patients and to examine their coping strategies in regard to optimism and QoL. The study included 41 patients with localized scleroderma. QoL was evaluated using the SKINDEX questionnaire, and levels of dispositional optimism were assessed using the Life Orientation Test-Revised. In addition, individual coping strategy was determined using the Mini-MAC scale and physical condition was assessed using the Localized Scleroderma Severity Index. The mean QoL score amounted to 51.10 points, with mean scores for individual components as follows: symptoms = 13.49 points, emotions = 21.29 points, and functioning = 16.32 points. A relationship was detected between QoL and the level of dispositional optimism as well as with coping strategies known as anxious preoccupation and helplessness-hopelessness. Higher levels of optimism predicted a higher general QoL. In turn, greater intensity of anxious preoccupied and helpless-hopeless behaviors predicted a lower QoL. Based on these results, it may be stated that localized scleroderma patients have a relatively high QoL, which is accompanied by optimism as well as a lower frequency of behaviors typical of emotion-focused coping strategies.

Optimal matching for prostate brachytherapy seed localization with dimension reduction.

Science.gov (United States)

Lee, Junghoon; Labat, Christian; Jain, Ameet K; Song, Danny Y; Burdette, Everette C; Fichtinger, Gabor; Prince, Jerry L

2009-01-01

In prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. More recent developments have made possible 3D localization of the implanted radioactive seeds. This is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. It is, however, NP-hard, and the problem is even harder in practice due to the significant number of hidden seeds. In this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. It solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. Simulation results show the robustness of the algorithm. It was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of > or = 97.6% and reconstruction error of < or = 1.2 mm. This is considered to be clinically excellent performance.

Notions of local controllability and optimal feedforward control for quantum systems

International Nuclear Information System (INIS)

Chakrabarti, Raj

2011-01-01

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Notions of local controllability and optimal feedforward control for quantum systems

Energy Technology Data Exchange (ETDEWEB)

Chakrabarti, Raj, E-mail: rchakra@purdue.edu [School of Chemical Engineering, Purdue University, West Lafayette, IN 47907 (United States)

2011-05-06

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Energy network dispatch optimization under emergency of local energy shortage

International Nuclear Information System (INIS)

Cai, Tianxing; Zhao, Chuanyu; Xu, Qiang

2012-01-01

The consequence of short-time energy shortage under extreme conditions, such as earthquake, tsunami, and hurricane, may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. In this paper, a novel methodology is developed for energy network dispatch optimization under emergency of local energy shortage, which includes four stages of work. First, emergency-area-centered energy network needs to be characterized, where the capacity, quantity, and availability of various energy sources are determined. Second, the energy initial situation under emergency conditions needs to be identified. Then, the energy dispatch optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage. Finally, the sensitivity of the minimum dispatch time with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces. The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions. -- Highlights: ► Address the energy network dispatch problem under emergency of local energy shortage. ► Minimize the energy restoration time for the entire energy network under emergency events. ► Develop a new MILP model and a sensitivity analysis method with respect to uncertainties.

Localized probability of improvement for kriging based multi-objective optimization

Science.gov (United States)

Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

2017-12-01

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Optimization of distribution piping network in district cooling system using genetic algorithm with local search

International Nuclear Information System (INIS)

Chan, Apple L.S.; Hanby, Vic I.; Chow, T.T.

2007-01-01

A district cooling system is a sustainable means of distribution of cooling energy through mass production. A cooling medium like chilled water is generated at a central refrigeration plant and supplied to serve a group of consumer buildings through a piping network. Because of the substantial capital investment involved, an optimal design of the distribution piping configuration is one of the crucial factors for successful implementation of the district cooling scheme. In the present study, genetic algorithm (GA) incorporated with local search techniques was developed to find the optimal/near optimal configuration of the piping network in a hypothetical site. The effect of local search, mutation rate and frequency of local search on the performance of the GA in terms of both solution quality and computation time were investigated and presented in this paper

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

Science.gov (United States)

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.

Portfolio Optimization under Local-Stochastic Volatility: Coefficient Taylor Series Approximations & Implied Sharpe Ratio

OpenAIRE

Lorig, Matthew; Sircar, Ronnie

2015-01-01

We study the finite horizon Merton portfolio optimization problem in a general local-stochastic volatility setting. Using model coefficient expansion techniques, we derive approximations for the both the value function and the optimal investment strategy. We also analyze the `implied Sharpe ratio' and derive a series approximation for this quantity. The zeroth-order approximation of the value function and optimal investment strategy correspond to those obtained by Merton (1969) when the risky...

Formulation analysis and computation of an optimization-based local-to-nonlocal coupling method.

Energy Technology Data Exchange (ETDEWEB)

D' Elia, Marta [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research; Bochev, Pavel Blagoveston [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research

2017-01-01

In this paper, we present an optimization-based coupling method for local and nonlocal continuum models. Our approach couches the coupling of the models into a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the local and nonlocal problem domains, and the virtual controls are the nonlocal volume constraint and the local boundary condition. We present the method in the context of Local-to-Nonlocal di usion coupling. Numerical examples illustrate the theoretical properties of the approach.

Design Optimization of Laminated Composite Structures with Many Local Strength Criteria

DEFF Research Database (Denmark)

Lund, Erik

2012-01-01

This paper presents different strategies for handling very many local strength criteria in structural optimization of laminated composites. Global strength measures using Kreisselmeier-Steinhauser or p-norm functions are introduced for patch-wise parameterizations, and the efficiency of the metho...

Local-scaling density-functional method: Intraorbit and interorbit density optimizations

International Nuclear Information System (INIS)

Koga, T.; Yamamoto, Y.; Ludena, E.V.

1991-01-01

The recently proposed local-scaling density-functional theory provides us with a practical method for the direct variational determination of the electron density function ρ(r). The structure of ''orbits,'' which ensures the one-to-one correspondence between the electron density ρ(r) and the N-electron wave function Ψ({r k }), is studied in detail. For the realization of the local-scaling density-functional calculations, procedures for intraorbit and interorbit optimizations of the electron density function are proposed. These procedures are numerically illustrated for the helium atom in its ground state at the beyond-Hartree-Fock level

Asymptotically optimal data analysis for rejecting local realism

International Nuclear Information System (INIS)

Zhang, Yanbao; Glancy, Scott; Knill, Emanuel

2011-01-01

Reliable experimental demonstrations of violations of local realism are highly desirable for fundamental tests of quantum mechanics. One can quantify the violation witnessed by an experiment in terms of a statistical p value, which can be defined as the maximum probability according to local realism of a violation at least as high as that witnessed. Thus, high violation corresponds to small p value. We propose a prediction-based-ratio (PBR) analysis protocol whose p values are valid even if the prepared quantum state varies arbitrarily and local realistic models can depend on previous measurement settings and outcomes. It is therefore not subject to the memory loophole [J. Barrett et al., Phys. Rev. A 66, 042111 (2002)]. If the prepared state does not vary in time, the p values are asymptotically optimal. For comparison, we consider protocols derived from the number of standard deviations of violation of a Bell inequality and from martingale theory [R. Gill, e-print arXiv:quant-ph/0110137]. We find that the p values of the former can be too small and are therefore not statistically valid, while those derived from the latter are suboptimal. PBR p values do not require a predetermined Bell inequality and can be used to compare results from different tests of local realism independent of experimental details.

Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm

Directory of Open Access Journals (Sweden)

V. D. Sulimov

2014-01-01

Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search

Sub-Optimal Management of Type 2 Diabetes Mellitus – A Local Audit

African Journals Online (AJOL)

Original Research: Sub-Optimal Management of Type 2 Diabetes Mellitus – A Local Audit ... despite clinical trial data documenting improved outcomes associated not ... were used to define the Metabolic Syndrome.9 Central obesity was.

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

Energy Technology Data Exchange (ETDEWEB)

Li, Dengwang; Wang, Jie [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States)

2015-06-15

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

International Nuclear Information System (INIS)

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

2015-01-01

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

RDEL: Restart Differential Evolution algorithm with Local Search Mutation for global numerical optimization

Directory of Open Access Journals (Sweden)

Ali Wagdy Mohamed

2014-11-01

Full Text Available In this paper, a novel version of Differential Evolution (DE algorithm based on a couple of local search mutation and a restart mechanism for solving global numerical optimization problems over continuous space is presented. The proposed algorithm is named as Restart Differential Evolution algorithm with Local Search Mutation (RDEL. In RDEL, inspired by Particle Swarm Optimization (PSO, a novel local mutation rule based on the position of the best and the worst individuals among the entire population of a particular generation is introduced. The novel local mutation scheme is joined with the basic mutation rule through a linear decreasing function. The proposed local mutation scheme is proven to enhance local search tendency of the basic DE and speed up the convergence. Furthermore, a restart mechanism based on random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme is combined to avoid stagnation and/or premature convergence. Additionally, an exponent increased crossover probability rule and a uniform scaling factors of DE are introduced to promote the diversity of the population and to improve the search process, respectively. The performance of RDEL is investigated and compared with basic differential evolution, and state-of-the-art parameter adaptive differential evolution variants. It is discovered that the proposed modifications significantly improve the performance of DE in terms of quality of solution, efficiency and robustness.

A model of optimization for local energy infrastructure development

International Nuclear Information System (INIS)

Juroszek, Zbigniew; Kudelko, Mariusz

2016-01-01

The authors present a non-linear, optimization model supporting the planning of local energy systems development. The model considers two forms of final energy – heat and electricity. The model reflects both private and external costs and is designed to show the social perspective. It considers the variability of the marginal costs attributed to local renewable resources. In order to demonstrate the capacity of the model, the authors present a case study by modelling the development of the energy infrastructure in a municipality located in the south of Poland. The ensuing results show that a swift and significant shift in the local energy policy of typical central European municipalities is needed. The modelling is done in two scenarios – with and without the internalization of external environmental costs. The results confirm that the internalization of the external costs of energy production on a local scale leads to a significant improvement in the allocation of resources. - Highlights: • A model for municipal energy system development in Central European environment has been developed. • The variability of marginal costs of local, renewable fuels is considered. • External, environmental costs are considered. • The model reflects both network and individual energy infrastructure (e.g. individual housing boilers). • A swift change in Central European municipal energy infrastructure is necessary.

Global-Local Analysis and Optimization of a Composite Civil Tilt-Rotor Wing

Science.gov (United States)

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).

Local Optimization Strategies in Urban Vehicular Mobility.

Directory of Open Access Journals (Sweden)

Pierpaolo Mastroianni

Full Text Available The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints--physical, environmental, social, economic--that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.

Is local participation always optimal for sustainable action? The costs of consensus-building in Local Agenda 21.

Science.gov (United States)

Brandt, Urs Steiner; Svendsen, Gert Tinggaard

2013-11-15

Is local participation always optimal for sustainable action? Here, Local Agenda 21 is a relevant case as it broadly calls for consensus-building among stakeholders. Consensus-building is, however, costly. We show that the costs of making local decisions are likely to rapidly exceed the benefits. Why? Because as the number of participants grows, the more likely it is that the group will include individuals who have an extreme position and are unwilling to make compromises. Thus, the net gain of self-organization should be compared with those of its alternatives, for example voting, market-solutions, or not making any choices at all. Even though the informational value of meetings may be helpful to policy makers, the model shows that it also decreases as the number of participants increase. Overall, the result is a thought provoking scenario for Local Agenda 21 as it highlights the risk of less sustainable action in the future. Copyright © 2013 Elsevier Ltd. All rights reserved.

An optimized ensemble local mean decomposition method for fault detection of mechanical components

International Nuclear Information System (INIS)

Zhang, Chao; Chen, Shuai; Wang, Jianguo; Li, Zhixiong; Hu, Chao; Zhang, Xiaogang

2017-01-01

Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error ( Relative RMSE ) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE , corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions. (paper)

An optimized ensemble local mean decomposition method for fault detection of mechanical components

Science.gov (United States)

Zhang, Chao; Li, Zhixiong; Hu, Chao; Chen, Shuai; Wang, Jianguo; Zhang, Xiaogang

2017-03-01

Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.

GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

R. Rajakumar

2017-01-01

Full Text Available Seyedali Mirjalili et al. (2014 introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO. This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO and modified bat algorithm (MBA. The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.

Damage approach: A new method for topology optimization with local stress constraints

DEFF Research Database (Denmark)

Verbart, Alexander; Langelaar, Matthijs; van Keulen, Fred

2016-01-01

In this paper, we propose a new method for topology optimization with local stress constraints. In this method, material in which a stress constraint is violated is considered as damaged. Since damaged material will contribute less to the overall performance of the structure, the optimizer...... will promote a design with a minimal amount of damaged material. We tested the method on several benchmark problems, and the results show that the method is a viable alternative for conventional stress-based approaches based on constraint relaxation followed by constraint aggregation....

Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection

Directory of Open Access Journals (Sweden)

Wendong Xiao

2015-04-01

Full Text Available Device-free localization (DFL is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, city emergency response and infrastructure security. In DFL, wireless devices are used as sensors that can sense the target by transmitting and receiving wireless signals collaboratively. Many DFL systems are implemented based on received signal strength (RSS measurements and the location of the target is estimated by detecting the changes of the RSS measurements of the wireless links. Due to the uncertainty of the wireless channel, certain links may be seriously polluted and result in erroneous detection. In this paper, we propose a novel nonlinear optimization approach with outlier link rejection (NOOLR for RSS-based DFL. It consists of three key strategies, including: (1 affected link identification by differential RSS detection; (2 outlier link rejection via geometrical positional relationship among links; (3 target location estimation by formulating and solving a nonlinear optimization problem. Experimental results demonstrate that NOOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI approach.

Bilayer Local Search Enhanced Particle Swarm Optimization for the Capacitated Vehicle Routing Problem

Directory of Open Access Journals (Sweden)

A. K. M. Foysal Ahmed

2018-03-01

Full Text Available The classical capacitated vehicle routing problem (CVRP is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches.

Metallic surface description in a localized representation

International Nuclear Information System (INIS)

Kirtman, B.; Melo, C.P. de

1981-01-01

Binding orders for a three-dimensional system (cubium) are obtained. The study of convergence of these values with the progressive interiorization in the solid gives an indication of the perturbation magnitude introduced with the surface creation. Following Goddard's hint in which the nickel reactivity is denominated by the 4s orbitals, such a model is applied to this metal. The base transformation of atomic orbitals for the correspondent Wannier functions is obtained. (L.C.) [pt

Particle Swarm Optimization Based on Local Attractors of Ordinary Differential Equation System

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Wenyu Yang

2014-01-01

Full Text Available Particle swarm optimization (PSO is inspired by sociological behavior. In this paper, we interpret PSO as a finite difference scheme for solving a system of stochastic ordinary differential equations (SODE. In this framework, the position points of the swarm converge to an equilibrium point of the SODE and the local attractors, which are easily defined by the present position points, also converge to the global attractor. Inspired by this observation, we propose a class of modified PSO iteration methods (MPSO based on local attractors of the SODE. The idea of MPSO is to choose the next update state near the present local attractor, rather than the present position point as in the original PSO, according to a given probability density function. In particular, the quantum-behaved particle swarm optimization method turns out to be a special case of MPSO by taking a special probability density function. The MPSO methods with six different probability density functions are tested on a few benchmark problems. These MPSO methods behave differently for different problems. Thus, our framework not only gives an interpretation for the ordinary PSO but also, more importantly, provides a warehouse of PSO-like methods to choose from for solving different practical problems.

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

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Weitian Lin

2014-01-01

Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

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

Science.gov (United States)

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.

Reliability based topology optimization for continuum structures with local failure constraints

DEFF Research Database (Denmark)

Luo, Yangjun; Zhou, Mingdong; Wang, Michael Yu

2014-01-01

This paper presents an effective method for stress constrained topology optimization problems under load and material uncertainties. Based on the Performance Measure Approach (PMA), the optimization problem is formulated as to minimize the objective function under a large number of (stress......-related) target performance constraints. In order to overcome the stress singularity phenomenon caused by the combined stress and reliability constraints, a reduction strategy on target reliability index is proposed and utilized together with the ε-relaxation approach. Meanwhile, an enhanced aggregation method...... is employed to aggregate the selected active constraints using a general K–S function, which avoids expensive computational cost from the large-scale nature of local failure constraints. Several numerical examples are given to demonstrate the validity of the present method....

Optimal growth entails risky localization in population dynamics

Science.gov (United States)

Gueudré, Thomas; Martin, David G.

2018-03-01

Essential to each other, growth and exploration are jointly observed in alive and inanimate entities, such as animals, cells or goods. But how the environment's structural and temporal properties weights in this balance remains elusive. We analyze a model of stochastic growth with time correlations and diffusive dynamics that sheds light on the way populations grow and spread over general networks. This model suggests natural explanations of empirical facts in econo-physics or ecology, such as the risk-return trade-off and the Zipf law. We conclude that optimal growth leads to a localized population distribution, but such risky position can be mitigated through the space geometry. These results have broad applicability and are subsequently illustrated over an empirical study of financial data.

Optimal Audiovisual Integration in the Ventriloquism Effect But Pervasive Deficits in Unisensory Spatial Localization in Amblyopia.

Science.gov (United States)

Richards, Michael D; Goltz, Herbert C; Wong, Agnes M F

2018-01-01

Classically understood as a deficit in spatial vision, amblyopia is increasingly recognized to also impair audiovisual multisensory processing. Studies to date, however, have not determined whether the audiovisual abnormalities reflect a failure of multisensory integration, or an optimal strategy in the face of unisensory impairment. We use the ventriloquism effect and the maximum-likelihood estimation (MLE) model of optimal integration to investigate integration of audiovisual spatial information in amblyopia. Participants with unilateral amblyopia (n = 14; mean age 28.8 years; 7 anisometropic, 3 strabismic, 4 mixed mechanism) and visually normal controls (n = 16, mean age 29.2 years) localized brief unimodal auditory, unimodal visual, and bimodal (audiovisual) stimuli during binocular viewing using a location discrimination task. A subset of bimodal trials involved the ventriloquism effect, an illusion in which auditory and visual stimuli originating from different locations are perceived as originating from a single location. Localization precision and bias were determined by psychometric curve fitting, and the observed parameters were compared with predictions from the MLE model. Spatial localization precision was significantly reduced in the amblyopia group compared with the control group for unimodal visual, unimodal auditory, and bimodal stimuli. Analyses of localization precision and bias for bimodal stimuli showed no significant deviations from the MLE model in either the amblyopia group or the control group. Despite pervasive deficits in localization precision for visual, auditory, and audiovisual stimuli, audiovisual integration remains intact and optimal in unilateral amblyopia.

Theoretical Aspects of Optimizing the Allocation of Public Financial Resources at Local Level

OpenAIRE

Eugen DOGARIU

2010-01-01

The allocation of financial resources at local, but also at central level, is an issue especially since in times of crisis, finding the optimum way to spend public funds concerns all authorities. This paper aims to identify the ways in which, by leaving from the division of powers based on the allocation of resources and tools available, the local authorities can identify an optimal level of public expenditure so as to achieve a maximum level of using them. Also, the paper seeks to identify t...

Optimization of the fiber laser parameters for local high-temperature impact on metal

Science.gov (United States)

Yatsko, Dmitrii S.; Polonik, Marina V.; Dudko, Olga V.

2016-11-01

This paper presents the local laser heating process of surface layer of the metal sample. The aim is to create the molten pool with the required depth by laser thermal treatment. During the heating the metal temperature at any point of the molten zone should not reach the boiling point of the main material. The laser power, exposure time and the spot size of a laser beam are selected as the variable parameters. The mathematical model for heat transfer in a semi-infinite body, applicable to finite slab, is used for preliminary theoretical estimation of acceptable parameters values of the laser thermal treatment. The optimization problem is solved by using an algorithm based on the scanning method of the search space (the zero-order method of conditional optimization). The calculated values of the parameters (the optimal set of "laser radiation power - exposure time - spot radius") are used to conduct a series of natural experiments to obtain a molten pool with the required depth. A two-stage experiment consists of: a local laser treatment of metal plate (steel) and then the examination of the microsection of the laser irradiated region. According to the experimental results, we can judge the adequacy of the ongoing calculations within the selected models.

Development and Optimization of Polymeric Self-Emulsifying Nanocapsules for Localized Drug Delivery: Design of Experiment Approach

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Jyoti Wadhwa

2014-01-01

Full Text Available The purpose of the present study was to formulate polymeric self-emulsifying curcumin nanocapsules with high encapsulation efficiency, good emulsification ability, and optimal globule size for localized targeting in the colon. Formulations were prepared using modified quasiemulsion solvent diffusion method. Concentration of formulation variables, namely, X1 (oil, X2 (polymeric emulsifier, and X3 (adsorbent, was optimized by design of experiments using Box-Behnken design, for its impact on mean globule size (Y1 and encapsulation efficiency (Y2 of the formulation. Polymeric nanocapsules with an average diameter of 100–180 nm and an encapsulation efficiency of 64.85 ± 0.12% were obtained. In vitro studies revealed that formulations released the drug after 5 h lag time corresponding to the time to reach the colonic region. Pronounced localized action was inferred from the plasma concentration profile (Cmax 200 ng/mL that depicts limited systemic absorption. Roentgenography study confirms the localized presence of carrier (0–2 h in upper GIT; 2–4 h in small intestine; and 4–24 h in the lower intestine. Optimized formulation showed significantly higher cytotoxicity (IC50 value 20.32 μM in HT 29 colonic cancer cell line. The present study demonstrates systematic development of polymeric self-emulsifying nanocapsule formulation of curcumin for localized targeting in colon.

Optimal reducibility of all W states equivalent under stochastic local operations and classical communication

Energy Technology Data Exchange (ETDEWEB)

Rana, Swapan; Parashar, Preeti [Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 BT Road, Kolkata (India)

2011-11-15

We show that all multipartite pure states that are stochastic local operation and classical communication (SLOCC) equivalent to the N-qubit W state can be uniquely determined (among arbitrary states) from their bipartite marginals. We also prove that only (N-1) of the bipartite marginals are sufficient and that this is also the optimal number. Thus, contrary to the Greenberger-Horne-Zeilinger (GHZ) class, W-type states preserve their reducibility under SLOCC. We also study the optimal reducibility of some larger classes of states. The generic Dicke states |GD{sub N}{sup l}> are shown to be optimally determined by their (l+1)-partite marginals. The class of ''G'' states (superposition of W and W) are shown to be optimally determined by just two (N-2)-partite marginals.

One-step electric-field driven methane and formaldehyde synthesis from liquid methanol

Czech Academy of Sciences Publication Activity Database

Cassone, Giuseppe; Pietrucci, F.; Saija, F.; Guyot, Y.; Saitta, A. M.

2017-01-01

Roč. 8, č. 3 (2017), s. 2329-2336 ISSN 2041-6520 Institutional support: RVO:68081707 Keywords : localized wannier functions * molecular -dynamics Subject RIV: CE - Biochemistry OBOR OECD: Biochemistry and molecular biology Impact factor: 8.668, year: 2016

Excitons in insulators

International Nuclear Information System (INIS)

Grasser, R.; Scharmann, A.

1983-01-01

This chapter investigates absorption, reflectivity, and intrinsic luminescence spectra of free and/or self-trapped (localized) excitons in alkali halides and rare gas solids. Introduces the concepts underlying the Wannier-Mott and Frenkel exciton models, two extreme pictures of an exciton in crystalline materials. Discusses the theoretical and experimental background; excitons in alkali halides; and excitons in rare gas solids. Shows that the intrinsic optical behavior of wide gap insulators in the range of the fundamental absorption edge is controlled by modified Wannier-Mott excitons. Finds that while that alkali halides only show free and relaxed molecular-like exciton emission, in rare gas crystals luminescence due to free, single and double centered localized excitons is observed. Indicates that the simultaneous existence of free and self-trapped excitons in these solid requires an energy barrier for self-trapping

A note on eigenfrequency sensitivities and structural eigenfrequency optimization based on local sub-domain frequencies

DEFF Research Database (Denmark)

Pedersen, Pauli; Pedersen, Niels Leergaard

2014-01-01

foundation. A numerical heuristic redesign procedure is proposed and illustrated with examples. For the ideal case, an optimality criterion is fulfilled if the design have the same sub-domain frequency (local Rayleigh quotient). Sensitivity analysis shows an important relation between squared system...... eigenfrequency and squared local sub-domain frequency for a given eigenmode. Higher order eigenfrequenciesmay also be controlled in this manner. The presented examples are based on 2D finite element models with the use of subspace iteration for analysis and a heuristic recursive design procedure based...... on the derived optimality condition. The design that maximize a frequency depend on the total amount of available material and on a necessary interpolation as illustrated by different design cases.In this note we have assumed a linear and conservative eigenvalue problem without multiple eigenvalues. The presence...

OPTIMIZATION OF THE POSITION OF THE LOCAL DISTRIBUTION CENTRE OF THE REGIONAL POST LOGISTICS NETWORK

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Paweł DROŹDZIEL

2017-09-01

Full Text Available The phenomenon of the present postal services is the fact that, customers expect the lowest price while maintaining the availability, security and on time delivery of mail items. We can find that, the costs associated with transport of the postal substrate is one of the most important factors affecting the total cost of the postal services. These transport costs depend on various factors such as the investment in vehicles purchase, operational costs of the postal vehicles (costs of maintenance, repairs, fuel costs of the vehicle, etc. labour costs of the drivers and so on. For this reason, it is important to find such an operational - organizational solutions that can reduce the costs associated with the transportation of postal shipments, resulting in reducing the total cost of postal services. One option to do this is to minimize the length of postal transportation routes. This article presents the approach based on the application of graph theory to optimize existing connections of postal logistics network. Published results is oriented to revaluate existing position of local centre and find a location for the new local distribution centre potentially. New location of local distribution centre can to optimize (minimize the total transport costs of the local postal transportation network in area of the Lublin Province.

PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks

DEFF Research Database (Denmark)

Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk

2013-01-01

This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii) r...

Matrix Completion Optimization for Localization in Wireless Sensor Networks for Intelligent IoT

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Thu L. N. Nguyen

2016-05-01

Full Text Available Localization in wireless sensor networks (WSNs is one of the primary functions of the intelligent Internet of Things (IoT that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach.

Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

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Petr Maca

2014-01-01

Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

Science.gov (United States)

Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.

2018-05-01

The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).

VECTOR THEORY AND OPTIMAL CHOICE OF ANTIMICROBIAL DRUG FOR LOCAL WOUND TREATMENT

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Boyko N. N

2016-12-01

Full Text Available Introduction. One of important problems in the field of medicine and pharmacy is an optimal choice among several alternatives. For example, the choice of drugs for treatment among several analogs, selection of excipients among analogs for development of pharmaceutical forms with optimal pharmacological, technological and economical parameters, etc.The aim of the work is to show the possibility of vector theory use for optimal choice of antimicrobial drugs for local wound treatment among analogs taking into account several criteria at the same time. Materials and methods. For our investigation we have chosen ten drugs with antimicrobial properties for local wound treatment in different pharmaceutical forms (ointment, liniment, water and glycerin solution, tincture. We have determined antibacterial activity of drugs by agar well diffusion method on six test-stain microorganisms: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Proteus vulgaris ATCC 4636, Bacillus subtilis ATCC 6633, and Candida albicans ATCC 885-653. Well diameter was 10 mm, the volume of drug in the well was 0.27±0.02 ml, microbial burden of agar upper layer was 107 CFU/ml, and total layer height in Petri dish was 4.0±0.5 mm. In order to integrate various qualitative and quantitative parameters into one index (vector object in multidimensional factors’ space we modify these parameters to non-dimensional normalized values. For this purpose we use a desirability theory. We have chosen the following criteria for optimal choice of the drug: antimicrobial activity (integrated index of drug’s antimicrobial activity, drug’s price, pharmacological and technological index, spectrum of drug’s action on test strains of microorganisms studied. Results and their discussions. Using vector and desirability theory, we have obtained the following range of drugs in decreasing order: Laevomecol ointment, Ioddicerinum, Tincture of Sophora

Online Robot Dead Reckoning Localization Using Maximum Relative Entropy Optimization With Model Constraints

International Nuclear Information System (INIS)

Urniezius, Renaldas

2011-01-01

The principle of Maximum relative Entropy optimization was analyzed for dead reckoning localization of a rigid body when observation data of two attached accelerometers was collected. Model constraints were derived from the relationships between the sensors. The experiment's results confirmed that accelerometers each axis' noise can be successfully filtered utilizing dependency between channels and the dependency between time series data. Dependency between channels was used for a priori calculation, and a posteriori distribution was derived utilizing dependency between time series data. There was revisited data of autocalibration experiment by removing the initial assumption that instantaneous rotation axis of a rigid body was known. Performance results confirmed that such an approach could be used for online dead reckoning localization.

Electronic Structure and Transport in Solids from First Principles

Science.gov (United States)

Mustafa, Jamal Ibrahim

The focus of this dissertation is the determination of the electronic structure and trans- port properties of solids. We first review some of the theory and computational methodology used in the calculation of electronic structure and materials properties. Throughout the dissertation, we make extensive use of state-of-the-art software packages that implement density functional theory, density functional perturbation theory, and the GW approximation, in addition to specialized methods for interpolating matrix elements for extremely accurate results. The first application of the computational framework introduced is the determination of band offsets in semiconductor heterojunctions using a theory of quantum dipoles at the interface. This method is applied to the case of heterojunction formed between a new metastable phase of silicon, with a rhombohedral structure, and cubic silicon. Next, we introduce a novel method for the construction of localized Wannier functions, which we have named the optimized projection functions method (OPFM). We illustrate the method on a variety of systems and find that it can reliably construct localized Wannier functions with minimal user intervention. We further develop the OPFM to investigate a class of materials called topological insulators, which are insulating in the bulk but have conductive surface states. These properties are a result of a nontrivial topology in their band structure, which has interesting effects on the character of the Wannier functions. In the last sections of the main text, the noble metals are studied in great detail, including their electronic properties and carrier dynamics. In particular, we investigate, the Fermi surface properties of the noble metals, specifically electron-phonon scattering lifetimes, and subsequently the transport properties determined by carriers on the Fermi surface. To achieve this, a novel sampling technique is developed, with wide applicability to transport calculations

Optimized curve design for image analysis using localized geodesic distance transformations

Science.gov (United States)

Braithwaite, Billy; Niska, Harri; Pöllänen, Irene; Ikonen, Tiia; Haataja, Keijo; Toivanen, Pekka; Tolonen, Teemu

2015-03-01

We consider geodesic distance transformations for digital images. Given a M × N digital image, a distance image is produced by evaluating local pixel distances. Distance Transformation on Curved Space (DTOCS) evaluates shortest geodesics of a given pixel neighborhood by evaluating the height displacements between pixels. In this paper, we propose an optimization framework for geodesic distance transformations in a pattern recognition scheme, yielding more accurate machine learning based image analysis, exemplifying initial experiments using complex breast cancer images. Furthermore, we will outline future research work, which will complete the research work done for this paper.

The Optimization of the Local Public Policies’ Development Process Through Modeling And Simulation

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Minodora URSĂCESCU

2012-06-01

Full Text Available The local public policies development in Romania represents an empirically realized measure, the strategic management practices in this domain not being based on a scientific instrument capable to anticipate and evaluate the results of implementing a local public policy in a logic of needs-policies-effects type. Beginning from this motivation, the purpose of the paper resides in the reconceptualization of the public policies process on functioning principles of the dynamic systems with inverse connection, by means of mathematical modeling and techniques simulation. Therefore, the research is oriented in the direction of developing an optimization method for the local public policies development process, using as instruments the mathematical modeling and the techniques simulation. The research’s main results are on the one side constituted by generating a new process concept of the local public policies, and on the other side by proposing the conceptual model of a complex software product which will permit the parameterized modeling in a virtual environment of these policies development process. The informatic product’s finality resides in modeling and simulating each local public policy type, taking into account the respective policy’s characteristics, but also the value of their appliance environment parameters in a certain moment.

A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

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Zhigang Lian

2010-01-01

Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

The relationship between the bladder volume and optimal treatment planning in definitive radiotherapy for localized prostate cancer

International Nuclear Information System (INIS)

Nakamura, Naoki; Sekiguchi, Kenji; Akahane, Keiko; Shikama, Naoto; Takahashi, Osamu; Hama, Yukihiro; Nakagawa, Keiichi

2012-01-01

Background and purpose: There is no current consensus regarding the optimal bladder volumes in definitive radiotherapy for localized prostate cancer. The aim of this study was to clarify the relationship between the bladder volume and optimal treatment planning in radiotherapy for localized prostate cancer. Material and methods: Two hundred and forty-three patients underwent definitive radiotherapy with helical tomotherapy for intermediate- and high-risk localized prostate cancer. The prescribed dose defined as 95 % of the planning target volume (PTV) receiving 100 % of the prescription dose was 76 Gy in 38 fractions. The clinical target volume (CTV) was defined as the prostate with a 5-mm margin and 2 cm of the proximal seminal vesicle. The PTV was defined as the CTV with a 5-mm margin. Treatment plans were optimized to satisfy the dose constraints defined by in-house protocols for PTV and organs at risk (rectum wall, bladder wall, sigmoid colon and small intestine). If all dose constraints were satisfied, the plan was defined as an optimal plan (OP). Results: An OP was achieved with 203 patients (84%). Mean bladder volume (± 1 SD) was 266 ml (± 130 ml) among those with an OP and 214 ml (±130 ml) among those without an OP (p = 0.02). Logistic regression analysis also showed that bladder volumes below 150 ml decreased the possibility of achieving an OP. However, the percentage of patients with an OP showed a plateau effect at bladder volumes above 150 ml. Conclusions. Bladder volume is a significant factor affecting OP rates. However, our results suggest that bladder volumes exceeding 150 ml may not help meet planning dose constraints

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

Science.gov (United States)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing

2014-12-01

Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

DESTINATION MARKETING STRATEGY IN BALI THROUGH OPTIMIZING THE POTENTIAL OF LOCAL PRODUCTS

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I Gusti Ayu Oka Suryawardani

2014-03-01

Full Text Available This study was designed to study destination marketing strategy in Bali through optimizing the potential of local products. Seventy nine of hotel managers were interviewed based on cluster sampling method to gain their point of view. The results show that destination must build their images around unique attributes that provide them sustainable competitive advantage including its attraction which should be designed to meet the needs of the target market and should be served by local products. The results also show that hotel managers thought that foreign tourists always preferred imported products, meanwhile previous statistical results indicate that foreign tourists significantly look for local products. There is a need to encourage hotel managers to change their perception and attitude about local and imported products. In fact, hoteliers expressed willingness to use local products as long as these meet the quality standard. As tourism involves four types of activities, namely something to see, something to do, something to buy, something to learn, destination product development could be focused in the above activities through offering foreign tourist, such as to stay in hotels, homestays or villas owned by Balinese; to eat in restaurants owned by Balinese by choosing the authentic local foods that are using local meat, seafood and vegetables, exotic local fruits and beverages; and to buy products that are produced by the Balinese. By promoting vacation on the real Balinese atmosphere such as stay in accommodations owned by the Balinese supported by the authenticity of local Balinese foods, fruits and beverages, these will strengthen the local economy, so the benefit of tourism development can be more beneficial to the local Balinese. The results suggests that destination management related to improvement of service and hospitality are really important through improvement of human resource by giving training to their employees, educate

A computational framework for the optimal design of morphing processes in locally activated smart material structures

International Nuclear Information System (INIS)

Wang, Shuang; Brigham, John C

2012-01-01

A proof-of-concept study is presented for a strategy to obtain maximally efficient and accurate morphing structures composed of active materials such as shape memory polymers (SMP) through synchronization of adaptable and localized activation and actuation. The work focuses on structures or structural components entirely composed of thermo-responsive SMP, and particularly utilizes the ability of such materials to display controllable variable stiffness. The study presents and employs a computational inverse mechanics approach that combines a computational representation of the SMP thermo-mechanical behavior with a nonlinear optimization algorithm to determine location, magnitude and sequencing of the activation and actuation to obtain a desired shape change subject to design objectives such as prevention of damage. Two numerical examples are presented in which the synchronization of the activation and actuation and the location of activation excitation were optimized with respect to the combined thermal and mechanical energy for design concepts in morphing skeletal structural components. In all cases the concept of localized activation along with the optimal design strategy were able to produce far more energy efficient morphing structures and more accurately reach the desired shape change in comparison to traditional methods that require complete structural activation prior to actuation. (paper)

Contribution to the development of a food guide in Benin: linear programming for the optimization of local diets.

Science.gov (United States)

Levesque, Sarah; Delisle, Hélène; Agueh, Victoire

2015-03-01

Food guides are important tools for nutrition education. While developing a food guide in Benin, the objective was to determine the daily number of servings per food group and the portion sizes of common foods to be recommended. Linear programming (LP) was used to determine, for each predefined food group, the optimal number and size of servings of commonly consumed foods. Two types of constraints were introduced into the LP models: (i) WHO/FAO Recommended Nutrient Intakes and dietary guidelines for the prevention of chronic diseases; and (ii) dietary patterns based on local food consumption data recently collected in southern Benin in 541 adults. Dietary intakes of the upper tertile of participants for diet quality based on prevention and micronutrient adequacy scores were used in the LP algorithms. Southern area of the Republic of Benin. Local key-players in nutrition (n 30) from the government, academic institutions, international organizations and civil society were partners in the development of the food guide directed at the population. The number of servings per food group and the portion size for eight age-sex groups were determined. For four limiting micronutrients (Fe, Ca, folate and Zn), local diets could be optimized to meet only 70 % of the Recommended Nutrient Intakes, not 100 %. It was possible to determine the daily number of servings and the portion sizes of common foods that can be recommended in Benin with the help of LP to optimize local diets, although Recommended Nutrient Intakes were not fully met for a few critical micronutrients.

OPTIMIZING LOCAL BUDGET BALANCING IN ROMANIA

Directory of Open Access Journals (Sweden)

Gyorgy Adina Crsitina

2011-07-01

Full Text Available The importance of the local public finance is growing in accordance with the increasing proportion of the decentralization process. The mechanism of resource allocation, and especially the allocation criteria used, constitutes subjects of debate. Our objective pursued is to assess whether the avoidance of the first step for balancing the allocation of funds can provide enhanced fairness in balancing the local budgets across the country. Local budgets in Romania receive significant resources from the state budget in the form of amounts and quotas distributed from certain taxes, which are revenues for the state budget. Some of these amounts are designed to balance the local budgets. The distribution of funds from the state budget to the local budgets requires two steps. Firstly, the amounts are divided by county, secondly, these amounts are directed within the county especially towards localities which have a lower financial standing. Given the significant disparities between counties, we believe that this mechanism does not ensure fairness in the allocation because the funds distributed according to the first step may not use fair criteria to meet the requirements for balanced local budgets. Therefore, we intend to simulate a balanced allocation of national funds for eliminating the first step that produces the most significant inequities. Direct application of the second step of allocation, with its two phases, will provide more funds serving those local administrative units for the income tax per capita is lower than the national average. Comparing the values allocated for the year 2011 with those obtained in the simulation we will examine changes that occur after the application of this method which seems to be more equitable and appropriate. This work was supported by CNCSISUEFISCSU, project number PNII-IDEI 1780/2008

Strategies for discovery and optimization of thermoelectric materials: Role of real objects and local fields

Science.gov (United States)

Zhu, Hao; Xiao, Chong

2018-06-01

Thermoelectric materials provide a renewable and eco-friendly solution to mitigate energy shortages and to reduce environmental pollution via direct heat-to-electricity conversion. Discovery of the novel thermoelectric materials and optimization of the state-of-the-art material systems lie at the core of the thermoelectric society, the basic concept behind these being comprehension and manipulation of the physical principles and transport properties regarding thermoelectric materials. In this mini-review, certain examples for designing high-performance bulk thermoelectric materials are presented from the perspectives of both real objects and local fields. The highlights of this topic involve the Rashba effect, Peierls distortion, local magnetic field, and local stress field, which cover several aspects in the field of thermoelectric research. We conclude with an overview of future developments in thermoelectricity.

A comparison of optimization algorithms for localized in vivo B0 shimming.

Science.gov (United States)

Nassirpour, Sahar; Chang, Paul; Fillmer, Ariane; Henning, Anke

2018-02-01

To compare several different optimization algorithms currently used for localized in vivo B 0 shimming, and to introduce a novel, fast, and robust constrained regularized algorithm (ConsTru) for this purpose. Ten different optimization algorithms (including samples from both generic and dedicated least-squares solvers, and a novel constrained regularized inversion method) were implemented and compared for shimming in five different shimming volumes on 66 in vivo data sets from both 7 T and 9.4 T. The best algorithm was chosen to perform single-voxel spectroscopy at 9.4 T in the frontal cortex of the brain on 10 volunteers. The results of the performance tests proved that the shimming algorithm is prone to unstable solutions if it depends on the value of a starting point, and is not regularized to handle ill-conditioned problems. The ConsTru algorithm proved to be the most robust, fast, and efficient algorithm among all of the chosen algorithms. It enabled acquisition of spectra of reproducible high quality in the frontal cortex at 9.4 T. For localized in vivo B 0 shimming, the use of a dedicated linear least-squares solver instead of a generic nonlinear one is highly recommended. Among all of the linear solvers, the constrained regularized method (ConsTru) was found to be both fast and most robust. Magn Reson Med 79:1145-1156, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

NSGA-II Algorithm with a Local Search Strategy for Multiobjective Optimal Design of Dry-Type Air-Core Reactor

Directory of Open Access Journals (Sweden)

Chengfen Zhang

2015-01-01

Full Text Available Dry-type air-core reactor is now widely applied in electrical power distribution systems, for which the optimization design is a crucial issue. In the optimization design problem of dry-type air-core reactor, the objectives of minimizing the production cost and minimizing the operation cost are both important. In this paper, a multiobjective optimal model is established considering simultaneously the two objectives of minimizing the production cost and minimizing the operation cost. To solve the multi-objective optimization problem, a memetic evolutionary algorithm is proposed, which combines elitist nondominated sorting genetic algorithm version II (NSGA-II with a local search strategy based on the covariance matrix adaptation evolution strategy (CMA-ES. NSGA-II can provide decision maker with flexible choices among the different trade-off solutions, while the local-search strategy, which is applied to nondominated individuals randomly selected from the current population in a given generation and quantity, can accelerate the convergence speed. Furthermore, another modification is that an external archive is set in the proposed algorithm for increasing the evolutionary efficiency. The proposed algorithm is tested on a dry-type air-core reactor made of rectangular cross-section litz-wire. Simulation results show that the proposed algorithm has high efficiency and it converges to a better Pareto front.

Wannier-Bloch approach to localization in high-harmonics generation in solids

Czech Academy of Sciences Publication Activity Database

Osika, E.N.; Chacon, A.; Ortmann, L.; Suarez, N.; Perez-Hernandez, J.A.; Szafran, B.; Ciappina, Marcelo F.; Sols, F.; Landsman, A.S.; Lewenstein, M.

2017-01-01

Roč. 7, č. 2 (2017), 1-14, č. článku 021017. ISSN 2160-3308 R&D Projects: GA MŠk EF15_008/0000162; GA MŠk LQ1606 EU Projects: European Commission(XE) 654148 - LASERLAB-EUROPE Grant - others:ELI Beamlines(XE) CZ.02.1.01/0.0/0.0/15_008/0000162 Institutional support: RVO:68378271 Keywords : field * photoemission * spectroscopy * ionization Subject RIV: BL - Plasma and Gas Discharge Physics OBOR OECD: Fluids and plasma physics (including surface physics) Impact factor: 12.789, year: 2016

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

Directory of Open Access Journals (Sweden)

Mauro Castelli

2015-01-01

Full Text Available Energy consumption forecasting (ECF is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

Scalable unit commitment by memory-bounded ant colony optimization with A{sup *} local search

Energy Technology Data Exchange (ETDEWEB)

Saber, Ahmed Yousuf; Alshareef, Abdulaziz Mohammed [Department of Electrical and Computer Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589 (Saudi Arabia)

2008-07-15

Ant colony optimization (ACO) is successfully applied in optimization problems. Performance of the basic ACO for small problems with moderate dimension and searching space is satisfactory. As the searching space grows exponentially in the large-scale unit commitment problem, the basic ACO is not applicable for the vast size of pheromone matrix of ACO in practical time and physical computer-memory limit. However, memory-bounded methods prune the least-promising nodes to fit the system in computer memory. Therefore, the authors propose memory-bounded ant colony optimization (MACO) in this paper for the scalable (no restriction for system size) unit commitment problem. This MACO intelligently solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A{sup *} heuristic is introduced to increase local searching ability and probabilistic nearest neighbor method is applied to estimate pheromone intensity for the forgotten value. Finally, the benchmark data sets and existing methods are used to show the effectiveness of the proposed method. (author)

Multiple-copy state discrimination: Thinking globally, acting locally

International Nuclear Information System (INIS)

Higgins, B. L.; Pryde, G. J.; Wiseman, H. M.; Doherty, A. C.; Bartlett, S. D.

2011-01-01

We theoretically investigate schemes to discriminate between two nonorthogonal quantum states given multiple copies. We consider a number of state discrimination schemes as applied to nonorthogonal, mixed states of a qubit. In particular, we examine the difference that local and global optimization of local measurements makes to the probability of obtaining an erroneous result, in the regime of finite numbers of copies N, and in the asymptotic limit as N→∞. Five schemes are considered: optimal collective measurements over all copies, locally optimal local measurements in a fixed single-qubit measurement basis, globally optimal fixed local measurements, locally optimal adaptive local measurements, and globally optimal adaptive local measurements. Here an adaptive measurement is one in which the measurement basis can depend on prior measurement results. For each of these measurement schemes we determine the probability of error (for finite N) and the scaling of this error in the asymptotic limit. In the asymptotic limit, it is known analytically (and we verify numerically) that adaptive schemes have no advantage over the optimal fixed local scheme. Here we show moreover that, in this limit, the most naive scheme (locally optimal fixed local measurements) is as good as any noncollective scheme except for states with less than 2% mixture. For finite N, however, the most sophisticated local scheme (globally optimal adaptive local measurements) is better than any other noncollective scheme for any degree of mixture.

Automatic multi-cycle reload design of pressurized water reactor using particle swarm optimization algorithm and local search

International Nuclear Information System (INIS)

Lin, Chaung; Hung, Shao-Chun

2013-01-01

Highlights: • An automatic multi-cycle core reload design tool, which searches the fresh fuel assembly composition, is developed. • The search method adopts particle swarm optimization and local search. • The design objectives are to achieve required cycle energy, minimum fuel cost, and the satisfactory constraints. • The constraints include the hot zero power moderator temperature coefficient and the hot channel factor. - Abstract: An automatic multi-cycle core reload design tool, which searches the fresh fuel assembly composition, is developed using particle swarm optimization and local search. The local search uses heuristic rules to change the current search result a little so that the result can be improved. The composition of the fresh fuel assemblies should provide the required cycle energy and satisfy the constraints, such as the hot zero power moderator temperature coefficient and the hot channel factor. Instead of designing loading pattern for each FA composition during search process, two fixed loading patterns are used to calculate the core status and the better fitness function value is used in the search process. The fitness function contains terms which reflect the design objectives such as cycle energy, constraints, and fuel cost. The results show that the developed tool can achieve the desire objective

A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

Directory of Open Access Journals (Sweden)

J.-M. Beckers

2014-10-01

Full Text Available We present a method in which the optimal interpolation of multi-scale processes can be expanded into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the different mathematical equivalent formulations, we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well-controlled test case. The clear guidelines deduced from this experiment are then applied to a real situation in which we combine large-scale analysis of hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI satellite images using data interpolating empirical orthogonal functions (DINEOF with a local optimal interpolation using a Gaussian covariance. It is shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data.

Optimization modeling of U.S. renewable electricity deployment using local input variables

Science.gov (United States)

Bernstein, Adam

For the past five years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four key trends currently developing: (i) lower natural gas prices, (ii) slower growth in electricity demand, (iii) challenges of system balancing intermittent RE within the U.S. transmission regions, and (iv) fewer economical sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes' lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables on marginal RE capacity additions. A state-by-state analysis is necessary to study the U.S. electric sector and to generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2031 significantly lower than thoseimplied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Additionally, the majority of states do not achieve their RPS targets in our forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment, forecasting, and ongoing revision of variables as parameter values evolve through time. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE parameters. Further, our results can be tested against the empirical outcomes that will be observed over the coming years, and the forecast deviation from the actuals can be attributed to discrete parameter

Correlation induced localization of lattice trapped bosons coupled to a Bose–Einstein condensate

Science.gov (United States)

Keiler, Kevin; Krönke, Sven; Schmelcher, Peter

2018-03-01

We investigate the ground state properties of a lattice trapped bosonic system coupled to a Lieb–Liniger type gas. Our main goal is the description and in depth exploration and analysis of the two-species many-body quantum system including all relevant correlations beyond the standard mean-field approach. To achieve this, we use the multi-configuration time-dependent Hartree method for mixtures (ML-MCTDHX). Increasing the lattice depth and the interspecies interaction strength, the wave function undergoes a transition from an uncorrelated to a highly correlated state, which manifests itself in the localization of the lattice atoms in the latter regime. For small interspecies couplings, we identify the process responsible for this cross-over in a single-particle-like picture. Moreover, we give a full characterization of the wave function’s structure in both regimes, using Bloch and Wannier states of the lowest band, and we find an order parameter, which can be exploited as a corresponding experimental signature. To deepen the understanding, we use an effective Hamiltonian approach, which introduces an induced interaction and is valid for small interspecies interaction. We finally compare the ansatz of the effective Hamiltonian with the results of the ML-MCTDHX simulations.

Towards Optimal Event Detection and Localization in Acyclic Flow Networks

KAUST Repository

Agumbe Suresh, Mahima

2012-01-03

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.

Intranet and village community: optimization of public service based on electronic government at the local level

Science.gov (United States)

Pradana, G. W.; Fanida, E. H.; Niswah, F.

2018-01-01

The demand for good governance is directed towards the realization of efficiency, effectiveness, and clean government. The move is demonstrated through national and regional levels to develop and implement electronic government concepts. Through the development of electronic government is done structuring management systems and work processes in the government environment by optimizing the utilization of information technology. One of the real forms of electronic government (e-Gov) implementation at the local level is the Intranet Sub-District program in Sukodono Sub-District, Sidoarjo. Intranet Sub-District is an innovation whose purpose is to realize the availability of information on the utilization of management, distribution, and storage of official scripts, and also the optimal delivery of information and communication in the implementation of guidance and supervision of local administration. The type of this paper is descriptive with a qualitative approach and focus on the implementation of the Intranet District Program in Sukodono District, Sidoarjo. The findings of the study are the limited number of human resources who have mastered ICT, the uneven network, the adequacy of institutional needs and the existence of budget support from the authorized institution and the information system has not accommodated all the service needs.

A localized navigation algorithm for Radiation Evasion for nuclear facilities. Part II: Optimizing the “Nearest Exit” Criterion

Energy Technology Data Exchange (ETDEWEB)

Khasawneh, Mohammed A., E-mail: mkha@ieee.org [Department of Electrical Engineering, Jordan University of Science and Technology (Jordan); Al-Shboul, Zeina Aman M., E-mail: xeinaaman@gmail.com [Department of Electrical Engineering, Jordan University of Science and Technology (Jordan); Jaradat, Mohammad A., E-mail: majaradat@just.edu.jo [Department of Mechanical Engineering, Jordan University of Science and Technology (Jordan); Malkawi, Mohammad I., E-mail: mmalkawi@aimws.com [College of Engineering, Jadara University, Irbid 221 10 (Jordan)

2013-06-15

Highlights: ► A new navigation algorithm for Radiation Evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this extension from part I (Khasawneh et al., in press), we modify the navigation algorithm which was presented with the objective of optimizing the “Radiation Evasion” Criterion so that navigation would optimize the criterion of “Nearest Exit”. Under this modification, algorithm would yield navigation paths that would guide occupational workers towards Nearest Exit points. Again, under this optimization criterion, algorithm leverages the use of localized information acquired through a well designed and distributed wireless sensor network, as it averts the need for any long-haul communication links or centralized decision and monitoring facility thereby achieving a more reliable performance under dynamic environments. As was done in part I, the proposed algorithm under the “Nearest Exit” Criterion is designed to leverage nearest neighbor information coming in through the sensory network overhead, in computing successful navigational paths from one point to another. For comparison purposes, the proposed algorithm is tested under the two optimization criteria: “Radiation Evasion” and “Nearest Exit”, for different numbers of step look-ahead. We verify the performance of the algorithm by means of simulations, whereby navigational paths are calculated for different radiation fields. We, via simulations, also, verify the performance of the algorithm in comparison with a well-known global navigation algorithm upon which we draw our conclusions.

A localized navigation algorithm for Radiation Evasion for nuclear facilities. Part II: Optimizing the “Nearest Exit” Criterion

International Nuclear Information System (INIS)

Khasawneh, Mohammed A.; Al-Shboul, Zeina Aman M.; Jaradat, Mohammad A.; Malkawi, Mohammad I.

2013-01-01

Highlights: ► A new navigation algorithm for Radiation Evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this extension from part I (Khasawneh et al., in press), we modify the navigation algorithm which was presented with the objective of optimizing the “Radiation Evasion” Criterion so that navigation would optimize the criterion of “Nearest Exit”. Under this modification, algorithm would yield navigation paths that would guide occupational workers towards Nearest Exit points. Again, under this optimization criterion, algorithm leverages the use of localized information acquired through a well designed and distributed wireless sensor network, as it averts the need for any long-haul communication links or centralized decision and monitoring facility thereby achieving a more reliable performance under dynamic environments. As was done in part I, the proposed algorithm under the “Nearest Exit” Criterion is designed to leverage nearest neighbor information coming in through the sensory network overhead, in computing successful navigational paths from one point to another. For comparison purposes, the proposed algorithm is tested under the two optimization criteria: “Radiation Evasion” and “Nearest Exit”, for different numbers of step look-ahead. We verify the performance of the algorithm by means of simulations, whereby navigational paths are calculated for different radiation fields. We, via simulations, also, verify the performance of the algorithm in comparison with a well-known global navigation algorithm upon which we draw our conclusions

Mathematical programming solver based on local search

CERN Document Server

Gardi, Frédéric; Darlay, Julien; Estellon, Bertrand; Megel, Romain

2014-01-01

This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern about industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces ex...

SU-F-J-11: Radiobiologically Optimized Patient Localization During Prostate External Beam Localization

Energy Technology Data Exchange (ETDEWEB)

Huang, Y; Gardner, S; Liu, C; Zhao, B; Wen, N; Brown, S; Chetty, I [Henry Ford Health System, Detroit, MI (United States)

2016-06-15

Purpose: To present a novel positioning strategy which optimizes radiation delivery with radiobiological response knowledge, and to evaluate its application during prostate external beam radiotherapy. Methods: Ten patients with low or intermediate risk prostate cancer were evaluated retrospectively in this IRB-approved study. For each patient, a VMAT plan was generated on the planning CT (PCT) to deliver 78 Gy in 39 fractions with PTV = prostate + 7 mm margin, except for 5mm in the posterior direction. Five representative pretreatment CBCT images were selected for each patient, and prostate, rectum, and bladder were delineated on all CBCT images. Each CBCT was auto-registered to the corresponding PCT. Starting from this auto-matched position (AM-position), a search for optimal treatment position was performed utilizing a score function based on radiobiological and dosimetric indices (D98-DTV, NTCP-rectum, and NTCP-bladder) for the daily target volume (DTV), rectum, and bladder. DTV was defined as prostate + 4 mm margin to account for intra-fraction motion as well as contouring variability on CBCT. We termed the optimal treatment position the radiobiologically optimized couch shift position (ROCS-position). Results: The indices, averaged over the 10 patients’ treatment plans, were (mean±SD): 77.7±0.2 Gy (D98-PTV), 12.3±2.7% (NTCP-rectum), and 53.2±11.2% (NTCP-bladder). The corresponding values calculated on all 50 CBCT images at the AM-positions were 72.9±11.3 Gy (D98-DTV), 15.8±6.4% (NTCP-rectum), and 53.0±21.1% (NTCP-bladder), respectively. In comparison, calculated on CBCT at the ROCS-positions, the indices were 77.0±2.1 Gy (D98-DTV), 12.1±5.7% (NTCP-rectum), and 60.7±16.4% (NTCP-bladder). Compared to autoregistration, ROCS-optimization recovered dose coverage to target volume and lowered the risk to rectum. Moreover, NTCPrectum for one patient remained high after ROCS-optimization and therefore could potentially benefit from adaptive planning

Automatic fuel lattice design in a boiling water reactor using a particle swarm optimization algorithm and local search

International Nuclear Information System (INIS)

Lin Chaung; Lin, Tung-Hsien

2012-01-01

Highlights: ► The automatic procedure was developed to design the radial enrichment and gadolinia (Gd) distribution of fuel lattice. ► The method is based on a particle swarm optimization algorithm and local search. ► The design goal were to achieve the minimum local peaking factor. ► The number of fuel pins with Gd and Gd concentration are fixed to reduce search complexity. ► In this study, three axial sections are design and lattice performance is calculated using CASMO-4. - Abstract: The axial section of fuel assembly in a boiling water reactor (BWR) consists of five or six different distributions; this requires a radial lattice design. In this study, an automatic procedure based on a particle swarm optimization (PSO) algorithm and local search was developed to design the radial enrichment and gadolinia (Gd) distribution of the fuel lattice. The design goals were to achieve the minimum local peaking factor (LPF), and to come as close as possible to the specified target average enrichment and target infinite multiplication factor (k ∞ ), in which the number of fuel pins with Gd and Gd concentration are fixed. In this study, three axial sections are designed, and lattice performance is calculated using CASMO-4. Finally, the neutron cross section library of the designed lattice is established by CMSLINK; the core status during depletion, such as thermal limits, cold shutdown margin and cycle length, are then calculated using SIMULATE-3 in order to confirm that the lattice design satisfies the design requirements.

Linear-scaling explicitly correlated treatment of solids: Periodic local MP2-F12 method

Energy Technology Data Exchange (ETDEWEB)

Usvyat, Denis, E-mail: denis.usvyat@chemie.uni-regensburg.de [Institute of Physical and Theoretical Chemistry, University of Regensburg, Universitätsstraße 31, D-93040 Regensburg (Germany)

2013-11-21

Theory and implementation of the periodic local MP2-F12 method in the 3*A fixed-amplitude ansatz is presented. The method is formulated in the direct space, employing local representation for the occupied, virtual, and auxiliary orbitals in the form of Wannier functions (WFs), projected atomic orbitals (PAOs), and atom-centered Gaussian-type orbitals, respectively. Local approximations are introduced, restricting the list of the explicitly correlated pairs, as well as occupied, virtual, and auxiliary spaces in the strong orthogonality projector to the pair-specific domains on the basis of spatial proximity of respective orbitals. The 4-index two-electron integrals appearing in the formalism are approximated via the direct-space density fitting technique. In this procedure, the fitting orbital spaces are also restricted to local fit-domains surrounding the fitted densities. The formulation of the method and its implementation exploits the translational symmetry and the site-group symmetries of the WFs. Test calculations are performed on LiH crystal. The results show that the periodic LMP2-F12 method substantially accelerates basis set convergence of the total correlation energy, and even more so the correlation energy differences. The resulting energies are quite insensitive to the resolution-of-the-identity domain sizes and the quality of the auxiliary basis sets. The convergence with the orbital domain size is somewhat slower, but still acceptable. Moreover, inclusion of slightly more diffuse functions, than those usually used in the periodic calculations, improves the convergence of the LMP2-F12 correlation energy with respect to both the size of the PAO-domains and the quality of the orbital basis set. At the same time, the essentially diffuse atomic orbitals from standard molecular basis sets, commonly utilized in molecular MP2-F12 calculations, but problematic in the periodic context, are not necessary for LMP2-F12 treatment of crystals.

Optimal classical-communication-assisted local model of n-qubit Greenberger-Horne-Zeilinger correlations

International Nuclear Information System (INIS)

Tessier, Tracey E.; Caves, Carlton M.; Deutsch, Ivan H.; Eastin, Bryan; Bacon, Dave

2005-01-01

We present a model, motivated by the criterion of reality put forward by Einstein, Podolsky, and Rosen and supplemented by classical communication, which correctly reproduces the quantum-mechanical predictions for measurements of all products of Pauli operators on an n-qubit GHZ state (or 'cat state'). The n-2 bits employed by our model are shown to be optimal for the allowed set of measurements, demonstrating that the required communication overhead scales linearly with n. We formulate a connection between the generation of the local values utilized by our model and the stabilizer formalism, which leads us to conjecture that a generalization of this method will shed light on the content of the Gottesman-Knill theorem

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ying Zhang

2016-02-01

Full Text Available Due to their special environment, Underwater Wireless Sensor Networks (UWSNs are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms.

Science.gov (United States)

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-02-06

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.

Including screening in van der Waals corrected density functional theory calculations: The case of atoms and small molecules physisorbed on graphene

Energy Technology Data Exchange (ETDEWEB)

Silvestrelli, Pier Luigi; Ambrosetti, Alberto [Dipartimento di Fisica e Astronomia, Università di Padova, via Marzolo 8, I–35131 Padova, Italy and DEMOCRITOS National Simulation Center of the Italian Istituto Officina dei Materiali (IOM) of the Italian National Research Council (CNR), Trieste (Italy)

2014-03-28

The Density Functional Theory (DFT)/van der Waals-Quantum Harmonic Oscillator-Wannier function (vdW-QHO-WF) method, recently developed to include the vdW interactions in approximated DFT by combining the quantum harmonic oscillator model with the maximally localized Wannier function technique, is applied to the cases of atoms and small molecules (X=Ar, CO, H{sub 2}, H{sub 2}O) weakly interacting with benzene and with the ideal planar graphene surface. Comparison is also presented with the results obtained by other DFT vdW-corrected schemes, including PBE+D, vdW-DF, vdW-DF2, rVV10, and by the simpler Local Density Approximation (LDA) and semilocal generalized gradient approximation approaches. While for the X-benzene systems all the considered vdW-corrected schemes perform reasonably well, it turns out that an accurate description of the X-graphene interaction requires a proper treatment of many-body contributions and of short-range screening effects, as demonstrated by adopting an improved version of the DFT/vdW-QHO-WF method. We also comment on the widespread attitude of relying on LDA to get a rough description of weakly interacting systems.

Subcontract Report: Diffusion Mechanisms and Bond Dynamics in Solid Electrolyte Ion-Conductors

Energy Technology Data Exchange (ETDEWEB)

Zevgolis, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hall, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Alvez, T. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mehmedovic, Z. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Shea, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Varley, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Wood, B. C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Adelstein, N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-10-03

We employ first-principles molecular dynamics simulations and Maximally Localized Wannier Function (MLWF) analysis to explore how halide substitution and nano-phase microstructures affect diffusivity, through the activation energy barrier - E_a and D₀, in the solid electrolyte Li₃InBr_6-xCl_x. We find that nano-phase microstructures with x=3 (50-50 Br-Cl) mixed composition have a higher diffusivity compared to x=2 and x=3 solid solutions. There is a positive linear relationship between ln(D₀.) and E_a, which suggests that for superionic conductivity optimizing both the activation energy and the D₀ is important. Bond frustration due to mismatch in crystal geometry and ideal coordination number leads to especially high diffusivity through a high D₀ in the x=3 composition.

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

Directory of Open Access Journals (Sweden)

Chuang Lin

2015-01-01

Full Text Available Kernel Locality Preserving Projection (KLPP algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.

A systematic review of optimal treatment strategies for localized Ewing's sarcoma of bone after neo-adjuvant chemotherapy.

Science.gov (United States)

Werier, Joel; Yao, Xiaomei; Caudrelier, Jean-Michel; Di Primio, Gina; Ghert, Michelle; Gupta, Abha A; Kandel, Rita; Verma, Shailendra

2016-03-01

To perform a systematic review to investigate the optimal treatment strategy among the options of surgery alone, radiotherapy (RT) alone, and the combination of RT plus surgery in the management of localized Ewing's sarcoma of bone following neo-adjuvant chemotherapy. MEDLINE and EMBASE (1999 to February 2015), the Cochrane Library, and relevant conferences were searched. Two systematic reviews and eight full texts met the pre-planned study selection criteria. When RT was compared with surgery, a meta-analysis combining two papers showed that surgery resulted in a higher event-free survival (EFS) than RT in any location (HR = 1.50, 95% CI 1.12-2.00; p = 0.007). However another paper did not find a statistically significant difference in patients with pelvic disease, and no papers identified a significant difference in overall survival. When surgery plus RT was compared with surgery alone, a meta-analysis did not demonstrate a statistically significant difference for EFS between the two groups (HR = 1.21, 95% CI 0.90-1.63). Both surgical morbidities and radiation toxicities were reported. The existing evidence is based on very low aggregate quality as assessed by the GRADE approach. In patients with localized Ewing's sarcoma, either surgery alone (if complete surgical excision with clear margin can be achieved) or RT alone may be a reasonable treatment option. The optimal local treatment for an individual patient should be decided through consideration of patient characteristics, the potential benefit and harm of the treatment options, and patient preference. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Optimal Solubility of Diclofenac β-Cyclodextrin in Combination with Local Anaesthetics for Mesotherapy Applications.

Science.gov (United States)

Tringali, Giuseppe; Navarra, Pierluigi

2017-01-01

Because of low injection volume, the recently marketed injectable solution of diclofenac in complex with β -cyclodextrin (Akis®, IBSA Farmaceutici Italia) is an ideal candidate for mesotherapy applications. In this study, we investigated the solubility of Akis, 25 and 50 mg/kg, in combination with various local anaesthetics (lidocaine, mepivacaine, bupivacaine, levobupivacaine, and ropivacaine) at different concentrations in aqueous vehicles (normal saline, sterile water, or bicarbonate). Final injection mixtures were classified as limpid, turbid, or milky at visual analysis under standardized conditions. We found that (i) the use of sterile water for injections or normal saline as vehicles to dilute Akis in combination with whatever local anaesthetic normally results in milky solutions and therefore is not recommended; (ii) using bicarbonate, optimal solubility was obtained combining Akis with lidocaine, both 1 and 2%, or mepivacaine, both 1 and 2%, whereas solutions were turbid in combination with bupivacaine, levobupivacaine, or ropivacaine. Thus, we recommend that Akis is used in combination with lidocaine or mepivacaine in a bicarbonate vehicle.

Optimal Solubility of Diclofenac β-Cyclodextrin in Combination with Local Anaesthetics for Mesotherapy Applications

Directory of Open Access Journals (Sweden)

Giuseppe Tringali

2017-01-01

Full Text Available Because of low injection volume, the recently marketed injectable solution of diclofenac in complex with β-cyclodextrin (Akis®, IBSA Farmaceutici Italia is an ideal candidate for mesotherapy applications. In this study, we investigated the solubility of Akis, 25 and 50 mg/kg, in combination with various local anaesthetics (lidocaine, mepivacaine, bupivacaine, levobupivacaine, and ropivacaine at different concentrations in aqueous vehicles (normal saline, sterile water, or bicarbonate. Final injection mixtures were classified as limpid, turbid, or milky at visual analysis under standardized conditions. We found that (i the use of sterile water for injections or normal saline as vehicles to dilute Akis in combination with whatever local anaesthetic normally results in milky solutions and therefore is not recommended; (ii using bicarbonate, optimal solubility was obtained combining Akis with lidocaine, both 1 and 2%, or mepivacaine, both 1 and 2%, whereas solutions were turbid in combination with bupivacaine, levobupivacaine, or ropivacaine. Thus, we recommend that Akis is used in combination with lidocaine or mepivacaine in a bicarbonate vehicle.

Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

Directory of Open Access Journals (Sweden)

Yuliang Su

2015-04-01

Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

Advantages and limitations of navigation-based multicriteria optimization (MCO) for localized prostate cancer IMRT planning

International Nuclear Information System (INIS)

McGarry, Conor K.; Bokrantz, Rasmus; O’Sullivan, Joe M.; Hounsell, Alan R.

2014-01-01

Efficacy of inverse planning is becoming increasingly important for advanced radiotherapy techniques. This study’s aims were to validate multicriteria optimization (MCO) in RayStation (v2.4, RaySearch Laboratories, Sweden) against standard intensity-modulated radiation therapy (IMRT) optimization in Oncentra (v4.1, Nucletron BV, the Netherlands) and characterize dose differences due to conversion of navigated MCO plans into deliverable multileaf collimator apertures. Step-and-shoot IMRT plans were created for 10 patients with localized prostate cancer using both standard optimization and MCO. Acceptable standard IMRT plans with minimal average rectal dose were chosen for comparison with deliverable MCO plans. The trade-off was, for the MCO plans, managed through a user interface that permits continuous navigation between fluence-based plans. Navigated MCO plans were made deliverable at incremental steps along a trajectory between maximal target homogeneity and maximal rectal sparing. Dosimetric differences between navigated and deliverable MCO plans were also quantified. MCO plans, chosen as acceptable under navigated and deliverable conditions resulted in similar rectal sparing compared with standard optimization (33.7 ± 1.8 Gy vs 35.5 ± 4.2 Gy, p = 0.117). The dose differences between navigated and deliverable MCO plans increased as higher priority was placed on rectal avoidance. If the best possible deliverable MCO was chosen, a significant reduction in rectal dose was observed in comparison with standard optimization (30.6 ± 1.4 Gy vs 35.5 ± 4.2 Gy, p = 0.047). Improvements were, however, to some extent, at the expense of less conformal dose distributions, which resulted in significantly higher doses to the bladder for 2 of the 3 tolerance levels. In conclusion, similar IMRT plans can be created for patients with prostate cancer using MCO compared with standard optimization. Limitations exist within MCO regarding conversion of navigated plans to

Local Directional Probability Optimization for Quantification of Blurred Gray/White Matter Junction in Magnetic Resonance Image

Directory of Open Access Journals (Sweden)

Xiaoxia Qu

2017-09-01

Full Text Available The blurred gray/white matter junction is an important feature of focal cortical dysplasia (FCD lesions. FCD is the main cause of epilepsy and can be detected through magnetic resonance (MR imaging. Several earlier studies have focused on computing the gradient magnitude of the MR image and used the resulting map to model the blurred gray/white matter junction. However, gradient magnitude cannot quantify the blurred gray/white matter junction. Therefore, we proposed a novel algorithm called local directional probability optimization (LDPO for detecting and quantifying the width of the gray/white matter boundary (GWB within the lesional areas. The proposed LDPO method mainly consists of the following three stages: (1 introduction of a hidden Markov random field-expectation-maximization algorithm to compute the probability images of brain tissues in order to obtain the GWB region; (2 generation of local directions from gray matter (GM to white matter (WM passing through the GWB, considering the GWB to be an electric potential field; (3 determination of the optimal local directions for any given voxel of GWB, based on iterative searching of the neighborhood. This was then used to measure the width of the GWB. The proposed LDPO method was tested on real MR images of patients with FCD lesions. The results indicated that the LDPO method could quantify the GWB width. On the GWB width map, the width of the blurred GWB in the lesional region was observed to be greater than that in the non-lesional regions. The proposed GWB width map produced higher F-scores in terms of detecting the blurred GWB within the FCD lesional region as compared to that of FCD feature maps, indicating better trade-off between precision and recall.

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

Science.gov (United States)

Zhang, Yong-Feng; Chiang, Hsiao-Dong

2017-09-01

A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

Science.gov (United States)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

Role of controllability in optimizing quantum dynamics

International Nuclear Information System (INIS)

Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel

2011-01-01

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Longitudinally Vibrating Elastic Rods with Locally and Non-Locally Reacting Viscous Dampers

Directory of Open Access Journals (Sweden)

Şefaatdin Yüksel

2005-01-01

Full Text Available Eigencharacteristics of a longitudinally vibrating elastic rod with locally and non-locally reacting damping are analyzed. The rod is considered as a continuous system and complex eigenfrequencies are determined as solution of a characteristic equation. The variation of the damping ratios with respect to damper locations and damping coefficients for the first four eigenfrequencies are obtained. It is shown that at any mode of locally or non-locally damped elastic rod, the variation of damping ratio with damper location is linearly proportional to absolute value of the mode shape of undamped system. It is seen that the increasing damping coefficient does not always increase the damping ratio and there are optimal values for the damping ratio. Optimal values for external damping coefficients of viscous dampers and locations of the dampers are presented.

Optimal conditions for local NQR observation

International Nuclear Information System (INIS)

Grechishkin, V.S.; Grechishkina, P.V.

1998-01-01

At last the local NQR is used widely for detection of explosions and narcotics. Two methods are applied usually: one-sided detection and translucent detection (TD). These methods are analyzed in the paper. It is shown that the TD method has the greater sensitivity

Indoor Localization for Optimized Ambient Assisted Living Services

DEFF Research Database (Denmark)

Mitev, Miroslav; Mihovska, Albena Dimitrova; Poulkov, Vladimir

Indoor localization is very critical for the provision of Ambient Assisted Living (AAL) services, such as e-Health, smart home, etc. The success of deploying a real-time localization system depends on selecting the right performance characteristics. Bluetooth Low Energy (BLE) is a technology, which...

Unequal-thickness billet optimization in transitional region during isothermal local loading forming of Ti-alloy rib-web component using response surface method

Directory of Open Access Journals (Sweden)

Ke WEI

2018-04-01

Full Text Available Avoiding the folding defect and improving the die filling capability in the transitional region are desired in isothermal local loading forming of a large-scale Ti-alloy rib-web component (LTRC. To achieve a high-precision LTRC, the folding evolution and die filling process in the transitional region were investigated by 3D finite element simulation and experiment using an equal-thickness billet (ETB. It is found that the initial volume distribution in the second-loading region can greatly affect the amount of material transferred into the first-loading region during the second-loading step, and thus lead to the folding defect. Besides, an improper initial volume distribution results in non-concurrent die filling in the cavities of ribs after the second-loading step, and then causes die underfilling. To this end, an unequal-thickness billet (UTB was employed with the initial volume distribution optimized by the response surface method (RSM. For a certain eigenstructure, the critical value of the percentage of transferred material determined by the ETB was taken as a constraint condition for avoiding the folding defect in the UTB optimization process, and the die underfilling rate was considered as the optimization objective. Then, based on the RSM models of the percentage of transferred material and the die underfilling rate, non-folding parameter combinations and optimum die filling were achieved. Lastly, an optimized UTB was obtained and verified by the simulation and experiment. Keywords: Die filling, Folding defect, Isothermal local loading forming, Transitional region, Unequal-thickness billet optimization

Threshold Law For Positron Impact Ionization of Atoms

International Nuclear Information System (INIS)

Ihra, W.; Mota-Furtado, F.; OMahony, P.F.; Macek, J.H.; Macek, J.H.

1997-01-01

We demonstrate that recent experiments for positron impact ionization of He and H 2 can be interpreted by extending Wannier theory to higher energies. Anharmonicities in the expansion of the three-particle potential around the Wannier configuration give rise to corrections in the threshold behavior of the breakup cross section. These corrections are taken into account perturbatively by employing the hidden crossing theory. The resulting threshold law is σ(E)∝E 2.640 exp[ -0.73√(E)] . The actual energy range for which the Wannier law is valid is found to be smaller for positron impact ionization than for electron impact ionization. copyright 1997 The American Physical Society

Active Neural Localization

OpenAIRE

Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

2018-01-01

Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

Directory of Open Access Journals (Sweden)

Stefanos Georganos

2018-02-01

Full Text Available In object-based image analysis (OBIA, the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO. A popular USPO method does this through the optimization of a “global score” (GS, which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum/maximum threshold values for the intrasegment/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.

Exploring structural variability in X-ray crystallographic models using protein local optimization by torsion-angle sampling

International Nuclear Information System (INIS)

Knight, Jennifer L.; Zhou, Zhiyong; Gallicchio, Emilio; Himmel, Daniel M.; Friesner, Richard A.; Arnold, Eddy; Levy, Ronald M.

2008-01-01

Torsion-angle sampling, as implemented in the Protein Local Optimization Program (PLOP), is used to generate multiple structurally variable single-conformer models which are in good agreement with X-ray data. An ensemble-refinement approach to differentiate between positional uncertainty and conformational heterogeneity is proposed. Modeling structural variability is critical for understanding protein function and for modeling reliable targets for in silico docking experiments. Because of the time-intensive nature of manual X-ray crystallographic refinement, automated refinement methods that thoroughly explore conformational space are essential for the systematic construction of structurally variable models. Using five proteins spanning resolutions of 1.0–2.8 Å, it is demonstrated how torsion-angle sampling of backbone and side-chain libraries with filtering against both the chemical energy, using a modern effective potential, and the electron density, coupled with minimization of a reciprocal-space X-ray target function, can generate multiple structurally variable models which fit the X-ray data well. Torsion-angle sampling as implemented in the Protein Local Optimization Program (PLOP) has been used in this work. Models with the lowest R free values are obtained when electrostatic and implicit solvation terms are included in the effective potential. HIV-1 protease, calmodulin and SUMO-conjugating enzyme illustrate how variability in the ensemble of structures captures structural variability that is observed across multiple crystal structures and is linked to functional flexibility at hinge regions and binding interfaces. An ensemble-refinement procedure is proposed to differentiate between variability that is a consequence of physical conformational heterogeneity and that which reflects uncertainty in the atomic coordinates

Imaging local cerebral blood flow by xenon-enhanced computed tomography - technical optimization procedures

Energy Technology Data Exchange (ETDEWEB)

Meyer, J.S.; Shinohara, T.; Imai, A.; Kobari, M.; Sakai, F.; Hata, T.; Oravez, W.T.; Timpe, G.M.; Deville, T.; Solomon, E.

1988-08-01

Methods are described for non-invasive, computer-assisted serial scanning throughout the human brain during eight minutes of inhalation of 27%-30% xenon gas in order to measure local cerebral blood flow (LCBF). Optimized xenon-enhanced computed tomography (XeCT) was achieved by 5-second scanning at one-minute intervals utilizing a state-of-the-art CT scanner and rapid delivery of xenon gas via a face mask. Values for local brain-blood partition coefficients (Llambda) measured in vivo were utilized to calculate LCBF values. Previous methods assumed Llambda values to be normal, introducing the risk of systematic errors, because Llambda values differ throughout normal brain and may be altered by disease. Color-coded maps of Llambda and LCBF values were formatted directly onto CT images for exact correlation of function with anatomic and pathologic observations (spatial resolution: 26.5 cubic mm). Results were compared among eight normal volunteers, aged between 50 and 88 years. Mean cortical gray matter blood flow was 46.3 +- 7.7, for subcortical gray matter it was 50.3 +- 13.2 and for white matter it was 18.8 +- 3.2. Modern CT scanners provide stability, improved signal to noise ratio and minimal radiation scatter. Combining these advantages with rapid xenon saturation of the blood provides correlations of Llambda and LCBF with images of normal and abnormal brain in a safe, useful and non-invasive manner.

Application of the entropy generation minimization method to a solar heat exchanger: A pseudo-optimization design process based on the analysis of the local entropy generation maps

International Nuclear Information System (INIS)

Giangaspero, Giorgio; Sciubba, Enrico

2013-01-01

This paper presents an application of the entropy generation minimization method to the pseudo-optimization of the configuration of the heat exchange surfaces in a Solar Rooftile. An initial “standard” commercial configuration is gradually improved by introducing design changes aimed at the reduction of the thermodynamic losses due to heat transfer and fluid friction. Different geometries (pins, fins and others) are analysed with a commercial CFD (Computational Fluid Dynamics) code that also computes the local entropy generation rate. The design improvement process is carried out on the basis of a careful analysis of the local entropy generation maps and the rationale behind each step of the process is discussed in this perspective. The results are compared with other entropy generation minimization techniques available in the recent technical literature. It is found that the geometry with pin-fins has the best performance among the tested ones, and that the optimal pin array shape parameters (pitch and span) can be determined by a critical analysis of the integrated and local entropy maps and of the temperature contours. - Highlights: ► An entropy generation minimization method is applied to a solar heat exchanger. ► The approach is heuristic and leads to a pseudo-optimization process with CFD as main tool. ► The process is based on the evaluation of the local entropy generation maps. ► The geometry with pin-fins in general outperforms all other configurations. ► The entropy maps and temperature contours can be used to determine the optimal pin array design parameters

How do local governments decide on public policy in fiscal federalism?

DEFF Research Database (Denmark)

Köthenbürger, Marko

2011-01-01

Previous literature widely assumes that taxes are optimized in local public finance while expenditures adjust residually. This paper endogenizes the choice of the optimization variable. In particular, it analyzes how federal policy toward local governments influences the way local governments...... decide on public policy. Unlike the usual presumption, the paper shows that local governments may choose to optimize over expenditures. The result holds when federal policy subsidizes local taxation. The results offer a new perspective of the efficiency implications of federal policy toward local...

Impact Ionization in Monoclinic $\\beta-Ga_2O_3$

OpenAIRE

Ghosh, Krishnendu; Singisetti, Uttam

2017-01-01

We report a theoretical investigation of extremely high field transport in an emerging widebandgap material $\\beta-Ga_2O_3$ from first principles. The signature high-field effect explored here is impact ionization. Interaction between a ground-state electron and an excited electron is computed from the matrix elements of a screened Coulomb operator. Maximally localized Wannier functions (MLWF) are utilized in computing the electron-electron self-energy. A full-band Monte Carlo (FBMC) simulati...

Theory and Algorithms for Global/Local Design Optimization

National Research Council Canada - National Science Library

Haftka, Raphael T

2004-01-01

... the component and overall design as well as on exploration of global optimization algorithms. In the former category, heuristic decomposition was followed with proof that it solves the original problem...

Optimizing heliostat positions with local search metaheuristics using a ray tracing optical model

Science.gov (United States)

Reinholz, Andreas; Husenbeth, Christof; Schwarzbözl, Peter; Buck, Reiner

2017-06-01

The life cycle costs of solar tower power plants are mainly determined by the investment costs of its construction. Significant parts of these investment costs are used for the heliostat field. Therefore, an optimized placement of the heliostats gaining the maximal annual power production has a direct impact on the life cycle costs revenue ratio. We present a two level local search method implemented in MATLAB utilizing the Monte Carlo raytracing software STRAL [1] for the evaluation of the annual power output for a specific weighted annual time scheme. The algorithm was applied to a solar tower power plant (PS10) with 624 heliostats. Compared to former work of Buck [2], we were able to improve both runtime of the algorithm and quality of the output solutions significantly. Using the same environment for both algorithms, we were able to reach Buck's best solution with a speed up factor of about 20.

Near threshold TDCS for photo-double ionization of helium

International Nuclear Information System (INIS)

Dawber, G.; McConkey, A.G.; Rojas, H.; King, G.C.; MacDonald, M.A.

1995-01-01

TDCS for photo-double ionization of helium have been measured in a PhotoElectron-PhotoElectron COincidence (PEPECO) experiment. The TDCS have been obtained for the first time at very low excess energies E, 0.6 eV < E <2 eV, for both equal and unequal energy sharing between the two outgoing electrons. The measured data are compared with the predictions of the Wannier model, and also with recent, non-Wannier, ab initio calculations. In addition, also for the first time, the relative magnitudes of the various TDCS measured have been determined in this excess energy region, suggesting a departure from the predictions of the Wannier model at the largest excess energy studied, E = 2 eV. (Author)

Pseudolinear functions and optimization

CERN Document Server

Mishra, Shashi Kant

2015-01-01

Pseudolinear Functions and Optimization is the first book to focus exclusively on pseudolinear functions, a class of generalized convex functions. It discusses the properties, characterizations, and applications of pseudolinear functions in nonlinear optimization problems.The book describes the characterizations of solution sets of various optimization problems. It examines multiobjective pseudolinear, multiobjective fractional pseudolinear, static minmax pseudolinear, and static minmax fractional pseudolinear optimization problems and their results. The authors extend these results to locally

Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings

Science.gov (United States)

Wodecki, Jacek; Michalak, Anna; Zimroz, Radoslaw

2018-03-01

Harsh industrial conditions present in underground mining cause a lot of difficulties for local damage detection in heavy-duty machinery. For vibration signals one of the most intuitive approaches of obtaining signal with expected properties, such as clearly visible informative features, is prefiltration with appropriately prepared filter. Design of such filter is very broad field of research on its own. In this paper authors propose a novel approach to dedicated optimal filter design using progressive genetic algorithm. Presented method is fully data-driven and requires no prior knowledge of the signal. It has been tested against a set of real and simulated data. Effectiveness of operation has been proven for both healthy and damaged case. Termination criterion for evolution process was developed, and diagnostic decision making feature has been proposed for final result determinance.

Development of a novel method for surgical implant design optimization through noninvasive assessment of local bone properties.

Science.gov (United States)

Schiuma, D; Brianza, S; Tami, A E

2011-03-01

A method was developed to improve the design of locking implants by finding the optimal paths for the anchoring elements, based on a high resolution pQCT assessment of local bone mineral density (BMD) distribution and bone micro-architecture (BMA). The method consists of three steps: (1) partial fixation of the implant to the bone and creation of a reference system, (2) implant removal and pQCT scan of the bone, and (3) determination of BMD and BMA of all implant-anchoring locations along the actual and alternative directions. Using a PHILOS plate, the method uncertainty was tested on an artificial humerus bone model. A cadaveric humerus was used to quantify how the uncertainty of the method affects the assessment of bone parameters. BMD and BMA were determined along four possible alternative screw paths as possible criteria for implant optimization. The method is biased by a 0.87 ± 0.12 mm systematic uncertainty and by a 0.44 ± 0.09 mm random uncertainty in locating the virtual screw position. This study shows that this method can be used to find alternative directions for the anchoring elements, which may possess better bone properties. This modification will thus produce an optimized implant design. Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

How Do Local Governments Decide on Public Policy in Fiscal Federalism?

DEFF Research Database (Denmark)

Köthenbürger, Marko

2008-01-01

Previous literature widely assumes that taxes are optimized in local public finance while expenditures adjust residually. This paper endogenizes the choice of the optimization variable. In particular, it analyzes how federal policy toward local governments influences the way local governments...... decide on public policy. Unlike the presumption, the paper shows that local governments may choose to optimize over expenditures. The result most notably prevails when federal policy subsidizes local fiscal effort. The results offer a new perspective of the efficiency implications of federal policy...

A Non-Local, Energy-Optimized Kernel: Recovering Second-Order Exchange and Beyond in Extended Systems

Science.gov (United States)

Bates, Jefferson; Laricchia, Savio; Ruzsinszky, Adrienn

The Random Phase Approximation (RPA) is quickly becoming a standard method beyond semi-local Density Functional Theory that naturally incorporates weak interactions and eliminates self-interaction error. RPA is not perfect, however, and suffers from self-correlation error as well as an incorrect description of short-ranged correlation typically leading to underbinding. To improve upon RPA we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free for one and two electron systems in the high-density limit. By tuning the one free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy we obtain a non-local, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. To reduce the computational cost of the standard kernel-corrected RPA, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and non-metallic systems. Furthermore we stress that for norm-conserving implementations the accuracy of RPA and beyond RPA structural properties compared to experiment is inherently limited by the choice of pseudopotential. Current affiliation: King's College London.

Least median of squares filtering of locally optimal point matches for compressible flow image registration

International Nuclear Information System (INIS)

Castillo, Edward; Guerrero, Thomas; Castillo, Richard; White, Benjamin; Rojo, Javier

2012-01-01

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. (paper)

Introduction to Continuous Optimization

DEFF Research Database (Denmark)

Andreasson, Niclas; Evgrafov, Anton; Patriksson, Michael

optimal solutions for continuous optimization models. The main part of the mathematical material therefore concerns the analysis and linear algebra that underlie the workings of convexity and duality, and necessary/sufficient local/global optimality conditions for continuous optimization problems. Natural...... algorithms are then developed from these optimality conditions, and their most important convergence characteristics are analyzed. The book answers many more questions of the form “Why?” and “Why not?” than “How?”. We use only elementary mathematics in the development of the book, yet are rigorous throughout...

Imaging local cerebral blood flow by xenon-enhanced computed tomography - technical optimization procedures

International Nuclear Information System (INIS)

Meyer, J.S.; Shinohara, T.; Imai, A.; Kobari, M.; Solomon, E.

1988-01-01

Methods are described for non-invasive, computer-assisted serial scanning throughout the human brain during eight minutes of inhalation of 27%-30% xenon gas in order to measure local cerebral blood flow (LCBF). Optimized xenon-enhanced computed tomography (XeCT) was achieved by 5-second scanning at one-minute intervals utilizing a state-of-the-art CT scanner and rapid delivery of xenon gas via a face mask. Values for local brain-blood partition coefficients (Lλ) measured in vivo were utilized to calculate LCBF values. Previous methods assumed Lλ values to be normal, introducing the risk of systematic errors, because Lλ values differ throughout normal brain and may be altered by disease. Color-coded maps of Lλ and LCBF values were formatted directly onto CT images for exact correlation of function with anatomic and pathologic observations (spatial resolution: 26.5 cubic mm). Results were compared among eight normal volunteers, aged between 50 and 88 years. Mean cortical gray matter blood flow was 46.3 ± 7.7, for subcortical gray matter it was 50.3 ± 13.2 and for white matter it was 18.8 ± 3.2. Modern CT scanners provide stability, improved signal to noise ratio and minimal radiation scatter. Combining these advantages with rapid xenon saturation of the blood provides correlations of Lλ and LCBF with images of normal and abnormal brain in a safe, useful and non-invasive manner. (orig.)

[Optimization of labeling and localizing bacterial membrane and nucleus with FM4-64 and Hoechst dyes].

Science.gov (United States)

Wang, Jing; Han, Yanping; Yang, Ruifu; Zhao, Xingxu

2015-08-04

To observe cell membrane and nucleus in bacteria for subcellular localization. FM4-64 and Hoechst were dyed that can label cell membrane and nucleus, respectively. Both dyes were used to co-stain the membranes and nucleus of eight bacterial strains ( Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, Yersinia pestis, Legionella pneumonia, Vibrio cholerae and Bacillus anthracis). E. coli was dyed with different dye concentrations and times and then observed by confocal fluorescence microscopic imaging. Fluorescence intensity of cell membrane and nucleus is affected by dye concentrations and times. The optimal conditions were determined as follows: staining cell membrane with 20 μg/mL FM4-64 for 1 min and cell nucleus with 20 μg/mL Hoechst for 20 min. Gram-negative bacteria were dyed better than gram-positive bacteria with FM4-64dye. FM4-64 and Hoechst can be used to stain membrane and nucleus in different types of bacteria. Co-staining bacterial membrane and nucleus provides the reference to observe cell structure in prokaryotes for studying subcellular localization.

Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

Energy Technology Data Exchange (ETDEWEB)

Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)

2013-02-15

Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.

Topology optimization of continuum structure with dynamic constraints using mode identification

International Nuclear Information System (INIS)

Li, Jianhongyu; Chen, Shenyan; Huang, Hai

2015-01-01

For the problems such as mode exchange and localized modes in topology optimization of continuum structure with dynamic constraints, it is difficult to apply the traditional optimization model which considers fixed order mode frequencies as constraints in optimization calculation. A new optimization model is established, in which the dynamical constraints are changed as frequencies of structural principal vibrations. The order of the principal vibrations is recognized through modal identification in the optimization process, and the constraints are updated to make the optimization calculation execute smoothly. Localized mode elimination techniques are introduced to reduce the localized modes induced by the low density elements, which could improve the optimization efficiency. A new optimization process is designed, which achieves the purpose of overcoming mode exchange problem and localized mode problem at the cost of increasing several structural analyses. Optimization system is developed by using Nastran to perform structural analysis and sensitivity analysis and two-level multipoint approximation algorithm as optimizer. Numerical results verified that the presented method is effective and reasonable.

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

Science.gov (United States)

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.

Acceleration techniques in the univariate Lipschitz global optimization

Science.gov (United States)

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

2016-10-01

Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.

What is the optimal management of high risk, clinically localized prostate cancer?

Science.gov (United States)

Eastham, James A; Evans, Christopher P; Zietman, Anthony

2010-01-01

To summarize the presentations and debate regarding the optimal treatment of localized high-risk prostate cancer as presented at the 2009 Spring Meeting of the Society of Urologic Oncology. The debate was centered on presentations arguing for radical prostatectomy (RP) or radiotherapy as the optimal treatment for this condition. The meeting presentations are summarized by their respective presenters herein. Dr. James Eastham presents the varied definitions for "high-risk" prostate cancer as strongly influencing which patients end up in this cohort. Based upon this, between 3% and 38% of patients with high-risk features could be defined as "high-risk". Despite that, these men do not have a uniformly poor prognosis after RP, and attention to surgical principles as outlined improve outcomes. Disease-specific survival at 12 years is excellent and up to one-half of these men may not need adjuvant or salvage therapies, depending on their specific disease characteristics. Adjuvant or salvage radiotherapies improve outcomes and are part of a sequential approach to treating these patients. Dr. Anthony Zietman presented radiotherapy as the gold-standard based upon large, randomized clinical trials of intermediate- and high-risk prostate cancer patients. Compared with androgen deprivation alone, the addition of radiotherapy provided a 12% cancer-specific survival advantage and 10% overall survival advantage. Dose escalation seems to confer further improvements in cancer control without significant escalation of toxicities, with more data forthcoming. There are no randomized trials comparing RP to radiotherapy for any risk category. In high-risk prostate cancer patients, both approaches have potential benefits and cumulative toxicities that must be matched to disease characteristics and patient expectations in selecting a treatment course. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A brief introduction to continuous evolutionary optimization

CERN Document Server

Kramer, Oliver

2014-01-01

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

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

Science.gov (United States)

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.

Total-energy global optimizations using nonorthogonal localized orbitals

International Nuclear Information System (INIS)

Kim, J.; Mauri, F.; Galli, G.

1995-01-01

An energy functional for orbital-based O(N) calculations is proposed, which depends on a number of nonorthogonal, localized orbitals larger than the number of occupied states in the system, and on a parameter, the electronic chemical potential, determining the number of electrons. We show that the minimization of the functional with respect to overlapping localized orbitals can be performed so as to attain directly the ground-state energy, without being trapped at local minima. The present approach overcomes the multiple-minima problem present within the original formulation of orbital-based O(N) methods; it therefore makes it possible to perform O(N) calculations for an arbitrary system, without including any information about the system bonding properties in the construction of the input wave functions. Furthermore, while retaining the same computational cost as the original approach, our formulation allows one to improve the variational estimate of the ground-state energy, and the energy conservation during a molecular dynamics run. Several numerical examples for surfaces, bulk systems, and clusters are presented and discussed

Analysis of e-e angular correlations in near-threshold electron impact ionisation of helium

International Nuclear Information System (INIS)

Selles, P.; Huetz, A.; Mazeau, J.

1987-01-01

Using a coincidence technique in a coplanar geometry, triple differential cross sections (TDCS) for electron impact ionisation of helium are measured in the 0.5-2 eV energy range above threshold. As a few states (O <= L <= 2) of the two outgoing electrons are obviously involved in the process, their respective intensities appear as unknown parameters in the theoretical TDCS as deduced in the frame of the Wannier theory. The authors show that almost all these parameters can be determined through normalisation to the measured TDCS in two specific geometries: in the first one the two electrons are kept in opposite directions while in the second one they remain symmetrical with respect to the incident beam. A comparison with the complete set of data is then performed. The measured TDCS are in agreement with the Wannier theory for the lowest energies (0.5 and 1 eV). At 2 eV the overall agreement becomes poorer, although some predictions of the Wannier theory still apply. Finally specific measurements at 8 eV clearly show from consideration of symmetry that the Wannier theory no longer applies at this energy. (author)

Modelling and optimization of a local smart grid for an agro-industrial site

Directory of Open Access Journals (Sweden)

Enrico Fabrizio

2013-09-01

Full Text Available A smart grid is defined where different elements are interconnected between them and with the public utility grid. The development of smart grids is considered a strategic goal at both national and international levels and has been funded by many research programs. Within the BEE (Building Energy Ecosystems project, funded by the Piedmont Region under the European POR FESR 2007-13 scheme, the creation of an electricity smart grid at a local level in a small agroindustry was done. This industry is one of the so-called prosumer, that is both a producer and a consumer of energy. The energy production is done by means of solar photovoltaic and biomass. In this local smart grid, the elements were subdivided in two main groups: loads (process machineries in the case study and generators (PV and biomass in the case study. The loads may be further subdivided into permanent loads, mandatory loads and shiftable loads. The objective of the smart grid is the minimization of the exchanges between the local grid and the public utility grid. Even though no financial savings occur, this is important for the community grid. The problem is therefore to find the conditions that let the net exported energy going to zero at each time step, so arriving close to a self-sufficient system by modifying the shiftable loads. In a first phase of the study, the consumers were studied and, according to some characteristics of the machineries employed and the production requirements, grouped into production lines that can or not be switched off for intervals of time in order to compensate the smart grid fluctuations. The smart grid balancing may be done on an instantaneous basis, or in a predictive way considering the future weather forecasts and the future production requirements. The demo site was equipped with measurement instrumentation, data acquisition tools and a user interface that may be used to visualize all the quantities that are measured but also to perform the

Collimator optimization in myocardial perfusion SPECT using the ideal observer and realistic background variability for lesion detection and joint detection and localization tasks

Science.gov (United States)

Ghaly, Michael; Du, Yong; Links, Jonathan M.; Frey, Eric C.

2016-03-01

In SPECT imaging, collimators are a major factor limiting image quality and largely determine the noise and resolution of SPECT images. In this paper, we seek the collimator with the optimal tradeoff between image noise and resolution with respect to performance on two tasks related to myocardial perfusion SPECT: perfusion defect detection and joint detection and localization. We used the Ideal Observer (IO) operating on realistic background-known-statistically (BKS) and signal-known-exactly (SKE) data. The areas under the receiver operating characteristic (ROC) and localization ROC (LROC) curves (AUCd, AUCd+l), respectively, were used as the figures of merit for both tasks. We used a previously developed population of 54 phantoms based on the eXtended Cardiac Torso Phantom (XCAT) that included variations in gender, body size, heart size and subcutaneous adipose tissue level. For each phantom, organ uptakes were varied randomly based on distributions observed in patient data. We simulated perfusion defects at six different locations with extents and severities of 10% and 25%, respectively, which represented challenging but clinically relevant defects. The extent and severity are, respectively, the perfusion defect’s fraction of the myocardial volume and reduction of uptake relative to the normal myocardium. Projection data were generated using an analytical projector that modeled attenuation, scatter, and collimator-detector response effects, a 9% energy resolution at 140 keV, and a 4 mm full-width at half maximum (FWHM) intrinsic spatial resolution. We investigated a family of eight parallel-hole collimators that spanned a large range of sensitivity-resolution tradeoffs. For each collimator and defect location, the IO test statistics were computed using a Markov Chain Monte Carlo (MCMC) method for an ensemble of 540 pairs of defect-present and -absent images that included the aforementioned anatomical and uptake variability. Sets of test statistics were

Interactive Topology Optimization

DEFF Research Database (Denmark)

Nobel-Jørgensen, Morten

Interactivity is the continuous interaction between the user and the application to solve a task. Topology optimization is the optimization of structures in order to improve stiffness or other objectives. The goal of the thesis is to explore how topology optimization can be used in applications...... on theory of from human-computer interaction which is described in Chapter 2. Followed by a description of the foundations of topology optimization in Chapter 3. Our applications for topology optimization in 2D and 3D are described in Chapter 4 and a game which trains the human intuition of topology...... optimization is presented in Chapter 5. Topology optimization can also be used as an interactive modeling tool with local control which is presented in Chapter 6. Finally, Chapter 7 contains a summary of the findings and concludes the dissertation. Most of the presented applications of the thesis are available...

Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines

Directory of Open Access Journals (Sweden)

Boming Song

2017-01-01

Full Text Available Accurate target localization technology plays a very important role in ensuring mine safety production and higher production efficiency. The localization accuracy of a mine localization system is influenced by many factors. The most significant factor is the non-line of sight (NLOS propagation error of the localization signal between the access point (AP and the target node (Tag. In order to improve positioning accuracy, the NLOS error must be suppressed by an optimization algorithm. However, the traditional optimization algorithms are complex and exhibit poor optimization performance. To solve this problem, this paper proposes a new method for mine time of arrival (TOA localization based on the idea of comprehensive optimization. The proposed method utilizes particle filtering to reduce the TOA data error, and the positioning results are further optimized with fingerprinting based on the Manhattan distance. This proposed method combines the advantages of particle filtering and fingerprinting localization. It reduces algorithm complexity and has better error suppression performance. The experimental results demonstrate that, as compared to the symmetric double-sided two-way ranging (SDS-TWR method or received signal strength indication (RSSI based fingerprinting method, the proposed method has a significantly improved localization performance, and the environment adaptability is enhanced.

Matrix-product-state method with local basis optimization for nonequilibrium electron-phonon systems

Science.gov (United States)

Heidrich-Meisner, Fabian; Brockt, Christoph; Dorfner, Florian; Vidmar, Lev; Jeckelmann, Eric

We present a method for simulating the time evolution of quasi-one-dimensional correlated systems with strongly fluctuating bosonic degrees of freedom (e.g., phonons) using matrix product states. For this purpose we combine the time-evolving block decimation (TEBD) algorithm with a local basis optimization (LBO) approach. We discuss the performance of our approach in comparison to TEBD with a bare boson basis, exact diagonalization, and diagonalization in a limited functional space. TEBD with LBO can reduce the computational cost by orders of magnitude when boson fluctuations are large and thus it allows one to investigate problems that are out of reach of other approaches. First, we test our method on the non-equilibrium dynamics of a Holstein polaron and show that it allows us to study the regime of strong electron-phonon coupling. Second, the method is applied to the scattering of an electronic wave packet off a region with electron-phonon coupling. Our study reveals a rich physics including transient self-trapping and dissipation. Supported by Deutsche Forschungsgemeinschaft (DFG) via FOR 1807.

COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

Energy Technology Data Exchange (ETDEWEB)

Sun, Y.; Borland, Michael

2017-06-25

Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

Optimization with Extremal Dynamics

International Nuclear Information System (INIS)

Boettcher, Stefan; Percus, Allon G.

2001-01-01

We explore a new general-purpose heuristic for finding high-quality solutions to hard discrete optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively updates extremely undesirable variables of a single suboptimal solution, assigning them new, random values. Large fluctuations ensue, efficiently exploring many local optima. We use extremal optimization to elucidate the phase transition in the 3-coloring problem, and we provide independent confirmation of previously reported extrapolations for the ground-state energy of ±J spin glasses in d=3 and 4

Optimizing the Management of High-Risk, Localized Prostate Cancer

OpenAIRE

Sundi, Debasish; Jeong, Byong Chang; Lee, Seung Bae; Han, Misop

2012-01-01

Prostate cancer has a high prevalence and a rising incidence in many parts of the world. Although many screen-detected prostate cancers may be indolent, prostate cancer remains a major contributor to mortality in men. Therefore, the appropriate diagnosis and treatment of localized prostate cancer with lethal potential are of great importance. High-risk, localized prostate cancer has multiple definitions. Treatment options that should be individualized to each patient include observation, radi...

Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems.

Science.gov (United States)

Krohling, Renato A; Coelho, Leandro dos Santos

2006-12-01

In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

Directory of Open Access Journals (Sweden)

Zhi-Ren Tsai

2013-01-01

Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Adsorptive removal of crystal violet dye by a local clay and process optimization by response surface methodology

Science.gov (United States)

Loqman, Amal; El Bali, Brahim; Lützenkirchen, Johannes; Weidler, Peter G.; Kherbeche, Abdelhak

2017-11-01

The current study relates to the removal of a dye [crystal violet (CV)] from aqueous solutions through batch adsorption experiment onto a local clay from Morocco. The clay was characterized by X-ray diffraction, IR spectroscopy, X-ray fluorescence, scanning electron microscope, Brunauer-Emmett-Teller analysis and Fraunhofer diffraction method. The influence of independent variables on the removal efficiency was determined and optimized by response surface methodology using the Box-Behnken surface statistical design. The model predicted maximum adsorption of 81.62% under the optimum conditions of operational parameters (125 mg L-1 initial dye concentration, 2.5 g L-1 adsorbent dose and time of 43 min). Practically, the removal ranges in 27.4-95.3%.

Electromobility and renewable energies. Locally optimized use of grid-connected vehicles; Elektromobilitaet und erneuerbare Energien. Lokal optimierter Einsatz von netzgekoppelten Fahrzeugen

Energy Technology Data Exchange (ETDEWEB)

Link, Jochen

2012-07-01

To reach significant CO{sub 2} emission reduction with electric vehicles, electricity production based on renewable energies is required. The aim of this study is to determine different options for linking the charging times of electric vehicles with fluctuating local renewable energy production. Energy demand profiles for electric cars were generated on the basis of statistic mobility data for Germany. The impact of different charging strategies for a high market penetration of electric vehicles is analyzed considering the renewable energy production and the distribution grid of the city of Freiburg. If all cars were substituted by electric vehicles, the electrical energy demand would increase by a third. Load peaks and the capacity usage of electric installations depend strongly on the amount of power and the simultaneity of the charging process. Decentralized electric vehicle charging based on tariff incentives is one option to influence the charging behavior. In the Freiburg area the residual grid load was adjusted by shifting the charging time to periods with high renewable energy production or low electric energy consumption. Another important aspect of this study is the development and realization of a pilot system for tariff incentive based load shifting. The so called ''mobile Dispatcher'' is principally used for the determination of the optimal charging strategy considering all relevant factors (e.g. user input data, time variable feed-in tariffs, energy consumption tariffs and battery degradation costs), the communication and data exchange with the charging station, as well as with the energy providers. Prior to the development of the ''mobile Dispatcher'', concepts to connect the electric vehicle to the grid were evaluated, and necessary modifications of the electricity marked structure were discussed. Based on the goals of the German national development plan, different grid integration strategies, such as mobile

Optimal Local Dimming for LC Image Formation With Controllable Backlighting

DEFF Research Database (Denmark)

Shu, Xiao; Wu, Xiaolin; Forchhammer, Søren

2013-01-01

Light emitting diode (LED)-backlit liquid crystal displays (LCDs) hold the promise of improving image quality while reducing the energy consumption with signal-dependent local dimming. However, most existing local dimming algorithms are mostly motivated by simple implementation, and they often la...

Novelty-driven Particle Swarm Optimization

DEFF Research Database (Denmark)

Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo

2015-01-01

Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However......, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm...

SU-F-J-09: Radioactive Seed Localization for Breast Lumpectomy - Towards Optimization

Energy Technology Data Exchange (ETDEWEB)

Aima, M; Viscariello, N; Patton, T; Bednarz, B [University of Wisconsin, Madison, WI (United States)

2016-06-15

Purpose: The aim of this work is to propose a method to optimize radioactive source localization (RSL) for non-palpable breast cancer surgery. RSL is commonly used as a guiding technique during surgery for excision of non-palpable tumors. A collimated hand-held detector is used to localize radioactive sources implanted in tumors. Incisions made by the surgeon are based on maximum observed detector counts, and tumors are subsequently resected based on an arbitrary estimate of the counts expected at the surgical margin boundary. This work focuses on building a framework to predict detector counts expected throughout the procedure to improve surgical margins. Methods: A gamma detection system called the Neoprobe GDS was used for this work. The probe consists of a cesium zinc telluride crystal and a collimator. For this work, an I-125 Best Medical model 2301 source was used. The source was placed in three different phantoms, a PMMA, a Breast (25%- glandular tissue/75%- adipose tissue) and a Breast (75-25) phantom with a backscatter thickness of 6 cm. Counts detected by the probe were recorded with varying amounts of phantom thicknesses placed on top of the source. A calibration curve was generated using MATLAB based on the counts recorded for the calibration dataset acquired with the PMMA phantom. Results: The observed detector counts data used as the validation set was accurately predicted to within ±3.2%, ±6.9%, ±8.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. The average difference between predicted and observed counts was −0.4%, 2.4%, 1.4% with a standard deviation of 1.2 %, 1.8%, 3.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. Conclusion: The results of this work provide a basis for characterization of a detector used for RSL. Counts were predicted to within ±9% for three different phantoms without the application of a density correction factor.

Workshop on Computational Optimization

CERN Document Server

2016-01-01

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.

Modeling Thermal Comfort and Optimizing Local Renewal Strategies—A Case Study of Dazhimen Neighborhood in Wuhan City

Directory of Open Access Journals (Sweden)

Chong Peng

2015-03-01

Full Text Available Modeling thermal comfort provides quantitative evidence and parameters for effective and efficient urban planning, design, and building construction particularly in a dense and narrow inner city, which has become one of many concerns for sustainable urban development. This paper aims to develop geometric and mathematical models of wind and thermal comfort and use them to examine the impacts of six small-scale renewal strategies on the wind and thermal environment at pedestrian level in Dazhimen neighborhood, Wuhan, which is a typical case study of urban renewal project in a mega-city. The key parameters such as the solar radiation, natural convection, relative humidity, ambient crosswind have been incorporated into the mathematical models by using user-defined-function (UDF method. Detailed temperature and velocity distributions under different strategies have been compared for the optimization of local renewal strategies. It is concluded that five rules generated from the simulation results can provide guidance for building demolition and reconstruction in a neighborhood and there is no need of large-scale demolition. Particularly, combining the local demolition and city virescence can both improve the air ventilation and decrease the temperature level in the study area.

Analytic Optimization of Near-Field Optical Chirality Enhancement

Science.gov (United States)

2017-01-01

We present an analytic derivation for the enhancement of local optical chirality in the near field of plasmonic nanostructures by tuning the far-field polarization of external light. We illustrate the results by means of simulations with an achiral and a chiral nanostructure assembly and demonstrate that local optical chirality is significantly enhanced with respect to circular polarization in free space. The optimal external far-field polarizations are different from both circular and linear. Symmetry properties of the nanostructure can be exploited to determine whether the optimal far-field polarization is circular. Furthermore, the optimal far-field polarization depends on the frequency, which results in complex-shaped laser pulses for broadband optimization. PMID:28239617

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

OpenAIRE

Zhang, Xiangsheng; Pan, Feng

2015-01-01

Aimed at the parameters optimization in support vector machine (SVM) for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO) algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effective...

Efficient evolutionary algorithms for optimal control

NARCIS (Netherlands)

López Cruz, I.L.

2002-01-01

If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use

Towards optimal design of locally resonant acoustic metamaterials

NARCIS (Netherlands)

Krushynska, A.O.; Kouznetsova, V.; Geers, M.G.D.

2014-01-01

The paper presents an in-depth analysis of solid locally resonant acoustic metamaterials (LRAMs) consisting of rubber-coated inclusions. Dispersion properties of two-dimensional LRAMs are studied by means of finite-element modal analysis. For an incompressible rubber, only one practically important

Diffusion Monte Carlo approach versus adiabatic computation for local Hamiltonians

Science.gov (United States)

Bringewatt, Jacob; Dorland, William; Jordan, Stephen P.; Mink, Alan

2018-02-01

Most research regarding quantum adiabatic optimization has focused on stoquastic Hamiltonians, whose ground states can be expressed with only real non-negative amplitudes and thus for whom destructive interference is not manifest. This raises the question of whether classical Monte Carlo algorithms can efficiently simulate quantum adiabatic optimization with stoquastic Hamiltonians. Recent results have given counterexamples in which path-integral and diffusion Monte Carlo fail to do so. However, most adiabatic optimization algorithms, such as for solving MAX-k -SAT problems, use k -local Hamiltonians, whereas our previous counterexample for diffusion Monte Carlo involved n -body interactions. Here we present a 6-local counterexample which demonstrates that even for these local Hamiltonians there are cases where diffusion Monte Carlo cannot efficiently simulate quantum adiabatic optimization. Furthermore, we perform empirical testing of diffusion Monte Carlo on a standard well-studied class of permutation-symmetric tunneling problems and similarly find large advantages for quantum optimization over diffusion Monte Carlo.

Optimization of the linear-scaling local natural orbital CCSD(T) method: Redundancy-free triples correction using Laplace transform

Science.gov (United States)

Nagy, Péter R.; Kállay, Mihály

2017-06-01

An improved algorithm is presented for the evaluation of the (T) correction as a part of our local natural orbital (LNO) coupled-cluster singles and doubles with perturbative triples [LNO-CCSD(T)] scheme [Z. Rolik et al., J. Chem. Phys. 139, 094105 (2013)]. The new algorithm is an order of magnitude faster than our previous one and removes the bottleneck related to the calculation of the (T) contribution. First, a numerical Laplace transformed expression for the (T) fragment energy is introduced, which requires on average 3 to 4 times fewer floating point operations with negligible compromise in accuracy eliminating the redundancy among the evaluated triples amplitudes. Second, an additional speedup factor of 3 is achieved by the optimization of our canonical (T) algorithm, which is also executed in the local case. These developments can also be integrated into canonical as well as alternative fragmentation-based local CCSD(T) approaches with minor modifications. As it is demonstrated by our benchmark calculations, the evaluation of the new Laplace transformed (T) correction can always be performed if the preceding CCSD iterations are feasible, and the new scheme enables the computation of LNO-CCSD(T) correlation energies with at least triple-zeta quality basis sets for realistic three-dimensional molecules with more than 600 atoms and 12 000 basis functions in a matter of days on a single processor.

Game-theoretic learning and distributed optimization in memoryless multi-agent systems

CERN Document Server

Tatarenko, Tatiana

2017-01-01

This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during scommunication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. .

Exploiting Sparsity in SDP Relaxation for Sensor Network Localization

NARCIS (Netherlands)

S. Kim (Sunyoung); M. Kojima; H. Waki (Hayato)

2008-01-01

htmlabstract A sensor network localization problem can be formulated as a quadratic optimization problem (QOP). For quadratic optimization problems, semidefinite programming (SDP) relaxation by Lasserre with relaxation order 1 for general polynomial optimization problems (POPs) is known to be

Exploiting Sparsity in SDP Relaxation for Sensor Network Localization

NARCIS (Netherlands)

S. Kim (Sunyoung); M. Kojima; H. Waki (Hayato)

2009-01-01

htmlabstract A sensor network localization problem can be formulated as a quadratic optimization problem (QOP). For quadratic optimization problems, semidefinite programming (SDP) relaxation by Lasserre with relaxation order 1 for general polynomial optimization problems (POPs) is known to be

Planning Target Volume D95 and Mean Dose Should Be Considered for Optimal Local Control for Stereotactic Ablative Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Zhao, Lina [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Zhou, Shouhao [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Balter, Peter [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Shen, Chan [Department of Health Service Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Gomez, Daniel R.; Welsh, James D.; Lin, Steve H. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chang, Joe Y., E-mail: jychang@mdanderson.org [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

2016-07-15

Purpose: To identify the optimal dose parameters predictive for local/lobar control after stereotactic ablative radiation therapy (SABR) in early-stage non-small cell lung cancer (NSCLC). Methods and Materials: This study encompassed a total of 1092 patients (1200 lesions) with NSCLC of clinical stage T1-T2 N0M0 who were treated with SABR of 50 Gy in 4 fractions or 70 Gy in 10 fractions, depending on tumor location/size, using computed tomography-based heterogeneity corrections and a convolution superposition calculation algorithm. Patients were monitored by chest CT or positron emission tomography/CT and/or biopsy after SABR. Factors predicting local/lobar recurrence (LR) were determined by competing risk multivariate analysis. Continuous variables were divided into 2 subgroups at cutoff values identified by receiver operating characteristic curves. Results: At a median follow-up time of 31.7 months (interquartile range, 14.8-51.3 months), the 5-year time to local recurrence within the same lobe and overall survival rates were 93.8% and 44.8%, respectively. Total cumulative number of patients experiencing LR was 40 (3.7%), occurring at a median time of 14.4 months (range, 4.8-46 months). Using multivariate competing risk analysis, independent predictive factors for LR after SABR were minimum biologically effective dose (BED{sub 10}) to 95% of planning target volume (PTVD95 BED{sub 10}) ≤86 Gy (corresponding to PTV D95 physics dose of 42 Gy in 4 fractions or 55 Gy in 10 fractions) and gross tumor volume ≥8.3 cm{sup 3}. The PTVmean BED{sub 10} was highly correlated with PTVD95 BED{sub 10.} In univariate analysis, a cutoff of 130 Gy for PTVmean BED{sub 10} (corresponding to PTVmean physics dose of 55 Gy in 4 fractions or 75 Gy in 10 fractions) was also significantly associated with LR. Conclusions: In addition to gross tumor volume, higher radiation dose delivered to the PTV predicts for better local/lobar control. We recommend that both PTVD95 BED

Periodic local MP2 method employing orbital specific virtuals

International Nuclear Information System (INIS)

Usvyat, Denis; Schütz, Martin; Maschio, Lorenzo

2015-01-01

We introduce orbital specific virtuals (OSVs) to represent the truncated pair-specific virtual space in periodic local Møller-Plesset perturbation theory of second order (LMP2). The OSVs are constructed by diagonalization of the LMP2 amplitude matrices which correspond to diagonal Wannier-function (WF) pairs. Only a subset of these OSVs is adopted for the subsequent OSV-LMP2 calculation, namely, those with largest contribution to the diagonal pair correlation energy and with the accumulated value of these contributions reaching a certain accuracy. The virtual space for a general (non diagonal) pair is spanned by the union of the two OSV sets related to the individual WFs of the pair. In the periodic LMP2 method, the diagonal LMP2 amplitude matrices needed for the construction of the OSVs are calculated in the basis of projected atomic orbitals (PAOs), employing very large PAO domains. It turns out that the OSVs are excellent to describe short range correlation, yet less appropriate for long range van der Waals correlation. In order to compensate for this bias towards short range correlation, we augment the virtual space spanned by the OSVs by the most diffuse PAOs of the corresponding minimal PAO domain. The Fock and overlap matrices in OSV basis are constructed in the reciprocal space. The 4-index electron repulsion integrals are calculated by local density fitting and, for distant pairs, via multipole approximation. New procedures for determining the fit-domains and the distant-pair lists, leading to higher efficiency in the 4-index integral evaluation, have been implemented. Generally, and in contrast to our previous PAO based periodic LMP2 method, the OSV-LMP2 method does not require anymore great care in the specification of the individual domains (to get a balanced description when calculating energy differences) and is in that sense a black box procedure. Discontinuities in potential energy surfaces, which may occur for PAO-based calculations if one is not

Periodic local MP2 method employing orbital specific virtuals

Energy Technology Data Exchange (ETDEWEB)

Usvyat, Denis, E-mail: denis.usvyat@chemie.uni-regensburg.de; Schütz, Martin, E-mail: martin.schuetz@chemie.uni-regensburg.de [Institute for Physical and Theoretical Chemistry, Universität Regensburg, Universitätsstraße 31, D-93040 Regensburg (Germany); Maschio, Lorenzo, E-mail: lorenzo.maschio@unito.it [Dipartimento di Chimica, and Centre of Excellence NIS (Nanostructured Interfaces and Surfaces), Università di Torino, via Giuria 5, I-10125 Torino (Italy)

2015-09-14

We introduce orbital specific virtuals (OSVs) to represent the truncated pair-specific virtual space in periodic local Møller-Plesset perturbation theory of second order (LMP2). The OSVs are constructed by diagonalization of the LMP2 amplitude matrices which correspond to diagonal Wannier-function (WF) pairs. Only a subset of these OSVs is adopted for the subsequent OSV-LMP2 calculation, namely, those with largest contribution to the diagonal pair correlation energy and with the accumulated value of these contributions reaching a certain accuracy. The virtual space for a general (non diagonal) pair is spanned by the union of the two OSV sets related to the individual WFs of the pair. In the periodic LMP2 method, the diagonal LMP2 amplitude matrices needed for the construction of the OSVs are calculated in the basis of projected atomic orbitals (PAOs), employing very large PAO domains. It turns out that the OSVs are excellent to describe short range correlation, yet less appropriate for long range van der Waals correlation. In order to compensate for this bias towards short range correlation, we augment the virtual space spanned by the OSVs by the most diffuse PAOs of the corresponding minimal PAO domain. The Fock and overlap matrices in OSV basis are constructed in the reciprocal space. The 4-index electron repulsion integrals are calculated by local density fitting and, for distant pairs, via multipole approximation. New procedures for determining the fit-domains and the distant-pair lists, leading to higher efficiency in the 4-index integral evaluation, have been implemented. Generally, and in contrast to our previous PAO based periodic LMP2 method, the OSV-LMP2 method does not require anymore great care in the specification of the individual domains (to get a balanced description when calculating energy differences) and is in that sense a black box procedure. Discontinuities in potential energy surfaces, which may occur for PAO-based calculations if one is not

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform.

Science.gov (United States)

Bai, Guanbing; Liu, Jinghong; Song, Yueming; Zuo, Yujia

2017-01-06

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: σ B = 1.63 × 10 - 4 ( ° ) , σ L = 1.35 × 10 - 4 ( ° ) , σ H = 15.8 ( m ) , σ s u m = 27.6 ( m ) , where σ B represents the longitude error, σ L represents the latitude error, σ H represents the altitude error, and σ s u m represents the error radius.

Robust optimization of the billet for isothermal local loading transitional region of a Ti-alloy rib-web component based on dual-response surface method

Science.gov (United States)

Wei, Ke; Fan, Xiaoguang; Zhan, Mei; Meng, Miao

2018-03-01

Billet optimization can greatly improve the forming quality of the transitional region in the isothermal local loading forming (ILLF) of large-scale Ti-alloy ribweb components. However, the final quality of the transitional region may be deteriorated by uncontrollable factors, such as the manufacturing tolerance of the preforming billet, fluctuation of the stroke length, and friction factor. Thus, a dual-response surface method (RSM)-based robust optimization of the billet was proposed to address the uncontrollable factors in transitional region of the ILLF. Given that the die underfilling and folding defect are two key factors that influence the forming quality of the transitional region, minimizing the mean and standard deviation of the die underfilling rate and avoiding folding defect were defined as the objective function and constraint condition in robust optimization. Then, the cross array design was constructed, a dual-RSM model was established for the mean and standard deviation of the die underfilling rate by considering the size parameters of the billet and uncontrollable factors. Subsequently, an optimum solution was derived to achieve the robust optimization of the billet. A case study on robust optimization was conducted. Good results were attained for improving the die filling and avoiding folding defect, suggesting that the robust optimization of the billet in the transitional region of the ILLF was efficient and reliable.

Multi-Objective Optimization in Physical Synthesis of Integrated Circuits

CERN Document Server

A Papa, David

2013-01-01

This book introduces techniques that advance the capabilities and strength of modern software tools for physical synthesis, with the ultimate goal to improve the quality of leading-edge semiconductor products.  It provides a comprehensive introduction to physical synthesis and takes the reader methodically from first principles through state-of-the-art optimizations used in cutting edge industrial tools. It explains how to integrate chip optimizations in novel ways to create powerful circuit transformations that help satisfy performance requirements. Broadens the scope of physical synthesis optimization to include accurate transformations operating between the global and local scales; Integrates groups of related transformations to break circular dependencies and increase the number of circuit elements that can be jointly optimized to escape local minima;  Derives several multi-objective optimizations from first observations through complete algorithms and experiments; Describes integrated optimization te...

Optimization of fractionated radiotherapy of tumors

International Nuclear Information System (INIS)

Ivanov, V.K.

1984-01-01

Underlying modern conceptions of clinical radiobiology and mathematic methods in system theory a model of radiation therapy for tumors is developed. To obtain optimal fractionating conditions the principle of gradual optimization is used. A optimal therapeutic method permits to minimize the survival of a tumor cell population with localized lesions of the intact tissue. An analytic research is carried out for the simplest variant of the model. By help of a SORT-program unit the conditions are ascertained for gradual optimization of radiotherapy. (author)

A novel approach for optimal chiller loading using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Ardakani, A. Jahanbani; Ardakani, F. Fattahi; Hosseinian, S.H. [Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, Tehran 15875-4413 (Iran, Islamic Republic of)

2008-07-01

This study employs two new methods to solve optimal chiller loading (OCL) problem. These methods are continuous genetic algorithm (GA) and particle swarm optimization (PSO). Because of continuous nature of variables in OCL problem, continuous GA and PSO easily overcome deficiencies in other conventional optimization methods. Partial load ratio (PLR) of the chiller is chosen as the variable to be optimized and consumption power of the chiller is considered as fitness function. Both of these methods find the optimal solution while the equality constraint is exactly satisfied. Some of the major advantages of proposed approaches over other conventional methods can be mentioned as fast convergence, escaping from getting into local optima, simple implementation as well as independency of the solution from the problem. Abilities of proposed methods are examined with reference to an example system. To demonstrate these abilities, results are compared with binary genetic algorithm method. The proposed approaches can be perfectly applied to air-conditioning systems. (author)

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

Directory of Open Access Journals (Sweden)

Xiangsheng Zhang

2015-01-01

Full Text Available Aimed at the parameters optimization in support vector machine (SVM for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

Optimization of large-scale industrial systems : an emerging method

Energy Technology Data Exchange (ETDEWEB)

Hammache, A.; Aube, F.; Benali, M.; Cantave, R. [Natural Resources Canada, Varennes, PQ (Canada). CANMET Energy Technology Centre

2006-07-01

This paper reviewed optimization methods of large-scale industrial production systems and presented a novel systematic multi-objective and multi-scale optimization methodology. The methodology was based on a combined local optimality search with global optimality determination, and advanced system decomposition and constraint handling. The proposed method focused on the simultaneous optimization of the energy, economy and ecology aspects of industrial systems (E{sup 3}-ISO). The aim of the methodology was to provide guidelines for decision-making strategies. The approach was based on evolutionary algorithms (EA) with specifications including hybridization of global optimality determination with a local optimality search; a self-adaptive algorithm to account for the dynamic changes of operating parameters and design variables occurring during the optimization process; interactive optimization; advanced constraint handling and decomposition strategy; and object-oriented programming and parallelization techniques. Flowcharts of the working principles of the basic EA were presented. It was concluded that the EA uses a novel decomposition and constraint handling technique to enhance the Pareto solution search procedure for multi-objective problems. 6 refs., 9 figs.

Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

Directory of Open Access Journals (Sweden)

Kanagasabai Lenin

2015-04-01

Full Text Available In this paper, a novel approach Modified Monkey optimization (MMO algorithm for solving optimal reactive power dispatch problem has been presented. MMO is a population based stochastic meta-heuristic algorithm and it is inspired by intelligent foraging behaviour of monkeys. This paper improves both local leader and global leader phases.  The proposed (MMO algorithm has been tested in standard IEEE 30 bus test system and simulation results show the worthy performance of the proposed algorithm in reducing the real power loss.

Maximization of eigenvalues using topology optimization

DEFF Research Database (Denmark)

Pedersen, Niels Leergaard

2000-01-01

to localized modes in low density areas. The topology optimization problem is formulated using the SIMP method. Special attention is paid to a numerical method for removing localized eigenmodes in low density areas. The method is applied to numerical examples of maximizing the first eigenfrequency, One example...

Local and nonlocal information in a traffic network: how important is the horizon?

International Nuclear Information System (INIS)

Petri, G.

2010-01-01

Recent advances in distributed sensor network technology have changed the landscape of traffic optimization in which small, mobile devices are able to sense local information and communicate in real time with one another. Naive optimization algorithms that operate solely on the local or global level are inherently flawed, as global optimization requires every local sensor to communicate with a centralized base-station, creating prohibitive bandwidth, robustness, and security concerns, while local optimization methods are limited by a near information horizon as they are unable to propagate or react to information beyond their immediate vicinity. This paper investigates an intermediate approach where individual sensors are able to propagate congestion information over a variable distance that is determined in real-time. This strategy consistently out-performs a naive strategy where every car simply takes the shortest path to its destination, but does worse than a simpler optimization algorithm that only incorporates local information. This is most likely because the intermediate solution directs cars along the same alternate path when attempting to free a congested area, thus creating new congestion along the detour. The results suggest that local information might set an upper bound on performance in models of cascading in- formation. Further work is required to confirm this observation and develop an algorithm able to join both local and global information to effectively diffuse traffic around congestion. (author)

Optimal placement of capacito

Directory of Open Access Journals (Sweden)

N. Gnanasekaran

2016-06-01

Full Text Available Optimal size and location of shunt capacitors in the distribution system plays a significant role in minimizing the energy loss and the cost of reactive power compensation. This paper presents a new efficient technique to find optimal size and location of shunt capacitors with the objective of minimizing cost due to energy loss and reactive power compensation of distribution system. A new Shark Smell Optimization (SSO algorithm is proposed to solve the optimal capacitor placement problem satisfying the operating constraints. The SSO algorithm is a recently developed metaheuristic optimization algorithm conceptualized using the shark’s hunting ability. It uses a momentum incorporated gradient search and a rotational movement based local search for optimization. To demonstrate the applicability of proposed method, it is tested on IEEE 34-bus and 118-bus radial distribution systems. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

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

International Nuclear Information System (INIS)

Makino, K.; Berz, M.

2011-01-01

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

Interpretation of scanning tunneling quasiparticle interference and impurity states in cuprates.

Science.gov (United States)

Kreisel, A; Choubey, Peayush; Berlijn, T; Ku, W; Andersen, B M; Hirschfeld, P J

2015-05-29

We apply a recently developed method combining first principles based Wannier functions with solutions to the Bogoliubov-de Gennes equations to the problem of interpreting STM data in cuprate superconductors. We show that the observed images of Zn on the surface of Bi_{2}Sr_{2}CaCu_{2}O_{8} can only be understood by accounting for the tails of the Cu Wannier functions, which include significant weight on apical O sites in neighboring unit cells. This calculation thus puts earlier crude "filter" theories on a microscopic foundation and solves a long-standing puzzle. We then study quasiparticle interference phenomena induced by out-of-plane weak potential scatterers, and show how patterns long observed in cuprates can be understood in terms of the interference of Wannier functions above the surface. Our results show excellent agreement with experiment and enable a better understanding of novel phenomena in the cuprates via STM imaging.

Smart grid technologies in local electric grids

Science.gov (United States)

Lezhniuk, Petro D.; Pijarski, Paweł; Buslavets, Olga A.

2017-08-01

The research is devoted to the creation of favorable conditions for the integration of renewable sources of energy into electric grids, which were designed to be supplied from centralized generation at large electric power stations. Development of distributed generation in electric grids influences the conditions of their operation - conflict of interests arises. The possibility of optimal functioning of electric grids and renewable sources of energy, when complex criterion of the optimality is balance reliability of electric energy in local electric system and minimum losses of electric energy in it. Multilevel automated system for power flows control in electric grids by means of change of distributed generation of power is developed. Optimization of power flows is performed by local systems of automatic control of small hydropower stations and, if possible, solar power plants.

The concept of 'optimal' path in classical mechanics

International Nuclear Information System (INIS)

Passos, E.J.V. de; Cruz, F.F. de S.

1986-01-01

The significance of the concept of 'optimal' path in the framework of classical mechanics is discussed. The derivation of the local harmonic approximation and self-consistent collective coordinate method equations of the optimal path is based on a careful study of the concepts of local maximal decoupling and global maximal decoupling respectively. This exhibits the nature of the differences between these two theories and allows one to establish the conditions under which they become equivalent. (author)

Learning from Experiments in Optimization

DEFF Research Database (Denmark)

Winthereik, Brit Ross; Jensen, Casper Bruun

2017-01-01

This article examines attempts by professionals in the Danish branch of the environmental NGO NatureAid to optimize their practice by developing a local standard. Describing these efforts as an experiment in optimization, we outline a post-critical alternative to critiques that centre on the redu...... of management as ‘broken up;’ as a distributed, ambient activity, variably performed by different actors using different standards....

Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

Science.gov (United States)

Xin Yang; Kwang-Ting Cheng

2014-06-01

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile augmented reality (AR) system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.

Optimal perturbations for nonlinear systems using graph-based optimal transport

Science.gov (United States)

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.

Performance analysis of the partial use of a local optimization operator on the genetic algorithm for the Travelling Salesman Problem

Directory of Open Access Journals (Sweden)

Milan Djordjevic

2012-01-01

Full Text Available Background: The Travelling Salesman Problem is an NP-hard problem in combinatorial optimization with a number of practical implications. There are many heuristic algorithms and exact methods for solving the problem. Objectives: In this paper we study the influence of hybridization of a genetic algorithm with a local optimizer on solving instances of the Travelling Salesman Problem. Methods/ Approach: Our algorithm uses hybridization that occurs at various percentages of generations of a genetic algorithm. Moreover, we have also studied at which generations to apply the hybridization and hence applied it at random generations, at the initial generations, and at the last ones. Results: We tested our algorithm on instances with sizes ranging from 76 to 439 cities. On the one hand, the less frequent application of hybridization decreased the average running time of the algorithm from 14.62 sec to 2.78 sec at 100% and 10% hybridization respectively, while on the other hand, the quality of the solution on average deteriorated only from 0.21% till 1.40% worse than the optimal solution. Conclusions: In the paper we have shown that even a small hybridization substantially improves the quality of the result. Moreover, the hybridization in fact does not deteriorate the running time too much. Finally, our experiments show that the best results are obtained when hybridization occurs in the last generations of the genetic algorithm.

Direct Optimization of Printed Reflectarrays for Contoured Beam Satellite Antenna Applications

DEFF Research Database (Denmark)

Zhou, Min; Sorensen, Stig B.; Kim, Oleksiy S.

2013-01-01

An accurate and efficient direct optimization technique for the design of contoured beam reflectarrays is presented. It is based on the spectral domain method of moments assuming local periodicity and minimax optimization. Contrary to the conventional phase-only optimization techniques, the geome......An accurate and efficient direct optimization technique for the design of contoured beam reflectarrays is presented. It is based on the spectral domain method of moments assuming local periodicity and minimax optimization. Contrary to the conventional phase-only optimization techniques......, the geometrical parameters of the array elements are directly optimized to fulfill the contoured beam requirements, thus maintaining a direct relation between optimization goals and optimization variables, and hence resulting in more optimal designs. Both co- and cross-polar radiation patterns of the reflectarray...... can be optimized for multiple frequencies, polarizations, and feed illuminations. Several contoured beam reflectarrays, that radiate a high-gain beam on a European coverage, have been designed and compared to similar designs obtained using the phase-only optimization technique. The comparisons show...

Multicriteria optimization of the spatial dose distribution

International Nuclear Information System (INIS)

Schlaefer, Alexander; Viulet, Tiberiu; Muacevic, Alexander; Fürweger, Christoph

2013-01-01

Purpose: Treatment planning for radiation therapy involves trade-offs with respect to different clinical goals. Typically, the dose distribution is evaluated based on few statistics and dose–volume histograms. Particularly for stereotactic treatments, the spatial dose distribution represents further criteria, e.g., when considering the gradient between subregions of volumes of interest. The authors have studied how to consider the spatial dose distribution using a multicriteria optimization approach.Methods: The authors have extended a stepwise multicriteria optimization approach to include criteria with respect to the local dose distribution. Based on a three-dimensional visualization of the dose the authors use a software tool allowing interaction with the dose distribution to map objectives with respect to its shape to a constrained optimization problem. Similarly, conflicting criteria are highlighted and the planner decides if and where to relax the shape of the dose distribution.Results: To demonstrate the potential of spatial multicriteria optimization, the tool was applied to a prostate and meningioma case. For the prostate case, local sparing of the rectal wall and shaping of a boost volume are achieved through local relaxations and while maintaining the remaining dose distribution. For the meningioma, target coverage is improved by compromising low dose conformality toward noncritical structures. A comparison of dose–volume histograms illustrates the importance of spatial information for achieving the trade-offs.Conclusions: The results show that it is possible to consider the location of conflicting criteria during treatment planning. Particularly, it is possible to conserve already achieved goals with respect to the dose distribution, to visualize potential trade-offs, and to relax constraints locally. Hence, the proposed approach facilitates a systematic exploration of the optimal shape of the dose distribution

Aeroelastic Wingbox Stiffener Topology Optimization

Science.gov (United States)

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.

Stress-based topology optimization of concrete structures with prestressing reinforcements

Science.gov (United States)

Luo, Yangjun; Wang, Michael Yu; Deng, Zichen

2013-11-01

Following the extended two-material density penalization scheme, a stress-based topology optimization method for the layout design of prestressed concrete structures is proposed. The Drucker-Prager yield criterion is used to predict the asymmetrical strength failure of concrete. The prestress is considered by making a reasonable assumption on the prestressing orientation in each element and adding an additional load vector to the structural equilibrium function. The proposed optimization model is thus formulated as to minimize the reinforcement material volume under Drucker-Prager yield constraints on elemental concrete local stresses. In order to give a reasonable definition of concrete local stress and prevent the stress singularity phenomenon, the local stress interpolation function and the ɛ -relaxation technique are adopted. The topology optimization problem is solved using the method of moving asymptotes combined with an active set strategy. Numerical examples are given to show the efficiency of the proposed optimization method in the layout design of prestressed concrete structures.

Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization

Science.gov (United States)

Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei

2014-04-01

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

Science.gov (United States)

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Absence of multiple local minima effects in intensity modulated optimization with dose-volume constraints

Energy Technology Data Exchange (ETDEWEB)

Llacer, Jorge [EC Engineering Consultants, LLC 130, Forest Hill Drive, Los Gatos, CA (United States); Deasy, Joseph O [Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO (United States); Bortfeld, Thomas R [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, 30 Fruit Street, Boston, MA (United States); Solberg, Timothy D [Department of Radiation Oncology, University of California, Los Angeles, CA (United States); Promberger, Claus [BrainLAB AG, Ammerthalstrasse 8, 85551 Heimstetten (Germany)

2003-01-21

This paper reports on the analysis of intensity modulated radiation treatment optimization problems in the presence of non-convex feasible parameter spaces caused by the specification of dose-volume constraints for the organs-at-risk (OARs). The main aim was to determine whether the presence of those non-convex spaces affects the optimization of clinical cases in any significant way. This was done in two phases: (1) Using a carefully designed two-dimensional mathematical phantom that exhibits two controllable minima and with randomly initialized beamlet weights, we developed a methodology for exploring the nature of the convergence characteristics of quadratic cost function optimizations (deterministic or stochastic). The methodology is based on observing the statistical behaviour of the residual cost at the end of optimizations in which the stopping criterion is progressively more demanding and carrying out those optimizations to very small error changes per iteration. (2) Seven clinical cases were then analysed with dose-volume constraints that are stronger than originally used in the clinic. The clinical cases are two prostate cases differently posed, a meningioma case, two head-and-neck cases, a spleen case and a spine case. Of the 14 different sets of optimizations (with and without the specification of maximum doses allowed for the OARs), 12 fail to show any effect due to the existence of non-convex feasible spaces. The remaining two sets of optimizations show evidence of multiple minima in the solutions, but those minima are very close to each other in cost and the resulting treatment plans are practically identical, as measured by the quality of the dose-volume histograms (DVHs). We discuss the differences between fluence maps resulting from those similar treatment plans. We provide a possible reason for the observed results and conclude that, although the study is necessarily limited, the annealing characteristics of a simulated annealing method may not be

Selection of the optimal radiotherapy technique for locally advanced hepatocellular carcinoma

International Nuclear Information System (INIS)

Lee, Ik-Jae; Seong, Jinsil; Koom, Woong-Sub; Kim, Yong-Bae; Jeon, Byeong-Chul; Kim, Joo-Ho; Han, Kwang-Hyub

2011-01-01

Various techniques are available for radiotherapy of hepatocellular carcinoma, including three-dimensional conformal radiotherapy, linac-based intensity-modulated radiotherapy and helical tomotherapy. The purpose of this study was to determine the optimal radiotherapy technique for hepatocellular carcinoma. Between 2006 and 2007, 12 patients underwent helical tomotherapy for locally advanced hepatocellular carcinoma. Helical tomotherapy computerized radiotherapy planning was compared with the best computerized radiotherapy planning for three-dimensional conformal radiotherapy and linac-based intensity-modulated radiotherapy for the delivery of 60 Gy in 30 fractions. Tumor coverage was assessed by conformity index, radical dose homogeneity index and moderated dose homogeneity index. Computerized radiotherapy planning was also compared according to the tumor location. Tumor coverage was shown to be significantly superior with helical tomotherapy as assessed by conformity index and moderated dose homogeneity index (P=0.002 and 0.03, respectively). Helical tomotherapy showed significantly lower irradiated liver volume at 40, 50 and 60 Gy (V40, V50 and V60, P=0.04, 0.03 and 0.01, respectively). On the contrary, the dose-volume of three-dimensional conformal radiotherapy at V20 was significantly smaller than those of linac-based intensity-modulated radiotherapy and helical tomotherapy in the remaining liver (P=0.03). Linac-based intensity-modulated radiotherapy showed better sparing of the stomach compared with helical tomotherapy in the case of separated lesions in both lobes (12.3 vs. 24.6 Gy). Helical tomotherapy showed the high dose-volume exposure to the left kidney due to helical delivery in the right lobe lesion. Helical tomotherapy achieved the best tumor coverage of the remaining normal liver. However, helical tomotherapy showed much exposure to the remaining liver at the lower dose region and left kidney. (author)

Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial

Energy Technology Data Exchange (ETDEWEB)

Salavati, Ali [Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, PA (United States); University of Minnesota, Department of Radiology, Minneapolis, MN (United States); Duan, Fenghai [Brown University School of Public Health, Department of Biostatistics and Center for Statistical Sciences, Providence, RI (United States); Snyder, Bradley S. [Brown University School of Public Health, Center for Statistical Sciences, Providence, RI (United States); Wei, Bo [Emory University, Department of Biostatistics, Rollins School of Public Health, Atlanta, GA (United States); Houshmand, Sina; Alavi, Abass [Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, PA (United States); Khiewvan, Benjapa [Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, PA (United States); Mahidol University, Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine Siriraj Hospital, Bangkok (Thailand); Opanowski, Adam [ACR Center for Research and Innovation, American College of Radiology, Philadelphia, PA (United States); Simone, Charles B. [University of Maryland Medical Center, Department of Radiation Oncology, Baltimore, MD (United States); Siegel, Barry A. [Washington University School of Medicine, Mallinckrodt Institute of Radiology and the Alvin J. Siteman Cancer Center, St, Louis, MO (United States); Machtay, Mitchell [Case Western Reserve University and University Hospitals Case Medical Center, Department of Radiation Oncology, Cleveland, OH (United States)

2017-11-15

In recent years, multiple studies have demonstrated the value of volumetric FDG-PET/CT parameters as independent prognostic factors in patients with non-small cell lung cancer (NSCLC). We aimed to determine the optimal cut-off points of pretreatment volumetric FDG-PET/CT parameters in predicting overall survival (OS) in patients with locally advanced NSCLC and to recommend imaging biomarkers appropriate for routine clinical applications. Patients with inoperable stage IIB/III NSCLC enrolled in ACRIN 6668/RTOG 0235 were included. Pretreatment FDG-PET scans were quantified using semiautomatic adaptive contrast-oriented thresholding and local-background partial-volume-effect-correction algorithms. For each patient, the following indices were measured: metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean for the whole-body, primary tumor, and regional lymph nodes. The association between each index and patient outcome was assessed using Cox proportional hazards regression. Optimal cut-off points were estimated using recursive binary partitioning in a conditional inference framework and used in Kaplan-Meier curves with log-rank testing. The discriminatory ability of each index was examined using time-dependent receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC(t)). The study included 196 patients. Pretreatment whole-body and primary tumor MTV, TLG, and pvcTLG were independently prognostic of OS. Optimal cut-off points were 175.0, 270.9, and 35.5 cm{sup 3} for whole-body TLG, pvcTLG, and MTV, and were 168.2, 239.8, and 17.4 cm{sup 3} for primary tumor TLG, pvcTLG, and MTV, respectively. In time-dependent ROC analysis, AUC(t) for MTV and TLG were uniformly higher than that of SUV measures over all time points. Primary tumor and whole-body parameters demonstrated similar patterns of separation for those patients above versus below the optimal cut

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

Science.gov (United States)

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.

Welding Robot Collision-Free Path Optimization

Directory of Open Access Journals (Sweden)

Xuewu Wang

2017-02-01

Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.

A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO

Directory of Open Access Journals (Sweden)

Mehdi Neshat

2015-11-01

Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

Enabling Controlling Complex Networks with Local Topological Information.

Science.gov (United States)

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.

[Safe local anesthesia in patients with bronchial asthma].

Science.gov (United States)

Anisimova, E N; Gromovik, M V

The paper presents the analysis of studies of local anesthesia in patients with bronchial asthma. It was found that the diagnosis of hypersensitivity to sodium metabisulfite in patients with bronchial asthma must be optimized for development of local anesthesia selection algorithm in outpatient dentistry.

Basic studies of atomic dynamics. Technical progress report, August 1, 1984-June 30, 1985

International Nuclear Information System (INIS)

Fano, U.

1985-01-01

A procedure has been developed to represent the critical propagation of wave functions on potential ridges by constructing and superposing approximate wave functions that represent Wannier's diverging and converging modes. This procedure also serves to represent optimized adiabatic propagation in potential valleys and is designed for application to reactive collisions as well as to spectra. The pilot studies of the effect of channel coupling in open valence shells has been completed very successfully. Its method is now available for application throughout the periodic system, to collisions as well as to spectra. Renewed analysis of the theory of atomic orientation by electron collisions is providing new insights

Identification of distinct phenotypes of locally advanced pancreatic adenocarcinoma.

LENUS (Irish Health Repository)

Teo, Minyuen

2013-03-01

A significant number of pancreatic ductal adenocarcinoma present as locally advanced disease. Optimal treatment remains controversial. We sought to analyze the clinical course of locally advanced pancreatic adenocarcinoma (LAPC) in order to identify potential distinct clinical phenotypes.

Hierarchical models and iterative optimization of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

2016-06-08

A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

Symmetrized local co-registration optimization for anomalous change detection

Energy Technology Data Exchange (ETDEWEB)

Wohlberg, Brendt E [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

2009-01-01

The goal of anomalous change detection (ACD) is to identify what unusual changes have occurred in a scene, based on two images of the scene taken at different times and under different conditions. The actual anomalous changes need to be distinguished from the incidental differences that occur throughout the imagery, and one of the most common and confounding of these incidental differences is due to the misregistration of the images, due to limitations of the registration pre-processing applied to the image pair. We propose a general method to compensate for residual misregistration in any ACD algorithm which constructs an estimate of the degree of 'anomalousness' for every pixel in the image pair. The method computes a modified misregistration-insensitive anomalousness by making local re-registration adjustments to minimize the local anomalousness. In this paper we describe a symmetrized version of our initial algorithm, and find significant performance improvements in the anomalous change detection ROC curves for a number of real and synthetic data sets.

Landscape encodings enhance optimization.

Directory of Open Access Journals (Sweden)

Konstantin Klemm

Full Text Available Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state.

Landscape Encodings Enhance Optimization

Science.gov (United States)

Klemm, Konstantin; Mehta, Anita; Stadler, Peter F.

2012-01-01

Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state. PMID:22496860

Infill Optimization for Additive Manufacturing-Approaching Bone-Like Porous Structures.

Science.gov (United States)

Wu, Jun; Aage, Niels; Westermann, Rudiger; Sigmund, Ole

2018-02-01

Porous structures such as trabecular bone are widely seen in nature. These structures are lightweight and exhibit strong mechanical properties. In this paper, we present a method to generate bone-like porous structures as lightweight infill for additive manufacturing. Our method builds upon and extends voxel-wise topology optimization. In particular, for the purpose of generating sparse yet stable structures distributed in the interior of a given shape, we propose upper bounds on the localized material volume in the proximity of each voxel in the design domain. We then aggregate the local per-voxel constraints by their p-norm into an equivalent global constraint, in order to facilitate an efficient optimization process. Implemented on a high-resolution topology optimization framework, our results demonstrate mechanically optimized, detailed porous structures which mimic those found in nature. We further show variants of the optimized structures subject to different design specifications, and we analyze the optimality and robustness of the obtained structures.

Optimization Formulations for the Maximum Nonlinear Buckling Load of Composite Structures

DEFF Research Database (Denmark)

Lindgaard, Esben; Lund, Erik

2011-01-01

This paper focuses on criterion functions for gradient based optimization of the buckling load of laminated composite structures considering different types of buckling behaviour. A local criterion is developed, and is, together with a range of local and global criterion functions from literature......, benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems...... solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization. © 2010 Springer-Verlag....

Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.

Science.gov (United States)

Wang, Xinghu; Hong, Yiguang; Ji, Haibo

2016-07-01

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

Towards an improved continuum theory for phase transformations

International Nuclear Information System (INIS)

Tijssens, M.G.A.; James, R.D.

2003-01-01

We develop a continuum theory for martensitic phase transformations in which explicit use is made of atomistic calculations based on density functional theory. Following the work of Rabe and coworkers, branches of the phonon-dispersion relation with imaginary frequencies are selected to construct a localized basis tailored to the symmetry of the crystal lattice. This so-called Wannier basis helps to construct an effective Hamiltonian of a particularly simple form. We extend the methodology by incorporating finite deformations and passing the effective Hamiltonian fully to continuum level. The developments so far are implemented on the shape memory material NiTi

RNA Localization in Astrocytes

DEFF Research Database (Denmark)

Thomsen, Rune

2012-01-01

, regulation of the blood brain barrier and glial scar tissue formation. Despite the involvement in various CNS functions only a limited number of studies have addressed mRNA localization in astrocytes. This PhD project was initially focused on developing and implementing methods that could be used to asses mRNA......Messenger RNA (mRNA) localization is a mechanism by which polarized cells can regulate protein synthesis to specific subcellular compartments in a spatial and temporal manner, and plays a pivotal role in multiple physiological processes from embryonic development to cell differentiation...... localization in astrocyte protrusions, and following look into the subcellular localization pattern of specific mRNA species of both primary astrocytes isolated from cortical hemispheres of newborn mice, and the mouse astrocyte cell line, C8S. The Boyden chamber cell fractionation assay was optimized, in a way...

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

Open space preservation, property value, and optimal spatial configuration

Science.gov (United States)

Yong Jiang; Stephen K. Swallow

2007-01-01

The public has increasingly demonstrated a strong support for open space preservation. How to finance the socially efficient level of open space with the optimal spatial structure is of high policy relevance to local governments. In this study, we developed a spatially explicit open space model to help identify the socially optimal amount and optimal spatial...

A scalable coevolutionary multi-objective particle swarm optimizer

Directory of Open Access Journals (Sweden)

Xiangwei Zheng

2010-11-01

Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

Using complete measurement statistics for optimal device-independent randomness evaluation

International Nuclear Information System (INIS)

Nieto-Silleras, O; Pironio, S; Silman, J

2014-01-01

The majority of recent works investigating the link between non-locality and randomness, e.g. in the context of device-independent cryptography, do so with respect to some specific Bell inequality, usually the CHSH inequality. However, the joint probabilities characterizing the measurement outcomes of a Bell test are richer than just the degree of violation of a single Bell inequality. In this work we show how to take this extra information into account in a systematic manner in order to optimally evaluate the randomness that can be certified from non-local correlations. We further show that taking into account the complete set of outcome probabilities is equivalent to optimizing over all possible Bell inequalities, thereby allowing us to determine the optimal Bell inequality for certifying the maximal amount of randomness from a given set of non-local correlations. (paper)

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

Directory of Open Access Journals (Sweden)

Xiaohua Nie

2017-01-01

Full Text Available 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.

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

Science.gov (United States)

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.

An Approximate Method for Solving Optimal Control Problems for Discrete Systems Based on Local Approximation of an Attainability Set

Directory of Open Access Journals (Sweden)

V. A. Baturin

2017-03-01

Full Text Available An optimal control problem for discrete systems is considered. A method of successive improvements along with its modernization based on the expansion of the main structures of the core algorithm about the parameter is suggested. The idea of the method is based on local approximation of attainability set, which is described by the zeros of the Bellman function in the special problem of optimal control. The essence of the problem is as follows: from the end point of the phase is required to find a path that minimizes functional deviations of the norm from the initial state. If the initial point belongs to the attainability set of the original controlled system, the value of the Bellman function equal to zero, otherwise the value of the Bellman function is greater than zero. For this special task Bellman equation is considered. The support approximation and Bellman equation are selected. The Bellman function is approximated by quadratic terms. Along the allowable trajectory, this approximation gives nothing, because Bellman function and its expansion coefficients are zero. We used a special trick: an additional variable is introduced, which characterizes the degree of deviation of the system from the initial state, thus it is obtained expanded original chain. For the new variable initial nonzero conditions is selected, thus obtained trajectory is lying outside attainability set and relevant Bellman function is greater than zero, which allows it to hold a non-trivial approximation. As a result of these procedures algorithms of successive improvements is designed. Conditions for relaxation algorithms and conditions for the necessary conditions of optimality are also obtained.

Fuel shuffling optimization for the Delft research reactor

Energy Technology Data Exchange (ETDEWEB)

Geemert, R. van; Hoogenboom, J.E.; Gibcus, H.P.M. [Delft Univ. of Technology, Interfaculty Reactor Inst., Delft (Netherlands); Quist, A.J. [Delft Univ., Fac. of Applied Mathematics and Informatics, Delft (Netherlands)

1997-07-01

A fuel shuffling optimization procedure is proposed for the Hoger Onderwijs Reactor (HOR) in Delft, the Netherlands, a 2 MWth swimming-pool type research reactor. In order to cope with the fluctuatory behaviour of objective functions in loading pattern optimization, the proposed cyclic permutation optimization procedure features a gradual transition from global to local search behaviour via the introduction of stochastic tests for the number of fuel assemblies involved in a cyclic permutation. The possible objectives and the safety and operation constraints, as well as the optimization procedure, are discussed, followed by some optimization results for the HOR. (author) 5 figs., 4 refs.

Fuel shuffling optimization for the Delft research reactor

International Nuclear Information System (INIS)

Geemert, R. van; Hoogenboom, J.E.; Gibcus, H.P.M.; Quist, A.J.

1997-01-01

A fuel shuffling optimization procedure is proposed for the Hoger Onderwijs Reactor (HOR) in Delft, the Netherlands, a 2 MWth swimming-pool type research reactor. In order to cope with the fluctuatory behaviour of objective functions in loading pattern optimization, the proposed cyclic permutation optimization procedure features a gradual transition from global to local search behaviour via the introduction of stochastic tests for the number of fuel assemblies involved in a cyclic permutation. The possible objectives and the safety and operation constraints, as well as the optimization procedure, are discussed, followed by some optimization results for the HOR. (author)

Fuel shuffling optimization for the Delft research reactor

Energy Technology Data Exchange (ETDEWEB)

Geemert, R. van; Hoogenboom, J.E.; Gibcus, H.P.M. [Delft Univ. of Technology, Interfaculty Reactor Inst., Delft (Netherlands); Quist, A.J. [Delft Univ., Fac. of Applied Mathematics and Informatics, Delft (Netherlands)

1997-07-01

A fuel shuffling optimization procedure is proposed for the Hoger Onderwijs Reactor (HOR) in Delft, the Netherlands, a 2 MWth swimming-pool type research reactor. In order to cope with the fluctuatory behaviour of objective functions in loading pattern optimization, the proposed cyclic permutation optimization procedure features a gradual transition from global to local search behaviour via the introduction of stochastic tests for the number of fuel assemblies involved in a cyclic permutation. The possible objectives and the safety and operation constraints, as well as the optimization procedure, are discussed, followed by some optimization results for the HOR. (author)

Lens design and local minima

International Nuclear Information System (INIS)

Brixner, B.

1981-01-01

The widespread belief that local minima exist in the least squares lens-design error function is not confirmed by the Los Alamos Scientific Laboratory (LASL) optimization program. LASL finds the optimum-mimimum region, which is characterized by small parameter gradients of similar size, small performance improvement per iteration, and many designs that give similar performance. Local minima and unique prescriptions have not been found in many-parameter problems. The reason for these absences is that image errors caused by a change in one parameter can be compensated by changes in the remaining parameters. False local minima have been found, and four cases are discussed

Maximizing dosimetric benefits of IMRT in the treatment of localized prostate cancer through multicriteria optimization planning

International Nuclear Information System (INIS)

Wala, Jeremiah; Craft, David; Paly, Jon; Zietman, Anthony; Efstathiou, Jason

2013-01-01

We examine the quality of plans created using multicriteria optimization (MCO) treatment planning in intensity-modulated radiation therapy (IMRT) in treatment of localized prostate cancer. Nine random cases of patients receiving IMRT to the prostate were selected. Each case was associated with a clinically approved plan created using Corvus. The cases were replanned using MCO-based planning in RayStation. Dose-volume histogram data from both planning systems were presented to 2 radiation oncologists in a blinded evaluation, and were compared at a number of dose-volume points. Both physicians rated all 9 MCO plans as superior to the clinically approved plans (p −5 ). Target coverage was equivalent (p = 0.81). Maximum doses to the prostate and bladder and the V50 and V70 to the anterior rectum were reduced in all MCO plans (p<0.05). Treatment planning time with MCO took approximately 60 minutes per case. MCO-based planning for prostate IMRT is efficient and produces high-quality plans with good target homogeneity and sparing of the anterior rectum, bladder, and femoral heads, without sacrificing target coverage

Maximizing dosimetric benefits of IMRT in the treatment of localized prostate cancer through multicriteria optimization planning

Energy Technology Data Exchange (ETDEWEB)

Wala, Jeremiah; Craft, David [Harvard Medical School, Boston, MA (United States); Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA (United States); Paly, Jon [Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA (United States); Zietman, Anthony [Harvard Medical School, Boston, MA (United States); Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA (United States); Efstathiou, Jason, E-mail: jefstathiou@partners.org [Harvard Medical School, Boston, MA (United States); Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA (United States)

2013-10-01

We examine the quality of plans created using multicriteria optimization (MCO) treatment planning in intensity-modulated radiation therapy (IMRT) in treatment of localized prostate cancer. Nine random cases of patients receiving IMRT to the prostate were selected. Each case was associated with a clinically approved plan created using Corvus. The cases were replanned using MCO-based planning in RayStation. Dose-volume histogram data from both planning systems were presented to 2 radiation oncologists in a blinded evaluation, and were compared at a number of dose-volume points. Both physicians rated all 9 MCO plans as superior to the clinically approved plans (p<10{sup −5}). Target coverage was equivalent (p = 0.81). Maximum doses to the prostate and bladder and the V50 and V70 to the anterior rectum were reduced in all MCO plans (p<0.05). Treatment planning time with MCO took approximately 60 minutes per case. MCO-based planning for prostate IMRT is efficient and produces high-quality plans with good target homogeneity and sparing of the anterior rectum, bladder, and femoral heads, without sacrificing target coverage.

THE NECESSITY OF IMPLEMENTING REFORMS IN THE FIELD OF LOCAL PUBLIC FINANCE

Directory of Open Access Journals (Sweden)

Vezure Oana Sabina

2013-06-01

Full Text Available The difficulties faced by local authorities as a result of the austerity conditions in which they work, the elements inherited from the previous regime, the need for additional resources to optimize public finance to meet the needs, optimally, if possible, citizens, require the design and continue the reform of public finances at the local level that correspond to these requirements. Optimization of the reform process in local public finances depend to a great extent on the use of financial levers of fiscal efficiency, fulfilment of the functions of public finance, the way resources are provided and how their administration for economic and social development. The uneven development of economic weakness of the assembly reflect and are unacceptable because, in their turn, become a source of economic and political instability. Responsibility for ensuring sufficient local revenue must not belong to a large measure, the central authorities, the context in which local authorities should prioritize finding solutions to supplement the local budget and obtain funds from the central budget. At the same time, cannot be intended directions of reform in the field of public administration without taking into account the financial implications reflected in the budgets for each level of Government, pyramid-shaped, from central to local level.

Optimized probes for dose rate measurements at local government sites and in emergency planning zones and their integration into measurement networks

International Nuclear Information System (INIS)

Kuca, Petr; Helebrant, Jan; Cespirova, Irena; Judas, Libor; Skala, Lukas

2015-01-01

The results of a security project aimed at the development of a radiation situation monitoring system using optimized probes for dose rate measurements are described. The system is suitable for use at local government sites as well as at other sites. The system includes dose rate measurement probes with the variable configuration functionality (detection part), equipment for data transfer to a central workplace (communication part) and application for collection, storage and administration of the results and their presentation at a website (presentation part). The dosimetric and other operational properties of the probes were tested and the feasibility of their integration into measurement networks using the IMS central application was examined. (orig.)

Newton-type method for the variational discretization of topology optimization problems

DEFF Research Database (Denmark)

Evgrafov, Anton

2013-01-01

We present a locally quadratically convergent optimization algorithm for solving topology optimization problems. The distinguishing feature of the algorithm is to treat the design as a smooth function of the state and not vice versa as in the traditional nested approach to topology optimization, ...

Optimal Placing of Wind Turbines: Modelling the Uncertainty

NARCIS (Netherlands)

Leenman, T.S.; Phillipson, F.

2015-01-01

When looking at the optimal place to locate a wind turbine, trade-offs have to be made between local placement and spreading: transmission loss favours local placements and the correlation between the stochastic productions of wind turbines favours spreading. In this paper steps are described to

Optimal Placing of Wind Turbines: Modelling the Uncertainty

NARCIS (Netherlands)

Leenman, T.S.; Phillipson, F.

2014-01-01

When looking at the optimal place to locate a wind turbine, trade-offs have to be made between local placement and spreading: transmission loss favours local placements and the correlation between the stochastic productions of wind turbines favours spreading. In this paper steps are described to

Conference on "State of the Art in Global Optimization : Computational Methods and Applications"

CERN Document Server

Pardalos, P

1996-01-01

Optimization problems abound in most fields of science, engineering, and technology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob­ lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver­ age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solvin...

Configuration space analysis of common cost functions in radiotherapy beam-weight optimization algorithms

Energy Technology Data Exchange (ETDEWEB)

Rowbottom, Carl Graham [Joint Department of Physics, Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey (United Kingdom); Webb, Steve [Joint Department of Physics, Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey (United Kingdom)

2002-01-07

The successful implementation of downhill search engines in radiotherapy optimization algorithms depends on the absence of local minima in the search space. Such techniques are much faster than stochastic optimization methods but may become trapped in local minima if they exist. A technique known as 'configuration space analysis' was applied to examine the search space of cost functions used in radiotherapy beam-weight optimization algorithms. A downhill-simplex beam-weight optimization algorithm was run repeatedly to produce a frequency distribution of final cost values. By plotting the frequency distribution as a function of final cost, the existence of local minima can be determined. Common cost functions such as the quadratic deviation of dose to the planning target volume (PTV), integral dose to organs-at-risk (OARs), dose-threshold and dose-volume constraints for OARs were studied. Combinations of the cost functions were also considered. The simple cost function terms such as the quadratic PTV dose and integral dose to OAR cost function terms are not susceptible to local minima. In contrast, dose-threshold and dose-volume OAR constraint cost function terms are able to produce local minima in the example case studied. (author)

Multicriteria Optimization in Intensity-Modulated Radiation Therapy Treatment Planning for Locally Advanced Cancer of the Pancreatic Head

International Nuclear Information System (INIS)

Hong, Theodore S.; Craft, David L.; Carlsson, Fredrik; Bortfeld, Thomas R.

2008-01-01

Purpose: Intensity-modulated radiation therapy (IMRT) affords the potential to decrease radiation therapy-associated toxicity by creating highly conformal dose distributions. However, the inverse planning process can create a suboptimal plan despite meeting all constraints. Multicriteria optimization (MCO) may reduce the time-consuming iteration loop necessary to develop a satisfactory plan while providing information regarding trade-offs between different treatment planning goals. In this exploratory study, we examine the feasibility and utility of MCO in physician plan selection in patients with locally advanced pancreatic cancer (LAPC). Methods and Materials: The first 10 consecutive patients with LAPC treated with IMRT were evaluated. A database of plans (Pareto surface) was created that met the inverse planning goals. The physician then navigated to an 'optimal' plan from the point on the Pareto surface at which kidney dose was minimized. Results: Pareto surfaces were created for all 10 patients. A physician was able to select a plan from the Pareto surface within 10 minutes for all cases. Compared with the original (treated) IMRT plans, the plan selected from the Pareto surface had a lower stomach mean dose in 9 of 10 patients, although often at the expense of higher kidney dose than with the treated plan. Conclusion: The MCO is feasible in patients with LAPC and allows the physician to choose a satisfactory plan quickly. Generally, when given the opportunity, the physician will choose a plan with a lower stomach dose. The MCO enables a physician to provide greater active clinical input into the IMRT planning process

Optimization of Acquisition time of 68Ga-PSMA-Ligand PET/MRI in Patients with Local and Metastatic Prostate Cancer.

Science.gov (United States)

Lütje, Susanne; Blex, Sebastian; Gomez, Benedikt; Schaarschmidt, Benedikt M; Umutlu, Lale; Forsting, Michael; Jentzen, Walter; Bockisch, Andreas; Poeppel, Thorsten D; Wetter, Axel

2016-01-01

The aim of this optimization study was to minimize the acquisition time of 68Ga-HBED-CC-PSMA positron emission tomography/magnetic resonance imaging (PET/MRI) in patients with local and metastatic prostate cancer (PCa) to obtain a sufficient image quality and quantification accuracy without any appreciable loss. Twenty patients with PCa were administered intravenously with the 68Ga-HBED-CC-PSMA ligand (mean activity 99 MBq/patient, range 76-148 MBq) and subsequently underwent PET/MRI at, on average, 168 min (range 77-320 min) after injection. PET and MR imaging data were acquired simultaneously. PET acquisition was performed in list mode and PET images were reconstructed at different time intervals (1, 2, 4, 6, 8, and 10 min). Data were analyzed regarding radiotracer uptake in tumors and muscle tissue and PET image quality. Tumor uptake was quantified in terms of the maximum and mean standardized uptake value (SUVmax, SUVmean) within a spherical volume of interest (VOI). Reference VOIs were drawn in the gluteus maximus muscle on the right side. PET image quality was evaluated by experienced nuclear physicians/radiologists using a five-point ordinal scale from 5-1 (excellent-insufficient). Lesion detectability linearly increased with increasing acquisition times, reaching its maximum at PET acquisition times of 4 min. At this image acquisition time, tumor lesions in 19/20 (95%) patients were detected. PET image quality showed a positive correlation with increasing acquisition time, reaching a plateau at 4-6 min image acquisition. Both SUVmax and SUVmean correlated inversely with acquisition time and reached a plateau at acquisition times after 4 min. In the applied image acquisition settings, the optimal acquisition time of 68Ga-PSMA-ligand PET/MRI in patients with local and metastatic PCa was identified to be 4 min per bed position. At this acquisition time, PET image quality and lesion detectability reach a maximum while SUVmax and SUVmean do not change

Optimization of Acquisition time of 68Ga-PSMA-Ligand PET/MRI in Patients with Local and Metastatic Prostate Cancer.

Directory of Open Access Journals (Sweden)

Susanne Lütje

Full Text Available The aim of this optimization study was to minimize the acquisition time of 68Ga-HBED-CC-PSMA positron emission tomography/magnetic resonance imaging (PET/MRI in patients with local and metastatic prostate cancer (PCa to obtain a sufficient image quality and quantification accuracy without any appreciable loss.Twenty patients with PCa were administered intravenously with the 68Ga-HBED-CC-PSMA ligand (mean activity 99 MBq/patient, range 76-148 MBq and subsequently underwent PET/MRI at, on average, 168 min (range 77-320 min after injection. PET and MR imaging data were acquired simultaneously. PET acquisition was performed in list mode and PET images were reconstructed at different time intervals (1, 2, 4, 6, 8, and 10 min. Data were analyzed regarding radiotracer uptake in tumors and muscle tissue and PET image quality. Tumor uptake was quantified in terms of the maximum and mean standardized uptake value (SUVmax, SUVmean within a spherical volume of interest (VOI. Reference VOIs were drawn in the gluteus maximus muscle on the right side. PET image quality was evaluated by experienced nuclear physicians/radiologists using a five-point ordinal scale from 5-1 (excellent-insufficient.Lesion detectability linearly increased with increasing acquisition times, reaching its maximum at PET acquisition times of 4 min. At this image acquisition time, tumor lesions in 19/20 (95% patients were detected. PET image quality showed a positive correlation with increasing acquisition time, reaching a plateau at 4-6 min image acquisition. Both SUVmax and SUVmean correlated inversely with acquisition time and reached a plateau at acquisition times after 4 min.In the applied image acquisition settings, the optimal acquisition time of 68Ga-PSMA-ligand PET/MRI in patients with local and metastatic PCa was identified to be 4 min per bed position. At this acquisition time, PET image quality and lesion detectability reach a maximum while SUVmax and SUVmean do not change

Tractable Pareto Optimization of Temporal Preferences

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Swarm algorithms with chaotic jumps for optimization of multimodal functions

Science.gov (United States)

Krohling, Renato A.; Mendel, Eduardo; Campos, Mauro

2011-11-01

In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested. The jump strategy is based on the chaotic logistic map. The hybrid algorithm was tested for all three versions of PSO and simulation results show that the addition of the jump strategy improves the performance of swarm algorithms for most of the investigated optimization problems. Comparison with the off-the-shelf PSO with local topology (l best model) has also been performed and indicates the superior performance of the standard PSO with chaotic jump over the standard both using local topology (l best model).

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

International Nuclear Information System (INIS)

Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

2014-01-01

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

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

Science.gov (United States)

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.

Supervisor Localization: A Top-Down Approach to Distributed Control of Discrete-Event Systems

International Nuclear Information System (INIS)

Cai, K.; Wonham, W. M.

2009-01-01

A purely distributed control paradigm is proposed for discrete-event systems (DES). In contrast to control by one or more external supervisors, distributed control aims to design built-in strategies for individual agents. First a distributed optimal nonblocking control problem is formulated. To solve it, a top-down localization procedure is developed which systematically decomposes an external supervisor into local controllers while preserving optimality and nonblockingness. An efficient localization algorithm is provided to carry out the computation, and an automated guided vehicles (AGV) example presented for illustration. Finally, the 'easiest' and 'hardest' boundary cases of localization are discussed.

A quantum annealing architecture with all-to-all connectivity from local interactions.

Science.gov (United States)

Lechner, Wolfgang; Hauke, Philipp; Zoller, Peter

2015-10-01

Quantum annealers are physical devices that aim at solving NP-complete optimization problems by exploiting quantum mechanics. The basic principle of quantum annealing is to encode the optimization problem in Ising interactions between quantum bits (qubits). A fundamental challenge in building a fully programmable quantum annealer is the competing requirements of full controllable all-to-all connectivity and the quasi-locality of the interactions between physical qubits. We present a scalable architecture with full connectivity, which can be implemented with local interactions only. The input of the optimization problem is encoded in local fields acting on an extended set of physical qubits. The output is-in the spirit of topological quantum memories-redundantly encoded in the physical qubits, resulting in an intrinsic fault tolerance. Our model can be understood as a lattice gauge theory, where long-range interactions are mediated by gauge constraints. The architecture can be realized on various platforms with local controllability, including superconducting qubits, NV-centers, quantum dots, and atomic systems.

A quantum annealing architecture with all-to-all connectivity from local interactions

Science.gov (United States)

Lechner, Wolfgang; Hauke, Philipp; Zoller, Peter

2015-01-01

Quantum annealers are physical devices that aim at solving NP-complete optimization problems by exploiting quantum mechanics. The basic principle of quantum annealing is to encode the optimization problem in Ising interactions between quantum bits (qubits). A fundamental challenge in building a fully programmable quantum annealer is the competing requirements of full controllable all-to-all connectivity and the quasi-locality of the interactions between physical qubits. We present a scalable architecture with full connectivity, which can be implemented with local interactions only. The input of the optimization problem is encoded in local fields acting on an extended set of physical qubits. The output is—in the spirit of topological quantum memories—redundantly encoded in the physical qubits, resulting in an intrinsic fault tolerance. Our model can be understood as a lattice gauge theory, where long-range interactions are mediated by gauge constraints. The architecture can be realized on various platforms with local controllability, including superconducting qubits, NV-centers, quantum dots, and atomic systems. PMID:26601316

Applying Four Different Risk Models in Local Ore Selection

International Nuclear Information System (INIS)

Richmond, Andrew

2002-01-01

Given the uncertainty in grade at a mine location, a financially risk-averse decision-maker may prefer to incorporate this uncertainty into the ore selection process. A FORTRAN program risksel is presented to calculate local risk-adjusted optimal ore selections using a negative exponential utility function and three dominance models: mean-variance, mean-downside risk, and stochastic dominance. All four methods are demonstrated in a grade control environment. In the case study, optimal selections range with the magnitude of financial risk that a decision-maker is prepared to accept. Except for the stochastic dominance method, the risk models reassign material from higher cost to lower cost processing options as the aversion to financial risk increases. The stochastic dominance model usually was unable to determine the optimal local selection

The watershed-scale optimized and rearranged landscape design (WORLD) model and local biomass processing depots for sustainable biofuel production: Integrated life cycle assessments

Energy Technology Data Exchange (ETDEWEB)

Eranki, Pragnya L.; Manowitz, David H.; Bals, Bryan D.; Izaurralde, Roberto C.; Kim, Seungdo; Dale, Bruce E.

2013-07-23

An array of feedstock is being evaluated as potential raw material for cellulosic biofuel production. Thorough assessments are required in regional landscape settings before these feedstocks can be cultivated and sustainable management practices can be implemented. On the processing side, a potential solution to the logistical challenges of large biorefi neries is provided by a network of distributed processing facilities called local biomass processing depots. A large-scale cellulosic ethanol industry is likely to emerge soon in the United States. We have the opportunity to influence the sustainability of this emerging industry. The watershed-scale optimized and rearranged landscape design (WORLD) model estimates land allocations for different cellulosic feedstocks at biorefinery scale without displacing current animal nutrition requirements. This model also incorporates a network of the aforementioned depots. An integrated life cycle assessment is then conducted over the unified system of optimized feedstock production, processing, and associated transport operations to evaluate net energy yields (NEYs) and environmental impacts.

Adaptive optimization for active queue management supporting TCP flows

NARCIS (Netherlands)

Baldi, S.; Kosmatopoulos, Elias B.; Pitsillides, Andreas; Lestas, Marios; Ioannou, Petros A.; Wan, Y.; Chiu, George; Johnson, Katie; Abramovitch, Danny

2016-01-01

An adaptive decentralized strategy for active queue management of TCP flows over communication networks is presented. The proposed strategy solves locally, at each link, an optimal control problem, minimizing a cost composed of residual capacity and buffer queue size. The solution of the optimal

Multi-stage and multi-response process optimization in Taguchi ...

African Journals Online (AJOL)

Product quality is all about reducing variations of key performance indicators. However, product manufacturing often, requires multiple processes with multiple indicators, which make reducing variation a complex task. There are tools used to optimize a single stage process independently which ensure local optimization ...

Tapping mode SPM local oxidation nanolithography with sub-10 nm resolution

International Nuclear Information System (INIS)

Nishimura, S; Ogino, T; Shirakashi, J; Takemura, Y

2008-01-01

Tapping mode SPM local oxidation nanolithography with sub-10 nm resolution is investigated by optimizing the applied bias voltage (V), scanning speed (S) and the oscillation amplitude of the cantilever (A). We fabricated Si oxide wires with an average width of 9.8 nm (V = 17.5 V, S 250 nm/s, A = 292 nm). In SPM local oxidation with tapping mode operation, it is possible to decrease the size of the water meniscus by enhancing the oscillation amplitude of cantilever. Hence, it seems that the water meniscus with sub-10 nm dimensions could be formed by precisely optimizing the oxidation conditions. Moreover, we quantitatively explain the size (width and height) of Si oxide wires with a model based on the oxidation ratio, which is defined as the oxidation time divided by the period of the cantilever oscillation. The model allows us to understand the mechanism of local oxidation in tapping mode operation with amplitude modulation. The results imply that the sub-10 nm resolution could be achieved using tapping mode SPM local oxidation technique with the optimization of the cantilever dynamics

Exchange and Dzyaloshinskii-Moriya interactions in bulk FeGe: Effects of atomic vacancies

Science.gov (United States)

Loh, G. C.; Gan, C. K.

2017-05-01

We examine the effects of atomic vacancies on the (1) spin interaction, and (2) electronic character in the cubic B20 chiral magnet FeGe. For the former, Heisenberg exchange and Dzyaloshinskii-Moriya (DM) interactions are studied. The latter is done via a particular Wannier flavor of the Hamiltonian in the form of maximally-localized Wannier functions (MLWFs). Using first-principles calculations based on full-potential linearized augmented plane-wave (FLAPW)-based density functional theory (DFT), the spin order of bulk FeGe, in its pristine form, and with a Fe (Fe75%Ge100%) or Ge vacancy (Fe100%Ge75%) is investigated. Despite the presence of vacancies, the ground state of FeGe remains helimagnetic, i.e. spin spirals in FeGe are fairly robust. The energetic stability of FeGe increases in the presence of the vacancies. The spiral size is increased by approximately 40%, suggesting that vacancies can be introduced to manipulate the chiral order. The vacancies lift the band degeneracy in the valence manifold of the Wannier-interpolated band structures. Only the spin-down Fermi surfaces are substantially different between the pristine and defective FeGe; it is electron-like in the pristine case, but largely hole-like in the defective ones. The Ge vacancy splits the Fermi surface more than the Fe vacancy. The Heisenberg exchange between nearest Fe pairs is ferromagnetic in pristine FeGe. This Fe-Fe interaction remains ferromagnetic, albeit a slight decrease in strength, in the presence of a Fe vacancy. In contrast, a Ge vacancy in FeGe induces anti-ferromagnetism between nearest Fe pairs. By including spin-orbit coupling effects, we find that the DM interaction of defective FeGe is reversed in sign, and it is more uniform in strength along the three highly symmetric directions, relative to that in pristine FeGe. All in all, the versatility of FeGe makes it an excellent functional material, especially in data storage and spintronics applications.

Multi-Objective Optimization of Managed Aquifer Recharge.

Science.gov (United States)

Fatkhutdinov, Aybulat; Stefan, Catalin

2018-04-27

This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady-state and transient scenarios. The steady-state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global - the Non-Dominated Sorting Genetic Algorithm (NSGA-2), and local - the Nelder-Mead Downhill Simplex search algorithms. The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared. This article is protected by copyright. All rights reserved.

Complete local search with memory

NARCIS (Netherlands)

Ghosh, D.; Sierksma, G.

2000-01-01

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic

A localized navigation algorithm for radiation evasion for nuclear facilities: Optimizing the “Radiation Evasion” criterion: Part I

International Nuclear Information System (INIS)

Khasawneh, Mohammed A.; Al-Shboul, Zeina Aman M.; Jaradat, Mohammad A.

2013-01-01

Highlights: ► A new navigation algorithm for radiation evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this paper, we introduce a navigation algorithm having general utility for occupational workers at nuclear facilities and places where radiation poses serious health hazards. This novel algorithm leverages the use of localized information for its operation. Therefore, the need for central processing and decision resources is avoided, since information processing and the ensuing decision-making are done aboard a man-borne device. To acquire the information needed for path planning in radiation avoidance, a well-designed and distributed wireless sensory infrastructure is needed. This will automatically benefit from the most recent trends in technology developments in both sensor networks and wireless communication. When used to navigate based on local radiation information, the algorithm will behave more reliably when accidents happen, since no long-haul communication links are required for information exchange. In essence, the proposed algorithm is designed to leverage nearest neighbor information coming in through the sensory network overhead, to compute successful navigational paths from one point to another. The proposed algorithm is tested under the “Radiation Evasion” criterion. It is also tested for the case when more information, beyond nearest neighbors, is made available; here, we test its operation for different numbers of step look-ahead. We verify algorithm performance by means of simulations, whereby navigational paths are calculated for different radiation fields

A localized navigation algorithm for radiation evasion for nuclear facilities: Optimizing the “Radiation Evasion” criterion: Part I

Energy Technology Data Exchange (ETDEWEB)

Khasawneh, Mohammed A., E-mail: mkha@ieee.org [Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan); Al-Shboul, Zeina Aman M., E-mail: xeinaaman@gmail.com [Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan); Jaradat, Mohammad A., E-mail: majaradat@just.edu.jo [Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan)

2013-06-15

Highlights: ► A new navigation algorithm for radiation evasion around nuclear facilities. ► An optimization criteria minimized under algorithm operation. ► A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. ► Benefits of using localized navigation as opposed to global navigation schemas. ► A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. -- Abstract: In this paper, we introduce a navigation algorithm having general utility for occupational workers at nuclear facilities and places where radiation poses serious health hazards. This novel algorithm leverages the use of localized information for its operation. Therefore, the need for central processing and decision resources is avoided, since information processing and the ensuing decision-making are done aboard a man-borne device. To acquire the information needed for path planning in radiation avoidance, a well-designed and distributed wireless sensory infrastructure is needed. This will automatically benefit from the most recent trends in technology developments in both sensor networks and wireless communication. When used to navigate based on local radiation information, the algorithm will behave more reliably when accidents happen, since no long-haul communication links are required for information exchange. In essence, the proposed algorithm is designed to leverage nearest neighbor information coming in through the sensory network overhead, to compute successful navigational paths from one point to another. The proposed algorithm is tested under the “Radiation Evasion” criterion. It is also tested for the case when more information, beyond nearest neighbors, is made available; here, we test its operation for different numbers of step look-ahead. We verify algorithm performance by means of simulations, whereby navigational paths are calculated for different radiation fields.

Intensity-modulated radiation therapy (IMRT) for locally advanced paranasal sinus tumors: incorporating clinical decisions in the optimization process

International Nuclear Information System (INIS)

Tsien, Christina; Eisbruch, Avraham; McShan, Daniel; Kessler, Marc; Marsh, Robin C.; Fraass, Benedick

2003-01-01

Purpose: Intensity-modulated radiotherapy (IMRT) plans require decisions about priorities and tradeoffs among competing goals. This study evaluates the incorporation of various clinical decisions into the optimization system, using locally advanced paranasal sinus tumors as a model. Methods and Materials: Thirteen patients with locally advanced paranasal sinus tumors were retrospectively replanned using inverse planning. Two clinical decisions were assumed: (1) Spare both optic pathways (OP), or (2) Spare only the contralateral OP. In each case, adequate tumor coverage (treated to 70 Gy in 35 fractions) was required. Two beamlet IMRT plans were thus developed for each patient using a class solution cost function. By altering one key variable at a time, different levels of risk of OP toxicity and planning target volume (PTV) compromise were compared in a systematic manner. The resulting clinical tradeoffs were analyzed using dosimetric criteria, tumor control probability (TCP), equivalent uniform dose (EUD), and normal tissue complication probability. Results: Plan comparisons representing the two clinical decisions (sparing both OP and sparing only the contralateral OP), with respect to minimum dose, TCP, V 95 , and EUD, demonstrated small, yet statistically significant, differences. However, when individual cases were analyzed further, significant PTV underdosage (>5%) was present in most cases for plans sparing both OP. In 6/13 cases (46%), PTV underdosage was between 5% and 15%, and in 3 cases (23%) was greater than 15%. By comparison, adequate PTV coverage was present in 8/13 cases (62%) for plans sparing only the contralateral OP. Mean target EUD comparisons between the two plans (including 9 cases where a clinical tradeoff between PTV coverage and OP sparing was required) were similar: 68.6 Gy and 69.1 Gy, respectively (p=0.02). Mean TCP values for those 9 cases were 56.5 vs. 61.7, respectively (p=0.006). Conclusions: In IMRT plans for paranasal sinus tumors

Truss systems and shape optimization

Science.gov (United States)

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.

Benders decomposition for discrete material optimization in laminate design with local failure criteria

DEFF Research Database (Denmark)

Munoz, Eduardo; Stolpe, Mathias; Bendsøe, Martin P.

2009-01-01

in any discrete angle optimization design, or material selection problems. The mathematical modeling of this problem is more general than the one of standard topology optimization. When considering only two material candidates with a considerable difference in stiffness, it corresponds exactly...... to a topology optimization problem. The problem is modeled as a discrete design problem coming from a finite element discretization of the continuum problem. This discretization is made of shell or plate elements. For each element (selection domain), only one of the material candidates must be selected...... of the relaxed master problem and the current best compliance (weight) found get close enough with respect to certain tolerance. The method is investigated by computational means, using the finite element method to solve the analysis problems, and a commercial branch and cut method for solving the relaxed master...

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

Science.gov (United States)

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

Optimizing the Thermoacoustic Pulse Tube Refrigerator Performances

Directory of Open Access Journals (Sweden)

E. V. Blagin

2014-01-01

Full Text Available The article deals with research and optimization of the thermoacoustic pulse tube refrigerator to reach a cryogenic temperature level. The refrigerator is considered as a thermoacoustic converter based on the modified Stirling cycle with helium working fluid. A sound pressure generator runs as a compressor. Plant model comprises an inner heat exchanger, a regenerative heat exchanger, a pulse tube, hot and cold heat exchangers at its ends, an inertial tube with the throttle, and a reservoir. A model to calculate the pulse tube thermoacoustic refrigerator using the DeltaEC software package has been developed to be a basis for calculation techniques of the pulse tube refrigerator. Momentum, continuity, and energy equations for helium refrigerant are solved according to calculation algorithm taking into account the porosity of regenerator and heat exchangers. Optimization of the main geometric parameters resulted in decreasing temperature of cold heat exchanger by 41,7 K. After optimization this value became equal to 115,01 K. The following parameters have been optimized: diameters of the feeding and pulse tube and heat exchangers, regenerator, lengths of the regenerator and pulse and inertial tubes, as well as initial pressure. Besides, global minimum of temperatures has been searched at a point of local minima corresponding to the optimal values of abovementioned parameters. A global-local minima difference is 0,1%. Optimized geometric and working parameters of the thermoacoustic pulse tube refrigerator are presented.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

Science.gov (United States)

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

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

Science.gov (United States)

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.

Local randomness: Examples and application

Science.gov (United States)

Fu, Honghao; Miller, Carl A.

2018-03-01

When two players achieve a superclassical score at a nonlocal game, their outputs must contain intrinsic randomness. This fact has many useful implications for quantum cryptography. Recently it has been observed [C. Miller and Y. Shi, Quantum Inf. Computat. 17, 0595 (2017)] that such scores also imply the existence of local randomness—that is, randomness known to one player but not to the other. This has potential implications for cryptographic tasks between two cooperating but mistrustful players. In the current paper we bring this notion toward practical realization, by offering near-optimal bounds on local randomness for the CHSH game, and also proving the security of a cryptographic application of local randomness (single-bit certified deletion).

Graph Design via Convex Optimization: Online and Distributed Perspectives

Science.gov (United States)

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

Scalable algorithms for optimal control of stochastic PDEs

KAUST Repository

Ghattas, Omar

2016-01-07

We present methods for the optimal control of systems governed by partial differential equations with infinite-dimensional uncertain parameters. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control formulation. To make the optimal control problem computationally tractable, we employ a local quadratic approximation of the objective with respect to the uncertain parameter. This enables computation of the mean and variance of the control objective analytically. The resulting risk-averse optimization problem is formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs for the first and second-order derivatives of the quantity of interest with respect to the uncertain parameter, and with an objective that involves the trace of a covariance-preconditioned Hessian (of the objective with respect to the uncertain parameters) operator. A randomized trace estimator is used to make tractable the trace computation. Adjoint-based techniques are used to derive an expression for the infinite-dimensional gradient of the risk-averse objective function via the Lagrangian, leading to a quasi-Newton method for solution of the optimal control problem. A specific problem of optimal control of a linear elliptic PDE that describes flow of a fluid in a porous medium with uncertain permeability field is considered. We present numerical results to study the consequences of the local quadratic approximation and the efficiency of the method.

Scalable algorithms for optimal control of stochastic PDEs

KAUST Repository

Ghattas, Omar; Alexanderian, Alen; Petra, Noemi; Stadler, Georg

2016-01-01

We present methods for the optimal control of systems governed by partial differential equations with infinite-dimensional uncertain parameters. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control formulation. To make the optimal control problem computationally tractable, we employ a local quadratic approximation of the objective with respect to the uncertain parameter. This enables computation of the mean and variance of the control objective analytically. The resulting risk-averse optimization problem is formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs for the first and second-order derivatives of the quantity of interest with respect to the uncertain parameter, and with an objective that involves the trace of a covariance-preconditioned Hessian (of the objective with respect to the uncertain parameters) operator. A randomized trace estimator is used to make tractable the trace computation. Adjoint-based techniques are used to derive an expression for the infinite-dimensional gradient of the risk-averse objective function via the Lagrangian, leading to a quasi-Newton method for solution of the optimal control problem. A specific problem of optimal control of a linear elliptic PDE that describes flow of a fluid in a porous medium with uncertain permeability field is considered. We present numerical results to study the consequences of the local quadratic approximation and the efficiency of the method.

Sensor Network Localization with Imprecise Distances

NARCIS (Netherlands)

Cao, M.; Morse, A.S.; Anderson, B.D.O.

2006-01-01

An approach to formulate geometric relations among distances between nodes as equality constraints is introduced in this paper to study the localization problem with imprecise distance information in sensor networks. These constraints can be further used to formulate optimization problems for

Making it local: Beacon Communities use health information technology to optimize care management.

Science.gov (United States)

Allen, Amy; Des Jardins, Terrisca R; Heider, Arvela; Kanger, Chatrian R; Lobach, David F; McWilliams, Lee; Polello, Jennifer M; Rein, Alison L; Schachter, Abigail A; Singh, Ranjit; Sorondo, Barbara; Tulikangas, Megan C; Turske, Scott A

2014-06-01

Care management aims to provide cost-effective, coordinated, non-duplicative care to improve care quality, population health, and reduce costs. The 17 communities receiving funding from the Office of the National Coordinator for Health Information Technology through the Beacon Community Cooperative Agreement Program are leaders in building and strengthening their health information technology (health IT) infrastructure to provide more effective and efficient care management. This article profiles 6 Beacon Communities' health IT-enabled care management programs, highlighting the influence of local context on program strategy and design, and describing challenges, lessons learned, and policy implications for care delivery and payment reform. The unique needs (eg, disease burden, demographics), community partnerships, and existing resources and infrastructure all exerted significant influence on the overall priorities and design of each community's care management program. Though each Beacon Community needed to engage in a similar set of care management tasks--including patient identification, stratification, and prioritization; intervention; patient engagement; and evaluation--the contextual factors helped shape the specific strategies and tools used to carry out these tasks and achieve their objectives. Although providers across the country are striving to deliver standardized, high-quality care, the diverse contexts in which this care is delivered significantly influence the priorities, strategies, and design of community-based care management interventions. Gaps and challenges in implementing effective community-based care management programs include: optimizing allocation of care management services; lack of available technology tailored to care management needs; lack of standards and interoperability; integrating care management into care settings; evaluating impact; and funding and sustainability.

Analytical models of optical response in one-dimensional semiconductors

International Nuclear Information System (INIS)

Pedersen, Thomas Garm

2015-01-01

The quantum mechanical description of the optical properties of crystalline materials typically requires extensive numerical computation. Including excitonic and non-perturbative field effects adds to the complexity. In one dimension, however, the analysis simplifies and optical spectra can be computed exactly. In this paper, we apply the Wannier exciton formalism to derive analytical expressions for the optical response in four cases of increasing complexity. Thus, we start from free carriers and, in turn, switch on electrostatic fields and electron–hole attraction and, finally, analyze the combined influence of these effects. In addition, the optical response of impurity-localized excitons is discussed. - Highlights: • Optical response of one-dimensional semiconductors including excitons. • Analytical model of excitonic Franz–Keldysh effect. • Computation of optical response of impurity-localized excitons

Impact of Chaos Functions on Modern Swarm Optimizers.

Directory of Open Access Journals (Sweden)

E Emary

Full Text Available Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO, antlion optimizer (ALO and moth-flame optimizer (MFO in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.

Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation

OpenAIRE

Maeda, Yutaka; Matsushita, Naoto

2009-01-01

In this paper, we presented hardware implementation of the particle swarm optimization algorithm which is combination of the ordinary particle swarm optimization and the simultaneous perturbation method. FPGA is used to realize the system. This algorithm utilizes local information of objective function effectively without lack of advantage of the original particle swarm optimization. Moreover, the FPGA implementation gives higher operation speed effectively using parallelism of the particle s...

A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material

Energy Technology Data Exchange (ETDEWEB)

Yu, S.W.; Ding, C.; Zhu, K.J. [China University of Geoscience, Wuhan (China)

2011-08-15

In the open vehicle routing problem (OVRP), the objective is to minimize the number of vehicles and the total distance (or time) traveled. This study primarily focuses on solving an open vehicle routing problem (OVRP) by applying a novel hybrid genetic algorithm and the Tabu search (GA-TS), which combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. Firstly, the proposed algorithm uses natural number coding according to the customer demands and the captivity of the vehicle for globe optimization. Secondly, individuals of population do TS local search with a certain degree of probability, namely, do the local routing optimization of all customer sites belong to one vehicle. The mechanism not only improves the ability of global optimization, but also ensures the speed of operation. The algorithm was used in Zhengzhou Coal Mine and Power Supply Co., Ltd.'s transport vehicle routing optimization.

Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications

Science.gov (United States)

Athaudage, Chandranath R. N.; Bradley, Alan B.; Lech, Margaret

2003-12-01

A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.

Some New Locally Optimal Control Laws for Sailcraft Dynamics in Heliocentric Orbits

Directory of Open Access Journals (Sweden)

F. A. Abd El-Salam

2013-01-01

Full Text Available The concept of solar sailing and its developing spacecraft is presented. The gravitational and solar radiation forces are considered. The effect of source of radiation pressure and the force due to coronal mass ejections and solar wind on the sailcraft configurations is modeled. Some analytical control laws with some mentioned input constraints for optimizing sailcraft dynamics in heliocentric orbit using lagrange’s planetary equations are obtained. Optimum force vector in a required direction is maximized by deriving optimal sail cone angle. Ignoring the absorbed and diffusely reflected parts of the radiation, some special cases are obtained. New control laws that maximize thrust to obtain certain required maximization in some particular orbital element are obtained.

On projection methods, convergence and robust formulations in topology optimization

DEFF Research Database (Denmark)

Wang, Fengwen; Lazarov, Boyan Stefanov; Sigmund, Ole

2011-01-01

alleviated using various projection methods. In this paper we show that simple projection methods do not ensure local mesh-convergence and propose a modified robust topology optimization formulation based on erosion, intermediate and dilation projections that ensures both global and local mesh-convergence.......Mesh convergence and manufacturability of topology optimized designs have previously mainly been assured using density or sensitivity based filtering techniques. The drawback of these techniques has been gray transition regions between solid and void parts, but this problem has recently been...

Optimal local dimming for LED-backlit LCD displays via linear programming

DEFF Research Database (Denmark)

Shu, Xiao; Wu, Xiaolin; Forchhammer, Søren

2012-01-01

and the attenuations of LCD pixels. The objective is to minimize the distortion in luminance reproduction due to the leakage of LCD and the coarse granularity of the LED lights. The optimization problem is formulated as one of linear programming, and both exact and approximate algorithms are proposed. Simulation...

Robust sampling-sourced numerical retrieval algorithm for optical energy loss function based on log–log mesh optimization and local monotonicity preserving Steffen spline

Energy Technology Data Exchange (ETDEWEB)

Maglevanny, I.I., E-mail: sianko@list.ru [Volgograd State Social Pedagogical University, 27 Lenin Avenue, Volgograd 400131 (Russian Federation); Smolar, V.A. [Volgograd State Technical University, 28 Lenin Avenue, Volgograd 400131 (Russian Federation)

2016-01-15

We introduce a new technique of interpolation of the energy-loss function (ELF) in solids sampled by empirical optical spectra. Finding appropriate interpolation methods for ELFs poses several challenges. The sampled ELFs are usually very heterogeneous, can originate from various sources thus so called “data gaps” can appear, and significant discontinuities and multiple high outliers can be present. As a result an interpolation based on those data may not perform well at predicting reasonable physical results. Reliable interpolation tools, suitable for ELF applications, should therefore satisfy several important demands: accuracy and predictive power, robustness and computational efficiency, and ease of use. We examined the effect on the fitting quality due to different interpolation schemes with emphasis on ELF mesh optimization procedures and we argue that the optimal fitting should be based on preliminary log–log scaling data transforms by which the non-uniformity of sampled data distribution may be considerably reduced. The transformed data are then interpolated by local monotonicity preserving Steffen spline. The result is a piece-wise smooth fitting curve with continuous first-order derivatives that passes through all data points without spurious oscillations. Local extrema can occur only at grid points where they are given by the data, but not in between two adjacent grid points. It is found that proposed technique gives the most accurate results and also that its computational time is short. Thus, it is feasible using this simple method to address practical problems associated with interaction between a bulk material and a moving electron. A compact C++ implementation of our algorithm is also presented.

Robust sampling-sourced numerical retrieval algorithm for optical energy loss function based on log–log mesh optimization and local monotonicity preserving Steffen spline

International Nuclear Information System (INIS)

Maglevanny, I.I.; Smolar, V.A.

2016-01-01

We introduce a new technique of interpolation of the energy-loss function (ELF) in solids sampled by empirical optical spectra. Finding appropriate interpolation methods for ELFs poses several challenges. The sampled ELFs are usually very heterogeneous, can originate from various sources thus so called “data gaps” can appear, and significant discontinuities and multiple high outliers can be present. As a result an interpolation based on those data may not perform well at predicting reasonable physical results. Reliable interpolation tools, suitable for ELF applications, should therefore satisfy several important demands: accuracy and predictive power, robustness and computational efficiency, and ease of use. We examined the effect on the fitting quality due to different interpolation schemes with emphasis on ELF mesh optimization procedures and we argue that the optimal fitting should be based on preliminary log–log scaling data transforms by which the non-uniformity of sampled data distribution may be considerably reduced. The transformed data are then interpolated by local monotonicity preserving Steffen spline. The result is a piece-wise smooth fitting curve with continuous first-order derivatives that passes through all data points without spurious oscillations. Local extrema can occur only at grid points where they are given by the data, but not in between two adjacent grid points. It is found that proposed technique gives the most accurate results and also that its computational time is short. Thus, it is feasible using this simple method to address practical problems associated with interaction between a bulk material and a moving electron. A compact C++ implementation of our algorithm is also presented.

Vasoconstrictors in local anesthesia for dentistry.

Science.gov (United States)

Sisk, A. L.

1992-01-01

Addition of a vasoconstrictor to a local anesthetic may have several beneficial effects: a decrease in the peak plasma concentration of the local anesthetic agent, increase in the duration and the quality of anesthesia, reduction of the minimum concentration of anesthetic needed for nerve block, and decrease of blood loss during surgical procedures. The addition of a vasoconstrictor to a local anesthetic may also have detrimental effects. A review of the literature indicates that vasoconstrictor concentrations in local anesthetics marketed for dental use in the United States are not always optimal to achieve the purposes for which they are added. In most cases, a reduced concentration of vasoconstrictor could achieve the same goal as the marketed higher concentration, with less side-effect liability. PMID:8250339

Protein Structure Refinement by Optimization

DEFF Research Database (Denmark)

Carlsen, Martin

on whether the three-dimensional structure of a homologous sequence is known. Whether or not a protein model can be used for industrial purposes depends on the quality of the predicted structure. A model can be used to design a drug when the quality is high. The overall goal of this project is to assess...... that correlates maximally to a native-decoy distance. The main contribution of this thesis is methods developed for analyzing the performance of metrically trained knowledge-based potentials and for optimizing their performance while making them less dependent on the decoy set used to define them. We focus...... being at-least a local minimum of the potential. To address how far the current functional form of the potential is from an ideal potential we present two methods for finding the optimal metrically trained potential that simultaneous has a number of native structures as a local minimum. Our results...

Optimal algebraic multilevel preconditioning for local refinement along a line

NARCIS (Netherlands)

Margenov, S.D.; Maubach, J.M.L.

1995-01-01

The application of some recently proposed algebraic multilevel methods for the solution of two-dimensional finite element problems on nonuniform meshes is studied. The locally refined meshes are created by the newest vertex mesh refinement method. After the introduction of this refinement technique

Large negative differential resistance in graphene nanoribbon superlattices

Science.gov (United States)

Tseng, P.; Chen, C. H.; Hsu, S. A.; Hsueh, W. J.

2018-05-01

A graphene nanoribbon superlattice with a large negative differential resistance (NDR) is proposed. Our results show that the peak-to-valley ratio (PVR) of the graphene superlattices can reach 21 at room temperature with bias voltages between 90-220 mV, which is quite large compared with the one of traditional graphene-based devices. It is found that the NDR is strongly influenced by the thicknesses of the potential barrier. Therefore, the NDR effect can be optimized by designing a proper barrier thickness. The large NDR effect can be attributed to the splitting of the gap in transmission spectrum (segment of Wannier-Stark ladder) with larger thicknesses of barrier when the applied voltage increases.

Design optimization for active twist rotor blades

Science.gov (United States)

Mok, Ji Won

This dissertation introduces the process of optimizing active twist rotor blades in the presence of embedded anisotropic piezo-composite actuators. Optimum design of active twist blades is a complex task, since it involves a rich design space with tightly coupled design variables. The study presents the development of an optimization framework for active helicopter rotor blade cross-sectional design. This optimization framework allows for exploring a rich and highly nonlinear design space in order to optimize the active twist rotor blades. Different analytical components are combined in the framework: cross-sectional analysis (UM/VABS), an automated mesh generator, a beam solver (DYMORE), a three-dimensional local strain recovery module, and a gradient based optimizer within MATLAB. Through the mathematical optimization problem, the static twist actuation performance of a blade is maximized while satisfying a series of blade constraints. These constraints are associated with locations of the center of gravity and elastic axis, blade mass per unit span, fundamental rotating blade frequencies, and the blade strength based on local three-dimensional strain fields under worst loading conditions. Through pre-processing, limitations of the proposed process have been studied. When limitations were detected, resolution strategies were proposed. These include mesh overlapping, element distortion, trailing edge tab modeling, electrode modeling and foam implementation of the mesh generator, and the initial point sensibility of the current optimization scheme. Examples demonstrate the effectiveness of this process. Optimization studies were performed on the NASA/Army/MIT ATR blade case. Even though that design was built and shown significant impact in vibration reduction, the proposed optimization process showed that the design could be improved significantly. The second example, based on a model scale of the AH-64D Apache blade, emphasized the capability of this framework to

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

Science.gov (United States)

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.

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

Negatively correlated local and global stock externalities: tax or subsidy?

International Nuclear Information System (INIS)

Zili Yang

2006-01-01

Fossil fuel combustion generates both CO 2 and SO 2 . CO 2 is the most important greenhouse gas; SO 2 can cause serious local pollution. But it can alleviate the potential global warming because of negative radiative forcing. Such a phenomenon can be characterized as negatively correlated local and global stock externalities. In this paper, we set up an optimal control problem of negatively correlated local and global stock externality provision. The efficiency conditions for this problem are derived. These conditions modify the Samuelson rules for optimal provision of externalities. In addition, we examine several policy related scenarios of negatively correlated local and global stock externality provisions. Finally, we discuss policy implications and limitation of the theoretical results derived in this paper. We also indicate applications of the theoretical results here to empirical research, particularly to economic analysis of multiple-gas issues in climate change. (Author)

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

Science.gov (United States)

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)…

Preliminary analysis on in-core fuel management optimization of molten salt pebble-bed reactor

International Nuclear Information System (INIS)

Xia Bing; Jing Xingqing; Xu Xiaolin; Lv Yingzhong

2013-01-01

The Nuclear Hot Spring (NHS) is a molten salt pebble-bed reactor featured by full power natural circulation. The unique horizontal coolant flow of the NHS demands the fuel recycling schemes based on radial zoning refueling and the corresponding method of fuel management optimization. The local searching algorithm (LSA) and the simulated annealing algorithm (SAA), the stochastic optimization methods widely used in the refueling optimization problems in LWRs, were applied to the analysis of refueling optimization of the NHS. The analysis results indicate that, compared with the LSA, the SAA can survive the traps of local optimized solutions and reach the global optimized solution, and the quality of optimization of the SAA is independent of the choice of the initial solution. The optimization result gives excellent effects on the in-core power flattening and the suppression of fuel center temperature. For the one-dimensional zoning refueling schemes of the NHS, the SAA is an appropriate optimization method. (authors)

Chemotherapy, brachytherapy and surgery of locally evolved uterine cervix carcinomas: prognosis factors of local control and global survival; Chimioradiotherapie, curietherapie et chirurgie des cancers du col uterin localement evolues: facteurs pronostiques de controle local et de survie globale

Energy Technology Data Exchange (ETDEWEB)

Laude, C.; Montella, A.; Montbarbon, X.; Malet, C.; Racadot, S.; Pommier, P. [Centre Leon-Berard, 69 - Lyon (France); Mathevet, P. [Hopital Femme-Mere-Enfant, Hospices Civils de Lyon, 69 - Lyon (France); Buenerd, A. [Centre de Pathologie Est, Hospices Civils de Lyon, 69 - Lyon (France)

2009-10-15

The protocol used allows an excellent local control of the uterine cervix carcinoma with an acceptable morbidity. To anticipate the presence of a tumor residue can be an evolution in the therapy management after external radiotherapy, particularly in optimized image-guided brachytherapy (MRI and PET)New utero vaginal applicators with parameters implantation allow to realise the dose complement at the distal parameters. These advances make consider an improvement of results in the management of locally evolved uterine cervix carcinomas. (N.C.)

Lumped Mass Modeling for Local-Mode-Suppressed Element Connectivity

DEFF Research Database (Denmark)

Joung, Young Soo; Yoon, Gil Ho; Kim, Yoon Young

2005-01-01

connectivity parameterization (ECP) is employed. On the way to the ultimate crashworthy structure optimization, we are now developing a local mode-free topology optimization formulation that can be implemented in the ECP method. In fact, the local mode-freeing strategy developed here can be also used directly...... experiencing large structural changes, appears to be still poor. In ECP, the nodes of the domain-discretizing elements are connected by zero-length one-dimensional elastic links having varying stiffness. For computational efficiency, every elastic link is now assumed to have two lumped masses at its ends....... Choosing appropriate penalization functions for lumped mass and link stiffness is important for local mode-free results. However, unless the objective and constraint functions are carefully selected, it is difficult to obtain clear black-and-white results. It is shown that the present formulation is also...

Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

Directory of Open Access Journals (Sweden)

Zhiwen Hu

2015-01-01

Full Text Available The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR. We also show that the proposed new method has a robust detection performance for broad SNR region.

Towards Optimal Transport Networks

Directory of Open Access Journals (Sweden)

Erik P. Vargo

2010-08-01

Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly affect the network dynamics. First, we present simulation results which support a connection between maximizing the first non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semidefinite programming (SDP relaxations. A modification of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.

Efficient "on-the-fly" calculation of Raman spectra from ab-initio molecular dynamics: Application to hydrophobic/hydrophilic solutes in bulk water.

Science.gov (United States)

Partovi-Azar, Pouya; Kühne, Thomas D

2015-11-05

We present a novel computational method to accurately calculate Raman spectra from first principles. Together with an extension of the second-generation Car-Parrinello method of Kühne et al. (Phys. Rev. Lett. 2007, 98, 066401) to propagate maximally localized Wannier functions together with the nuclei, a speed-up of one order of magnitude can be observed. This scheme thus allows to routinely calculate finite-temperature Raman spectra "on-the-fly" by means of ab-initio molecular dynamics simulations. To demonstrate the predictive power of this approach we investigate the effect of hydrophobic and hydrophilic solutes in water solution on the infrared and Raman spectra. © 2015 Wiley Periodicals, Inc.

Software for CATV Design and Frequency Plan Optimization

OpenAIRE

Hala, O.

2007-01-01

The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Methods for optimizing over the efficient and weakly efficient sets of an affine fractional vector optimization program

DEFF Research Database (Denmark)

Le, T.H.A.; Pham, D. T.; Canh, Nam Nguyen

2010-01-01

Both the efficient and weakly efficient sets of an affine fractional vector optimization problem, in general, are neither convex nor given explicitly. Optimization problems over one of these sets are thus nonconvex. We propose two methods for optimizing a real-valued function over the efficient...... and weakly efficient sets of an affine fractional vector optimization problem. The first method is a local one. By using a regularization function, we reformulate the problem into a standard smooth mathematical programming problem that allows applying available methods for smooth programming. In case...... the objective function is linear, we have investigated a global algorithm based upon a branch-and-bound procedure. The algorithm uses Lagrangian bound coupling with a simplicial bisection in the criteria space. Preliminary computational results show that the global algorithm is promising....

A kind of balance between exploitation and exploration on kriging for global optimization of expensive functions

International Nuclear Information System (INIS)

Dong, Huachao; Song, Baowei; Wang, Peng; Huang, Shuai

2015-01-01

In this paper, a novel kriging-based algorithm for global optimization of computationally expensive black-box functions is presented. This algorithm utilizes a multi-start approach to find all of the local optimal values of the surrogate model and performs searches within the neighboring area around these local optimal positions. Compared with traditional surrogate-based global optimization method, this algorithm provides another kind of balance between exploitation and exploration on kriging-based model. In addition, a new search strategy is proposed and coupled into this optimization process. The local search strategy employs a kind of improved 'Minimizing the predictor' method, which dynamically adjusts search direction and radius until finds the optimal value. Furthermore, the global search strategy utilizes the advantage of kriging-based model in predicting unexplored regions to guarantee the reliability of the algorithm. Finally, experiments on 13 test functions with six algorithms are set up and the results show that the proposed algorithm is very promising.

Global optimization methods for engineering design

Science.gov (United States)

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.

Partial discharge localization in power transformers based on the sequential quadratic programming-genetic algorithm adopting acoustic emission techniques

Science.gov (United States)

Liu, Hua-Long; Liu, Hua-Dong

2014-10-01

Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And

e - 2e Collisions near ionization threshold - electron correlations

International Nuclear Information System (INIS)

Mazeau, J.; Huetz, A.; Selles, P.

1986-01-01

The results presented in this report constitute the first direct experimental proof that a few (LSΠ) states definitely contribute to the near threshold ionization cross section. The Wannier Peterkop Rau theory is an useful tool to their understanding and a more precise determination of the angular correlation width is still needed. It has been shown that the values of the a LSΠ coefficients can be extracted from the observations. These are physically interesting quantities as they are directly related to the probability of forming Wannier ridge riding states above the double escape threshold, and considerable theoretical effort is presently in progress to investigate such states. (Auth.)

Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Lopez, P. Reche; Reyes, N. Ruiz; Gonzalez, M. Gomez; Jurado, F.

2008-01-01

With sufficient territory and abundant biomass resources Spain appears to have suitable conditions to develop biomass utilization technologies. As an important decentralized power technology, biomass gasification and power generation has a potential market in making use of biomass wastes. This paper addresses biomass fuelled generation of electricity in the specific aspect of finding the best location and the supply area of the electric generation plant for three alternative technologies (gas motor, gas turbine and fuel cell-microturbine hybrid power cycle), taking into account the variables involved in the problem, such as the local distribution of biomass resources, transportation costs, distance to existing electric lines, etc. For each technology, not only optimal location and supply area of the biomass plant, but also net present value and generated electric power are determined by an own binary variant of Particle Swarm Optimization (PSO). According to the values derived from the optimization algorithm, the most profitable technology can be chosen. Computer simulations show the good performance of the proposed binary PSO algorithm to optimize biomass fuelled systems for distributed power generation. (author)

Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

Directory of Open Access Journals (Sweden)

Prabha Umapathy

2010-01-01

Full Text Available This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO. The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW, a time-varying inertia weight (TVIW, and global-local best inertia weight (GLbestIW, are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

IMRT optimization: Variability of solutions and its radiobiological impact

International Nuclear Information System (INIS)

Mattia, Maurizio; Del Giudice, Paolo; Caccia, Barbara

2004-01-01

We aim at (1) defining and measuring a 'complexity' index for the optimization process of an intensity modulated radiation therapy treatment plan (IMRT TP), (2) devising an efficient approximate optimization strategy, and (3) evaluating the impact of the complexity of the optimization process on the radiobiological quality of the treatment. In this work, for a prostate therapy case, the IMRT TP optimization problem has been formulated in terms of dose-volume constraints. The cost function has been minimized in order to achieve the optimal solution, by means of an iterative procedure, which is repeated for many initial modulation profiles, and for each of them the final optimal solution is recorded. To explore the complexity of the space of such solutions we have chosen to minimize the cost function with an algorithm that is unable to avoid local minima. The size of the (sub)optimal solutions distribution is taken as an indicator of the complexity of the optimization problem. The impact of the estimated complexity on the probability of success of the therapy is evaluated using radiobiological indicators (Poissonian TCP model [S. Webb and A. E. Nahum, Phys. Med. Biol. 38(6), 653-666 (1993)] and NTCP relative seriality model [Kallman et al., Int. J. Radiat. Biol. 62(2), 249-262 (1992)]). We find in the examined prostate case a nontrivial distribution of local minima, which has symmetry properties allowing a good estimate of near-optimal solutions with a moderate computational load. We finally demonstrate that reducing the a priori uncertainty in the optimal solution results in a significant improvement of the probability of success of the TP, based on TCP and NTCP estimates

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

Science.gov (United States)

Gros, Charley; De Leener, Benjamin; Dupont, Sara M; Martin, Allan R; Fehlings, Michael G; Bakshi, Rohit; Tummala, Subhash; Auclair, Vincent; McLaren, Donald G; Callot, Virginie; Cohen-Adad, Julien; Sdika, Michaël

2018-02-01

During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T 2 -weighted n = 287, T 1 -weighted n = 120, T 2 ∗ -weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a

Estimating cellular parameters through optimization procedures: elementary principles and applications

Directory of Open Access Journals (Sweden)

Akatsuki eKimura

2015-03-01

Full Text Available 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.

Optimization of a polygeneration system for energy demands of a livestock farm

Directory of Open Access Journals (Sweden)

Mančić Marko V.

2016-01-01

Full Text Available A polygeneration system is an energy system capable of providing multiple utility outputs to meet local demands by application of process integration. This paper addresses the problem of pinpointing the optimal polygeneration energy supply system for the local energy demands of a livestock farm in terms of optimal system configuration and optimal system capacity. The optimization problem is presented and solved for a case study of a pig farm in the paper. Energy demands of the farm, as well as the super-structure of the polygeneration system were modelled using TRNSYS software. Based on the locally available resources, the following polygeneration modules were chosen for the case study analysis: a biogas fired internal combustion engine co-generation module, a gas boiler, a chiller, a ground water source heat pump, solar thermal collectors, photovoltaic collectors, and heat and cold storage. Capacities of the polygeneration modules were used as optimization variables for the TRNSYS-GenOpt optimization, whereas net present value, system primary energy consumption, and CO2 emissions were used as goal functions for optimization. A hybrid system composed of biogas fired internal combustion engine based co-generation system, adsorption chiller solar thermal and photovoltaic collectors, and heat storage is found to be the best option. Optimal heating capacity of the biogas co-generation and adsorption units was found equal to the design loads, whereas the optimal surface of the solar thermal array is equal to the south office roof area, and the optimal surface of the PV array corresponds to the south facing animal housing building rooftop area. [Projekat Ministarstva nauke Republike Srbije, br. III 42006: Research and development of energy and environmentally highly effective polygeneration systems based on using renewable energy sources

An age estimation method using brain local features for T1-weighted images.

Science.gov (United States)

Kondo, Chihiro; Ito, Koichi; Kai Wu; Sato, Kazunori; Taki, Yasuyuki; Fukuda, Hiroshi; Aoki, Takafumi

2015-08-01

Previous statistical analysis studies using large-scale brain magnetic resonance (MR) image databases have examined that brain tissues have age-related morphological changes. This fact indicates that one can estimate the age of a subject from his/her brain MR image by evaluating morphological changes with healthy aging. This paper proposes an age estimation method using local features extracted from T1-weighted MR images. The brain local features are defined by volumes of brain tissues parcellated into local regions defined by the automated anatomical labeling atlas. The proposed method selects optimal local regions to improve the performance of age estimation. We evaluate performance of the proposed method using 1,146 T1-weighted images from a Japanese MR image database. We also discuss the medical implication of selected optimal local regions.

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

Science.gov (United States)

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.

Measurement and Optimization of Local Coupling from RHIC BPM Data

CERN Document Server

Calaga, Rama; Bai, Mei; Fischer, Wolfram; Franchi, Andrea; Tomas, Rogelio

2005-01-01

Global coupling in RHIC is routinely corrected by using three skew quadrupole families to minimize the tune split. In this paper we aim to re-optimize the coupling at top energy by minimizing resonance driving terms and the C-matrix in two steps: 1. Find the best configuration of the three skew quadrupole families and 2. Identify locations with coupling sources by inspection of the driving terms and the C-matrix around the ring. The measurements of resonance terms and C-matrix are presented.

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

Science.gov (United States)

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.

Role of beam orientation optimization in intensity-modulated radiation therapy

International Nuclear Information System (INIS)

Pugachev, Andrei; Li, Jonathan G.; Boyer, Arthur L.; Hancock, Steven L.; Le, Quynh-Thu; Donaldson, Sarah S.; Lei Xing

2001-01-01

Purpose: To investigate the role of beam orientation optimization in intensity-modulated radiation therapy (IMRT) and to examine the potential benefits of noncoplanar intensity-modulated beams. Methods and Materials: A beam orientation optimization algorithm was implemented. For this purpose, system variables were divided into two groups: beam position (gantry and table angles) and beam profile (beamlet weights). Simulated annealing was used for beam orientation optimization and the simultaneous iterative inverse treatment planning algorithm (SIITP) for beam intensity profile optimization. Three clinical cases were studied: a localized prostate cancer, a nasopharyngeal cancer, and a paraspinal tumor. Nine fields were used for all treatments. For each case, 3 types of treatment plan optimization were performed: (1) beam intensity profiles were optimized for 9 equiangular spaced coplanar beams; (2) orientations and intensity profiles were optimized for 9 coplanar beams; (3) orientations and intensity profiles were optimized for 9 noncoplanar beams. Results: For the localized prostate case, all 3 types of optimization described above resulted in dose distributions of a similar quality. For the nasopharynx case, optimized noncoplanar beams provided a significant gain in the gross tumor volume coverage. For the paraspinal case, orientation optimization using noncoplanar beams resulted in better kidney sparing and improved gross tumor volume coverage. Conclusion: The sensitivity of an IMRT treatment plan with respect to the selection of beam orientations varies from site to site. For some cases, the choice of beam orientations is important even when the number of beams is as large as 9. Noncoplanar beams provide an additional degree of freedom for IMRT treatment optimization and may allow for notable improvement in the quality of some complicated plans

A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

Directory of Open Access Journals (Sweden)

Liu Yan

2018-01-01

Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

Time and Frequency Localized Pulse Shape for Resolution Enhancement in STFT-BOTDR

Directory of Open Access Journals (Sweden)

Linqing Luo

2016-01-01

Full Text Available Short-Time Fourier Transform-Brillouin Optical Time-Domain Reflectometry (STFT-BOTDR implements STFT over the full frequency spectrum to measure the distributed temperature and strain along the optic fiber, providing new research advances in dynamic distributed sensing. The spatial and frequency resolution of the dynamic sensing are limited by the Signal to Noise Ratio (SNR and the Time-Frequency (T-F localization of the input pulse shape. T-F localization is fundamentally important for the communication system, which suppresses interchannel interference (ICI and intersymbol interference (ISI to improve the transmission quality in multicarrier modulation (MCM. This paper demonstrates that the T-F localized input pulse shape can enhance the SNR and the spatial and frequency resolution in STFT-BOTDR. Simulation and experiments of T-F localized different pulses shapes are conducted to compare the limitation of the system resolution. The result indicates that rectangular pulse should be selected to optimize the spatial resolution and Lorentzian pulse could be chosen to optimize the frequency resolution, while Gaussian shape pulse can be used in general applications for its balanced performance in both spatial and frequency resolution. Meanwhile, T-F localization is proved to be useful in the pulse shape selection for system resolution optimization.

Multiobjective memetic estimation of distribution algorithm based on an incremental tournament local searcher.

Science.gov (United States)

Yang, Kaifeng; Mu, Li; Yang, Dongdong; Zou, Feng; Wang, Lei; Jiang, Qiaoyong

2014-01-01

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

Directory of Open Access Journals (Sweden)

Kaifeng Yang

2014-01-01

Full Text Available A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

A novel chaotic particle swarm optimization approach using Henon map and implicit filtering local search for economic load dispatch

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos; Mariani, Viviana Cocco

2009-01-01

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic systems theory, this paper proposed a novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using Henon map sequences which increases its convergence rate and resulting precision. The chaotic PSO approach is used to produce good potential solutions, and the IF is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results are promising and show the effectiveness of the proposed approach.

Optimal Design of Composite Structures Under Manufacturing Constraints

DEFF Research Database (Denmark)

Marmaras, Konstantinos

algorithms to perform the global optimization. The efficiency of the proposed models is examined on a set of well–defined discrete multi material and thickness optimization problems originating from the literature. The inclusion of manufacturing limitations along with structural considerations in the early...... mixed integer 0–1 programming problems. The manufacturing constraints have been treated by developing explicit models with favorable properties. In this thesis we have developed and implemented special purpose global optimization methods and heuristic techniques for solving this class of problems......This thesis considers discrete multi material and thickness optimization of laminated composite structures including local failure criteria and manufacturing constraints. Our models closely follow an immediate extension of the Discrete Material Optimization scheme, which allows simultaneous...

Orbitals from local RDMFT: Are they Kohn-Sham or natural orbitals?

International Nuclear Information System (INIS)

Theophilou, Iris; Helbig, Nicole; Lathiotakis, Nektarios N.; Gidopoulos, Nikitas I.; Rubio, Angel

2015-01-01

Recently, an approximate theoretical framework was introduced, called local reduced density matrix functional theory (local-RDMFT), where functionals of the one-body reduced density matrix (1-RDM) are minimized under the additional condition that the optimal orbitals satisfy a single electron Schrödinger equation with a local potential. In the present work, we focus on the character of these optimal orbitals. In particular, we compare orbitals obtained by local-RDMFT with those obtained with the full minimization (without the extra condition) by contrasting them against the exact NOs and orbitals from a density functional calculation using the local density approximation (LDA). We find that the orbitals from local-RMDFT are very close to LDA orbitals, contrary to those of the full minimization that resemble the exact NOs. Since local RDMFT preserves the good quality of the description of strong static correlation, this finding opens the way to a mixed density/density matrix scheme, where Kohn-Sham orbitals obtain fractional occupations from a minimization of the occupation numbers using 1-RDM functionals. This will allow for a description of strong correlation at a cost only minimally higher than a density functional calculation

Optimization of convergent collimators for pixelated SPECT systems

International Nuclear Information System (INIS)

Capote, Ricardo M.; Matela, Nuno; Conceição, Raquel C.; Almeida, Pedro

2013-01-01

Purpose: The optimization of the collimator design is essential to obtain the best possible sensitivity in single photon emission computed tomography imaging. The aim of this work is to present a methodology for maximizing the sensitivity of convergent collimators, specifically designed to match the pitch of pixelated detectors, for a fixed spatial resolution value and to present some initial results using this approach. Methods: Given the matched constraint, the optimal collimator design cannot be simply found by allowing the highest level of septal penetration and spatial resolution consistent with the imposed restrictions, as it is done for the optimization of conventional collimators. Therefore, an algorithm that interactively calculates the collimator dimensions, with the maximum sensitivity, which respect the imposed restrictions was developed and used to optimize cone and fan beam collimators with tapered square-shaped holes for low (60–300 keV) and high energy radiation (300–511 keV). The optimal collimator dimensions were locally calculated based on the premise that each hole and septa of the convergent collimator should locally resemble an appropriate optimal matched parallel collimator. Results: The optimal collimator dimensions, calculated for subcentimeter resolutions (3 and 7.5 mm), common pixel sizes (1.6, 2.1, and 2.5 mm), and acceptable septal penetration at 140 keV, were approximately constant throughout the collimator, despite their different hole incidence angles. By using these input parameters and a less strict septal penetration value of 5%, the optimal collimator dimensions and the corresponding mass per detector area were calculated for 511 keV. It is shown that a low value of focal distance leads to improvements in the average sensitivity at a fixed source-collimator distance and resolution. The optimal cone beam performance outperformed that of other optimal collimation geometries (fan and parallel beam) in imaging objects close to

Software for CATV Design and Frequency Plan Optimization

Directory of Open Access Journals (Sweden)

O. Hala

2007-09-01

Full Text Available The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Estimating local atmosphere-surface fluxes using eddy covariance and numerical Ogive optimization

DEFF Research Database (Denmark)

Sievers, Jakob; Papakyriakou, Tim; Larsen, Søren

2014-01-01

Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low-frequency cont......Estimating representative surface-fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modeling efforts, low...

Optimal Foraging in Semantic Memory

Science.gov (United States)

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…

Landmark based localization in urban environment

Science.gov (United States)

Qu, Xiaozhi; Soheilian, Bahman; Paparoditis, Nicolas

2018-06-01

A landmark based localization with uncertainty analysis based on cameras and geo-referenced landmarks is presented in this paper. The system is developed to adapt different camera configurations for six degree-of-freedom pose estimation. Local bundle adjustment is applied for optimization and the geo-referenced landmarks are integrated to reduce the drift. In particular, the uncertainty analysis is taken into account. On the one hand, we estimate the uncertainties of poses to predict the precision of localization. On the other hand, uncertainty propagation is considered for matching, tracking and landmark registering. The proposed method is evaluated on both KITTI benchmark and the data acquired by a mobile mapping system. In our experiments, decimeter level accuracy can be reached.

Optimization algorithm based on densification and dynamic canonical descent

Science.gov (United States)

Bousson, K.; Correia, S. D.

2006-07-01

Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.

Smoothing optimization of supporting quadratic surfaces with Zernike polynomials

Science.gov (United States)

Zhang, Hang; Lu, Jiandong; Liu, Rui; Ma, Peifu

2018-03-01

A new optimization method to get a smooth freeform optical surface from an initial surface generated by the supporting quadratic method (SQM) is proposed. To smooth the initial surface, a 9-vertex system from the neighbor quadratic surface and the Zernike polynomials are employed to establish a linear equation system. A local optimized surface to the 9-vertex system can be build by solving the equations. Finally, a continuous smooth optimization surface is constructed by stitching the above algorithm on the whole initial surface. The spot corresponding to the optimized surface is no longer discrete pixels but a continuous distribution.

Chemotherapy, brachytherapy and surgery of locally evolved uterine cervix carcinomas: prognosis factors of local control and global survival

International Nuclear Information System (INIS)

Laude, C.; Montella, A.; Montbarbon, X.; Malet, C.; Racadot, S.; Pommier, P.; Mathevet, P.; Buenerd, A.

2009-01-01

The protocol used allows an excellent local control of the uterine cervix carcinoma with an acceptable morbidity. To anticipate the presence of a tumor residue can be an evolution in the therapy management after external radiotherapy, particularly in optimized image-guided brachytherapy (MRI and PET)New utero vaginal applicators with parameters implantation allow to realise the dose complement at the distal parameters. These advances make consider an improvement of results in the management of locally evolved uterine cervix carcinomas. (N.C.)

A Joint Audio-Visual Approach to Audio Localization

DEFF Research Database (Denmark)

Jensen, Jesper Rindom; Christensen, Mads Græsbøll

2015-01-01

Localization of audio sources is an important research problem, e.g., to facilitate noise reduction. In the recent years, the problem has been tackled using distributed microphone arrays (DMA). A common approach is to apply direction-of-arrival (DOA) estimation on each array (denoted as nodes), a...... time-of-flight cameras. Moreover, we propose an optimal method for weighting such DOA and range information for audio localization. Our experiments on both synthetic and real data show that there is a clear, potential advantage of using the joint audiovisual localization framework....

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

Science.gov (United States)

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.

Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

Directory of Open Access Journals (Sweden)

Geng Zhang

2015-01-01

Full Text Available Although the original particle swarm optimizer (PSO method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO. In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Wei Li

2015-01-01

Full Text Available We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Reload pattern optimization by application of multiple cyclic interchange algorithms

Energy Technology Data Exchange (ETDEWEB)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E. [Technische Univ. Delft (Netherlands)

1996-09-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the `elite` cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Reload pattern optimization by application of multiple cyclic interchange algorithms

International Nuclear Information System (INIS)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E.

1996-01-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the 'elite' cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Topology Optimization Using Multiscale Finite Element Method for High-Contrast Media

DEFF Research Database (Denmark)

Lazarov, Boyan Stefanov

2014-01-01

The focus of this paper is on the applicability of multiscale finite element coarse spaces for reducing the computational burden in topology optimization. The coarse spaces are obtained by solving a set of local eigenvalue problems on overlapping patches covering the computational domain. The app......The focus of this paper is on the applicability of multiscale finite element coarse spaces for reducing the computational burden in topology optimization. The coarse spaces are obtained by solving a set of local eigenvalue problems on overlapping patches covering the computational domain...

Comparison of the convergence properties of linear-scaling electronic-structure schemes for nonorthogonal bases

International Nuclear Information System (INIS)

Stephan, Uwe

2000-01-01

This paper presents a detailed comparison of the convergence properties of density-matrix and localized-orbital O(N) functionals within 512-atom cells of amorphous carbon using a first-principles local-orbital Hamiltonian. The functionals were minimized by means of the conventional but tensorially incorrect covariant derivatives as well as the correct contravariant derivatives. While the correct derivatives result in a much faster minimization, the energies obtained in this case are somewhat higher compared to using the covariant derivatives. However, we present a representation of the density-matrix functional which requires shorter minimization times and yet returns more accurate energies for practical sizes of the localization regions. Furthermore, while the density-matrix functional is superior in efficiency to the orbital-based functional when using the incorrect derivatives, both functionals exhibit similar decay properties in terms of conjugate-gradient iterations for the correct derivatives. This makes the orbital-based functional faster, especially when minimal sets of Wannier-like functions and projected initial functions can be used

Oscillator strengths, first-order properties, and nuclear gradients for local ADC(2)

Energy Technology Data Exchange (ETDEWEB)

Schütz, Martin, E-mail: martin.schuetz@chemie.uni-regensburg.de [Institute of Physical and Theoretical Chemistry, University of Regensburg, Universitätsstraße 31, D-93040 Regensburg (Germany)

2015-06-07

We describe theory and implementation of oscillator strengths, orbital-relaxed first-order properties, and nuclear gradients for the local algebraic diagrammatic construction scheme through second order. The formalism is derived via time-dependent linear response theory based on a second-order unitary coupled cluster model. The implementation presented here is a modification of our previously developed algorithms for Laplace transform based local time-dependent coupled cluster linear response (CC2LR); the local approximations thus are state specific and adaptive. The symmetry of the Jacobian leads to considerable simplifications relative to the local CC2LR method; as a result, a gradient evaluation is about four times less expensive. Test calculations show that in geometry optimizations, usually very similar geometries are obtained as with the local CC2LR method (provided that a second-order method is applicable). As an exemplary application, we performed geometry optimizations on the low-lying singlet states of chlorophyllide a.

Oscillator strengths, first-order properties, and nuclear gradients for local ADC(2).

Science.gov (United States)

Schütz, Martin

2015-06-07

We describe theory and implementation of oscillator strengths, orbital-relaxed first-order properties, and nuclear gradients for the local algebraic diagrammatic construction scheme through second order. The formalism is derived via time-dependent linear response theory based on a second-order unitary coupled cluster model. The implementation presented here is a modification of our previously developed algorithms for Laplace transform based local time-dependent coupled cluster linear response (CC2LR); the local approximations thus are state specific and adaptive. The symmetry of the Jacobian leads to considerable simplifications relative to the local CC2LR method; as a result, a gradient evaluation is about four times less expensive. Test calculations show that in geometry optimizations, usually very similar geometries are obtained as with the local CC2LR method (provided that a second-order method is applicable). As an exemplary application, we performed geometry optimizations on the low-lying singlet states of chlorophyllide a.

Oscillator strengths, first-order properties, and nuclear gradients for local ADC(2)

International Nuclear Information System (INIS)

Schütz, Martin

2015-01-01

We describe theory and implementation of oscillator strengths, orbital-relaxed first-order properties, and nuclear gradients for the local algebraic diagrammatic construction scheme through second order. The formalism is derived via time-dependent linear response theory based on a second-order unitary coupled cluster model. The implementation presented here is a modification of our previously developed algorithms for Laplace transform based local time-dependent coupled cluster linear response (CC2LR); the local approximations thus are state specific and adaptive. The symmetry of the Jacobian leads to considerable simplifications relative to the local CC2LR method; as a result, a gradient evaluation is about four times less expensive. Test calculations show that in geometry optimizations, usually very similar geometries are obtained as with the local CC2LR method (provided that a second-order method is applicable). As an exemplary application, we performed geometry optimizations on the low-lying singlet states of chlorophyllide a

Economic dispatch using particle swarm optimization. A review

International Nuclear Information System (INIS)

Mahor, Amita; Rangnekar, Saroj; Prasad, Vishnu

2009-01-01

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. (author)

Evolutionary optimization of an experimental apparatus

DEFF Research Database (Denmark)

Geisel, Ilka; Cordes, Kai; Mahnke, Jan

2013-01-01

algorithm based on differential evolution. We demonstrate that this algorithm optimizes 21 correlated parameters and that it is robust against local maxima and experimental noise. The algorithm is flexible and easy to implement. Thus, the presented scheme can be applied to a wide range of experimental...

Evaluating Small Sphere Limit of the Wang-Yau Quasi-Local Energy

Science.gov (United States)

Chen, Po-Ning; Wang, Mu-Tao; Yau, Shing-Tung

2018-01-01

In this article, we study the small sphere limit of the Wang-Yau quasi-local energy defined in Wang and Yau (Phys Rev Lett 102(2):021101, 2009, Commun Math Phys 288(3):919-942, 2009). Given a point p in a spacetime N, we consider a canonical family of surfaces approaching p along its future null cone and evaluate the limit of the Wang-Yau quasi-local energy. The evaluation relies on solving an "optimal embedding equation" whose solutions represent critical points of the quasi-local energy. For a spacetime with matter fields, the scenario is similar to that of the large sphere limit found in Chen et al. (Commun Math Phys 308(3):845-863, 2011). Namely, there is a natural solution which is a local minimum, and the limit of its quasi-local energy recovers the stress-energy tensor at p. For a vacuum spacetime, the quasi-local energy vanishes to higher order and the solution of the optimal embedding equation is more complicated. Nevertheless, we are able to show that there exists a solution that is a local minimum and that the limit of its quasi-local energy is related to the Bel-Robinson tensor. Together with earlier work (Chen et al. 2011), this completes the consistency verification of the Wang-Yau quasi-local energy with all classical limits.

Modification of species-based differential evolution for multimodal optimization

Science.gov (United States)

Idrus, Said Iskandar Al; Syahputra, Hermawan; Firdaus, Muliawan

2015-12-01

At this time optimization has an important role in various fields as well as between other operational research, industry, finance and management. Optimization problem is the problem of maximizing or minimizing a function of one variable or many variables, which include unimodal and multimodal functions. Differential Evolution (DE), is a random search technique using vectors as an alternative solution in the search for the optimum. To localize all local maximum and minimum on multimodal function, this function can be divided into several domain of fitness using niching method. Species-based niching method is one of method that build sub-populations or species in the domain functions. This paper describes the modification of species-based previously to reduce the computational complexity and run more efficiently. The results of the test functions show species-based modifications able to locate all the local optima in once run the program.

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

Directory of Open Access Journals (Sweden)

Simon Fong

2014-01-01

Full Text Available 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

Science.gov (United States)

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

Augmented Lagrange Programming Neural Network for Localization Using Time-Difference-of-Arrival Measurements.

Science.gov (United States)

Han, Zifa; Leung, Chi Sing; So, Hing Cheung; Constantinides, Anthony George

2017-08-15

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

Generation of Articulated Mechanisms by Optimization Techniques

DEFF Research Database (Denmark)

Kawamoto, Atsushi

2004-01-01

optimization [Paper 2] 3. Branch and bound global optimization [Paper 3] 4. Path-generation problems [Paper 4] In terms of the objective of the articulated mechanism design problems, the first to third papers deal with maximization of output displacement, while the fourth paper solves prescribed path...... generation problems. From a mathematical programming point of view, the methods proposed in the first and third papers are categorized as deterministic global optimization, while those of the second and fourth papers are categorized as gradient-based local optimization. With respect to design variables, only...... directly affects the result of the associated sensitivity analysis. Another critical issue for mechanism design is the concept of mechanical degrees of freedom and this should be also considered for obtaining a proper articulated mechanism. The thesis treats this inherently discrete criterion in some...

Integration and Optimization of Alternative Sources of Energy in a Remote Region

Science.gov (United States)

Berberi, Pellumb; Inodnorjani, Spiro; Aleti, Riza

2010-01-01

In a remote coastal region supply of energy from national grid is insufficient for a sustainable development. Integration and optimization of local alternative renewable energy sources is an optional solution of the problem. In this paper we have studied the energetic potential of local sources of renewable energy (water, solar, wind and biomass). A bottom-up energy system optimization model is proposed in order to support planning policies for promoting the use of renewable energy sources. A software, based on multiple factors and constrains analysis for optimization energy flow is proposed, which provides detailed information for exploitation each source of energy, power and heat generation, GHG emissions and end-use sectors. Economical analysis shows that with existing technologies both stand alone and regional facilities may be feasible. Improving specific legislation will foster investments from Central or Local Governments and also from individuals, private companies or small families. The study is carried on the frame work of a FP6 project "Integrated Renewable Energy System."

Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review

Directory of Open Access Journals (Sweden)

M. K. Sakharov

2015-01-01

Full Text Available In recent decades, evolutionary algorithms have proven themselves as the powerful optimization techniques of search engine. Their popularity is due to the fact that they are easy to implement and can be used in all areas, since they are based on the idea of universal evolution. For example, in the problems of a large number of local optima, the traditional optimization methods, usually, fail in finding the global optimum. To solve such problems using a variety of stochastic methods, in particular, the so-called population-based algorithms, which are a kind of evolutionary methods. The main disadvantage of this class of methods is their slow convergence to the exact solution in the neighborhood of the global optimum, as these methods incapable to use the local information about the landscape of the function. This often limits their use in largescale real-world problems where the computation time is a critical factor.One of the promising directions in the field of modern evolutionary computation are memetic algorithms, which can be regarded as a combination of population search of the global optimum and local procedures for verifying solutions, which gives a synergistic effect. In the context of memetic algorithms, the meme is an implementation of the local optimization method to refine solution in the search.The concept of memetic algorithms provides ample opportunities for the development of various modifications of these algorithms, which can vary the frequency of the local search, the conditions of its end, and so on. The practically significant memetic algorithm modifications involve the simultaneous use of different memes. Such algorithms are called multi-memetic.The paper gives statement of the global problem of nonlinear unconstrained optimization, describes the most promising areas of AI modifications, including hybridization and metaoptimization. The main content of the work is the classification and review of existing varieties of

Optimization of nonlinear controller with an enhanced biogeography approach

Directory of Open Access Journals (Sweden)

Mohammed Salem

2014-07-01

Full Text Available This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO.

Improved Beam Angle Arrangement in Intensity Modulated Proton Therapy Treatment Planning for Localized Prostate Cancer

International Nuclear Information System (INIS)

Cao, Wenhua; Lim, Gino J.; Li, Yupeng; Zhu, X. Ronald; Zhang, Xiaodong

2015-01-01

Purpose: This study investigates potential gains of an improved beam angle arrangement compared to a conventional fixed gantry setup in intensity modulated proton therapy (IMPT) treatment for localized prostate cancer patients based on a proof of principle study. Materials and Methods: Three patients with localized prostate cancer retrospectively selected from our institution were studied. For each patient, IMPT plans were designed using two, three and four beam angles, respectively, obtained from a beam angle optimization algorithm. Those plans were then compared with ones using two lateral parallel-opposed beams according to the conventional planning protocol for localized prostate cancer adopted at our institution. Results: IMPT plans with two optimized angles achieved significant improvements in rectum sparing and moderate improvements in bladder sparing against those with two lateral angles. Plans with three optimized angles further improved rectum sparing significantly over those two-angle plans, whereas four-angle plans found no advantage over three-angle plans. A possible three-beam class solution for localized prostate patients was suggested and demonstrated with preserved dosimetric benefits because individually optimized three-angle solutions were found sharing a very similar pattern. Conclusions: This study has demonstrated the potential of using an improved beam angle arrangement to better exploit the theoretical dosimetric benefits of proton therapy and provided insights of selecting quality beam angles for localized prostate cancer treatment

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

Science.gov (United States)

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

Localization from near-source quasi-static electromagnetic fields

Energy Technology Data Exchange (ETDEWEB)

Mosher, John Compton [Univ. of Southern California, Los Angeles, CA (United States)

1993-09-01

A wide range of research has been published on the problem of estimating the parameters of electromagnetic and acoustical sources from measurements of signals measured at an array of sensors. In the quasi-static electromagnetic cases examined here, the signal variation from a point source is relatively slow with respect to the signal propagation and the spacing of the array of sensors. As such, the location of the point sources can only be determined from the spatial diversity of the received signal across the array. The inverse source localization problem is complicated by unknown model order and strong local minima. The nonlinear optimization problem is posed for solving for the parameters of the quasi-static source model. The transient nature of the sources can be exploited to allow subspace approaches to separate out the signal portion of the spatial correlation matrix. Decomposition techniques are examined for improved processing, and an adaptation of MUtiple SIgnal Characterization (MUSIC) is presented for solving the source localization problem. Recent results on calculating the Cramer-Rao error lower bounds are extended to the multidimensional problem here. This thesis focuses on the problem of source localization in magnetoencephalography (MEG), with a secondary application to thunderstorm source localization. Comparisons are also made between MEG and its electrical equivalent, electroencephalography (EEG). The error lower bounds are examined in detail for several MEG and EEG configurations, as well as localizing thunderstorm cells over Cape Canaveral and Kennedy Space Center. Time-eigenspectrum is introduced as a parsing technique for improving the performance of the optimization problem.

Procuring load curtailment from local customers under uncertainty.

Science.gov (United States)

Mijatović, Aleksandar; Moriarty, John; Vogrinc, Jure

2017-08-13

Demand side response (DSR) provides a flexible approach to managing constrained power network assets. This is valuable if future asset utilization is uncertain. However there may be uncertainty over the process of procurement of DSR from customers. In this context we combine probabilistic modelling, simulation and optimization to identify economically optimal procurement policies from heterogeneous customers local to the asset, under chance constraints on the adequacy of the procured DSR. Mathematically this gives rise to a search over permutations, and we provide an illustrative example implementation and case study.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

Optimization of a local district heating plant under fuel flexibility and performance

DEFF Research Database (Denmark)

Rudra, Souman; Rosendahl, Lasse; From, Niels

2011-01-01

are calculated for various local fuels in energyPRO. A comparison has been made between the reference model and the basis for individual solutions. The greatest reduction in heat price is obtained by replacing one engine with a new biogas where heat production is divided by 66% of biogas, 13% natural gas engines......, an investigation has been made to reduce the use of fossil fuels for district heating system and make use of the local renewable resources (Biogas, Solar and Geothermal) for district heating purpose. In this article, the techno-economic assessment is achieved through the development of a suite of models...

Numerically exact dynamics of the interacting many-body Schroedinger equation for Bose-Einstein condensates. Comparison to Bose-Hubbard and Gross-Pitaevskii theory

Energy Technology Data Exchange (ETDEWEB)

Sakmann, Kaspar

2010-07-21

In this thesis, the physics of trapped, interacting Bose-Einstein condensates is analyzed by solving the many-body Schroedinger equation. Particular emphasis is put on coherence, fragmentation and reduced density matrices. First, the ground state of a trapped Bose-Einstein condensate and its correlation functions are obtained. Then the dynamics of a bosonic Josephson junction is investigated by solving the time-dependent many-body Schroedinger equation numerically exactly. These are the first exact results in literature in this context. It is shown that the standard approximations of the field, Gross-Pitaevskii theory and the Bose-Hubbard model fail at weak interaction strength and within their range of expected validity. For stronger interactions the dynamics becomes strongly correlated and a new equilibration phenomenon is discovered. By comparison with exact results it is shown that a symmetry of the Bose- Hubbard model between attractive and repulsive interactions must be considered an artefact of the model. A conceptual innovation of this thesis are time-dependent Wannier functions. Equations of motion for time-dependent Wannier functions are derived from the variational principle. By comparison with exact results it is shown that lattice models can be greatly improved at little computational cost by letting the Wannier functions of a lattice model become time-dependent. (orig.)

Optimal contracts for wind power producers in electricity markets

KAUST Repository

Bitar, E.

2010-12-01

This paper is focused on optimal contracts for an independent wind power producer in conventional electricity markets. Starting with a simple model of the uncertainty in the production of power from a wind turbine farm and a model for the electric energy market, we derive analytical expressions for optimal contract size and corresponding expected optimal profit. We also address problems involving overproduction penalties, cost of reserves, and utility of additional sensor information. We obtain analytical expressions for marginal profits from investing in local generation and energy storage. ©2010 IEEE.

Loading pattern optimization using ant colony algorithm

International Nuclear Information System (INIS)

Hoareau, Fabrice

2008-01-01

Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications - Comparison of automated optimization techniques

International Nuclear Information System (INIS)

Gurcan, Metin N.; Sahiner, Berkman; Chan Heangping; Hadjiiski, Lubomir; Petrick, Nicholas

2001-01-01

Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are 'optimized' by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area A z under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost

Localized Multiple Kernel Learning A Convex Approach

Science.gov (United States)

2016-11-22

data. All the aforementioned approaches to localized MKL are formulated in terms of non-convex optimization problems, and deep the- oretical...learning. IEEE Transactions on Neural Networks, 22(3):433–446, 2011. Jingjing Yang, Yuanning Li, Yonghong Tian, Lingyu Duan, and Wen Gao. Group-sensitive

Strategy for solving a coupled problem of the electromagnetic load analysis and design optimization for local conducting structures to support the ITER blanket development

International Nuclear Information System (INIS)

Rozov, Vladimir; Belyakov, V.; Kukhtin, V.; Lamzin, E.; Mazul, I.; Sytchevsky, S.

2014-01-01

Highlights: • We present the way of modeling transient electro-magnetic loads on local conductive domains in the large magnetic system. • Simplification is achieved by decomposing of the problem, multi-scale integral-differential modeling and use of integral parameters. • The intrinsic scale of loads on a localized conductor with eddy is quantified through the load susceptibility tensor. • Solution is searched as response of a simple equivalent dynamic simulator, using control theory methods. • The concept is exemplified with multi-scenario assessment of EM eddy loads on ITER blanket modules. - Abstract: The complexity of the electromagnetic (EM) response of the tokamak structures is one of the key and design-driving issues for the ITER. We consider the specifics of the assessment of ponderomotive forces, acting on local components of a large electro-physical device during electromagnetic transients. A strategy and approach is proposed for the operative EM loads modeling and analysis that enables design optimization at early phases of development. The paper describes a method of principal simplification of the mathematical model, based on the analysis and exploiting specific features and peculiarities of the relevant technical problem, determined by the design and operation of the device and system under consideration. The application of the method for predictive EM loads analysis and corresponding numerical calculations are exemplified for the localized ITER blanket components — shield modules. The example demonstrates the efficiency of EM load analysis in complex electromagnetic systems via a set of simplified models with different scope, contents and level of detail

Strategy for solving a coupled problem of the electromagnetic load analysis and design optimization for local conducting structures to support the ITER blanket development

Energy Technology Data Exchange (ETDEWEB)

Rozov, Vladimir, E-mail: vladimir.rozov@iter.org [ITER Organization, Route de Vinon sur Verdon, 13115 Saint Paul-lez-Durance (France); Belyakov, V.; Kukhtin, V.; Lamzin, E.; Mazul, I.; Sytchevsky, S. [D.V. Efremov Scientific Research Institute, 196641 St. Petersburg (Russian Federation)

2014-11-15

Highlights: • We present the way of modeling transient electro-magnetic loads on local conductive domains in the large magnetic system. • Simplification is achieved by decomposing of the problem, multi-scale integral-differential modeling and use of integral parameters. • The intrinsic scale of loads on a localized conductor with eddy is quantified through the load susceptibility tensor. • Solution is searched as response of a simple equivalent dynamic simulator, using control theory methods. • The concept is exemplified with multi-scenario assessment of EM eddy loads on ITER blanket modules. - Abstract: The complexity of the electromagnetic (EM) response of the tokamak structures is one of the key and design-driving issues for the ITER. We consider the specifics of the assessment of ponderomotive forces, acting on local components of a large electro-physical device during electromagnetic transients. A strategy and approach is proposed for the operative EM loads modeling and analysis that enables design optimization at early phases of development. The paper describes a method of principal simplification of the mathematical model, based on the analysis and exploiting specific features and peculiarities of the relevant technical problem, determined by the design and operation of the device and system under consideration. The application of the method for predictive EM loads analysis and corresponding numerical calculations are exemplified for the localized ITER blanket components — shield modules. The example demonstrates the efficiency of EM load analysis in complex electromagnetic systems via a set of simplified models with different scope, contents and level of detail.

Genetic Algorithm Optimizes Q-LAW Control Parameters

Science.gov (United States)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Singularities in Structural Optimization of the Ziegler Pendulum

Directory of Open Access Journals (Sweden)

O. N. Kirillov

2011-01-01

Full Text Available Structural optimization of non-conservative systems with respect to stability criteria is a research area with important applications in fluid-structure interactions, friction-induced instabilities, and civil engineering. In contrast to optimization of conservative systems where rigorously proven optimal solutions in buckling problems have been found, for nonconservative optimization problems only numerically optimized designs have been reported. The proof of optimality in non-conservative optimization problems is a mathematical challenge related to multiple eigenvalues, singularities in the stability domain, and non-convexity of the merit functional. We present here a study of optimal mass distribution in a classical Ziegler pendulum where local and global extrema can be found explicitly. In particular, for the undamped case, the two maxima of the critical flutter load correspond to a vanishing mass either in a joint or at the free end of the pendulum; in the minimum, the ratio of the masses is equal to the ratio of the stiffness coefficients. The role of the singularities on the stability boundary in the optimization is highlighted, and an extension to the damped case as well as to the case of higher degrees of freedom is discussed.

An effective 2-band eg model of sulfur hydride H3S for high-Tc superconductivity

Science.gov (United States)

Nishiguchi, Kazutaka; Teranishi, Shingo; Miyao, Satoaki; Matsushita, Goh; Kusakabe, Koichi

To understand high transition temperature (Tc) superconductivity in sulfur hydride H3S, we propose an effective 2-band model having the eg symmetry as the minimal model for H3S. Two eg orbitals centered on a sulfur S atom are chosen for the smallest representation of relevant bands with the van-Hove singularity around the Fermi levels except for the Γ-centered small hole pockets by the sulfur 3 p orbitals. By using the maximally localized Wannier functions, we derive the minimal effective model preserving the body-centered cubic (bcc) crystal symmetry of the H3S phase having the highest Tc ( 203 K under pressures) among the other polymorphs of H3S.

Dialocalization: Acoustic speaker diarization and visual localization as joint optimization problem

NARCIS (Netherlands)

Friedland, G.; Yeo, C.; Hung, H.

2010-01-01

The following article presents a novel audio-visual approach for unsupervised speaker localization in both time and space and systematically analyzes its unique properties. Using recordings from a single, low-resolution room overview camera and a single far-field microphone, a state-of-the-art

Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits

Directory of Open Access Journals (Sweden)

Kajsa Ljungberg

2010-10-01

Full Text Available Kajsa Ljungberg1, Kateryna Mishchenko2, Sverker Holmgren11Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden; 2Department of Mathematics and Physics, Mälardalen University College, Västerås, SwedenAbstract: We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called QTL, and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.Keywords: global optimization, QTL mapping, DIRECT 

Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Daniel M. Wonohadidjojo

2017-03-01

was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.

Implicit geometric representations for optimal design of gas turbine blades

International Nuclear Information System (INIS)

Mansour, T.; Ghaly, W.

2004-01-01

Shape optimization requires a proper geometric representation of the blade profile; the parameters of such a representation are usually taken as design variables in the optimization process. This implies that the model must possess three specific features: flexibility, efficiency, and accuracy. For the specific task of aerodynamic optimization for turbine blades, it is critical to have flexibility in both the global and local design spaces in order to obtain a successful optimization. This work is concerned with the development of two geometric representations of turbine blade profiles that are appropriate for aerodynamic optimization: the Modified Rapid Axial Turbine Design (MRATD) model where the blade is represented by five low-order curves that satisfy eleven designer parameters; this model is suitable for a global search of the design space. The second model is NURBS parameterization of the blade profile that can be used for a local refinement. The two models are presented and are assessed for flexibility and accuracy when representing several typical turbine blade profiles. The models will be further discussed in terms of curve smoothness and blade shape representation with a multi-NURBS curve versus one curve and its effect on the flow field, in particular the pressure distribution along the blade surfaces, will be elaborated. (author)

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

OpenAIRE

Jing Wang; Yourui Huang

2013-01-01

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

New Delivery Systems for Local Anaesthetics—Part 2

Directory of Open Access Journals (Sweden)

Edward A. Shipton

2012-01-01

Full Text Available Part 2 of this paper deals with the techniques for drug delivery of topical and injectable local anaesthetics. The various routes of local anaesthetic delivery (epidural, peripheral, wound catheters, intra-nasal, intra-vesical, intra-articular, intra-osseous are explored. To enhance transdermal local anaesthetic permeation, additional methods to the use of an eutectic mixture of local anaesthetics and the use of controlled heat can be used. These methods include iontophoresis, electroporation, sonophoresis, and magnetophoresis. The potential clinical uses of topical local anaesthetics are elucidated. Iontophoresis, the active transportation of a drug into the skin using a constant low-voltage direct current is discussed. It is desirable to prolong local anaesthetic blockade by extending its sensory component only. The optimal release and safety of the encapsulated local anaesthetic agents still need to be determined. The use of different delivery systems should provide the clinician with both an extended range and choice in the degree of prolongation of action of each agent.

Methodology for wind turbine blade geometry optimization

Energy Technology Data Exchange (ETDEWEB)

Perfiliev, D.

2013-11-01

Nowadays, the upwind three bladed horizontal axis wind turbine is the leading player on the market. It has been found to be the best industrial compromise in the range of different turbine constructions. The current wind industry innovation is conducted in the development of individual turbine components. The blade constitutes 20-25% of the overall turbine budget. Its optimal operation in particular local economic and wind conditions is worth investigating. The blade geometry, namely the chord, twist and airfoil type distributions along the span, responds to the output measures of the blade performance. Therefore, the optimal wind blade geometry can improve the overall turbine performance. The objectives of the dissertation are focused on the development of a methodology and specific tool for the investigation of possible existing wind blade geometry adjustments. The novelty of the methodology presented in the thesis is the multiobjective perspective on wind blade geometry optimization, particularly taking simultaneously into account the local wind conditions and the issue of aerodynamic noise emissions. The presented optimization objective approach has not been investigated previously for the implementation in wind blade design. The possibilities to use different theories for the analysis and search procedures are investigated and sufficient arguments derived for the usage of proposed theories. The tool is used for the test optimization of a particular wind turbine blade. The sensitivity analysis shows the dependence of the outputs on the provided inputs, as well as its relative and absolute divergences and instabilities. The pros and cons of the proposed technique are seen from the practical implementation, which is documented in the results, analysis and conclusion sections. (orig.)

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

Maintenance resources optimization applied to a manufacturing system

International Nuclear Information System (INIS)