Mental Computation or Standard Algorithm? Children's Strategy Choices on Multi-Digit Subtractions

Science.gov (United States)

Torbeyns, Joke; Verschaffel, Lieven

2016-01-01

This study analyzed children's use of mental computation strategies and the standard algorithm on multi-digit subtractions. Fifty-eight Flemish 4th graders of varying mathematical achievement level were individually offered subtractions that either stimulated the use of mental computation strategies or the standard algorithm in one choice and two…

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

Science.gov (United States)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Real-time recursive hyperspectral sample and band processing algorithm architecture and implementation

CERN Document Server

Chang, Chein-I

2017-01-01

This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data.

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

Science.gov (United States)

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

Cloud computing task scheduling strategy based on improved differential evolution algorithm

Science.gov (United States)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

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

Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.

Energy Technology Data Exchange (ETDEWEB)

Matulef, Kevin Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewer resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.

Soil sampling strategies: Evaluation of different approaches

Energy Technology Data Exchange (ETDEWEB)

De Zorzi, Paolo [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Metrologia Ambientale, Via di Castel Romano, 100-00128 Roma (Italy)], E-mail: paolo.dezorzi@apat.it; Barbizzi, Sabrina; Belli, Maria [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Metrologia Ambientale, Via di Castel Romano, 100-00128 Roma (Italy); Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia [Agenzia Regionale per la Prevenzione e Protezione dell' Ambiente del Veneto, ARPA Veneto, U.O. Centro Qualita Dati, Via Spalato, 14-36045 Vicenza (Italy)

2008-11-15

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2{sigma}, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies.

Soil sampling strategies: Evaluation of different approaches

International Nuclear Information System (INIS)

De Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia

2008-01-01

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2σ, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies

Soil sampling strategies: evaluation of different approaches.

Science.gov (United States)

de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia

2008-11-01

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2sigma, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies.

The variance quadtree algorithm: use for spatial sampling design

NARCIS (Netherlands)

Minasny, B.; McBratney, A.B.; Walvoort, D.J.J.

2007-01-01

Spatial sampling schemes are mainly developed to determine sampling locations that can cover the variation of environmental properties in the area of interest. Here we proposed the variance quadtree algorithm for sampling in an area with prior information represented as ancillary or secondary

A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm

Directory of Open Access Journals (Sweden)

Jiao Shi

2014-01-01

Full Text Available How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems.

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using

Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy

Directory of Open Access Journals (Sweden)

Jie-Sheng Wang

2015-01-01

Full Text Available According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS algorithm and hysteresis switching (HS strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

International Nuclear Information System (INIS)

Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

Energy Technology Data Exchange (ETDEWEB)

Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems. © 2013 Elsevier Inc.

An intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces.

Science.gov (United States)

Ying, Xiang; Xin, Shi-Qing; Sun, Qian; He, Ying

2013-09-01

Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IR(n). To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.

Spent nuclear fuel sampling strategy

International Nuclear Information System (INIS)

Bergmann, D.W.

1995-01-01

This report proposes a strategy for sampling the spent nuclear fuel (SNF) stored in the 105-K Basins (105-K East and 105-K West). This strategy will support decisions concerning the path forward SNF disposition efforts in the following areas: (1) SNF isolation activities such as repackaging/overpacking to a newly constructed staging facility; (2) conditioning processes for fuel stabilization; and (3) interim storage options. This strategy was developed without following the Data Quality Objective (DQO) methodology. It is, however, intended to augment the SNF project DQOS. The SNF sampling is derived by evaluating the current storage condition of the SNF and the factors that effected SNF corrosion/degradation

Fast sampling algorithm for the simulation of photon Compton scattering

International Nuclear Information System (INIS)

Brusa, D.; Salvat, F.

1996-01-01

A simple algorithm for the simulation of Compton interactions of unpolarized photons is described. The energy and direction of the scattered photon, as well as the active atomic electron shell, are sampled from the double-differential cross section obtained by Ribberfors from the relativistic impulse approximation. The algorithm consistently accounts for Doppler broadening and electron binding effects. Simplifications of Ribberfors' formula, required for efficient random sampling, are discussed. The algorithm involves a combination of inverse transform, composition and rejection methods. A parameterization of the Compton profile is proposed from which the simulation of Compton events can be performed analytically in terms of a few parameters that characterize the target atom, namely shell ionization energies, occupation numbers and maximum values of the one-electron Compton profiles. (orig.)

Classical boson sampling algorithms with superior performance to near-term experiments

Science.gov (United States)

Neville, Alex; Sparrow, Chris; Clifford, Raphaël; Johnston, Eric; Birchall, Patrick M.; Montanaro, Ashley; Laing, Anthony

2017-12-01

It is predicted that quantum computers will dramatically outperform their conventional counterparts. However, large-scale universal quantum computers are yet to be built. Boson sampling is a rudimentary quantum algorithm tailored to the platform of linear optics, which has sparked interest as a rapid way to demonstrate such quantum supremacy. Photon statistics are governed by intractable matrix functions, which suggests that sampling from the distribution obtained by injecting photons into a linear optical network could be solved more quickly by a photonic experiment than by a classical computer. The apparently low resource requirements for large boson sampling experiments have raised expectations of a near-term demonstration of quantum supremacy by boson sampling. Here we present classical boson sampling algorithms and theoretical analyses of prospects for scaling boson sampling experiments, showing that near-term quantum supremacy via boson sampling is unlikely. Our classical algorithm, based on Metropolised independence sampling, allowed the boson sampling problem to be solved for 30 photons with standard computing hardware. Compared to current experiments, a demonstration of quantum supremacy over a successful implementation of these classical methods on a supercomputer would require the number of photons and experimental components to increase by orders of magnitude, while tackling exponentially scaling photon loss.

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

Science.gov (United States)

Marojevic, Vuk; Gomez-Miguelez, Ismael; Gelonch, Antoni

2012-09-01

Software defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Homem-de-Mello, Tito [University of Illinois at Chicago, Department of Mechanical and Industrial Engineering, Chicago, IL (United States); Matos, Vitor L. de; Finardi, Erlon C. [Universidade Federal de Santa Catarina, LabPlan - Laboratorio de Planejamento de Sistemas de Energia Eletrica, Florianopolis (Brazil)

2011-03-15

The long-term hydrothermal scheduling is one of the most important problems to be solved in the power systems area. This problem aims to obtain an optimal policy, under water (energy) resources uncertainty, for hydro and thermal plants over a multi-annual planning horizon. It is natural to model the problem as a multi-stage stochastic program, a class of models for which algorithms have been developed. The original stochastic process is represented by a finite scenario tree and, because of the large number of stages, a sampling-based method such as the Stochastic Dual Dynamic Programming (SDDP) algorithm is required. The purpose of this paper is two-fold. Firstly, we study the application of two alternative sampling strategies to the standard Monte Carlo - namely, Latin hypercube sampling and randomized quasi-Monte Carlo - for the generation of scenario trees, as well as for the sampling of scenarios that is part of the SDDP algorithm. Secondly, we discuss the formulation of stopping criteria for the optimization algorithm in terms of statistical hypothesis tests, which allows us to propose an alternative criterion that is more robust than that originally proposed for the SDDP. We test these ideas on a problem associated with the whole Brazilian power system, with a three-year planning horizon. (orig.)

User-driven sampling strategies in image exploitation

Science.gov (United States)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

Science.gov (United States)

Abedini, M. J.; Nasseri, M.; Burn, D. H.

2012-04-01

In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

Evaluation of sampling strategies to estimate crown biomass

Directory of Open Access Journals (Sweden)

Krishna P Poudel

2015-01-01

Full Text Available Background Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass. Methods Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE, relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12 are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass. Results Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases. Conclusions Use of

Fast algorithms for finding proper strategies in game trees

DEFF Research Database (Denmark)

Miltersen, Peter Bro; Sørensen, Troels Bjerre

2008-01-01

We show how to find a normal form proper equilibrium in behavior strategies of a given two-player zero-sum extensive form game with imperfect information but perfect recall. Our algorithm solves a finite sequence of linear programs and runs in polynomial time. For the case of a perfect informatio...

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam; Jacobs, Sam Ade; Sharma, Shishir; Amato, Nancy M.; Rauchwerger, Lawrence

2014-01-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam

2014-05-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

Observing System Simulation Experiments for the assessment of temperature sampling strategies in the Mediterranean Sea

Directory of Open Access Journals (Sweden)

F. Raicich

2003-01-01

Full Text Available For the first time in the Mediterranean Sea various temperature sampling strategies are studied and compared to each other by means of the Observing System Simulation Experiment technique. Their usefulness in the framework of the Mediterranean Forecasting System (MFS is assessed by quantifying their impact in a Mediterranean General Circulation Model in numerical twin experiments via univariate data assimilation of temperature profiles in summer and winter conditions. Data assimilation is performed by means of the optimal interpolation algorithm implemented in the SOFA (System for Ocean Forecasting and Analysis code. The sampling strategies studied here include various combinations of eXpendable BathyThermograph (XBT profiles collected along Volunteer Observing Ship (VOS tracks, Airborne XBTs (AXBTs and sea surface temperatures. The actual sampling strategy adopted in the MFS Pilot Project during the Targeted Operational Period (TOP, winter-spring 2000 is also studied. The data impact is quantified by the error reduction relative to the free run. The most effective sampling strategies determine 25–40% error reduction, depending on the season, the geographic area and the depth range. A qualitative relationship can be recognized in terms of the spread of information from the data positions, between basin circulation features and spatial patterns of the error reduction fields, as a function of different spatial and seasonal characteristics of the dynamics. The largest error reductions are observed when samplings are characterized by extensive spatial coverages, as in the cases of AXBTs and the combination of XBTs and surface temperatures. The sampling strategy adopted during the TOP is characterized by little impact, as a consequence of a sampling frequency that is too low. Key words. Oceanography: general (marginal and semi-enclosed seas; numerical modelling

Observing System Simulation Experiments for the assessment of temperature sampling strategies in the Mediterranean Sea

Directory of Open Access Journals (Sweden)

F. Raicich

Full Text Available For the first time in the Mediterranean Sea various temperature sampling strategies are studied and compared to each other by means of the Observing System Simulation Experiment technique. Their usefulness in the framework of the Mediterranean Forecasting System (MFS is assessed by quantifying their impact in a Mediterranean General Circulation Model in numerical twin experiments via univariate data assimilation of temperature profiles in summer and winter conditions. Data assimilation is performed by means of the optimal interpolation algorithm implemented in the SOFA (System for Ocean Forecasting and Analysis code. The sampling strategies studied here include various combinations of eXpendable BathyThermograph (XBT profiles collected along Volunteer Observing Ship (VOS tracks, Airborne XBTs (AXBTs and sea surface temperatures. The actual sampling strategy adopted in the MFS Pilot Project during the Targeted Operational Period (TOP, winter-spring 2000 is also studied.

The data impact is quantified by the error reduction relative to the free run. The most effective sampling strategies determine 25–40% error reduction, depending on the season, the geographic area and the depth range. A qualitative relationship can be recognized in terms of the spread of information from the data positions, between basin circulation features and spatial patterns of the error reduction fields, as a function of different spatial and seasonal characteristics of the dynamics. The largest error reductions are observed when samplings are characterized by extensive spatial coverages, as in the cases of AXBTs and the combination of XBTs and surface temperatures. The sampling strategy adopted during the TOP is characterized by little impact, as a consequence of a sampling frequency that is too low.

Key words. Oceanography: general (marginal and semi-enclosed seas; numerical modelling

Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm

Directory of Open Access Journals (Sweden)

Ali Akbar Hasani

2016-11-01

Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies

International Nuclear Information System (INIS)

Chambari, Amirhossain; Najafi, Amir Abbas; Rahmati, Seyed Habib A.; Karimi, Aida

2013-01-01

The redundancy allocation problem (RAP) is an important reliability optimization problem. This paper studies a specific RAP in which redundancy strategies are chosen. To do so, the choice of the redundancy strategies among active and cold standby is considered as decision variables. The goal is to select the redundancy strategy, component, and redundancy level for each subsystem such that the system reliability is maximized. Since RAP is a NP-hard problem, we propose an efficient simulated annealing algorithm (SA) to solve it. In addition, to evaluating the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. The results of the performance analysis show a relatively satisfactory efficiency of the proposed SA algorithm

Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm

International Nuclear Information System (INIS)

Kim, Heungseob; Kim, Pansoo

2017-01-01

To maximize the reliability of a system, the traditional reliability–redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems. - Highlights: • Optimal strategy is proposed to solve reliability redundancy allocation problem. • The redundancy strategy uses parallel genetic algorithm. • Improved reliability function for a cold standby subsystem is suggested. • Proposed redundancy strategy enhances the system reliability.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Directory of Open Access Journals (Sweden)

Leilei Cao

2016-01-01

Full Text Available A Guiding Evolutionary Algorithm (GEA with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Use of a Radon Stripping Algorithm for Retrospective Assessment of Air Filter Samples

International Nuclear Information System (INIS)

Hayes, Robert

2009-01-01

An evaluation of a large number of air sample filters was undertaken using a commercial alpha and beta spectroscopy system employing a passive implanted planar silicon (PIPS) detector. Samples were only measured after air flow through the filters had ceased. Use of a commercial radon stripping algorithm was implemented to discriminate anthropogenic alpha and beta activity on the filters from the radon progeny. When uncontaminated air filters were evaluated, the results showed that there was a time-dependent bias in both average estimates and measurement dispersion with the relative bias being small compared to the dispersion. By also measuring environmental air sample filters simultaneously with electroplated alpha and beta sources, use of the radon stripping algorithm demonstrated a number of substantial unexpected deviations. Use of the current algorithm is therefore not recommended for assay applications and so use of the PIPS detector should only be utilized for gross counting without appropriate modifications to the curve fitting algorithm. As a screening method, the radon stripping algorithm might be expected to see elevated alpha and beta activities on air sample filters (not due to radon progeny) around the 200 dpm level

Plane-Based Sampling for Ray Casting Algorithm in Sequential Medical Images

Science.gov (United States)

Lin, Lili; Chen, Shengyong; Shao, Yan; Gu, Zichun

2013-01-01

This paper proposes a plane-based sampling method to improve the traditional Ray Casting Algorithm (RCA) for the fast reconstruction of a three-dimensional biomedical model from sequential images. In the novel method, the optical properties of all sampling points depend on the intersection points when a ray travels through an equidistant parallel plan cluster of the volume dataset. The results show that the method improves the rendering speed at over three times compared with the conventional algorithm and the image quality is well guaranteed. PMID:23424608

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade; Manavi, Kasra; Burgos, Juan; Denny, Jory; Thomas, Shawna; Amato, Nancy M.

2012-01-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade

2012-05-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

A clustering algorithm for sample data based on environmental pollution characteristics

Science.gov (United States)

Chen, Mei; Wang, Pengfei; Chen, Qiang; Wu, Jiadong; Chen, Xiaoyun

2015-04-01

Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution.

Effective traffic features selection algorithm for cyber-attacks samples

Science.gov (United States)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

Science.gov (United States)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Validated sampling strategy for assessing contaminants in soil stockpiles

International Nuclear Information System (INIS)

Lame, Frank; Honders, Ton; Derksen, Giljam; Gadella, Michiel

2005-01-01

Dutch legislation on the reuse of soil requires a sampling strategy to determine the degree of contamination. This sampling strategy was developed in three stages. Its main aim is to obtain a single analytical result, representative of the true mean concentration of the soil stockpile. The development process started with an investigation into how sample pre-treatment could be used to obtain representative results from composite samples of heterogeneous soil stockpiles. Combining a large number of random increments allows stockpile heterogeneity to be fully represented in the sample. The resulting pre-treatment method was then combined with a theoretical approach to determine the necessary number of increments per composite sample. At the second stage, the sampling strategy was evaluated using computerised models of contaminant heterogeneity in soil stockpiles. The now theoretically based sampling strategy was implemented by the Netherlands Centre for Soil Treatment in 1995. It was applied to all types of soil stockpiles, ranging from clean to heavily contaminated, over a period of four years. This resulted in a database containing the analytical results of 2570 soil stockpiles. At the final stage these results were used for a thorough validation of the sampling strategy. It was concluded that the model approach has indeed resulted in a sampling strategy that achieves analytical results representative of the mean concentration of soil stockpiles. - A sampling strategy that ensures analytical results representative of the mean concentration in soil stockpiles is presented and validated

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

Threshold-selecting strategy for best possible ground state detection with genetic algorithms

Science.gov (United States)

Lässig, Jörg; Hoffmann, Karl Heinz

2009-04-01

Genetic algorithms are a standard heuristic to find states of low energy in complex state spaces as given by physical systems such as spin glasses but also in combinatorial optimization. The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover. Many schemes have been considered in literature as possible crossover selection strategies. We show for a large class of quality measures that the best possible probability distribution for selecting individuals in each generation of the algorithm execution is a rectangular distribution over the individuals sorted by their energy values. This means uniform probabilities have to be assigned to a group of the individuals with lowest energy in the population but probabilities equal to zero to individuals which are corresponding to energy values higher than a fixed cutoff, which is equal to a certain rank in the vector sorted by the energy of the states in the current population. The considered strategy is dubbed threshold selecting. The proof applies basic arguments of Markov chains and linear optimization and makes only a few assumptions on the underlying principles and hence applies to a large class of algorithms.

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

Energy Technology Data Exchange (ETDEWEB)

Boardman, Beth Leigh [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-10-12

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

International Nuclear Information System (INIS)

Mandelli, Diego; Smith, Curtis Lee; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua Joseph

2015-01-01

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

Energy Technology Data Exchange (ETDEWEB)

Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Curtis Lee [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

DEFF Research Database (Denmark)

Grigoryan, Ruben; Arildsen, Thomas; Tandur, Deepaknath

2012-01-01

This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals...

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)

Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad; Janssen, Hans

2015-02-01

The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this

Algorithms

Indian Academy of Sciences (India)

to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

Science.gov (United States)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants

KAUST Repository

Liang, Faming; Jin, Ick-Hoon

2013-01-01

Simulating from distributions with intractable normalizing constants has been a long-standing problem inmachine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and exponential random graph models. Unlike other auxiliary variable Markov chain Monte Carlo (MCMC) algorithms, such as the Møller and exchange algorithms, the MCMH algorithm avoids the requirement for perfect sampling, and thus can be applied to many statistical models for which perfect sampling is not available or very expensive. TheMCMHalgorithm can also be applied to Bayesian inference for random effect models and missing data problems that involve simulations from a distribution with intractable integrals. © 2013 Massachusetts Institute of Technology.

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants

KAUST Repository

Liang, Faming

2013-08-01

Simulating from distributions with intractable normalizing constants has been a long-standing problem inmachine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and exponential random graph models. Unlike other auxiliary variable Markov chain Monte Carlo (MCMC) algorithms, such as the Møller and exchange algorithms, the MCMH algorithm avoids the requirement for perfect sampling, and thus can be applied to many statistical models for which perfect sampling is not available or very expensive. TheMCMHalgorithm can also be applied to Bayesian inference for random effect models and missing data problems that involve simulations from a distribution with intractable integrals. © 2013 Massachusetts Institute of Technology.

Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

Science.gov (United States)

Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

2016-09-01

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

Fast algorithms for finding proper strategies in game trees

DEFF Research Database (Denmark)

Miltersen, Peter Bro; Sørensen, Troels Bjerre

2008-01-01

We show how to find a normal form proper equilibrium in behavior strategies of a given two-player zero-sum extensive form game with imperfect information but perfect recall. Our algorithm solves a finite sequence of linear programs and runs in polynomial time. For the case of a perfect information...... game, we show how to find a normal form proper equilibrium in linear time by a simple backwards induction procedure....

An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method

International Nuclear Information System (INIS)

Campolina, Daniel; Lima, Paulo Rubens I.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2015-01-01

Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k eff was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

Science.gov (United States)

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Science.gov (United States)

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

Directory of Open Access Journals (Sweden)

Yu-Shuang Dong

2014-01-01

Full Text Available The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

An improved multi-domain convolution tracking algorithm

Science.gov (United States)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Note: A pure-sampling quantum Monte Carlo algorithm with independent Metropolis

Energy Technology Data Exchange (ETDEWEB)

Vrbik, Jan [Department of Mathematics, Brock University, St. Catharines, Ontario L2S 3A1 (Canada); Ospadov, Egor; Rothstein, Stuart M., E-mail: srothstein@brocku.ca [Department of Physics, Brock University, St. Catharines, Ontario L2S 3A1 (Canada)

2016-07-14

Recently, Ospadov and Rothstein published a pure-sampling quantum Monte Carlo algorithm (PSQMC) that features an auxiliary Path Z that connects the midpoints of the current and proposed Paths X and Y, respectively. When sufficiently long, Path Z provides statistical independence of Paths X and Y. Under those conditions, the Metropolis decision used in PSQMC is done without any approximation, i.e., not requiring microscopic reversibility and without having to introduce any G(x → x′; τ) factors into its decision function. This is a unique feature that contrasts with all competing reptation algorithms in the literature. An example illustrates that dependence of Paths X and Y has adverse consequences for pure sampling.

Note: A pure-sampling quantum Monte Carlo algorithm with independent Metropolis

International Nuclear Information System (INIS)

Vrbik, Jan; Ospadov, Egor; Rothstein, Stuart M.

2016-01-01

Recently, Ospadov and Rothstein published a pure-sampling quantum Monte Carlo algorithm (PSQMC) that features an auxiliary Path Z that connects the midpoints of the current and proposed Paths X and Y, respectively. When sufficiently long, Path Z provides statistical independence of Paths X and Y. Under those conditions, the Metropolis decision used in PSQMC is done without any approximation, i.e., not requiring microscopic reversibility and without having to introduce any G(x → x′; τ) factors into its decision function. This is a unique feature that contrasts with all competing reptation algorithms in the literature. An example illustrates that dependence of Paths X and Y has adverse consequences for pure sampling.

Hybrid algorithm of ensemble transform and importance sampling for assimilation of non-Gaussian observations

Directory of Open Access Journals (Sweden)

Shin'ya Nakano

2014-05-01

Full Text Available A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF and the importance sampling approach is proposed. Since the ETKF assumes a linear Gaussian observation model, the estimate obtained by the ETKF can be biased in cases with nonlinear or non-Gaussian observations. The particle filter (PF is based on the importance sampling technique, and is applicable to problems with nonlinear or non-Gaussian observations. However, the PF usually requires an unrealistically large sample size in order to achieve a good estimation, and thus it is computationally prohibitive. In the proposed hybrid algorithm, we obtain a proposal distribution similar to the posterior distribution by using the ETKF. A large number of samples are then drawn from the proposal distribution, and these samples are weighted to approximate the posterior distribution according to the importance sampling principle. Since the importance sampling provides an estimate of the probability density function (PDF without assuming linearity or Gaussianity, we can resolve the bias due to the nonlinear or non-Gaussian observations. Finally, in the next forecast step, we reduce the sample size to achieve computational efficiency based on the Gaussian assumption, while we use a relatively large number of samples in the importance sampling in order to consider the non-Gaussian features of the posterior PDF. The use of the ETKF is also beneficial in terms of the computational simplicity of generating a number of random samples from the proposal distribution and in weighting each of the samples. The proposed algorithm is not necessarily effective in case that the ensemble is located distant from the true state. However, monitoring the effective sample size and tuning the factor for covariance inflation could resolve this problem. In this paper, the proposed hybrid algorithm is introduced and its performance is evaluated through experiments with non-Gaussian observations.

Sampling strategies for estimating brook trout effective population size

Science.gov (United States)

Andrew R. Whiteley; Jason A. Coombs; Mark Hudy; Zachary Robinson; Keith H. Nislow; Benjamin H. Letcher

2012-01-01

The influence of sampling strategy on estimates of effective population size (Ne) from single-sample genetic methods has not been rigorously examined, though these methods are increasingly used. For headwater salmonids, spatially close kin association among age-0 individuals suggests that sampling strategy (number of individuals and location from...

Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

Energy Technology Data Exchange (ETDEWEB)

Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F.

2017-08-21

Molecular dynamics (MD) simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules but are limited by the timescale barrier, i.e., we may be unable to efficiently obtain properties because we need to run microseconds or longer simulations using femtoseconds time steps. While there are several existing methods to overcome this timescale barrier and efficiently sample thermodynamic and/or kinetic properties, problems remain in regard to being able to sample un- known systems, deal with high-dimensional space of collective variables, and focus the computational effort on slow timescales. Hence, a new sampling method, called the “Concurrent Adaptive Sampling (CAS) algorithm,” has been developed to tackle these three issues and efficiently obtain conformations and pathways. The method is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective vari- ables and uses macrostates (a partition of the collective variable space) to enhance the sampling. The exploration is done by running a large number of short simula- tions, and a clustering technique is used to accelerate the sampling. In this paper, we introduce the new methodology and show results from two-dimensional models and bio-molecules, such as penta-alanine and triazine polymer

A weighted sampling algorithm for the design of RNA sequences with targeted secondary structure and nucleotide distribution.

Science.gov (United States)

Reinharz, Vladimir; Ponty, Yann; Waldispühl, Jérôme

2013-07-01

The design of RNA sequences folding into predefined secondary structures is a milestone for many synthetic biology and gene therapy studies. Most of the current software uses similar local search strategies (i.e. a random seed is progressively adapted to acquire the desired folding properties) and more importantly do not allow the user to control explicitly the nucleotide distribution such as the GC-content in their sequences. However, the latter is an important criterion for large-scale applications as it could presumably be used to design sequences with better transcription rates and/or structural plasticity. In this article, we introduce IncaRNAtion, a novel algorithm to design RNA sequences folding into target secondary structures with a predefined nucleotide distribution. IncaRNAtion uses a global sampling approach and weighted sampling techniques. We show that our approach is fast (i.e. running time comparable or better than local search methods), seedless (we remove the bias of the seed in local search heuristics) and successfully generates high-quality sequences (i.e. thermodynamically stable) for any GC-content. To complete this study, we develop a hybrid method combining our global sampling approach with local search strategies. Remarkably, our glocal methodology overcomes both local and global approaches for sampling sequences with a specific GC-content and target structure. IncaRNAtion is available at csb.cs.mcgill.ca/incarnation/. Supplementary data are available at Bioinformatics online.

On e-business strategy planning and performance evaluation: An adaptive algorithmic managerial approach

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Alexandra Lipitakis

2017-07-01

Full Text Available A new e-business strategy planning and performance evaluation scheme based on adaptive algorithmic modelling techniques is presented. The effect of financial and non-financial performance of organizations on e-business strategy planning is investigated. The relationships between the four strategic planning parameters are examined, the directions of these relationships are given and six additional basic components are also considered. The new conceptual model has been constructed for e-business strategic planning and performance evaluation and an adaptive algorithmic modelling approach is presented. The new adaptive algorithmic modelling scheme including eleven dynamic modules, can be optimized and used effectively in e-business strategic planning and strategic planning evaluation of various e-services in very large organizations and businesses. A synoptic statistical analysis and comparative numerical results for the case of UK and Greece are given. The proposed e-business models indicate how e-business strategic planning may affect financial and non-financial performance in business and organizations by exploring whether models which are used for strategy planning can be applied to e-business planning and whether these models would be valid in different environments. A conceptual model has been constructed and qualitative research methods have been used for testing a predetermined number of considered hypotheses. The proposed models have been tested in the UK and Greece and the conclusions including numerical results and statistical analyses indicated existing relationships between considered dependent and independent variables. The proposed e-business models are expected to contribute to e-business strategy planning of businesses and organizations and managers should consider applying these models to their e-business strategy planning to improve their companies’ performances. This research study brings together elements of e

Hierarchical Control Strategy for Active Hydropneumatic Suspension Vehicles Based on Genetic Algorithms

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Jinzhi Feng

2015-02-01

Full Text Available A new hierarchical control strategy for active hydropneumatic suspension systems is proposed. This strategy considers the dynamic characteristics of the actuator. The top hierarchy controller uses a combined control scheme: a genetic algorithm- (GA- based self-tuning proportional-integral-derivative controller and a fuzzy logic controller. For practical implementations of the proposed control scheme, a GA-based self-learning process is initiated only when the defined performance index of vehicle dynamics exceeds a certain debounce time threshold. The designed control algorithm is implemented on a virtual prototype and cosimulations are performed with different road disturbance inputs. Cosimulation results show that the active hydropneumatic suspension system designed in this study significantly improves riding comfort characteristics of vehicles. The robustness and adaptability of the proposed controller are also examined when the control system is subjected to extremely rough road conditions.

An Intrinsic Algorithm for Parallel Poisson Disk Sampling on Arbitrary Surfaces.

Science.gov (United States)

Ying, Xiang; Xin, Shi-Qing; Sun, Qian; He, Ying

2013-03-08

Poisson disk sampling plays an important role in a variety of visual computing, due to its useful statistical property in distribution and the absence of aliasing artifacts. While many effective techniques have been proposed to generate Poisson disk distribution in Euclidean space, relatively few work has been reported to the surface counterpart. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. We propose a new technique for parallelizing the dart throwing. Rather than the conventional approaches that explicitly partition the spatial domain to generate the samples in parallel, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. It is worth noting that our algorithm is accurate as the generated Poisson disks are uniformly and randomly distributed without bias. Our method is intrinsic in that all the computations are based on the intrinsic metric and are independent of the embedding space. This intrinsic feature allows us to generate Poisson disk distributions on arbitrary surfaces. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

Science.gov (United States)

Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

2015-03-01

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

Evolution of Strategies for "Prisoner's Dilemma" using Genetic Algorithm

OpenAIRE

Heinz, Jan

2010-01-01

The subject of this thesis is the software application "Prisoner's Dilemma". The program creates a population of players of "Prisoner's Dilemma", has them play against each other, and - based on their results - performs an evolution of their strategies by means of a genetic algorithm (selection, mutation, and crossover). The program was written in Microsoft Visual Studio, in the C++ programming language, and its interface makes use of the .NET Framework. The thesis includes examples of strate...

Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform.

Science.gov (United States)

Mitra, Abhishek; Skrzypczak, Magdalena; Ginalski, Krzysztof; Rowicka, Maga

2015-01-01

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer's, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how

Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform.

Directory of Open Access Journals (Sweden)

Abhishek Mitra

Full Text Available Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding. Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants. Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer's, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively

Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.

Science.gov (United States)

Illouz, Tomer; Madar, Ravit; Louzon, Yoram; Griffioen, Kathleen J; Okun, Eitan

2016-02-01

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Optimal sampling strategies for detecting zoonotic disease epidemics.

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Jake M Ferguson

2014-06-01

Full Text Available The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

Optimal sampling strategies for detecting zoonotic disease epidemics.

Science.gov (United States)

Ferguson, Jake M; Langebrake, Jessica B; Cannataro, Vincent L; Garcia, Andres J; Hamman, Elizabeth A; Martcheva, Maia; Osenberg, Craig W

2014-06-01

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

A satellite digital controller or 'play that PID tune again, Sam'. [Position, Integral, Derivative feedback control algorithm for design strategy

Science.gov (United States)

Seltzer, S. M.

1976-01-01

The problem discussed is to design a digital controller for a typical satellite. The controlled plant is considered to be a rigid body acting in a plane. The controller is assumed to be a digital computer which, when combined with the proposed control algorithm, can be represented as a sampled-data system. The objective is to present a design strategy and technique for selecting numerical values for the control gains (assuming position, integral, and derivative feedback) and the sample rate. The technique is based on the parameter plane method and requires that the system be amenable to z-transform analysis.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring.

Science.gov (United States)

Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de

2017-11-05

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

Directory of Open Access Journals (Sweden)

Tongxin Shu

2017-11-01

Full Text Available Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA, while achieving around the same Normalized Mean Error (NME, DDASA is superior in saving 5.31% more battery energy.

Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

Science.gov (United States)

Odéen, Henrik; Todd, Nick; Diakite, Mahamadou; Minalga, Emilee; Payne, Allison; Parker, Dennis L.

2014-01-01

Purpose: To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications. Methods: Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemes utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm. Results: The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled “truth.” For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65 °C, 0.49 °C, and 0.35 °C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05 °C and 1.31 °C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes

Sample Data Synchronization and Harmonic Analysis Algorithm Based on Radial Basis Function Interpolation

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Huaiqing Zhang

2014-01-01

Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.

SAMPLING ADAPTIVE STRATEGY AND SPATIAL ORGANISATION ESTIMATION OF SOIL ANIMAL COMMUNITIES AT VARIOUS HIERARCHICAL LEVELS OF URBANISED TERRITORIES

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Baljuk J.A.

2014-12-01

Full Text Available In work the algorithm of adaptive strategy of optimum spatial sampling for studying of the spatial organisation of communities of soil animals in the conditions of an urbanization have been presented. As operating variables the principal components obtained as a result of the analysis of the field data on soil penetration resistance, soils electrical conductivity and density of a forest stand, collected on a quasiregular grid have been used. The locations of experimental polygons have been stated by means of program ESAP. The sampling has been made on a regular grid within experimental polygons. The biogeocoenological estimation of experimental polygons have been made on a basis of A.L.Belgard's ecomorphic analysis. The spatial configuration of biogeocoenosis types has been established on the basis of the data of earth remote sensing and the analysis of digital elevation model. The algorithm was suggested which allows to reveal the spatial organisation of soil animal communities at investigated point, biogeocoenosis, and landscape.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

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D. Ramyachitra

2015-09-01

Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

Science.gov (United States)

Ramyachitra, D; Sofia, M; Manikandan, P

2015-09-01

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

A Fast and Robust Feature-Based Scan-Matching Method in 3D SLAM and the Effect of Sampling Strategies

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Cihan Ulas

2013-11-01

Full Text Available Simultaneous localization and mapping (SLAM plays an important role in fully autonomous systems when a GNSS (global navigation satellite system is not available. Studies in both 2D indoor and 3D outdoor SLAM are based on the appearance of environments and utilize scan-matching methods to find rigid body transformation parameters between two consecutive scans. In this study, a fast and robust scan-matching method based on feature extraction is introduced. Since the method is based on the matching of certain geometric structures, like plane segments, the outliers and noise in the point cloud are considerably eliminated. Therefore, the proposed scan-matching algorithm is more robust than conventional methods. Besides, the registration time and the number of iterations are significantly reduced, since the number of matching points is efficiently decreased. As a scan-matching framework, an improved version of the normal distribution transform (NDT is used. The probability density functions (PDFs of the reference scan are generated as in the traditional NDT, and the feature extraction - based on stochastic plane detection - is applied to the only input scan. By using experimental dataset belongs to an outdoor environment like a university campus, we obtained satisfactory performance results. Moreover, the feature extraction part of the algorithm is considered as a special sampling strategy for scan-matching and compared to other sampling strategies, such as random sampling and grid-based sampling, the latter of which is first used in the NDT. Thus, this study also shows the effect of the subsampling on the performance of the NDT.

An implementation of differential evolution algorithm for inversion of geoelectrical data

Science.gov (United States)

Balkaya, Çağlayan

2013-11-01

Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system

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Kandathil Jom J.

2018-01-01

Full Text Available One of the primary ability of an intelligent mobile robot system is obstacle avoidance. BUG algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithms based on global knowledge, BUG algorithms assume only local knowledge of the environment and a global goal. Among the variations of the BUG algorithms that prevail, BUG-0, BUG-1 and BUG-2 are the more prominent versions. The exhaustive search algorithm present in BUG-1 makes it more reliable and safer for practical applications. Overall, this provides a more predictable and dependable performance. Hence, the essential focus in this paper is on implementing the BUG-1 algorithm across a group of robots to move them from a start location to a target location. The results are compared with the results from BUG-1 algorithm implemented on a single robot. The strategy developed in this work reduces the time involved in moving the robots from starting location to the target location. Further, the paper shows that the total distance covered by each robot in a multi robot-system is always lesser than or equal to that travelled by a single robot executing the same problem.

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

Science.gov (United States)

Cull, R. C.; Eltimsahy, A. H.

1983-01-01

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Classification and authentication of unknown water samples using machine learning algorithms.

Science.gov (United States)

Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

2011-07-01

This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Medical Image Retrieval Based On the Parallelization of the Cluster Sampling Algorithm

OpenAIRE

Ali, Hesham Arafat; Attiya, Salah; El-henawy, Ibrahim

2017-01-01

In this paper we develop parallel cluster sampling algorithms and show that a multi-chain version is embarrassingly parallel and can be used efficiently for medical image retrieval among other applications.

Efficient sampling of complex network with modified random walk strategies

Science.gov (United States)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.

Science.gov (United States)

Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch

2017-06-06

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

Estimating rare events in biochemical systems using conditional sampling

Science.gov (United States)

Sundar, V. S.

2017-01-01

The paper focuses on development of variance reduction strategies to estimate rare events in biochemical systems. Obtaining this probability using brute force Monte Carlo simulations in conjunction with the stochastic simulation algorithm (Gillespie's method) is computationally prohibitive. To circumvent this, important sampling tools such as the weighted stochastic simulation algorithm and the doubly weighted stochastic simulation algorithm have been proposed. However, these strategies require an additional step of determining the important region to sample from, which is not straightforward for most of the problems. In this paper, we apply the subset simulation method, developed as a variance reduction tool in the context of structural engineering, to the problem of rare event estimation in biochemical systems. The main idea is that the rare event probability is expressed as a product of more frequent conditional probabilities. These conditional probabilities are estimated with high accuracy using Monte Carlo simulations, specifically the Markov chain Monte Carlo method with the modified Metropolis-Hastings algorithm. Generating sample realizations of the state vector using the stochastic simulation algorithm is viewed as mapping the discrete-state continuous-time random process to the standard normal random variable vector. This viewpoint opens up the possibility of applying more sophisticated and efficient sampling schemes developed elsewhere to problems in stochastic chemical kinetics. The results obtained using the subset simulation method are compared with existing variance reduction strategies for a few benchmark problems, and a satisfactory improvement in computational time is demonstrated.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

Science.gov (United States)

Boudard, Mélanie; Bernauer, Julie; Barth, Dominique; Cohen, Johanne; Denise, Alain

2015-01-01

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

MUSIC ALGORITHM FOR LOCATING POINT-LIKE SCATTERERS CONTAINED IN A SAMPLE ON FLAT SUBSTRATE

Institute of Scientific and Technical Information of China (English)

Dong Heping; Ma Fuming; Zhang Deyue

2012-01-01

In this paper,we consider a MUSIC algorithm for locating point-like scatterers contained in a sample on flat substrate.Based on an asymptotic expansion of the scattering amplitude proposed by Ammari et al.,the reconstruction problem can be reduced to a calculation of Green function corresponding to the background medium.In addition,we use an explicit formulation of Green function in the MUSIC algorithm to simplify the calculation when the cross-section of sample is a half-disc.Numerical experiments are included to demonstrate the feasibility of this method.

Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Directory of Open Access Journals (Sweden)

Gonggui Chen

2017-01-01

Full Text Available The optimal power flow (OPF is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbest-guided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

Compressed sensing of roller bearing fault based on multiple down-sampling strategy

Science.gov (United States)

Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

2016-02-01

Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults.

Compressed sensing of roller bearing fault based on multiple down-sampling strategy

International Nuclear Information System (INIS)

Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

2016-01-01

Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults. (paper)

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.

Results of Evolution Supervised by Genetic Algorithms

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Lorentz JÄNTSCHI

2010-09-01

Full Text Available The efficiency of a genetic algorithm is frequently assessed using a series of operators of evolution like crossover operators, mutation operators or other dynamic parameters. The present paper aimed to review the main results of evolution supervised by genetic algorithms used to identify solutions to agricultural and horticultural hard problems and to discuss the results of using a genetic algorithms on structure-activity relationships in terms of behavior of evolution supervised by genetic algorithms. A genetic algorithm had been developed and implemented in order to identify the optimal solution in term of estimation power of a multiple linear regression approach for structure-activity relationships. Three survival and three selection strategies (proportional, deterministic and tournament were investigated in order to identify the best survival-selection strategy able to lead to the model with higher estimation power. The Molecular Descriptors Family for structure characterization of a sample of 206 polychlorinated biphenyls with measured octanol-water partition coefficients was used as case study. Evolution using different selection and survival strategies proved to create populations of genotypes living in the evolution space with different diversity and variability. Under a series of criteria of comparisons these populations proved to be grouped and the groups were showed to be statistically different one to each other. The conclusions about genetic algorithm evolution according to a number of criteria were also highlighted.

A Bayesian sampling strategy for hazardous waste site characterization

International Nuclear Information System (INIS)

Skalski, J.R.

1987-12-01

Prior knowledge based on historical records or physical evidence often suggests the existence of a hazardous waste site. Initial surveys may provide additional or even conflicting evidence of site contamination. This article presents a Bayes sampling strategy that allocates sampling at a site using this prior knowledge. This sampling strategy minimizes the environmental risks of missing chemical or radionuclide hot spots at a waste site. The environmental risk is shown to be proportional to the size of the undetected hot spot or inversely proportional to the probability of hot spot detection. 12 refs., 2 figs

Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems

International Nuclear Information System (INIS)

Thompson, M.P.; Sessions, J.; Hamann, J.D.

2009-01-01

Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a non spatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals.

Application of evolution strategy algorithm for optimization of a single-layer sound absorber

Directory of Open Access Journals (Sweden)

Morteza Gholamipoor

2014-12-01

Full Text Available Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past decades with innovative method of evolution strategy gaining more attention in the recent years. Based on their simplicity and straightforward mathematical representations, single-layer absorbers have been widely used in both engineering and industrial applications and an optimized design for these absorbers has become vital. In the present study, the method of evolution strategy algorithm is used for optimization of a single-layer absorber at both a particular frequency and an arbitrary frequency band. Results of the optimization have been compared against different methods of genetic algorithm and penalty functions which are proved to be favorable in both effectiveness and accuracy. Finally, a single-layer absorber is optimized in a desired range of frequencies that is the main goal of an industrial and engineering optimization process.

Optimal electricity dispatch on isolated mini-grids using a demand response strategy for thermal storage backup with genetic algorithms

International Nuclear Information System (INIS)

Neves, Diana; Silva, Carlos A.

2015-01-01

The present study uses the DHW (domestic hot water) electric backup from solar thermal systems to optimize the total electricity dispatch of an isolated mini-grid. The proposed approach estimates the hourly DHW load, and proposes and simulates different DR (demand response) strategies, from the supply side, to minimize the dispatch costs of an energy system. The case study consists on optimizing the electricity load, in a representative day with low solar radiation, in Corvo Island, Azores. The DHW backup is induced by three different demand patterns. The study compares different DR strategies: backup at demand (no strategy), pre-scheduled backup using two different imposed schedules, a strategy based on linear programming, and finally two strategies using genetic algorithms, with different formulations for DHW backup – one that assigns number of systems and another that assigns energy demand. It is concluded that pre-determined DR strategies may increase the generation costs, but DR strategies based on optimization algorithms are able to decrease generation costs. In particular, linear programming is the strategy that presents the lowest increase on dispatch costs, but the strategy based on genetic algorithms is the one that best minimizes both daily operation costs and total energy demand, of the system. - Highlights: • Integrated hourly model of DHW electric impact and electricity dispatch of isolated grid. • Proposal and comparison of different DR (demand response) strategies for DHW backup. • LP strategy presents 12% increase on total electric load, plus 5% on dispatch costs. • GA strategy presents 7% increase on total electric load, plus 8% on dispatch costs

Strategies of reconstruction algorithms for computerized tomography

International Nuclear Information System (INIS)

Garderet, P.

1984-10-01

Image reconstruction from projections has progressively spread out over all fields of medical imaging. As the mathematical aspects of the problem become more and more comprehensively explored a great variety of numerical solutions have been developed best suited to such-and-such imaging medical application and taking into account the physical phenomena related to data collection (a priori properties for signal and noise). The purpose of that survey is to present the general mathematical frame and the fundamental assumptions of various strategies; Fourier methods approximate explicit deterministic inversion formula for the Radon transform. Algebraic reconstruction techniques set up an a priori discrete model through a series expansion approach of the solution. The numerical system to be solved is huge when a fine grid of pixels is to be reconstructed; iterative solutions may then be found. Recently some least square procedures have been shown to be tractable which avoid the use of iterative methods. Finally maximum like hood approach incorporates accurately the Poisson nature of photon noise and are well adapted to emission computed tomography. The various strategies will be analysed from both aspects of theoretical assumptions needed for suitable use and of computing facilities, actual performance and cost. In the end we take a glimpse of the extension of the algorithms from two dimensional imaging to fully three dimensional volume analysis in preparation of the future medical imaging technologies

Sample-efficient Strategies for Learning in the Presence of Noise

DEFF Research Database (Denmark)

Cesa-Bianchi, N.; Dichterman, E.; Fischer, Paul

1999-01-01

In this paper, we prove various results about PAC learning in the presence of malicious noise. Our main interest is the sample size behavior of learning algorithms. We prove the first nontrivial sample complexity lower bound in this model by showing that order of &egr;/&Dgr;2 + d/&Dgr; (up...... to logarithmic factors) examples are necessary for PAC learning any target class of {#123;0,1}#125;-valued functions of VC dimension d, where &egr; is the desired accuracy and &eegr; = &egr;/(1 + &egr;) - &Dgr; the malicious noise rate (it is well known that any nontrivial target class cannot be PAC learned...... with accuracy &egr; and malicious noise rate &eegr; &egr;/(1 + &egr;), this irrespective to sample complexity). We also show that this result cannot be significantly improved in general by presenting efficient learning algorithms for the class of all subsets of d elements and the class of unions of at most d...

THE ALGORITHM IMPLEMENTATION OF THE DIVERSIFICATION STRATEGY IN SMALL AND MEDIUM-SIZED ENTERPRISES (FOR EXAMPLE, THE HOSPITALITY INDUSTRY

Directory of Open Access Journals (Sweden)

Наталья Николаевна Масюк

2013-09-01

Full Text Available Diversification in small businesses in the general sense is an extension of business activities to new areas of business (expanding the range of products, types of services provided, etc.. Application of the strategy of diversification in the small and medium business is justified in cases where the industry does not give us opportunities for further growth or when growth opportunities outside the industry more attractive. To determine whether diversification overdue and justified, the entrepreneur must clearly define the algorithm for their actions.Purpose: To determine the algorithm implementation of the strategy of diversification.Methodology: Desk research.Results: The developed algorithm.Practical implications: Management.DOI: http://dx.doi.org/10.12731/2218-7405-2013-9-19

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

Science.gov (United States)

Wang, Yan; Huang, Song; Ji, Zhicheng

2017-07-01

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

A preliminary evaluation of comminution and sampling strategies for radioactive cemented waste

Energy Technology Data Exchange (ETDEWEB)

Bilodeau, M.; Lastra, R.; Bouzoubaa, N. [Natural Resources Canada, Ottawa, ON (Canada); Chapman, M. [Atomic Energy of Canada Limited, Chalk River, ON (Canada)

2011-07-01

Lixiviation of Hg, U and Cs contaminants and micro-encapsulation of cemented radioactive waste (CRW) are the two main components of a CRW stabilization research project carried out at Natural Resources Canada in collaboration with Atomic Energy of Canada Limited. Unmolding CRW from the storage pail, its fragmentation into a size range suitable for both processes and the collection of a representative sample are three essential steps for providing optimal material conditions for the two studies. Separation of wires, metals and plastic incorporated into CRW samples is also required. A comminution and sampling strategy was developed to address all those needs. Dust emissions and other health and safety concerns were given full consideration. Surrogate cemented waste (SCW) was initially used for this comminution study where Cu was used as a substitute for U and Hg. SCW was characterized as a friable material through the measurement of the Bond work index of 7.7 kWh/t. A mineralogical investigation and the calibration of material heterogeneity parameters of the sampling error model showed that Cu, Hg and Cs are finely disseminated in the cement matrix. A sampling strategy was built from the model and successfully validated with radioactive waste. A larger than expected sampling error was observed with U due to the formation of large U solid phases, which were not observed with the Cu tracer. SCW samples were crushed and ground under different rock fragmentation mechanisms: compression (jaw and cone crushers, rod mill), impact (ball mill), attrition, high voltage disintegration and high pressure water (and liquid nitrogen) jetting. Cryogenic grinding was also tested with the attrition mill. Crushing and grinding technologies were assessed against criteria that were gathered from literature surveys, experiential know-how and discussion with the client and field experts. Water jetting and its liquid nitrogen variant were retained for pail cutting and waste unmolding while

A preliminary evaluation of comminution and sampling strategies for radioactive cemented waste

International Nuclear Information System (INIS)

Bilodeau, M.; Lastra, R.; Bouzoubaa, N.; Chapman, M.

2011-01-01

Lixiviation of Hg, U and Cs contaminants and micro-encapsulation of cemented radioactive waste (CRW) are the two main components of a CRW stabilization research project carried out at Natural Resources Canada in collaboration with Atomic Energy of Canada Limited. Unmolding CRW from the storage pail, its fragmentation into a size range suitable for both processes and the collection of a representative sample are three essential steps for providing optimal material conditions for the two studies. Separation of wires, metals and plastic incorporated into CRW samples is also required. A comminution and sampling strategy was developed to address all those needs. Dust emissions and other health and safety concerns were given full consideration. Surrogate cemented waste (SCW) was initially used for this comminution study where Cu was used as a substitute for U and Hg. SCW was characterized as a friable material through the measurement of the Bond work index of 7.7 kWh/t. A mineralogical investigation and the calibration of material heterogeneity parameters of the sampling error model showed that Cu, Hg and Cs are finely disseminated in the cement matrix. A sampling strategy was built from the model and successfully validated with radioactive waste. A larger than expected sampling error was observed with U due to the formation of large U solid phases, which were not observed with the Cu tracer. SCW samples were crushed and ground under different rock fragmentation mechanisms: compression (jaw and cone crushers, rod mill), impact (ball mill), attrition, high voltage disintegration and high pressure water (and liquid nitrogen) jetting. Cryogenic grinding was also tested with the attrition mill. Crushing and grinding technologies were assessed against criteria that were gathered from literature surveys, experiential know-how and discussion with the client and field experts. Water jetting and its liquid nitrogen variant were retained for pail cutting and waste unmolding while

Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

KAUST Repository

Elsheikh, A. H.

2013-12-01

Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.

The Wang-Landau Sampling Algorithm

Science.gov (United States)

Landau, David P.

2003-03-01

Over the past several decades Monte Carlo simulations[1] have evolved into a powerful tool for the study of wide-ranging problems in statistical/condensed matter physics. Standard methods sample the probability distribution for the states of the system, usually in the canonical ensemble, and enormous improvements have been made in performance through the implementation of novel algorithms. Nonetheless, difficulties arise near phase transitions, either due to critical slowing down near 2nd order transitions or to metastability near 1st order transitions, thus limiting the applicability of the method. We shall describe a new and different Monte Carlo approach [2] that uses a random walk in energy space to determine the density of states directly. Once the density of states is estimated, all thermodynamic properties can be calculated at all temperatures. This approach can be extended to multi-dimensional parameter spaces and has already found use in classical models of interacting particles including systems with complex energy landscapes, e.g., spin glasses, protein folding models, etc., as well as for quantum models. 1. A Guide to Monte Carlo Simulations in Statistical Physics, D. P. Landau and K. Binder (Cambridge U. Press, Cambridge, 2000). 2. Fugao Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001); Phys. Rev. E64, 056101-1 (2001).

A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy

Directory of Open Access Journals (Sweden)

Bingkun Zhu

2011-03-01

Full Text Available Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system.

A compatible control algorithm for greenhouse environment control based on MOCC strategy.

Science.gov (United States)

Hu, Haigen; Xu, Lihong; Zhu, Bingkun; Wei, Ruihua

2011-01-01

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system.

Reduction rules-based search algorithm for opportunistic replacement strategy of multiple life-limited parts

Directory of Open Access Journals (Sweden)

Xuyun FU

2018-01-01

Full Text Available The opportunistic replacement of multiple Life-Limited Parts (LLPs is a problem widely existing in industry. The replacement strategy of LLPs has a great impact on the total maintenance cost to a lot of equipment. This article focuses on finding a quick and effective algorithm for this problem. To improve the algorithm efficiency, six reduction rules are suggested from the perspectives of solution feasibility, determination of the replacement of LLPs, determination of the maintenance occasion and solution optimality. Based on these six reduction rules, a search algorithm is proposed. This search algorithm can identify one or several optimal solutions. A numerical experiment shows that these six reduction rules are effective, and the time consumed by the algorithm is less than 38 s if the total life of equipment is shorter than 55000 and the number of LLPs is less than 11. A specific case shows that the algorithm can obtain optimal solutions which are much better than the result of the traditional method in 10 s, and it can provide support for determining to-be-replaced LLPs when determining the maintenance workscope of an aircraft engine. Therefore, the algorithm is applicable to engineering applications concerning opportunistic replacement of multiple LLPs in aircraft engines.

Algorithm of developing competitive strategies and the trends of realizing them for agricultural enterprises

Directory of Open Access Journals (Sweden)

Viktoriia Boiko

2016-02-01

Full Text Available The paper specifies basic stages of developing and realizing the strategy of enhancing competitiveness of enterprises and represents an appropriate algorithm. The study analyzes the economic indexes and results of the activity of the agrarian enterprises in Kherson region and provides competitive strategies of efficient development of agrarian enterprises with different levels of competitiveness and specifies the ways of realizing them which will contribute to the optimal use of the available strategic potential

Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

KAUST Repository

Elsheikh, A. H.; Wheeler, M. F.; Hoteit, Ibrahim

2013-01-01

Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Tavakkoli-Moghaddam, R. [Department of Industrial Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365/4563, Tehran (Iran, Islamic Republic of); Department of Mechanical Engineering, The University of British Columbia, Vancouver (Canada)], E-mail: tavakoli@ut.ac.ir; Safari, J. [Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)], E-mail: jalalsafari@pideco.com; Sassani, F. [Department of Mechanical Engineering, The University of British Columbia, Vancouver (Canada)], E-mail: sassani@mech.ubc.ca

2008-04-15

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.

Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

International Nuclear Information System (INIS)

Tavakkoli-Moghaddam, R.; Safari, J.; Sassani, F.

2008-01-01

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed

Poisson-Box Sampling algorithms for three-dimensional Markov binary mixtures

Science.gov (United States)

Larmier, Coline; Zoia, Andrea; Malvagi, Fausto; Dumonteil, Eric; Mazzolo, Alain

2018-02-01

Particle transport in Markov mixtures can be addressed by the so-called Chord Length Sampling (CLS) methods, a family of Monte Carlo algorithms taking into account the effects of stochastic media on particle propagation by generating on-the-fly the material interfaces crossed by the random walkers during their trajectories. Such methods enable a significant reduction of computational resources as opposed to reference solutions obtained by solving the Boltzmann equation for a large number of realizations of random media. CLS solutions, which neglect correlations induced by the spatial disorder, are faster albeit approximate, and might thus show discrepancies with respect to reference solutions. In this work we propose a new family of algorithms (called 'Poisson Box Sampling', PBS) aimed at improving the accuracy of the CLS approach for transport in d-dimensional binary Markov mixtures. In order to probe the features of PBS methods, we will focus on three-dimensional Markov media and revisit the benchmark problem originally proposed by Adams, Larsen and Pomraning [1] and extended by Brantley [2]: for these configurations we will compare reference solutions, standard CLS solutions and the new PBS solutions for scalar particle flux, transmission and reflection coefficients. PBS will be shown to perform better than CLS at the expense of a reasonable increase in computational time.

A fast direct sampling algorithm for equilateral closed polygons

International Nuclear Information System (INIS)

Cantarella, Jason; Duplantier, Bertrand; Shonkwiler, Clayton; Uehara, Erica

2016-01-01

Sampling equilateral closed polygons is of interest in the statistical study of ring polymers. Over the past 30 years, previous authors have proposed a variety of simple Markov chain algorithms (but have not been able to show that they converge to the correct probability distribution) and complicated direct samplers (which require extended-precision arithmetic to evaluate numerically unstable polynomials). We present a simple direct sampler which is fast and numerically stable, and analyze its runtime using a new formula for the volume of equilateral polygon space as a Dirichlet-type integral. (paper)

Sampling strategies in antimicrobial resistance monitoring: evaluating how precision and sensitivity vary with the number of animals sampled per farm.

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Takehisa Yamamoto

Full Text Available Because antimicrobial resistance in food-producing animals is a major public health concern, many countries have implemented antimicrobial monitoring systems at a national level. When designing a sampling scheme for antimicrobial resistance monitoring, it is necessary to consider both cost effectiveness and statistical plausibility. In this study, we examined how sampling scheme precision and sensitivity can vary with the number of animals sampled from each farm, while keeping the overall sample size constant to avoid additional sampling costs. Five sampling strategies were investigated. These employed 1, 2, 3, 4 or 6 animal samples per farm, with a total of 12 animals sampled in each strategy. A total of 1,500 Escherichia coli isolates from 300 fattening pigs on 30 farms were tested for resistance against 12 antimicrobials. The performance of each sampling strategy was evaluated by bootstrap resampling from the observational data. In the bootstrapping procedure, farms, animals, and isolates were selected randomly with replacement, and a total of 10,000 replications were conducted. For each antimicrobial, we observed that the standard deviation and 2.5-97.5 percentile interval of resistance prevalence were smallest in the sampling strategy that employed 1 animal per farm. The proportion of bootstrap samples that included at least 1 isolate with resistance was also evaluated as an indicator of the sensitivity of the sampling strategy to previously unidentified antimicrobial resistance. The proportion was greatest with 1 sample per farm and decreased with larger samples per farm. We concluded that when the total number of samples is pre-specified, the most precise and sensitive sampling strategy involves collecting 1 sample per farm.

Effective sampling strategy to detect food and feed contamination

NARCIS (Netherlands)

Bouzembrak, Yamine; Fels, van der Ine

2018-01-01

Sampling plans for food safety hazards are aimed to be used to determine whether a lot of food is contaminated (with microbiological or chemical hazards) or not. One of the components of sampling plans is the sampling strategy. The aim of this study was to compare the performance of three

An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms

Directory of Open Access Journals (Sweden)

René Roland Colditz

2015-07-01

Full Text Available Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using classification and regression tree algorithms. In addition to measures of discrete and continuous map accuracy the correct estimation of the area is another important criteria. A subset of the 30 m national land cover dataset of 2006 (NLCD2006 of the United States was used as reference set to classify NADIR BRDF-adjusted surface reflectance time series of MODIS at 900 m spatial resolution. Results show that sampling of heterogeneous pixels and sample allocation according to the expected area of each class is best for classification trees. Regression trees for continuous land cover mapping should be trained with random allocation, and predictions should be normalized with a linear scaling function to correctly estimate the total area. From the tested algorithms random forest classification yields lower errors than boosted trees of C5.0, and Cubist shows higher accuracies than random forest regression.

Novel strategies for sample preparation in forensic toxicology.

Science.gov (United States)

Samanidou, Victoria; Kovatsi, Leda; Fragou, Domniki; Rentifis, Konstantinos

2011-09-01

This paper provides a review of novel strategies for sample preparation in forensic toxicology. The review initially outlines the principle of each technique, followed by sections addressing each class of abused drugs separately. The novel strategies currently reviewed focus on the preparation of various biological samples for the subsequent determination of opiates, benzodiazepines, amphetamines, cocaine, hallucinogens, tricyclic antidepressants, antipsychotics and cannabinoids. According to our experience, these analytes are the most frequently responsible for intoxications in Greece. The applications of techniques such as disposable pipette extraction, microextraction by packed sorbent, matrix solid-phase dispersion, solid-phase microextraction, polymer monolith microextraction, stir bar sorptive extraction and others, which are rapidly gaining acceptance in the field of toxicology, are currently reviewed.

A neural algorithm for the non-uniform and adaptive sampling of biomedical data.

Science.gov (United States)

Mesin, Luca

2016-04-01

Body sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. A real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. Four examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. The discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal Dynamic Strategies for Index Tracking and Algorithmic Trading

Science.gov (United States)

Ward, Brian

In this thesis we study dynamic strategies for index tracking and algorithmic trading. Tracking problems have become ever more important in Financial Engineering as investors seek to precisely control their portfolio risks and exposures over different time horizons. This thesis analyzes various tracking problems and elucidates the tracking errors and strategies one can employ to minimize those errors and maximize profit. In Chapters 2 and 3, we study the empirical tracking properties of exchange traded funds (ETFs), leveraged ETFs (LETFs), and futures products related to spot gold and the Chicago Board Option Exchange (CBOE) Volatility Index (VIX), respectively. These two markets provide interesting and differing examples for understanding index tracking. We find that static strategies work well in the nonleveraged case for gold, but fail to track well in the corresponding leveraged case. For VIX, tracking via neither ETFs, nor futures\\ portfolios succeeds, even in the nonleveraged case. This motivates the need for dynamic strategies, some of which we construct in these two chapters and further expand on in Chapter 4. There, we analyze a framework for index tracking and risk exposure control through financial derivatives. We derive a tracking condition that restricts our exposure choices and also define a slippage process that characterizes the deviations from the index over longer horizons. The framework is applied to a number of models, for example, Black Scholes model and Heston model for equity index tracking, as well as the Square Root (SQR) model and the Concatenated Square Root (CSQR) model for VIX tracking. By specifying how each of these models fall into our framework, we are able to understand the tracking errors in each of these models. Finally, Chapter 5 analyzes a tracking problem of a different kind that arises in algorithmic trading: schedule following for optimal execution. We formulate and solve a stochastic control problem to obtain the optimal

Small-angle X-ray scattering tensor tomography: model of the three-dimensional reciprocal-space map, reconstruction algorithm and angular sampling requirements.

Science.gov (United States)

Liebi, Marianne; Georgiadis, Marios; Kohlbrecher, Joachim; Holler, Mirko; Raabe, Jörg; Usov, Ivan; Menzel, Andreas; Schneider, Philipp; Bunk, Oliver; Guizar-Sicairos, Manuel

2018-01-01

Small-angle X-ray scattering tensor tomography, which allows reconstruction of the local three-dimensional reciprocal-space map within a three-dimensional sample as introduced by Liebi et al. [Nature (2015), 527, 349-352], is described in more detail with regard to the mathematical framework and the optimization algorithm. For the case of trabecular bone samples from vertebrae it is shown that the model of the three-dimensional reciprocal-space map using spherical harmonics can adequately describe the measured data. The method enables the determination of nanostructure orientation and degree of orientation as demonstrated previously in a single momentum transfer q range. This article presents a reconstruction of the complete reciprocal-space map for the case of bone over extended ranges of q. In addition, it is shown that uniform angular sampling and advanced regularization strategies help to reduce the amount of data required.

Limited-sampling strategies for anti-infective agents: systematic review.

Science.gov (United States)

Sprague, Denise A; Ensom, Mary H H

2009-09-01

Area under the concentration-time curve (AUC) is a pharmacokinetic parameter that represents overall exposure to a drug. For selected anti-infective agents, pharmacokinetic-pharmacodynamic parameters, such as AUC/MIC (where MIC is the minimal inhibitory concentration), have been correlated with outcome in a few studies. A limited-sampling strategy may be used to estimate pharmacokinetic parameters such as AUC, without the frequent, costly, and inconvenient blood sampling that would be required to directly calculate the AUC. To discuss, by means of a systematic review, the strengths, limitations, and clinical implications of published studies involving a limited-sampling strategy for anti-infective agents and to propose improvements in methodology for future studies. The PubMed and EMBASE databases were searched using the terms "anti-infective agents", "limited sampling", "optimal sampling", "sparse sampling", "AUC monitoring", "abbreviated AUC", "abbreviated sampling", and "Bayesian". The reference lists of retrieved articles were searched manually. Included studies were classified according to modified criteria from the US Preventive Services Task Force. Twenty studies met the inclusion criteria. Six of the studies (involving didanosine, zidovudine, nevirapine, ciprofloxacin, efavirenz, and nelfinavir) were classified as providing level I evidence, 4 studies (involving vancomycin, didanosine, lamivudine, and lopinavir-ritonavir) provided level II-1 evidence, 2 studies (involving saquinavir and ceftazidime) provided level II-2 evidence, and 8 studies (involving ciprofloxacin, nelfinavir, vancomycin, ceftazidime, ganciclovir, pyrazinamide, meropenem, and alpha interferon) provided level III evidence. All of the studies providing level I evidence used prospectively collected data and proper validation procedures with separate, randomly selected index and validation groups. However, most of the included studies did not provide an adequate description of the methods or

DSMC multicomponent aerosol dynamics: Sampling algorithms and aerosol processes

Science.gov (United States)

Palaniswaamy, Geethpriya

The post-accident nuclear reactor primary and containment environments can be characterized by high temperatures and pressures, and fission products and nuclear aerosols. These aerosols evolve via natural transport processes as well as under the influence of engineered safety features. These aerosols can be hazardous and may pose risk to the public if released into the environment. Computations of their evolution, movement and distribution involve the study of various processes such as coagulation, deposition, condensation, etc., and are influenced by factors such as particle shape, charge, radioactivity and spatial inhomogeneity. These many factors make the numerical study of nuclear aerosol evolution computationally very complicated. The focus of this research is on the use of the Direct Simulation Monte Carlo (DSMC) technique to elucidate the role of various phenomena that influence the nuclear aerosol evolution. In this research, several aerosol processes such as coagulation, deposition, condensation, and source reinforcement are explored for a multi-component, aerosol dynamics problem in a spatially homogeneous medium. Among the various sampling algorithms explored the Metropolis sampling algorithm was found to be effective and fast. Several test problems and test cases are simulated using the DSMC technique. The DSMC results obtained are verified against the analytical and sectional results for appropriate test problems. Results show that the assumption of a single mean density is not appropriate due to the complicated effect of component densities on the aerosol processes. The methods developed and the insights gained will also be helpful in future research on the challenges associated with the description of fission product and aerosol releases.

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies

International Nuclear Information System (INIS)

Hampton, Jerrad; Doostan, Alireza

2015-01-01

Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is known that bounding a probabilistic parameter, referred to as coherence, yields a bound on the number of samples necessary to identify coefficients in a sparse PC expansion via solution to an ℓ 1 -minimization problem. Utilizing results for orthogonal polynomials, we bound the coherence parameter for polynomials of Hermite and Legendre type under their respective natural sampling distribution. In both polynomial bases we identify an importance sampling distribution which yields a bound with weaker dependence on the order of the approximation. For more general orthonormal bases, we propose the coherence-optimal sampling: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support. We demonstrate these different sampling strategies numerically in both high-order and high-dimensional, manufactured PC expansions. In addition, the quality of each sampling method is compared in the identification of solutions to two differential equations, one with a high-dimensional random input and the other with a high-order PC expansion. In both cases, the coherence-optimal sampling scheme leads to similar or considerably improved accuracy

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies

Science.gov (United States)

Hampton, Jerrad; Doostan, Alireza

2015-01-01

Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is known that bounding a probabilistic parameter, referred to as coherence, yields a bound on the number of samples necessary to identify coefficients in a sparse PC expansion via solution to an ℓ1-minimization problem. Utilizing results for orthogonal polynomials, we bound the coherence parameter for polynomials of Hermite and Legendre type under their respective natural sampling distribution. In both polynomial bases we identify an importance sampling distribution which yields a bound with weaker dependence on the order of the approximation. For more general orthonormal bases, we propose the coherence-optimal sampling: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support. We demonstrate these different sampling strategies numerically in both high-order and high-dimensional, manufactured PC expansions. In addition, the quality of each sampling method is compared in the identification of solutions to two differential equations, one with a high-dimensional random input and the other with a high-order PC expansion. In both cases, the coherence-optimal sampling scheme leads to similar or considerably improved accuracy.

Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

International Nuclear Information System (INIS)

Wang, Xinli; Cai, Wenjian; Lu, Jiangang; Sun, Youxian; Zhao, Lei

2015-01-01

This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. - Highlights: • Present a model-based optimization strategy for energy saving in LDDS. • Energy predictive models for components in dehumidifier are developed. • The Optimization strategy are applied and tested in an actual LDDS. • Optimization strategy can achieve energy savings by 12% during operation

SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

International Nuclear Information System (INIS)

Tiwari, P; Xie, Y; Chen, Y; Deasy, J

2014-01-01

Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality

A comparative proteomics method for multiple samples based on a 18O-reference strategy and a quantitation and identification-decoupled strategy.

Science.gov (United States)

Wang, Hongbin; Zhang, Yongqian; Gui, Shuqi; Zhang, Yong; Lu, Fuping; Deng, Yulin

2017-08-15

Comparisons across large numbers of samples are frequently necessary in quantitative proteomics. Many quantitative methods used in proteomics are based on stable isotope labeling, but most of these are only useful for comparing two samples. For up to eight samples, the iTRAQ labeling technique can be used. For greater numbers of samples, the label-free method has been used, but this method was criticized for low reproducibility and accuracy. An ingenious strategy has been introduced, comparing each sample against a 18 O-labeled reference sample that was created by pooling equal amounts of all samples. However, it is necessary to use proportion-known protein mixtures to investigate and evaluate this new strategy. Another problem for comparative proteomics of multiple samples is the poor coincidence and reproducibility in protein identification results across samples. In present study, a method combining 18 O-reference strategy and a quantitation and identification-decoupled strategy was investigated with proportion-known protein mixtures. The results obviously demonstrated that the 18 O-reference strategy had greater accuracy and reliability than other previously used comparison methods based on transferring comparison or label-free strategies. By the decoupling strategy, the quantification data acquired by LC-MS and the identification data acquired by LC-MS/MS are matched and correlated to identify differential expressed proteins, according to retention time and accurate mass. This strategy made protein identification possible for all samples using a single pooled sample, and therefore gave a good reproducibility in protein identification across multiple samples, and allowed for optimizing peptide identification separately so as to identify more proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

MARKET-MAKING STRATEGY IN THE SYSTEM OF ALGORITHMIC HIGH-FREQUENCY TRADING

Directory of Open Access Journals (Sweden)

A. V. Toropov

2014-01-01

Full Text Available Market maker is the most important participant of modern exchange trading, it provides the increase of market liquidity and reduces the difference between bid and ask (spread. The paper presents automatic market-making strategy for quoting of options and other kinds of financial instruments on electronic markets. Quotes are based on theoretical pricing which is a resource-intensive task. Presented algorithmic optimizations, in particular quotes caching and smoothing of underlying asset price oscillation, give the possibility up to four times boost for quote modify scenario on real market data. Mechanism of quotes caching precalculates quotes in certain diapason around current underlying price. If underlying price changes within the diapason, algorithm sends already filled message for quote modification, instead of new complex computation. Smoothing of underlying asset price oscillation prevents permanent moving of the diapason and reacts only on significant market moving. A size of caching diapason which provides optimal correlation between speed of quotes modification and resource consumption has been defined experimentally (40 elements. In case of quoting 36 options on Eurex Exchange an average delay between underlying price change and quote modification is 277 usec. The measurements were carried out on the Sun X4170 M3: CPU(s: 2xXeon 2.9GHz RAM: 128 GB server under Solaris 10 operating system. Obtained results correspond to modern market-making requirements. The developed strategy is used by big European banks and trading firms.

Costs per Diagnosis of Acute HIV Infection in Community-based Screening Strategies: A Comparative Analysis of Four Screening Algorithms

Science.gov (United States)

Hoenigl, Martin; Graff-Zivin, Joshua; Little, Susan J.

2016-01-01

Background. In nonhealthcare settings, widespread screening for acute human immunodeficiency virus (HIV) infection (AHI) is limited by cost and decision algorithms to better prioritize use of resources. Comparative cost analyses for available strategies are lacking. Methods. To determine cost-effectiveness of community-based testing strategies, we evaluated annual costs of 3 algorithms that detect AHI based on HIV nucleic acid amplification testing (EarlyTest algorithm) or on HIV p24 antigen (Ag) detection via Architect (Architect algorithm) or Determine (Determine algorithm) as well as 1 algorithm that relies on HIV antibody testing alone (Antibody algorithm). The cost model used data on men who have sex with men (MSM) undergoing community-based AHI screening in San Diego, California. Incremental cost-effectiveness ratios (ICERs) per diagnosis of AHI were calculated for programs with HIV prevalence rates between 0.1% and 2.9%. Results. Among MSM in San Diego, EarlyTest was cost-savings (ie, ICERs per AHI diagnosis less than $13.000) when compared with the 3 other algorithms. Cost analyses relative to regional HIV prevalence showed that EarlyTest was cost-effective (ie, ICERs less than $69.547) for similar populations of MSM with an HIV prevalence rate >0.4%; Architect was the second best alternative for HIV prevalence rates >0.6%. Conclusions. Identification of AHI by the dual EarlyTest screening algorithm is likely to be cost-effective not only among at-risk MSM in San Diego but also among similar populations of MSM with HIV prevalence rates >0.4%. PMID:26508512

Algorithmic strategies for FPGA-based vision

OpenAIRE

Lim, Yoong Kang

2016-01-01

As demands for real-time computer vision applications increase, implementations on alternative architectures have been explored. These architectures include Field-Programmable Gate Arrays (FPGAs), which offer a high degree of flexibility and parallelism. A problem with this is that many computer vision algorithms have been optimized for serial processing, and this often does not map well to FPGA implementation. This thesis introduces the concept of FPGA-tailored computer vision algorithms...

Implementation of Evolution Strategies (ES Algorithm to Optimization Lovebird Feed Composition

Directory of Open Access Journals (Sweden)

Agung Mustika Rizki

2017-05-01

Full Text Available Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES. Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal fitness value of 0.3267 in the generation of 1400. 

Measurement of radioactivity in the environment - Soil - Part 2: Guidance for the selection of the sampling strategy, sampling and pre-treatment of samples

International Nuclear Information System (INIS)

2007-01-01

This part of ISO 18589 specifies the general requirements, based on ISO 11074 and ISO/IEC 17025, for all steps in the planning (desk study and area reconnaissance) of the sampling and the preparation of samples for testing. It includes the selection of the sampling strategy, the outline of the sampling plan, the presentation of general sampling methods and equipment, as well as the methodology of the pre-treatment of samples adapted to the measurements of the activity of radionuclides in soil. This part of ISO 18589 is addressed to the people responsible for determining the radioactivity present in soil for the purpose of radiation protection. It is applicable to soil from gardens, farmland, urban or industrial sites, as well as soil not affected by human activities. This part of ISO 18589 is applicable to all laboratories regardless of the number of personnel or the range of the testing performed. When a laboratory does not undertake one or more of the activities covered by this part of ISO 18589, such as planning, sampling or testing, the corresponding requirements do not apply. Information is provided on scope, normative references, terms and definitions and symbols, principle, sampling strategy, sampling plan, sampling process, pre-treatment of samples and recorded information. Five annexes inform about selection of the sampling strategy according to the objectives and the radiological characterization of the site and sampling areas, diagram of the evolution of the sample characteristics from the sampling site to the laboratory, example of sampling plan for a site divided in three sampling areas, example of a sampling record for a single/composite sample and example for a sample record for a soil profile with soil description. A bibliography is provided

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

Science.gov (United States)

Li, Bai; Gong, Li-gang; Yang, Wen-lun

2014-01-01

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Study on the Method of Association Rules Mining Based on Genetic Algorithm and Application in Analysis of Seawater Samples

Directory of Open Access Journals (Sweden)

Qiuhong Sun

2014-04-01

Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.

Study on the effects of sample selection on spectral reflectance reconstruction based on the algorithm of compressive sensing

International Nuclear Information System (INIS)

Zhang, Leihong; Liang, Dong

2016-01-01

In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)

Non-oblivious Strategy Improvement

Science.gov (United States)

Fearnley, John

We study strategy improvement algorithms for mean-payoff and parity games. We describe a structural property of these games, and we show that these structures can affect the behaviour of strategy improvement. We show how awareness of these structures can be used to accelerate strategy improvement algorithms. We call our algorithms non-oblivious because they remember properties of the game that they have discovered in previous iterations. We show that non-oblivious strategy improvement algorithms perform well on examples that are known to be hard for oblivious strategy improvement. Hence, we argue that previous strategy improvement algorithms fail because they ignore the structural properties of the game that they are solving.

Learning from nature: Nature-inspired algorithms

DEFF Research Database (Denmark)

Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin

2016-01-01

.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...

Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms

International Nuclear Information System (INIS)

Mahdad, Belkacem; Srairi, K.

2015-01-01

Highlights: • A generalized optimal security power system planning strategy for blackout risk prevention is proposed. • A Grey Wolf Optimizer dynamically coordinated with Pattern Search algorithm is proposed. • A useful optimized database dynamically generated considering margin loading stability under severe faults. • The robustness and feasibility of the proposed strategy is validated in the standard IEEE 30 Bus system. • The proposed planning strategy will be useful for power system protection coordination and control. - Abstract: Developing a flexible and reliable power system planning strategy under critical situations is of great importance to experts and industrials to minimize the probability of blackouts occurrence. This paper introduces the first stage of this practical strategy by the application of Grey Wolf Optimizer coordinated with pattern search algorithm for solving the security smart grid power system management under critical situations. The main objective of this proposed planning strategy is to prevent the practical power system against blackout due to the apparition of faults in generating units or important transmission lines. At the first stage the system is pushed to its margin stability limit, the critical loads shedding are selected using voltage stability index. In the second stage the generator control variables, the reactive power of shunt and dynamic compensators are adjusted in coordination with minimization the active and reactive power at critical loads to maintain the system at security state to ensure service continuity. The feasibility and efficiency of the proposed strategy is applied to IEEE 30-Bus test system. Results are promising and prove the practical efficiency of the proposed strategy to ensure system security under critical situations

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

Directory of Open Access Journals (Sweden)

Tinggui Chen

2014-01-01

Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

Directory of Open Access Journals (Sweden)

Bai Li

2014-01-01

Full Text Available Unmanned combat aerial vehicles (UCAVs have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC algorithm improved by a balance-evolution strategy (BES is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Mars Sample Return - Launch and Detection Strategies for Orbital Rendezvous

Science.gov (United States)

Woolley, Ryan C.; Mattingly, Richard L.; Riedel, Joseph E.; Sturm, Erick J.

2011-01-01

This study sets forth conceptual mission design strategies for the ascent and rendezvous phase of the proposed NASA/ESA joint Mars Sample Return Campaign. The current notional mission architecture calls for the launch of an acquisition/cache rover in 2018, an orbiter with an Earth return vehicle in 2022, and a fetch rover and ascent vehicle in 2024. Strategies are presented to launch the sample into a coplanar orbit with the Orbiter which facilitate robust optical detection, orbit determination, and rendezvous. Repeating ground track orbits exist at 457 and 572 km which provide multiple launch opportunities with similar geometries for detection and rendezvous.

Mars Sample Return: Launch and Detection Strategies for Orbital Rendezvous

Science.gov (United States)

Woolley, Ryan C.; Mattingly, Richard L.; Riedel, Joseph E.; Sturm, Erick J.

2011-01-01

This study sets forth conceptual mission design strategies for the ascent and rendezvous phase of the proposed NASA/ESA joint Mars Sample Return Campaign. The current notional mission architecture calls for the launch of an acquisition/ caching rover in 2018, an Earth return orbiter in 2022, and a fetch rover with ascent vehicle in 2024. Strategies are presented to launch the sample into a nearly coplanar orbit with the Orbiter which would facilitate robust optical detection, orbit determination, and rendezvous. Repeating ground track orbits existat 457 and 572 km which would provide multiple launch opportunities with similar geometries for detection and rendezvous.

Efficiency of alternative MCMC strategies illustrated using the reaction norm model

DEFF Research Database (Denmark)

Shariati, Mohammad Mahdi; Sørensen, D.

2008-01-01

The Markov chain Monte Carlo (MCMC) strategy provides remarkable flexibility for fitting complex hierarchical models. However, when parameters are highly correlated in their posterior distributions and their number is large, a particular MCMC algorithm may perform poorly and the resulting...... in the low correlation scenario where SG was the best strategy. The two LH proposals could not compete with any of the Gibbs sampling algorithms. In this study it was not possible to find an MCMC strategy that performs optimally across the range of target distributions and across all possible values...

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals.

Science.gov (United States)

Guven, Onur; Eftekhar, Amir; Kindt, Wilko; Constandinou, Timothy G

2016-06-01

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors' previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp-p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat.

Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion)

NARCIS (Netherlands)

Brus, D.J.; Gruijter, de J.J.

1997-01-01

Classical sampling theory has been repeatedly identified with classical statistics which assumes that data are identically and independently distributed. This explains the switch of many soil scientists from design-based sampling strategies, based on classical sampling theory, to the model-based

PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS

KAUST Repository

Prudencio, Ernesto; Cheung, Sai Hung

2012-01-01

In recent years, Bayesian model updating techniques based on measured data have been applied to many engineering and applied science problems. At the same time, parallel computational platforms are becoming increasingly more powerful and are being used more frequently by the engineering and scientific communities. Bayesian techniques usually require the evaluation of multi-dimensional integrals related to the posterior probability density function (PDF) of uncertain model parameters. The fact that such integrals cannot be computed analytically motivates the research of stochastic simulation methods for sampling posterior PDFs. One such algorithm is the adaptive multilevel stochastic simulation algorithm (AMSSA). In this paper we discuss the parallelization of AMSSA, formulating the necessary load balancing step as a binary integer programming problem. We present a variety of results showing the effectiveness of load balancing on the overall performance of AMSSA in a parallel computational environment.

A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

International Nuclear Information System (INIS)

Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

2014-01-01

Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

Adaptive sampling strategies with high-throughput molecular dynamics

Science.gov (United States)

Clementi, Cecilia

Despite recent significant hardware and software developments, the complete thermodynamic and kinetic characterization of large macromolecular complexes by molecular simulations still presents significant challenges. The high dimensionality of these systems and the complexity of the associated potential energy surfaces (creating multiple metastable regions connected by high free energy barriers) does not usually allow to adequately sample the relevant regions of their configurational space by means of a single, long Molecular Dynamics (MD) trajectory. Several different approaches have been proposed to tackle this sampling problem. We focus on the development of ensemble simulation strategies, where data from a large number of weakly coupled simulations are integrated to explore the configurational landscape of a complex system more efficiently. Ensemble methods are of increasing interest as the hardware roadmap is now mostly based on increasing core counts, rather than clock speeds. The main challenge in the development of an ensemble approach for efficient sampling is in the design of strategies to adaptively distribute the trajectories over the relevant regions of the systems' configurational space, without using any a priori information on the system global properties. We will discuss the definition of smart adaptive sampling approaches that can redirect computational resources towards unexplored yet relevant regions. Our approaches are based on new developments in dimensionality reduction for high dimensional dynamical systems, and optimal redistribution of resources. NSF CHE-1152344, NSF CHE-1265929, Welch Foundation C-1570.

Algorithmic Mechanism Design of Evolutionary Computation.

Science.gov (United States)

Pei, Yan

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Assessment of sampling strategies for estimation of site mean concentrations of stormwater pollutants.

Science.gov (United States)

McCarthy, David T; Zhang, Kefeng; Westerlund, Camilla; Viklander, Maria; Bertrand-Krajewski, Jean-Luc; Fletcher, Tim D; Deletic, Ana

2018-02-01

The estimation of stormwater pollutant concentrations is a primary requirement of integrated urban water management. In order to determine effective sampling strategies for estimating pollutant concentrations, data from extensive field measurements at seven different catchments was used. At all sites, 1-min resolution continuous flow measurements, as well as flow-weighted samples, were taken and analysed for total suspend solids (TSS), total nitrogen (TN) and Escherichia coli (E. coli). For each of these parameters, the data was used to calculate the Event Mean Concentrations (EMCs) for each event. The measured Site Mean Concentrations (SMCs) were taken as the volume-weighted average of these EMCs for each parameter, at each site. 17 different sampling strategies, including random and fixed strategies were tested to estimate SMCs, which were compared with the measured SMCs. The ratios of estimated/measured SMCs were further analysed to determine the most effective sampling strategies. Results indicate that the random sampling strategies were the most promising method in reproducing SMCs for TSS and TN, while some fixed sampling strategies were better for estimating the SMC of E. coli. The differences in taking one, two or three random samples were small (up to 20% for TSS, and 10% for TN and E. coli), indicating that there is little benefit in investing in collection of more than one sample per event if attempting to estimate the SMC through monitoring of multiple events. It was estimated that an average of 27 events across the studied catchments are needed for characterising SMCs of TSS with a 90% confidence interval (CI) width of 1.0, followed by E.coli (average 12 events) and TN (average 11 events). The coefficient of variation of pollutant concentrations was linearly and significantly correlated to the 90% confidence interval ratio of the estimated/measured SMCs (R 2  = 0.49; P sampling frequency needed to accurately estimate SMCs of pollutants. Crown

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

Science.gov (United States)

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

A Unified Differential Evolution Algorithm for Global Optimization

Energy Technology Data Exchange (ETDEWEB)

Qiang, Ji; Mitchell, Chad

2014-06-24

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.

Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm

Science.gov (United States)

Vrugt, Jasper A.; Beven, Keith J.

2018-04-01

This essay illustrates some recent developments to the DiffeRential Evolution Adaptive Metropolis (DREAM) MATLAB toolbox of Vrugt (2016) to delineate and sample the behavioural solution space of set-theoretic likelihood functions used within the GLUE (Limits of Acceptability) framework (Beven and Binley, 1992, 2014; Beven and Freer, 2001; Beven, 2006). This work builds on the DREAM(ABC) algorithm of Sadegh and Vrugt (2014) and enhances significantly the accuracy and CPU-efficiency of Bayesian inference with GLUE. In particular it is shown how lack of adequate sampling in the model space might lead to unjustified model rejection.

Development and analysis of an economizer control strategy algorithm to promote an opportunity for energy savings in air conditioning installations

Energy Technology Data Exchange (ETDEWEB)

Neto, Jose H.M.; Azevedo, Walter L. [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET), Belo Horizonte, MG (Brazil). Dept. de Engenharia Mecanica]. E-mail: henrique@daem.des.cefetmg.br

2000-07-01

This work presents an algorithm control strategy denominated enthalpy economizer. The objective of this algorithm strategy is to determine the adequate fractions of outside and return air flowrates entering a cooling coil based on the analysis of the outside, return and supply air enthalpies, rather than on the analysis of the dry bulb temperatures. The proposed algorithm predicts the actual opening position of the outside and return air dampers in order to provide the lower mixing air enthalpy. First, the psychometrics properties of the outside and return air are calculated from actual measurements of the dry and wet bulb temperatures. Then, three distinct cases are analyzed: the enthalpy of the outside air is lower than the enthalpy of the supply air (free cooling); the enthalpy of the outside air is higher than the enthalpy of the return air; the enthalpy of the outside air is lower than the enthalpy of the return air and higher than the temperature of the supply air. Different outside air conditions were selected in order to represent typical weather data of Brazilians cities, as well as typical return air conditions. It was found that the enthalpy control strategy could promote an opportunity for energy savings mainly during mild nights and wintertime periods as well as during warm afternoons and summertime periods, depending on the outside air relative humidity. The proposed algorithm works well and can be integrated in some commercial automation software to reduce energy consumption and electricity demand. (author)

Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

OpenAIRE

Lingna He; Qingshui Li; Linan Zhu

2012-01-01

In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the...

The new strategy for particle identification samples in Run 2 at LHCb

CERN Multimedia

Mathad, Abhijit

2017-01-01

For Run 2 of LHCb data taking, the selection of PID calibration samples is implemented in the high level trigger. A further processing is needed to provide background-subtracted samples to determine the PID performance, or to develop new algorithms for the evaluation of the detector performance in upgrade scenarios. This is achieved through a centralised production which makes efficient use of LHCb computing resources. This poster presents the major steps of the implementation.

Mendelian breeding units versus standard sampling strategies: mitochondrial DNA variation in southwest Sardinia

Directory of Open Access Journals (Sweden)

Daria Sanna

2011-01-01

Full Text Available We report a sampling strategy based on Mendelian Breeding Units (MBUs, representing an interbreeding group of individuals sharing a common gene pool. The identification of MBUs is crucial for case-control experimental design in association studies. The aim of this work was to evaluate the possible existence of bias in terms of genetic variability and haplogroup frequencies in the MBU sample, due to severe sample selection. In order to reach this goal, the MBU sampling strategy was compared to a standard selection of individuals according to their surname and place of birth. We analysed mitochondrial DNA variation (first hypervariable segment and coding region in unrelated healthy subjects from two different areas of Sardinia: the area around the town of Cabras and the western Campidano area. No statistically significant differences were observed when the two sampling methods were compared, indicating that the stringent sample selection needed to establish a MBU does not alter original genetic variability and haplogroup distribution. Therefore, the MBU sampling strategy can be considered a useful tool in association studies of complex traits.

Design for a Crane Metallic Structure Based on Imperialist Competitive Algorithm and Inverse Reliability Strategy

Science.gov (United States)

Fan, Xiao-Ning; Zhi, Bo

2017-07-01

Uncertainties in parameters such as materials, loading, and geometry are inevitable in designing metallic structures for cranes. When considering these uncertainty factors, reliability-based design optimization (RBDO) offers a more reasonable design approach. However, existing RBDO methods for crane metallic structures are prone to low convergence speed and high computational cost. A unilevel RBDO method, combining a discrete imperialist competitive algorithm with an inverse reliability strategy based on the performance measure approach, is developed. Application of the imperialist competitive algorithm at the optimization level significantly improves the convergence speed of this RBDO method. At the reliability analysis level, the inverse reliability strategy is used to determine the feasibility of each probabilistic constraint at each design point by calculating its α-percentile performance, thereby avoiding convergence failure, calculation error, and disproportionate computational effort encountered using conventional moment and simulation methods. Application of the RBDO method to an actual crane structure shows that the developed RBDO realizes a design with the best tradeoff between economy and safety together with about one-third of the convergence speed and the computational cost of the existing method. This paper provides a scientific and effective design approach for the design of metallic structures of cranes.

Robust species taxonomy assignment algorithm for 16S rRNA NGS reads: application to oral carcinoma samples

Directory of Open Access Journals (Sweden)

Nezar Noor Al-Hebshi

2015-09-01

Full Text Available Background: Usefulness of next-generation sequencing (NGS in assessing bacteria associated with oral squamous cell carcinoma (OSCC has been undermined by inability to classify reads to the species level. Objective: The purpose of this study was to develop a robust algorithm for species-level classification of NGS reads from oral samples and to pilot test it for profiling bacteria within OSCC tissues. Methods: Bacterial 16S V1-V3 libraries were prepared from three OSCC DNA samples and sequenced using 454's FLX chemistry. High-quality, well-aligned, and non-chimeric reads ≥350 bp were classified using a novel, multi-stage algorithm that involves matching reads to reference sequences in revised versions of the Human Oral Microbiome Database (HOMD, HOMD extended (HOMDEXT, and Greengene Gold (GGG at alignment coverage and percentage identity ≥98%, followed by assignment to species level based on top hit reference sequences. Priority was given to hits in HOMD, then HOMDEXT and finally GGG. Unmatched reads were subject to operational taxonomic unit analysis. Results: Nearly, 92.8% of the reads were matched to updated-HOMD 13.2, 1.83% to trusted-HOMDEXT, and 1.36% to modified-GGG. Of all matched reads, 99.6% were classified to species level. A total of 228 species-level taxa were identified, representing 11 phyla; the most abundant were Proteobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Actinobacteria. Thirty-five species-level taxa were detected in all samples. On average, Prevotella oris, Neisseria flava, Neisseria flavescens/subflava, Fusobacterium nucleatum ss polymorphum, Aggregatibacter segnis, Streptococcus mitis, and Fusobacterium periodontium were the most abundant. Bacteroides fragilis, a species rarely isolated from the oral cavity, was detected in two samples. Conclusion: This multi-stage algorithm maximizes the fraction of reads classified to the species level while ensuring reliable classification by giving priority to the

Sampling strategies for indoor radon investigations

International Nuclear Information System (INIS)

Prichard, H.M.

1983-01-01

Recent investigations prompted by concern about the environmental effects of residential energy conservation have produced many accounts of indoor radon concentrations far above background levels. In many instances time-normalized annual exposures exceeded the 4 WLM per year standard currently used for uranium mining. Further investigations of indoor radon exposures are necessary to judge the extent of the problem and to estimate the practicality of health effects studies. A number of trends can be discerned as more indoor surveys are reported. It is becoming increasingly clear that local geological factors play a major, if not dominant role in determining the distribution of indoor radon concentrations in a given area. Within a giving locale, indoor radon concentrations tend to be log-normally distributed, and sample means differ markedly from one region to another. The appreciation of geological factors and the general log-normality of radon distributions will improve the accuracy of population dose estimates and facilitate the design of preliminary health effects studies. The relative merits of grab samples, short and long term integrated samples, and more complicated dose assessment strategies are discussed in the context of several types of epidemiological investigations. A new passive radon sampler with a 24 hour integration time is described and evaluated as a tool for pilot investigations

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

Science.gov (United States)

Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

2012-01-01

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to

Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR data sets

International Nuclear Information System (INIS)

Stanek, Jan; Kozminski, Wiktor

2010-01-01

Spectra obtained by application of multidimensional Fourier Transformation (MFT) to sparsely sampled nD NMR signals are usually corrupted due to missing data. In the present paper this phenomenon is investigated on simulations and experiments. An effective iterative algorithm for artifact suppression for sparse on-grid NMR data sets is discussed in detail. It includes automated peak recognition based on statistical methods. The results enable one to study NMR spectra of high dynamic range of peak intensities preserving benefits of random sampling, namely the superior resolution in indirectly measured dimensions. Experimental examples include 3D 15 N- and 13 C-edited NOESY-HSQC spectra of human ubiquitin.

Non-oblivious Strategy Improvement

OpenAIRE

Fearnley, John

2010-01-01

We study strategy improvement algorithms for mean-payoff and parity games. We describe a structural property of these games, and we show that these structures can affect the behaviour of strategy improvement. We show how awareness of these structures can be used to accelerate strategy improvement algorithms. We call our algorithms non-oblivious because they remember properties of the game that they have discovered in previous iterations. We show that non-oblivious strategy improvement algorit...

Understand your Algorithm: Drill Down to Sample Visualizations in Jupyter Notebooks

Science.gov (United States)

Mapes, B. E.; Ho, Y.; Cheedela, S. K.; McWhirter, J.

2017-12-01

Statistics are the currency of climate dynamics, but the space of all possible algorithms is fathomless - especially for 4-dimensional weather-resolving data that many "impact" variables depend on. Algorithms are designed on data samples, but how do you know if they measure what you expect when turned loose on Big Data? We will introduce the year-1 prototype of a 3-year scientist-led, NSF-supported, Unidata-quality software stack called DRILSDOWN (https://brianmapes.github.io/EarthCube-DRILSDOWN/) for automatically extracting, integrating, and visualizing multivariate 4D data samples. Based on a customizable "IDV bundle" of data sources, fields and displays supplied by the user, the system will teleport its space-time coordinates to fetch Cases of Interest (edge cases, typical cases, etc.) from large aggregated repositories. These standard displays can serve as backdrops to overlay with your value-added fields (such as derived quantities stored on a user's local disk). Fields can be readily pulled out of the visualization object for further processing in Python. The hope is that algorithms successfully tested in this visualization space will then be lifted out and added to automatic processing toolchains, lending confidence in the next round of processing, to seek the next Cases of Interest, in light of a user's statistical measures of "Interest". To log the scientific work done in this vein, the visualizations are wrapped in iPython-based Jupyter notebooks for rich, human-readable documentation (indeed, quasi-publication with formatted text, LaTex math, etc.). Such notebooks are readable and executable, with digital replicability and provenance built in. The entire digital object of a case study can be stored in a repository, where libraries of these Case Study Notebooks can be examined in a browser. Model data (the session topic) are of course especially convenient for this system, but observations of all sorts can also be brought in, overlain, and differenced or

Chapter 2: Sampling strategies in forest hydrology and biogeochemistry

Science.gov (United States)

Roger C. Bales; Martha H. Conklin; Branko Kerkez; Steven Glaser; Jan W. Hopmans; Carolyn T. Hunsaker; Matt Meadows; Peter C. Hartsough

2011-01-01

Many aspects of forest hydrology have been based on accurate but not necessarily spatially representative measurements, reflecting the measurement capabilities that were traditionally available. Two developments are bringing about fundamental changes in sampling strategies in forest hydrology and biogeochemistry: (a) technical advances in measurement capability, as is...

AdaBoost-based algorithm for network intrusion detection.

Science.gov (United States)

Hu, Weiming; Hu, Wei; Maybank, Steve

2008-04-01

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

Sampling strategy to develop a primary core collection of apple ...

African Journals Online (AJOL)

PRECIOUS

2010-01-11

Jan 11, 2010 ... Physiology and Molecular Biology for Fruit, Tree, Beijing 100193, China. ... analyzed on genetic diversity to ensure their represen- .... strategy, cluster and random sampling. .... on isozyme data―A simulation study, Theor.

ALGORITHM OF PREPARATION OF THE TRAINING SAMPLE USING 3D-FACE MODELING

Directory of Open Access Journals (Sweden)

D. I. Samal

2016-01-01

Full Text Available The algorithm of preparation and sampling for training of the multiclass qualifier of support vector machines (SVM is provided. The described approach based on the modeling of possible changes of the face features of recognized person. Additional features like perspectives of shooting, conditions of lighting, tilt angles were introduced to get improved identification results. These synthetic generated changes have some impact on the classifier learning expanding the range of possible variations of the initial image. The classifier learned with such extended example is ready to recognize unknown objects better. The age, emotional looks, turns of the head, various conditions of lighting, noise, and also some combinations of the listed parameters are chosen as the key considered parameters for modeling. The third-party software ‘FaceGen’ allowing to model up to 150 parameters and available in a demoversion for free downloading is used for 3D-modeling.The SVM classifier was chosen to test the impact of the introduced modifications of training sample. The preparation and preliminary processing of images contains the following constituents like detection and localization of area of the person on the image, assessment of an angle of rotation and an inclination, extension of the range of brightness of pixels and an equalization of the histogram to smooth the brightness and contrast characteristics of the processed images, scaling of the localized and processed area of the person, creation of a vector of features of the scaled and processed image of the person by a Principal component analysis (algorithm NIPALS, training of the multiclass SVM-classifier.The provided algorithm of expansion of the training selection is oriented to be used in practice and allows to expand using 3D-models the processed range of 2D – photographs of persons that positively affects results of identification in system of face recognition. This approach allows to compensate

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm

Science.gov (United States)

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Energy Optimal Control Strategy of PHEV Based on PMP Algorithm

Directory of Open Access Journals (Sweden)

Tiezhou Wu

2017-01-01

Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.

An Efficient Algorithm for the Optimal Market Timing over Two Stocks

Institute of Scientific and Technical Information of China (English)

Hui Li; Hong-zhi An; Guo-fu Wu

2004-01-01

In this paper,the optimal trading strategy in timing the market by switching between two stocks is given.In order to deal with a large sample size with a fast turnaround computation time,we propose a class of recursive algorithm.A simulation is given to verify the efiectiveness of our method.

An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

Science.gov (United States)

Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

2016-01-01

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559

An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

Directory of Open Access Journals (Sweden)

Bruno Srbinovski

2016-03-01

Full Text Available Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind. Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources and power hungry sensors (ultrasonic wind sensor and gas sensors. The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application

KAUST Repository

Chambolle, Antonin; Ehrhardt, Matthias J.; Richtarik, Peter; Schö nlieb, Carola-Bibiane

2017-01-01

We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.

Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application

KAUST Repository

Chambolle, Antonin

2017-06-15

We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

Science.gov (United States)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Development and Demonstration of a Method to Evaluate Bio-Sampling Strategies Using Building Simulation and Sample Planning Software.

Science.gov (United States)

Dols, W Stuart; Persily, Andrew K; Morrow, Jayne B; Matzke, Brett D; Sego, Landon H; Nuffer, Lisa L; Pulsipher, Brent A

2010-01-01

In an effort to validate and demonstrate response and recovery sampling approaches and technologies, the U.S. Department of Homeland Security (DHS), along with several other agencies, have simulated a biothreat agent release within a facility at Idaho National Laboratory (INL) on two separate occasions in the fall of 2007 and the fall of 2008. Because these events constitute only two realizations of many possible scenarios, increased understanding of sampling strategies can be obtained by virtually examining a wide variety of release and dispersion scenarios using computer simulations. This research effort demonstrates the use of two software tools, CONTAM, developed by the National Institute of Standards and Technology (NIST), and Visual Sample Plan (VSP), developed by Pacific Northwest National Laboratory (PNNL). The CONTAM modeling software was used to virtually contaminate a model of the INL test building under various release and dissemination scenarios as well as a range of building design and operation parameters. The results of these CONTAM simulations were then used to investigate the relevance and performance of various sampling strategies using VSP. One of the fundamental outcomes of this project was the demonstration of how CONTAM and VSP can be used together to effectively develop sampling plans to support the various stages of response to an airborne chemical, biological, radiological, or nuclear event. Following such an event (or prior to an event), incident details and the conceptual site model could be used to create an ensemble of CONTAM simulations which model contaminant dispersion within a building. These predictions could then be used to identify priority area zones within the building and then sampling designs and strategies could be developed based on those zones.

Sampling strategy for a large scale indoor radiation survey - a pilot project

International Nuclear Information System (INIS)

Strand, T.; Stranden, E.

1986-01-01

Optimisation of a stratified random sampling strategy for large scale indoor radiation surveys is discussed. It is based on the results from a small scale pilot project where variances in dose rates within different categories of houses were assessed. By selecting a predetermined precision level for the mean dose rate in a given region, the number of measurements needed can be optimised. The results of a pilot project in Norway are presented together with the development of the final sampling strategy for a planned large scale survey. (author)

Quantum learning algorithms for quantum measurements

Energy Technology Data Exchange (ETDEWEB)

Bisio, Alessandro, E-mail: alessandro.bisio@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); D' Ariano, Giacomo Mauro, E-mail: dariano@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Perinotti, Paolo, E-mail: paolo.perinotti@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Sedlak, Michal, E-mail: michal.sedlak@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Institute of Physics, Slovak Academy of Sciences, Dubravska cesta 9, 845 11 Bratislava (Slovakia)

2011-09-12

We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.

Quantum learning algorithms for quantum measurements

International Nuclear Information System (INIS)

Bisio, Alessandro; D'Ariano, Giacomo Mauro; Perinotti, Paolo; Sedlak, Michal

2011-01-01

We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.

Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy.

Science.gov (United States)

Sadler, Georgia Robins; Lee, Hau-Chen; Lim, Rod Seung-Hwan; Fullerton, Judith

2010-09-01

Nurse researchers and educators often engage in outreach to narrowly defined populations. This article offers examples of how variations on the snowball sampling recruitment strategy can be applied in the creation of culturally appropriate, community-based information dissemination efforts related to recruitment to health education programs and research studies. Examples from the primary author's program of research are provided to demonstrate how adaptations of snowball sampling can be used effectively in the recruitment of members of traditionally underserved or vulnerable populations. The adaptation of snowball sampling techniques, as described in this article, helped the authors to gain access to each of the more-vulnerable population groups of interest. The use of culturally sensitive recruitment strategies is both appropriate and effective in enlisting the involvement of members of vulnerable populations. Adaptations of snowball sampling strategies should be considered when recruiting participants for education programs or for research studies when the recruitment of a population-based sample is not essential.

Sampling strategies for the analysis of reactive low-molecular weight compounds in air

NARCIS (Netherlands)

Henneken, H.

2006-01-01

Within this thesis, new sampling and analysis strategies for the determination of airborne workplace contaminants have been developed. Special focus has been directed towards the development of air sampling methods that involve diffusive sampling. In an introductory overview, the current

Selective epidemic vaccination under the performant routing algorithms

Science.gov (United States)

Bamaarouf, O.; Alweimine, A. Ould Baba; Rachadi, A.; EZ-Zahraouy, H.

2018-04-01

Despite the extensive research on traffic dynamics and epidemic spreading, the effect of the routing algorithms strategies on the traffic-driven epidemic spreading has not received an adequate attention. It is well known that more performant routing algorithm strategies are used to overcome the congestion problem. However, our main result shows unexpectedly that these algorithms favor the virus spreading more than the case where the shortest path based algorithm is used. In this work, we studied the virus spreading in a complex network using the efficient path and the global dynamic routing algorithms as compared to shortest path strategy. Some previous studies have tried to modify the routing rules to limit the virus spreading, but at the expense of reducing the traffic transport efficiency. This work proposed a solution to overcome this drawback by using a selective vaccination procedure instead of a random vaccination used often in the literature. We found that the selective vaccination succeeded in eradicating the virus better than a pure random intervention for the performant routing algorithm strategies.

A Geostatistical Approach to Indoor Surface Sampling Strategies

DEFF Research Database (Denmark)

Schneider, Thomas; Petersen, Ole Holm; Nielsen, Allan Aasbjerg

1990-01-01

Particulate surface contamination is of concern in production industries such as food processing, aerospace, electronics and semiconductor manufacturing. There is also an increased awareness that surface contamination should be monitored in industrial hygiene surveys. A conceptual and theoretical...... framework for designing sampling strategies is thus developed. The distribution and spatial correlation of surface contamination can be characterized using concepts from geostatistical science, where spatial applications of statistics is most developed. The theory is summarized and particulate surface...... contamination, sampled from small areas on a table, have been used to illustrate the method. First, the spatial correlation is modelled and the parameters estimated from the data. Next, it is shown how the contamination at positions not measured can be estimated with kriging, a minimum mean square error method...

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms.

Science.gov (United States)

Sze, Sing-Hoi; Parrott, Jonathan J; Tarone, Aaron M

2017-12-06

While the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies. We develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies. Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

A Monte Carlo algorithm for sampling rare events: application to a search for the Griffiths singularity

International Nuclear Information System (INIS)

Hukushima, K; Iba, Y

2008-01-01

We develop a recently proposed importance-sampling Monte Carlo algorithm for sampling rare events and quenched variables in random disordered systems. We apply it to a two dimensional bond-diluted Ising model and study the Griffiths singularity which is considered to be due to the existence of rare large clusters. It is found that the distribution of the inverse susceptibility has an exponential tail down to the origin which is considered the consequence of the Griffiths singularity

Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms

DEFF Research Database (Denmark)

Elfving, Tommy; Hansen, Per Christian; Nikazad, Touraj

2012-01-01

We give a detailed study of the semiconverg ence behavior of projected nonstationary simultaneous iterative reconstruction technique (SIRT) algorithms, including the projected Landweber algorithm. We also consider the use of a relaxation parameter strategy, proposed recently for the standard...... algorithms, for controlling the semiconvergence of the projected algorithms. We demonstrate the semiconvergence and the performance of our strategies by examples taken from tomographic imaging. © 2012 Society for Industrial and Applied Mathematics....

Finite sample performance of the E-M algorithm for ranks data modelling

Directory of Open Access Journals (Sweden)

Angela D'Elia

2007-10-01

Full Text Available We check the finite sample performance of the maximum likelihood estimators of the parameters of a mixture distribution recently introduced for modelling ranks/preference data. The estimates are derived by the E-M algorithm and the performance is evaluated both from an univariate and bivariate points of view. While the results are generally acceptable as far as it concerns the bias, the Monte Carlo experiment shows a different behaviour of the estimators efficiency for the two parameters of the mixture, mainly depending upon their location in the admissible parametric space. Some operative suggestions conclude the paer.

Enhanced TDMA Based Anti-Collision Algorithm with a Dynamic Frame Size Adjustment Strategy for Mobile RFID Readers

Directory of Open Access Journals (Sweden)

Kwang Cheol Shin

2009-02-01

Full Text Available In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm.

Enhanced TDMA Based Anti-Collision Algorithm with a Dynamic Frame Size Adjustment Strategy for Mobile RFID Readers.

Science.gov (United States)

Shin, Kwang Cheol; Park, Seung Bo; Jo, Geun Sik

2009-01-01

In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA) based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm.

Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

Directory of Open Access Journals (Sweden)

Zhang Fengjiao

2015-03-01

Full Text Available Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of electric vehicle regenerative braking system in typical brake conditions. The results show that optimization objectives achieved a good astringency, and the optimized control strategy can increase the brake energy recovery effectively under the condition of ensuring the braking stability.

Introduction to Evolutionary Algorithms

CERN Document Server

Yu, Xinjie

2010-01-01

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

Impact of sampling strategy on stream load estimates in till landscape of the Midwest

Science.gov (United States)

Vidon, P.; Hubbard, L.E.; Soyeux, E.

2009-01-01

Accurately estimating various solute loads in streams during storms is critical to accurately determine maximum daily loads for regulatory purposes. This study investigates the impact of sampling strategy on solute load estimates in streams in the US Midwest. Three different solute types (nitrate, magnesium, and dissolved organic carbon (DOC)) and three sampling strategies are assessed. Regardless of the method, the average error on nitrate loads is higher than for magnesium or DOC loads, and all three methods generally underestimate DOC loads and overestimate magnesium loads. Increasing sampling frequency only slightly improves the accuracy of solute load estimates but generally improves the precision of load calculations. This type of investigation is critical for water management and environmental assessment so error on solute load calculations can be taken into account by landscape managers, and sampling strategies optimized as a function of monitoring objectives. ?? 2008 Springer Science+Business Media B.V.

Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms

Science.gov (United States)

2013-01-01

Background The high burden and rising incidence of cardiovascular disease (CVD) in resource constrained countries necessitates implementation of robust and pragmatic primary and secondary prevention strategies. Many current CVD management guidelines recommend absolute cardiovascular (CV) risk assessment as a clinically sound guide to preventive and treatment strategies. Development of non-laboratory based cardiovascular risk assessment algorithms enable absolute risk assessment in resource constrained countries. The objective of this review is to evaluate the performance of existing non-laboratory based CV risk assessment algorithms using the benchmarks for clinically useful CV risk assessment algorithms outlined by Cooney and colleagues. Methods A literature search to identify non-laboratory based risk prediction algorithms was performed in MEDLINE, CINAHL, Ovid Premier Nursing Journals Plus, and PubMed databases. The identified algorithms were evaluated using the benchmarks for clinically useful cardiovascular risk assessment algorithms outlined by Cooney and colleagues. Results Five non-laboratory based CV risk assessment algorithms were identified. The Gaziano and Framingham algorithms met the criteria for appropriateness of statistical methods used to derive the algorithms and endpoints. The Swedish Consultation, Framingham and Gaziano algorithms demonstrated good discrimination in derivation datasets. Only the Gaziano algorithm was externally validated where it had optimal discrimination. The Gaziano and WHO algorithms had chart formats which made them simple and user friendly for clinical application. Conclusion Both the Gaziano and Framingham non-laboratory based algorithms met most of the criteria outlined by Cooney and colleagues. External validation of the algorithms in diverse samples is needed to ascertain their performance and applicability to different populations and to enhance clinicians’ confidence in them. PMID:24373202

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

Science.gov (United States)

Qin, Cheng-Zhi; Zhan, Lijun

2012-06-01

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU

Perspectives on land snails - sampling strategies for isotopic analyses

Science.gov (United States)

Kwiecien, Ola; Kalinowski, Annika; Kamp, Jessica; Pellmann, Anna

2017-04-01

Since the seminal works of Goodfriend (1992), several substantial studies confirmed a relation between the isotopic composition of land snail shells (d18O, d13C) and environmental parameters like precipitation amount, moisture source, temperature and vegetation type. This relation, however, is not straightforward and site dependent. The choice of sampling strategy (discrete or bulk sampling) and cleaning procedure (several methods can be used, but comparison of their effects in an individual shell has yet not been achieved) further complicate the shell analysis. The advantage of using snail shells as environmental archive lies in the snails' limited mobility, and therefore an intrinsic aptitude of recording local and site-specific conditions. Also, snail shells are often found at dated archaeological sites. An obvious drawback is that shell assemblages rarely make up a continuous record, and a single shell is only a snapshot of the environmental setting at a given time. Shells from archaeological sites might represent a dietary component and cooking would presumably alter the isotopic signature of aragonite material. Consequently, a proper sampling strategy is of great importance and should be adjusted to the scientific question. Here, we compare and contrast different sampling approaches using modern shells collected in Morocco, Spain and Germany. The bulk shell approach (fine-ground material) yields information on mean environmental parameters within the life span of analyzed individuals. However, despite homogenization, replicate measurements of bulk shell material returned results with a variability greater than analytical precision (up to 2‰ for d18O, and up to 1‰ for d13C), calling for caution analyzing only single individuals. Horizontal high-resolution sampling (single drill holes along growth lines) provides insights into the amplitude of seasonal variability, while vertical high-resolution sampling (multiple drill holes along the same growth line

Algorithm FIRE-Feynman Integral REduction

International Nuclear Information System (INIS)

Smirnov, A.V.

2008-01-01

The recently developed algorithm FIRE performs the reduction of Feynman integrals to master integrals. It is based on a number of strategies, such as applying the Laporta algorithm, the s-bases algorithm, region-bases and integrating explicitly over loop momenta when possible. Currently it is being used in complicated three-loop calculations.

Contemporary evolution strategies

CERN Document Server

Bäck, Thomas; Krause, Peter

2013-01-01

Evolution strategies have more than 50 years of history in the field of evolutionary computation. Since the early 1990s, many algorithmic variations of evolution strategies have been developed, characterized by the fact that they use the so-called derandomization concept for strategy parameter adaptation. Most importantly, the covariance matrix adaptation strategy (CMA-ES) and its successors are the key representatives of this group of contemporary evolution strategies. This book provides an overview of the key algorithm developments between 1990 and 2012, including brief descriptions of the a

Sample preparation strategies for food and biological samples prior to nanoparticle detection and imaging

DEFF Research Database (Denmark)

Larsen, Erik Huusfeldt; Löschner, Katrin

2014-01-01

microscopy (TEM) proved to be necessary for trouble shooting of results obtained from AFFF-LS-ICP-MS. Aqueous and enzymatic extraction strategies were tested for thorough sample preparation aiming at degrading the sample matrix and to liberate the AgNPs from chicken meat into liquid suspension. The resulting...... AFFF-ICP-MS fractograms, which corresponded to the enzymatic digests, showed a major nano-peak (about 80 % recovery of AgNPs spiked to the meat) plus new smaller peaks that eluted close to the void volume of the fractograms. Small, but significant shifts in retention time of AFFF peaks were observed...... for the meat sample extracts and the corresponding neat AgNP suspension, and rendered sizing by way of calibration with AgNPs as sizing standards inaccurate. In order to gain further insight into the sizes of the separated AgNPs, or their possible dissolved state, fractions of the AFFF eluate were collected...

On the Structure of a Best Possible Crossover Selection Strategy in Genetic Algorithms

Science.gov (United States)

Lässig, Jörg; Hoffmann, Karl Heinz

The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover to find a solution with high fitness for a given optimization problem. Many different schemes have been described in the literature as possible strategies for this task but so far comparisons have been predominantly empirical. It is shown that if one wishes to maximize any linear function of the final state probabilities, e.g. the fitness of the best individual in the final population of the algorithm, then a best probability distribution for selecting an individual in each generation is a rectangular distribution over the individuals sorted in descending sequence by their fitness values. This means uniform probabilities have to be assigned to a group of the best individuals of the population but probabilities equal to zero to individuals with lower fitness, assuming that the probability distribution to choose individuals from the current population can be chosen independently for each iteration and each individual. This result is then generalized also to typical practically applied performance measures, such as maximizing the expected fitness value of the best individual seen in any generation.

Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics

NARCIS (Netherlands)

Suarez Diez, M.; Saccenti, E.

2015-01-01

We investigated the effect of sample size and dimensionality on the performance of four algorithms (ARACNE, CLR, CORR, and PCLRC) when they are used for the inference of metabolite association networks. We report that as many as 100-400 samples may be necessary to obtain stable network estimations,

Toward human-centered algorithm design

Directory of Open Access Journals (Sweden)

Eric PS Baumer

2017-07-01

Full Text Available As algorithms pervade numerous facets of daily life, they are incorporated into systems for increasingly diverse purposes. These systems’ results are often interpreted differently by the designers who created them than by the lay persons who interact with them. This paper offers a proposal for human-centered algorithm design, which incorporates human and social interpretations into the design process for algorithmically based systems. It articulates three specific strategies for doing so: theoretical, participatory, and speculative. Drawing on the author’s work designing and deploying multiple related systems, the paper provides a detailed example of using a theoretical approach. It also discusses findings pertinent to participatory and speculative design approaches. The paper addresses both strengths and challenges for each strategy in helping to center the process of designing algorithmically based systems around humans.

Sampling strategy for estimating human exposure pathways to consumer chemicals

NARCIS (Netherlands)

Papadopoulou, Eleni; Padilla-Sanchez, Juan A.; Collins, Chris D.; Cousins, Ian T.; Covaci, Adrian; de Wit, Cynthia A.; Leonards, Pim E.G.; Voorspoels, Stefan; Thomsen, Cathrine; Harrad, Stuart; Haug, Line S.

2016-01-01

Human exposure to consumer chemicals has become a worldwide concern. In this work, a comprehensive sampling strategy is presented, to our knowledge being the first to study all relevant exposure pathways in a single cohort using multiple methods for assessment of exposure from each exposure pathway.

Dried blood spot measurement: application in tacrolimus monitoring using limited sampling strategy and abbreviated AUC estimation.

Science.gov (United States)

Cheung, Chi Yuen; van der Heijden, Jaques; Hoogtanders, Karin; Christiaans, Maarten; Liu, Yan Lun; Chan, Yiu Han; Choi, Koon Shing; van de Plas, Afke; Shek, Chi Chung; Chau, Ka Foon; Li, Chun Sang; van Hooff, Johannes; Stolk, Leo

2008-02-01

Dried blood spot (DBS) sampling and high-performance liquid chromatography tandem-mass spectrometry have been developed in monitoring tacrolimus levels. Our center favors the use of limited sampling strategy and abbreviated formula to estimate the area under concentration-time curve (AUC(0-12)). However, it is inconvenient for patients because they have to wait in the center for blood sampling. We investigated the application of DBS method in tacrolimus level monitoring using limited sampling strategy and abbreviated AUC estimation approach. Duplicate venous samples were obtained at each time point (C(0), C(2), and C(4)). To determine the stability of blood samples, one venous sample was sent to our laboratory immediately. The other duplicate venous samples, together with simultaneous fingerprick blood samples, were sent to the University of Maastricht in the Netherlands. Thirty six patients were recruited and 108 sets of blood samples were collected. There was a highly significant relationship between AUC(0-12), estimated from venous blood samples, and fingerprick blood samples (r(2) = 0.96, P AUC(0-12) strategy as drug monitoring.

Analysis and Improvement of Fireworks Algorithm

Directory of Open Access Journals (Sweden)

Xi-Guang Li

2017-02-01

Full Text Available The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA, this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.

A Probabilistic Mass Estimation Algorithm for a Novel 7- Channel Capacitive Sample Verification Sensor

Science.gov (United States)

Wolf, Michael

2012-01-01

A document describes an algorithm created to estimate the mass placed on a sample verification sensor (SVS) designed for lunar or planetary robotic sample return missions. A novel SVS measures the capacitance between a rigid bottom plate and an elastic top membrane in seven locations. As additional sample material (soil and/or small rocks) is placed on the top membrane, the deformation of the membrane increases the capacitance. The mass estimation algorithm addresses both the calibration of each SVS channel, and also addresses how to combine the capacitances read from each of the seven channels into a single mass estimate. The probabilistic approach combines the channels according to the variance observed during the training phase, and provides not only the mass estimate, but also a value for the certainty of the estimate. SVS capacitance data is collected for known masses under a wide variety of possible loading scenarios, though in all cases, the distribution of sample within the canister is expected to be approximately uniform. A capacitance-vs-mass curve is fitted to this data, and is subsequently used to determine the mass estimate for the single channel s capacitance reading during the measurement phase. This results in seven different mass estimates, one for each SVS channel. Moreover, the variance of the calibration data is used to place a Gaussian probability distribution function (pdf) around this mass estimate. To blend these seven estimates, the seven pdfs are combined into a single Gaussian distribution function, providing the final mean and variance of the estimate. This blending technique essentially takes the final estimate as an average of the estimates of the seven channels, weighted by the inverse of the channel s variance.

Improved multivariate polynomial factoring algorithm

International Nuclear Information System (INIS)

Wang, P.S.

1978-01-01

A new algorithm for factoring multivariate polynomials over the integers based on an algorithm by Wang and Rothschild is described. The new algorithm has improved strategies for dealing with the known problems of the original algorithm, namely, the leading coefficient problem, the bad-zero problem and the occurrence of extraneous factors. It has an algorithm for correctly predetermining leading coefficients of the factors. A new and efficient p-adic algorithm named EEZ is described. Bascially it is a linearly convergent variable-by-variable parallel construction. The improved algorithm is generally faster and requires less store then the original algorithm. Machine examples with comparative timing are included

Statistical sampling strategies

International Nuclear Information System (INIS)

Andres, T.H.

1987-01-01

Systems assessment codes use mathematical models to simulate natural and engineered systems. Probabilistic systems assessment codes carry out multiple simulations to reveal the uncertainty in values of output variables due to uncertainty in the values of the model parameters. In this paper, methods are described for sampling sets of parameter values to be used in a probabilistic systems assessment code. Three Monte Carlo parameter selection methods are discussed: simple random sampling, Latin hypercube sampling, and sampling using two-level orthogonal arrays. Three post-selection transformations are also described: truncation, importance transformation, and discretization. Advantages and disadvantages of each method are summarized

Energy-saving control strategy for lighting system based on multivariate extremum seeking with Newton algorithm

International Nuclear Information System (INIS)

Yin, Chun; Dadras, Sara; Huang, Xuegang; Mei, Jun; Malek, Hadi; Cheng, Yuhua

2017-01-01

Highlights: • An energy-saving control strategy is proposed for multi-group lighting sources. • The proposed controller is designed to minimize the light-energy consumption. • It is designed to speed up the convergence rate without increasing the oscillation. • The minimal energy usage is guaranteed, while keeping the desired lighting level. • Experimental results shows the superiorities of the energy-saving control strategy. - Abstract: In recent years, the energy problem has been a universal concern. In order to improve the lighting energy efficiency and reduce the electric energy consumption, this paper develops an energy-saving control strategy for the lighting system with multiple lighting sources. The control strategy presented in this paper includes two parts: a new multivariate extremum seeking control method with Newton algorithm is developed to minimize the light-energy consumption by separately manipulating the brightness of multiple lighting sources, and a proportion-integration-differentiation control approach is adopted to realize the desired lighting level. The proposed scheme can increase the convergence speed of the closed loop system toward the minimum light-energy consumption, meanwhile, the accuracy of the control strategy will be improved. Experimental results illustrate that the light-energy consumption via the proposed method can reach more rapidly to a smaller vicinity of the minimum energy point, so, the lighting energy efficiency is greatly increased accordingly.

Entropic sampling of simple polymer models within Wang-Landau algorithm

International Nuclear Information System (INIS)

Vorontsov-Velyaminov, P N; Volkov, N A; Yurchenko, A A

2004-01-01

In this paper we apply a new simulation technique proposed in Wang and Landau (WL) (2001 Phys. Rev. Lett. 86 2050) to sampling of three-dimensional lattice and continuous models of polymer chains. Distributions obtained by homogeneous (unconditional) random walks are compared with results of entropic sampling (ES) within the WL algorithm. While homogeneous sampling gives reliable results typically in the range of 4-5 orders of magnitude, the WL entropic sampling yields them in the range of 20-30 orders and even larger with comparable computer effort. A combination of homogeneous and WL sampling provides reliable data for events with probabilities down to 10 -35 . For the lattice model we consider both the athermal case (self-avoiding walks, SAWs) and the thermal case when an energy is attributed to each contact between nonbonded monomers in a self-avoiding walk. For short chains the simulation results are checked by comparison with the exact data. In WL calculations for chain lengths up to N = 300 scaling relations for SAWs are well reproduced. In the thermal case distribution over the number of contacts is obtained in the N-range up to N = 100 and the canonical averages - internal energy, heat capacity, excess canonical entropy, mean square end-to-end distance - are calculated as a result in a wide temperature range. The continuous model is studied in the athermal case. By sorting conformations of a continuous phantom freely joined N-bonded chain with a unit bond length over a stochastic variable, the minimum distance between nonbonded beads, we determine the probability distribution for the N-bonded chain with hard sphere monomer units over its diameter a in the complete diameter range, 0 ≤ a ≤ 2, within a single ES run. This distribution provides us with excess specific entropy for a set of diameters a in this range. Calculations were made for chain lengths up to N = 100 and results were extrapolated to N → ∞ for a in the range 0 ≤ a ≤ 1.25

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

Science.gov (United States)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

A Note on Information-Directed Sampling and Thompson Sampling

OpenAIRE

Zhou, Li

2015-01-01

This note introduce three Bayesian style Multi-armed bandit algorithms: Information-directed sampling, Thompson Sampling and Generalized Thompson Sampling. The goal is to give an intuitive explanation for these three algorithms and their regret bounds, and provide some derivations that are omitted in the original papers.

SeaWiFS technical report series. Volume 4: An analysis of GAC sampling algorithms. A case study

Science.gov (United States)

Yeh, Eueng-Nan (Editor); Hooker, Stanford B. (Editor); Hooker, Stanford B. (Editor); Mccain, Charles R. (Editor); Fu, Gary (Editor)

1992-01-01

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument will sample at approximately a 1 km resolution at nadir which will be broadcast for reception by realtime ground stations. However, the global data set will be comprised of coarser four kilometer data which will be recorded and broadcast to the SeaWiFS Project for processing. Several algorithms for degrading the one kilometer data to four kilometer data are examined using imagery from the Coastal Zone Color Scanner (CZCS) in an effort to determine which algorithm would best preserve the statistical characteristics of the derived products generated from the one kilometer data. Of the algorithms tested, subsampling based on a fixed pixel within a 4 x 4 pixel array is judged to yield the most consistent results when compared to the one kilometer data products.

A proposal of optimal sampling design using a modularity strategy

Science.gov (United States)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm

Science.gov (United States)

Rahman, Imran; Vasant, Pandian M.; Singh, Balbir Singh Mahinder; Abdullah-Al-Wadud, M.

2014-10-01

Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.

Recruiting hard-to-reach United States population sub-groups via adaptations of snowball sampling strategy

Science.gov (United States)

Sadler, Georgia Robins; Lee, Hau-Chen; Seung-Hwan Lim, Rod; Fullerton, Judith

2011-01-01

Nurse researchers and educators often engage in outreach to narrowly defined populations. This article offers examples of how variations on the snowball sampling recruitment strategy can be applied in the creation of culturally appropriate, community-based information dissemination efforts related to recruitment to health education programs and research studies. Examples from the primary author’s program of research are provided to demonstrate how adaptations of snowball sampling can be effectively used in the recruitment of members of traditionally underserved or vulnerable populations. The adaptation of snowball sampling techniques, as described in this article, helped the authors to gain access to each of the more vulnerable population groups of interest. The use of culturally sensitive recruitment strategies is both appropriate and effective in enlisting the involvement of members of vulnerable populations. Adaptations of snowball sampling strategies should be considered when recruiting participants for education programs or subjects for research studies when recruitment of a population based sample is not essential. PMID:20727089

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

Science.gov (United States)

El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

2015-04-01

This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game

OpenAIRE

Lara-Cabrera, Raúl; Cotta, Carlos; Fernández Leiva, Antonio J.

2013-01-01

This work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based ...

Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Directory of Open Access Journals (Sweden)

Jingxian Hao

2016-11-01

Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

Sampling and analyte enrichment strategies for ambient mass spectrometry.

Science.gov (United States)

Li, Xianjiang; Ma, Wen; Li, Hongmei; Ai, Wanpeng; Bai, Yu; Liu, Huwei

2018-01-01

Ambient mass spectrometry provides great convenience for fast screening, and has showed promising potential in analytical chemistry. However, its relatively low sensitivity seriously restricts its practical utility in trace compound analysis. In this review, we summarize the sampling and analyte enrichment strategies coupled with nine modes of representative ambient mass spectrometry (desorption electrospray ionization, paper vhspray ionization, wooden-tip spray ionization, probe electrospray ionization, coated blade spray ionization, direct analysis in real time, desorption corona beam ionization, dielectric barrier discharge ionization, and atmospheric-pressure solids analysis probe) that have dramatically increased the detection sensitivity. We believe that these advances will promote routine use of ambient mass spectrometry. Graphical abstract Scheme of sampling stretagies for ambient mass spectrometry.

Adaptive firefly algorithm: parameter analysis and its application.

Directory of Open Access Journals (Sweden)

Ngaam J Cheung

Full Text Available As a nature-inspired search algorithm, firefly algorithm (FA has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa. There are three strategies in AdaFa including (1 a distance-based light absorption coefficient; (2 a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3 five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

GENESIS 1.1: A hybrid-parallel molecular dynamics simulator with enhanced sampling algorithms on multiple computational platforms.

Science.gov (United States)

Kobayashi, Chigusa; Jung, Jaewoon; Matsunaga, Yasuhiro; Mori, Takaharu; Ando, Tadashi; Tamura, Koichi; Kamiya, Motoshi; Sugita, Yuji

2017-09-30

GENeralized-Ensemble SImulation System (GENESIS) is a software package for molecular dynamics (MD) simulation of biological systems. It is designed to extend limitations in system size and accessible time scale by adopting highly parallelized schemes and enhanced conformational sampling algorithms. In this new version, GENESIS 1.1, new functions and advanced algorithms have been added. The all-atom and coarse-grained potential energy functions used in AMBER and GROMACS packages now become available in addition to CHARMM energy functions. The performance of MD simulations has been greatly improved by further optimization, multiple time-step integration, and hybrid (CPU + GPU) computing. The string method and replica-exchange umbrella sampling with flexible collective variable choice are used for finding the minimum free-energy pathway and obtaining free-energy profiles for conformational changes of a macromolecule. These new features increase the usefulness and power of GENESIS for modeling and simulation in biological research. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

[Study of spatial stratified sampling strategy of Oncomelania hupensis snail survey based on plant abundance].

Science.gov (United States)

Xun-Ping, W; An, Z

2017-07-27

Objective To optimize and simplify the survey method of Oncomelania hupensis snails in marshland endemic regions of schistosomiasis, so as to improve the precision, efficiency and economy of the snail survey. Methods A snail sampling strategy (Spatial Sampling Scenario of Oncomelania based on Plant Abundance, SOPA) which took the plant abundance as auxiliary variable was explored and an experimental study in a 50 m×50 m plot in a marshland in the Poyang Lake region was performed. Firstly, the push broom surveyed data was stratified into 5 layers by the plant abundance data; then, the required numbers of optimal sampling points of each layer through Hammond McCullagh equation were calculated; thirdly, every sample point in the line with the Multiple Directional Interpolation (MDI) placement scheme was pinpointed; and finally, the comparison study among the outcomes of the spatial random sampling strategy, the traditional systematic sampling method, the spatial stratified sampling method, Sandwich spatial sampling and inference and SOPA was performed. Results The method (SOPA) proposed in this study had the minimal absolute error of 0.213 8; and the traditional systematic sampling method had the largest estimate, and the absolute error was 0.924 4. Conclusion The snail sampling strategy (SOPA) proposed in this study obtains the higher estimation accuracy than the other four methods.

Generalized-ensemble molecular dynamics and Monte Carlo algorithms beyond the limit of the multicanonical algorithm

International Nuclear Information System (INIS)

Okumura, Hisashi

2010-01-01

I review two new generalized-ensemble algorithms for molecular dynamics and Monte Carlo simulations of biomolecules, that is, the multibaric–multithermal algorithm and the partial multicanonical algorithm. In the multibaric–multithermal algorithm, two-dimensional random walks not only in the potential-energy space but also in the volume space are realized. One can discuss the temperature dependence and pressure dependence of biomolecules with this algorithm. The partial multicanonical simulation samples a wide range of only an important part of potential energy, so that one can concentrate the effort to determine a multicanonical weight factor only on the important energy terms. This algorithm has higher sampling efficiency than the multicanonical and canonical algorithms. (review)

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

Directory of Open Access Journals (Sweden)

Kai Hu

2013-01-01

Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

Automatic design of decision-tree induction algorithms

CERN Document Server

Barros, Rodrigo C; Freitas, Alex A

2015-01-01

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning, and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain o

A genetic algorithm-based framework for wavelength selection on sample categorization.

Science.gov (United States)

Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F

2017-08-01

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A sampling strategy for estimating plot average annual fluxes of chemical elements from forest soils

NARCIS (Netherlands)

Brus, D.J.; Gruijter, de J.J.; Vries, de W.

2010-01-01

A sampling strategy for estimating spatially averaged annual element leaching fluxes from forest soils is presented and tested in three Dutch forest monitoring plots. In this method sampling locations and times (days) are selected by probability sampling. Sampling locations were selected by

Multilevel Image Segmentation Based on an Improved Firefly Algorithm

Directory of Open Access Journals (Sweden)

Kai Chen

2016-01-01

Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

Sample preparation composite and replicate strategy for assay of solid oral drug products.

Science.gov (United States)

Harrington, Brent; Nickerson, Beverly; Guo, Michele Xuemei; Barber, Marc; Giamalva, David; Lee, Carlos; Scrivens, Garry

2014-12-16

In pharmaceutical analysis, the results of drug product assay testing are used to make decisions regarding the quality, efficacy, and stability of the drug product. In order to make sound risk-based decisions concerning drug product potency, an understanding of the uncertainty of the reportable assay value is required. Utilizing the most restrictive criteria in current regulatory documentation, a maximum variability attributed to method repeatability is defined for a drug product potency assay. A sampling strategy that reduces the repeatability component of the assay variability below this predefined maximum is demonstrated. The sampling strategy consists of determining the number of dosage units (k) to be prepared in a composite sample of which there may be a number of equivalent replicate (r) sample preparations. The variability, as measured by the standard error (SE), of a potency assay consists of several sources such as sample preparation and dosage unit variability. A sampling scheme that increases the number of sample preparations (r) and/or number of dosage units (k) per sample preparation will reduce the assay variability and thus decrease the uncertainty around decisions made concerning the potency of the drug product. A maximum allowable repeatability component of the standard error (SE) for the potency assay is derived using material in current regulatory documents. A table of solutions for the number of dosage units per sample preparation (r) and number of replicate sample preparations (k) is presented for any ratio of sample preparation and dosage unit variability.

Development and Demonstration of a Method to Evaluate Bio-Sampling Strategies Using Building Simulation and Sample Planning Software

OpenAIRE

Dols, W. Stuart; Persily, Andrew K.; Morrow, Jayne B.; Matzke, Brett D.; Sego, Landon H.; Nuffer, Lisa L.; Pulsipher, Brent A.

2010-01-01

In an effort to validate and demonstrate response and recovery sampling approaches and technologies, the U.S. Department of Homeland Security (DHS), along with several other agencies, have simulated a biothreat agent release within a facility at Idaho National Laboratory (INL) on two separate occasions in the fall of 2007 and the fall of 2008. Because these events constitute only two realizations of many possible scenarios, increased understanding of sampling strategies can be obtained by vir...

A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis

International Nuclear Information System (INIS)

Zhang, Chunwei; Cui, Guomin; Peng, Fuyu

2016-01-01

Highlights: • The chaotic ant swarm algorithm is proposed to avoid trapping into a local optimum. • The organization variables update strategy makes full use of advantages of the chaotic search. • The structure evolution strategy is developed to handle integer variables optimization. • Overall three cases taken form the literatures are investigated with better optima. - Abstract: The heat exchanger networks synthesis (HENS) still remains an open problem due to its combinatorial nature, which can easily result in suboptimal design and unacceptable calculation effort. In this paper, a novel hybrid chaotic ant swarm algorithm is proposed. The presented algorithm, which consists of a combination of chaotic ant swarm (CAS) algorithm, structure evolution strategy, local optimization strategy and organization variables update strategy, can simultaneously optimize continuous variables and integer variables. The CAS algorithm chaotically searches and generates new solutions in the given space, and subsequently the structure evolution strategy evolves the structures represented by the solutions and limits the search space. Furthermore, the local optimizing strategy and the organization variables update strategy are introduced to enhance the performance of the algorithm. The study of three different cases, found in the literature, revealed special search abilities in both structure space and continuous variable space.

Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies.

Science.gov (United States)

Gagnebin, Yoric; Tonoli, David; Lescuyer, Pierre; Ponte, Belen; de Seigneux, Sophie; Martin, Pierre-Yves; Schappler, Julie; Boccard, Julien; Rudaz, Serge

2017-02-22

Among the various biological matrices used in metabolomics, urine is a biofluid of major interest because of its non-invasive collection and its availability in large quantities. However, significant sources of variability in urine metabolomics based on UHPLC-MS are related to the analytical drift and variation of the sample concentration, thus requiring normalization. A sequential normalization strategy was developed to remove these detrimental effects, including: (i) pre-acquisition sample normalization by individual dilution factors to narrow the concentration range and to standardize the analytical conditions, (ii) post-acquisition data normalization by quality control-based robust LOESS signal correction (QC-RLSC) to correct for potential analytical drift, and (iii) post-acquisition data normalization by MS total useful signal (MSTUS) or probabilistic quotient normalization (PQN) to prevent the impact of concentration variability. This generic strategy was performed with urine samples from healthy individuals and was further implemented in the context of a clinical study to detect alterations in urine metabolomic profiles due to kidney failure. In the case of kidney failure, the relation between creatinine/osmolality and the sample concentration is modified, and relying only on these measurements for normalization could be highly detrimental. The sequential normalization strategy was demonstrated to significantly improve patient stratification by decreasing the unwanted variability and thus enhancing data quality. Copyright © 2016 Elsevier B.V. All rights reserved.

A strategy for quantum algorithm design assisted by machine learning

International Nuclear Information System (INIS)

Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin

2014-01-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)

A strategy for quantum algorithm design assisted by machine learning

Science.gov (United States)

Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

2014-07-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

Improved Algorithms OF CELF and CELF++ for Influence Maximization

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Jiaguo Lv

2014-06-01

Full Text Available Motivated by the wide application in some fields, such as viral marketing, sales promotion etc, influence maximization has been the most important and extensively studied problem in social network. However, the most classical KK-Greedy algorithm for influence maximization is inefficient. Two major sources of the algorithm’s inefficiency were analyzed in this paper. With the analysis of algorithms CELF and CELF++, all nodes in the influenced set of u would never bring any marginal gain when a new seed u was produced. Through this optimization strategy, a lot of redundant nodes will be removed from the candidate nodes. Basing on the strategy, two improved algorithms of Lv_CELF and Lv_CELF++ were proposed in this study. To evaluate the two algorithms, the two algorithms with their benchmark algorithms of CELF and CELF++ were conducted on some real world datasets. To estimate the algorithms, influence degree and running time were employed to measure the performance and efficiency respectively. Experimental results showed that, compared with benchmark algorithms of CELF and CELF++, matching effects and higher efficiency were achieved by the new algorithms Lv_CELF and Lv_CELF++. Solutions with the proposed optimization strategy can be useful for the decisionmaking problems under the scenarios related to the influence maximization problem.

Multimodal Estimation of Distribution Algorithms.

Science.gov (United States)

Yang, Qiang; Chen, Wei-Neng; Li, Yun; Chen, C L Philip; Xu, Xiang-Min; Zhang, Jun

2016-02-15

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.

Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers: Application in a Non-US Sample of 1,104 Children

Science.gov (United States)

de Bildt, Annelies; Sytema, Sjoerd; Zander, Eric; Bölte, Sven; Sturm, Harald; Yirmiya, Nurit; Yaari, Maya; Charman, Tony; Salomone, Erica; LeCouteur, Ann; Green, Jonathan; Bedia, Ricardo Canal; Primo, Patricia García; van Daalen, Emma; de Jonge, Maretha V.; Guðmundsdóttir, Emilía; Jóhannsdóttir, Sigurrós; Raleva, Marija; Boskovska, Meri; Rogé, Bernadette; Baduel, Sophie; Moilanen, Irma; Yliherva, Anneli; Buitelaar, Jan; Oosterling, Iris J.

2015-01-01

The current study aimed to investigate the Autism Diagnostic Interview-Revised (ADI-R) algorithms for toddlers and young preschoolers (Kim and Lord, "J Autism Dev Disord" 42(1):82-93, 2012) in a non-US sample from ten sites in nine countries (n = 1,104). The construct validity indicated a good fit of the algorithms. The diagnostic…

Sampling and analysis strategies to support waste form qualification

International Nuclear Information System (INIS)

Westsik, J.H. Jr.; Pulsipher, B.A.; Eggett, D.L.; Kuhn, W.L.

1989-04-01

As part of the waste acceptance process, waste form producers will be required to (1) demonstrate that their glass waste form will meet minimum specifications, (2) show that the process can be controlled to consistently produce an acceptable waste form, and (3) provide documentation that the waste form produced meets specifications. Key to the success of these endeavors is adequate sampling and chemical and radiochemical analyses of the waste streams from the waste tanks through the process to the final glass product. This paper suggests sampling and analysis strategies for meeting specific statistical objectives of (1) detection of compositions outside specification limits, (2) prediction of final glass product composition, and (3) estimation of composition in process vessels for both reporting and guiding succeeding process steps. 2 refs., 1 fig., 3 tabs

Strategy for thermo-gravimetric analysis of K East fuel samples

International Nuclear Information System (INIS)

Lawrence, L.A.

1997-01-01

A strategy was developed for the Thermo-Gravimetric Analysis (TGA) testing of K East fuel samples for oxidation rate determinations. Tests will first establish if there are any differences for dry air oxidation between the K West and K East fuel. These tests will be followed by moist inert gas oxidation rate measurements. The final series of tests will consider pure water vapor i.e., steam

A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology

Directory of Open Access Journals (Sweden)

Slutsker Laurence

2008-02-01

Full Text Available Abstract Background Urban malaria is likely to become increasingly important as a consequence of the growing proportion of Africans living in cities. A novel sampling strategy was developed for urban areas to generate a sample simultaneously representative of population and inhabited environments. Such a strategy should facilitate analysis of important epidemiological relationships in this ecological context. Methods Census maps and summary data for Kisumu, Kenya, were used to create a pseudo-sampling frame using the geographic coordinates of census-sampled structures. For every enumeration area (EA designated as urban by the census (n = 535, a sample of structures equal to one-tenth the number of households was selected. In EAs designated as rural (n = 32, a geographically random sample totalling one-tenth the number of households was selected from a grid of points at 100 m intervals. The selected samples were cross-referenced to a geographic information system, and coordinates transferred to handheld global positioning units. Interviewers found the closest eligible household to the sampling point and interviewed the caregiver of a child aged Results 4,336 interviews were completed in 473 of the 567 study area EAs from June 2002 through February 2003. EAs without completed interviews were randomly distributed, and non-response was approximately 2%. Mean distance from the assigned sampling point to the completed interview was 74.6 m, and was significantly less in urban than rural EAs, even when controlling for number of households. The selected sample had significantly more children and females of childbearing age than the general population, and fewer older individuals. Conclusion This method selected a sample that was simultaneously population-representative and inclusive of important environmental variation. The use of a pseudo-sampling frame and pre-programmed handheld GPS units is more efficient and may yield a more complete sample than

The Fixpoint-Iteration Algorithm for Parity Games

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Florian Bruse

2014-08-01

Full Text Available It is known that the model checking problem for the modal mu-calculus reduces to the problem of solving a parity game and vice-versa. The latter is realised by the Walukiewicz formulas which are satisfied by a node in a parity game iff player 0 wins the game from this node. Thus, they define her winning region, and any model checking algorithm for the modal mu-calculus, suitably specialised to the Walukiewicz formulas, yields an algorithm for solving parity games. In this paper we study the effect of employing the most straight-forward mu-calculus model checking algorithm: fixpoint iteration. This is also one of the few algorithms, if not the only one, that were not originally devised for parity game solving already. While an empirical study quickly shows that this does not yield an algorithm that works well in practice, it is interesting from a theoretical point for two reasons: first, it is exponential on virtually all families of games that were designed as lower bounds for very particular algorithms suggesting that fixpoint iteration is connected to all those. Second, fixpoint iteration does not compute positional winning strategies. Note that the Walukiewicz formulas only define winning regions; some additional work is needed in order to make this algorithm compute winning strategies. We show that these are particular exponential-space strategies which we call eventually-positional, and we show how positional ones can be extracted from them.

Optimal Fungal Space Searching Algorithms.

Science.gov (United States)

Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V

2016-10-01

Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.

A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.

Directory of Open Access Journals (Sweden)

Daniel Vasiliu

Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.

Local Strategy Improvement for Parity Game Solving

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Oliver Friedmann

2010-06-01

Full Text Available The problem of solving a parity game is at the core of many problems in model checking, satisfiability checking and program synthesis. Some of the best algorithms for solving parity game are strategy improvement algorithms. These are global in nature since they require the entire parity game to be present at the beginning. This is a distinct disadvantage because in many applications one only needs to know which winning region a particular node belongs to, and a witnessing winning strategy may cover only a fractional part of the entire game graph. We present a local strategy improvement algorithm which explores the game graph on-the-fly whilst performing the improvement steps. We also compare it empirically with existing global strategy improvement algorithms and the currently only other local algorithm for solving parity games. It turns out that local strategy improvement can outperform these others by several orders of magnitude.

Compensation Methods for Non-uniform and Incomplete Data Sampling in High Resolution PET with Multiple Scintillation Crystal Layers

International Nuclear Information System (INIS)

Lee, Jae Sung; Kim, Soo Mee; Lee, Dong Soo; Hong, Jong Hong; Sim, Kwang Souk; Rhee, June Tak

2008-01-01

To establish the methods for sinogram formation and correction in order to appropriately apply the filtered backprojection (FBP) reconstruction algorithm to the data acquired using PET scanner with multiple scintillation crystal layers. Formation for raw PET data storage and conversion methods from listmode data to histogram and sinogram were optimized. To solve the various problems occurred while the raw histogram was converted into sinogram, optimal sampling strategy and sampling efficiency correction method were investigated. Gap compensation methods that is unique in this system were also investigated. All the sinogram data were reconstructed using 2D filtered backprojection algorithm and compared to estimate the improvements by the correction algorithms. Optimal radial sampling interval and number of angular samples in terms of the sampling theorem and sampling efficiency correction algorithm were pitch/2 and 120, respectively. By applying the sampling efficiency correction and gap compensation, artifacts and background noise on the reconstructed image could be reduced. Conversion method from the histogram to sinogram was investigated for the FBP reconstruction of data acquired using multiple scintillation crystal layers. This method will be useful for the fast 2D reconstruction of multiple crystal layer PET data

Recursive algorithms for phylogenetic tree counting.

Science.gov (United States)

Gavryushkina, Alexandra; Welch, David; Drummond, Alexei J

2013-10-28

In Bayesian phylogenetic inference we are interested in distributions over a space of trees. The number of trees in a tree space is an important characteristic of the space and is useful for specifying prior distributions. When all samples come from the same time point and no prior information available on divergence times, the tree counting problem is easy. However, when fossil evidence is used in the inference to constrain the tree or data are sampled serially, new tree spaces arise and counting the number of trees is more difficult. We describe an algorithm that is polynomial in the number of sampled individuals for counting of resolutions of a constraint tree assuming that the number of constraints is fixed. We generalise this algorithm to counting resolutions of a fully ranked constraint tree. We describe a quadratic algorithm for counting the number of possible fully ranked trees on n sampled individuals. We introduce a new type of tree, called a fully ranked tree with sampled ancestors, and describe a cubic time algorithm for counting the number of such trees on n sampled individuals. These algorithms should be employed for Bayesian Markov chain Monte Carlo inference when fossil data are included or data are serially sampled.

Research of Ant Colony Migration Strategy in LF Algorithm%LF算法中蚁群移动策略的研究

Institute of Scientific and Technical Information of China (English)

牛永洁

2013-01-01

采用FM、误分类错误率和运行时间作为衡量改进的LF算法的评价指标,对算法中蚁群的不同移动策略进行研究.这些移动策略包括完全随机移动、局部记忆指导下的直接跳转、局部记忆指导下的定向随机靠近、全局记忆指导下的直接跳转、全局记忆指导下定向随机靠近和局部记忆与全局记忆共同指导下的定向随机靠近6种移动策略.针对每种策略,固定算法的其他运行参数,在UCI数据集的Iris数据和Wine数据上运行的结果表明,全局记忆指导下的定向随机靠近策略运行效果最好,而且收敛速度快,并能有效避免局部最优化的问题.%Using the FM,the misclassification error rate and running time as a measure of improved LF algorithm,the colony different mobile strategy algorithm research.These mobile strategy including completely random mobile,jump directly under the guidance of local memory,orientation under the guidance of local memory close to random,jump directly under the guidance of the global memory,global memory,under the guidance of directional random near and local memory and global memory common guiding under the directional random near the six kinds of mobile strategy.For each strategy,the operating parameters of the fixed algorithm,run on the UCI datasets Iris data and Wine data results show that the global memory under the guidance of directional random near the best strategy run effect,and the speed of convergence can effectively avoid local optimization problem.

Evaluation of optimization strategies and the effect of initial conditions on IMAT optimization using a leaf position optimization algorithm

International Nuclear Information System (INIS)

Oliver, Mike; Jensen, Michael; Chen, Jeff; Wong, Eugene

2009-01-01

Intensity-modulated arc therapy (IMAT) is a rotational variant of intensity-modulated radiation therapy (IMRT) that can be implemented with or without angular dose rate variation. The purpose of this study is to assess optimization strategies and initial conditions using a leaf position optimization (LPO) algorithm altered for variable dose rate IMAT. A concave planning target volume (PTV) with a central cylindrical organ at risk (OAR) was used in this study. The initial IMAT arcs were approximated by multiple static beams at 5 deg. angular increments where multi-leaf collimator (MLC) leaf positions were determined from the beam's eye view to irradiate the PTV but avoid the OAR. For the optimization strategy, two arcs with arc ranges of 280 deg. and 150 deg. were employed and plans were created using LPO alone, variable dose rate optimization (VDRO) alone, simultaneous LPO and VDRO and sequential combinations of these strategies. To assess the MLC initialization effect, three single 360 deg. arc plans with different initial MLC configurations were generated using the simultaneous LPO and VDRO. The effect of changing optimization degrees of freedom was investigated by employing 3 deg., 5 deg. and 10 deg. angular sampling intervals for the two 280 deg., two 150 deg. and single arc plans using LPO and VDRO. The objective function value, a conformity index, a dose homogeneity index, mean dose to OAR and normal tissues were computed and used to evaluate the treatment plans. This study shows that the best optimization strategy for a concave target is to use simultaneous MLC LPO and VDRO. We found that the optimization result is sensitive to the choice of initial MLC aperture shapes suggesting that an LPO-based IMAT plan may not be able to overcome local minima for this geometry. In conclusion, simultaneous MLC leaf position and VDRO are needed with the most appropriate initial conditions (MLC positions, arc ranges and number of arcs) for IMAT.

Molecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform.

Science.gov (United States)

Pronk, Sander; Pouya, Iman; Lundborg, Magnus; Rotskoff, Grant; Wesén, Björn; Kasson, Peter M; Lindahl, Erik

2015-06-09

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

Guidelines and algorithms: strategies for standardization of referral criteria in diagnostic radiology

International Nuclear Information System (INIS)

Kainberger, Franz; Pokieser, Peter; Imhof, Herwig; Czembirek, Heinrich; Fruehwald, Franz

2002-01-01

Guidelines can be regarded as special forms of algorithms and have been shown to be useful tools for supporting medical decision making. With the Council Directive 97/43/Euratom recommendations concerning referral criteria for medical exposure have to be implemented into national law of all EU member states. The time- and cost-consuming efforts of developing, implementing, and updating such guidelines are balanced by the acceptance in clinical practice and eventual better health outcomes. Clearly defined objectives with special attention drawn on national and regional differences among potential users, support from organisations with expertise in evidence-based medicine, separated development of the evidence component and the recommendations component, and large-scale strategies for distribution and implementation are necessary. Editors as well as users of guidelines for referral criteria have to be aware which expectations can be met and which cannot be fulfilled with this instrument; thus, dealing with guidelines requires a new form of ''diagnostic reasoning'' based on medical ethics. (orig.)

Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Directory of Open Access Journals (Sweden)

Teng Liu

2015-07-01

Full Text Available This paper presents a reinforcement learning (RL–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h than the Dyna algorithm (7 h, its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

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Lei Wang

2017-01-01

Full Text Available In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES and improved population diversity strategy (IPDS are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

Recommended Immunological Strategies to Screen for Botulinum Neurotoxin-Containing Samples

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Stéphanie Simon

2015-11-01

Full Text Available Botulinum neurotoxins (BoNTs cause the life-threatening neurological illness botulism in humans and animals and are divided into seven serotypes (BoNT/A–G, of which serotypes A, B, E, and F cause the disease in humans. BoNTs are classified as “category A” bioterrorism threat agents and are relevant in the context of the Biological Weapons Convention. An international proficiency test (PT was conducted to evaluate detection, quantification and discrimination capabilities of 23 expert laboratories from the health, food and security areas. Here we describe three immunological strategies that proved to be successful for the detection and quantification of BoNT/A, B, and E considering the restricted sample volume (1 mL distributed. To analyze the samples qualitatively and quantitatively, the first strategy was based on sensitive immunoenzymatic and immunochromatographic assays for fast qualitative and quantitative analyses. In the second approach, a bead-based suspension array was used for screening followed by conventional ELISA for quantification. In the third approach, an ELISA plate format assay was used for serotype specific immunodetection of BoNT-cleaved substrates, detecting the activity of the light chain, rather than the toxin protein. The results provide guidance for further steps in quality assurance and highlight problems to address in the future.

Recommended Immunological Strategies to Screen for Botulinum Neurotoxin-Containing Samples.

Science.gov (United States)

Simon, Stéphanie; Fiebig, Uwe; Liu, Yvonne; Tierney, Rob; Dano, Julie; Worbs, Sylvia; Endermann, Tanja; Nevers, Marie-Claire; Volland, Hervé; Sesardic, Dorothea; Dorner, Martin B

2015-11-26

Botulinum neurotoxins (BoNTs) cause the life-threatening neurological illness botulism in humans and animals and are divided into seven serotypes (BoNT/A-G), of which serotypes A, B, E, and F cause the disease in humans. BoNTs are classified as "category A" bioterrorism threat agents and are relevant in the context of the Biological Weapons Convention. An international proficiency test (PT) was conducted to evaluate detection, quantification and discrimination capabilities of 23 expert laboratories from the health, food and security areas. Here we describe three immunological strategies that proved to be successful for the detection and quantification of BoNT/A, B, and E considering the restricted sample volume (1 mL) distributed. To analyze the samples qualitatively and quantitatively, the first strategy was based on sensitive immunoenzymatic and immunochromatographic assays for fast qualitative and quantitative analyses. In the second approach, a bead-based suspension array was used for screening followed by conventional ELISA for quantification. In the third approach, an ELISA plate format assay was used for serotype specific immunodetection of BoNT-cleaved substrates, detecting the activity of the light chain, rather than the toxin protein. The results provide guidance for further steps in quality assurance and highlight problems to address in the future.

GMOtrack: generator of cost-effective GMO testing strategies.

Science.gov (United States)

Novak, Petra Krau; Gruden, Kristina; Morisset, Dany; Lavrac, Nada; Stebih, Dejan; Rotter, Ana; Zel, Jana

2009-01-01

Commercialization of numerous genetically modified organisms (GMOs) has already been approved worldwide, and several additional GMOs are in the approval process. Many countries have adopted legislation to deal with GMO-related issues such as food safety, environmental concerns, and consumers' right of choice, making GMO traceability a necessity. The growing extent of GMO testing makes it important to study optimal GMO detection and identification strategies. This paper formally defines the problem of routine laboratory-level GMO tracking as a cost optimization problem, thus proposing a shift from "the same strategy for all samples" to "sample-centered GMO testing strategies." An algorithm (GMOtrack) for finding optimal two-phase (screening-identification) testing strategies is proposed. The advantages of cost optimization with increasing GMO presence on the market are demonstrated, showing that optimization approaches to analytic GMO traceability can result in major cost reductions. The optimal testing strategies are laboratory-dependent, as the costs depend on prior probabilities of local GMO presence, which are exemplified on food and feed samples. The proposed GMOtrack approach, publicly available under the terms of the General Public License, can be extended to other domains where complex testing is involved, such as safety and quality assurance in the food supply chain.

A hybrid multiview stereo algorithm for modeling urban scenes.

Science.gov (United States)

Lafarge, Florent; Keriven, Renaud; Brédif, Mathieu; Vu, Hoang-Hiep

2013-01-01

We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.

Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

Science.gov (United States)

Debasish Saha; Armen R. Kemanian; Benjamin M. Rau; Paul R. Adler; Felipe Montes

2017-01-01

Annual cumulative soil nitrous oxide (N2O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2O flux estimates with a known uncertainty level. Daily soil N2O fluxes were simulated for Ames, IA (...

Evaluating sampling strategies for larval cisco (Coregonus artedi)

Science.gov (United States)

Myers, J.T.; Stockwell, J.D.; Yule, D.L.; Black, J.A.

2008-01-01

To improve our ability to assess larval cisco (Coregonus artedi) populations in Lake Superior, we conducted a study to compare several sampling strategies. First, we compared density estimates of larval cisco concurrently captured in surface waters with a 2 x 1-m paired neuston net and a 0.5-m (diameter) conical net. Density estimates obtained from the two gear types were not significantly different, suggesting that the conical net is a reasonable alternative to the more cumbersome and costly neuston net. Next, we assessed the effect of tow pattern (sinusoidal versus straight tows) to examine if propeller wash affected larval density. We found no effect of propeller wash on the catchability of larval cisco. Given the availability of global positioning systems, we recommend sampling larval cisco using straight tows to simplify protocols and facilitate straightforward measurements of volume filtered. Finally, we investigated potential trends in larval cisco density estimates by sampling four time periods during the light period of a day at individual sites. Our results indicate no significant trends in larval density estimates during the day. We conclude estimates of larval cisco density across space are not confounded by time at a daily timescale. Well-designed, cost effective surveys of larval cisco abundance will help to further our understanding of this important Great Lakes forage species.

Sampling microcanonical measures of the 2D Euler equations through Creutz’s algorithm: a phase transition from disorder to order when energy is increased

International Nuclear Information System (INIS)

Potters, Max; Vaillant, Timothee; Bouchet, Freddy

2013-01-01

The 2D Euler equations are basic examples of fluid models for which a microcanonical measure can be constructed from first principles. This measure is defined through finite-dimensional approximations and a limiting procedure. Creutz’s algorithm is a microcanonical generalization of the Metropolis–Hastings algorithm (to sample Gibbs measures, in the canonical ensemble). We prove that Creutz’s algorithm can sample finite-dimensional approximations of the 2D Euler microcanonical measures (incorporating fixed energy and other invariants). This is essential as microcanonical and canonical measures are known to be inequivalent at some values of energy and vorticity distribution. Creutz’s algorithm is used to check predictions from the mean-field statistical mechanics theory of the 2D Euler equations (the Robert–Sommeria–Miller theory). We find full agreement with theory. Three different ways to compute the temperature give consistent results. Using Creutz’s algorithm, a first-order phase transition never observed previously and a situation of statistical ensemble inequivalence are found and studied. Strikingly, and in contrast to the usual statistical mechanics interpretations, this phase transition appears from a disordered phase to an ordered phase (with fewer symmetries) when the energy is increased. We explain this paradox. (paper)

Population Pharmacokinetics and Optimal Sampling Strategy for Model-Based Precision Dosing of Melphalan in Patients Undergoing Hematopoietic Stem Cell Transplantation.

Science.gov (United States)

Mizuno, Kana; Dong, Min; Fukuda, Tsuyoshi; Chandra, Sharat; Mehta, Parinda A; McConnell, Scott; Anaissie, Elias J; Vinks, Alexander A

2018-05-01

High-dose melphalan is an important component of conditioning regimens for patients undergoing hematopoietic stem cell transplantation. The current dosing strategy based on body surface area results in a high incidence of oral mucositis and gastrointestinal and liver toxicity. Pharmacokinetically guided dosing will individualize exposure and help minimize overexposure-related toxicity. The purpose of this study was to develop a population pharmacokinetic model and optimal sampling strategy. A population pharmacokinetic model was developed with NONMEM using 98 observations collected from 15 adult patients given the standard dose of 140 or 200 mg/m 2 by intravenous infusion. The determinant-optimal sampling strategy was explored with PopED software. Individual area under the curve estimates were generated by Bayesian estimation using full and the proposed sparse sampling data. The predictive performance of the optimal sampling strategy was evaluated based on bias and precision estimates. The feasibility of the optimal sampling strategy was tested using pharmacokinetic data from five pediatric patients. A two-compartment model best described the data. The final model included body weight and creatinine clearance as predictors of clearance. The determinant-optimal sampling strategies (and windows) were identified at 0.08 (0.08-0.19), 0.61 (0.33-0.90), 2.0 (1.3-2.7), and 4.0 (3.6-4.0) h post-infusion. An excellent correlation was observed between area under the curve estimates obtained with the full and the proposed four-sample strategy (R 2  = 0.98; p strategy promises to achieve the target area under the curve as part of precision dosing.

Geometric approximation algorithms

CERN Document Server

Har-Peled, Sariel

2011-01-01

Exact algorithms for dealing with geometric objects are complicated, hard to implement in practice, and slow. Over the last 20 years a theory of geometric approximation algorithms has emerged. These algorithms tend to be simple, fast, and more robust than their exact counterparts. This book is the first to cover geometric approximation algorithms in detail. In addition, more traditional computational geometry techniques that are widely used in developing such algorithms, like sampling, linear programming, etc., are also surveyed. Other topics covered include approximate nearest-neighbor search, shape approximation, coresets, dimension reduction, and embeddings. The topics covered are relatively independent and are supplemented by exercises. Close to 200 color figures are included in the text to illustrate proofs and ideas.

A Fast Algorithm of Cartographic Sounding Selection

Institute of Scientific and Technical Information of China (English)

SUI Haigang; HUA Li; ZHAO Haitao; ZHANG Yongli

2005-01-01

An effective strategy and framework that adequately integrate the automated and manual processes for fast cartographic sounding selection is presented. The important submarine topographic features are extracted for important soundings selection, and an improved "influence circle" algorithm is introduced for sounding selection. For automatic configuration of soundings distribution pattern, a special algorithm considering multi-factors is employed. A semi-automatic method for solving the ambiguous conflicts is described. On the basis of the algorithms and strategies a system named HGIS for fast cartographic sounding selection is developed and applied in Chinese Marine Safety Administration Bureau (CMSAB). The application experiments show that the system is effective and reliable. At last some conclusions and the future work are given.

Sampling from a polytope and hard-disk Monte Carlo

International Nuclear Information System (INIS)

Kapfer, Sebastian C; Krauth, Werner

2013-01-01

The hard-disk problem, the statics and the dynamics of equal two-dimensional hard spheres in a periodic box, has had a profound influence on statistical and computational physics. Markov-chain Monte Carlo and molecular dynamics were first discussed for this model. Here we reformulate hard-disk Monte Carlo algorithms in terms of another classic problem, namely the sampling from a polytope. Local Markov-chain Monte Carlo, as proposed by Metropolis et al. in 1953, appears as a sequence of random walks in high-dimensional polytopes, while the moves of the more powerful event-chain algorithm correspond to molecular dynamics evolution. We determine the convergence properties of Monte Carlo methods in a special invariant polytope associated with hard-disk configurations, and the implications for convergence of hard-disk sampling. Finally, we discuss parallelization strategies for event-chain Monte Carlo and present results for a multicore implementation

WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning

Directory of Open Access Journals (Sweden)

Chunjing Song

2017-11-01

Full Text Available Wireless local area network (WLAN fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS samples for the fingerprint database is time-consuming and labor-intensive, hindering the use of this technique. The popular crowdsourcing sampling technique has been introduced to reduce the workload of sample collection, but has two challenges: one is the heterogeneity of devices, which can significantly affect the positioning accuracy; the other is the requirement of users’ intervention in traditional crowdsourcing, which reduces the practicality of the system. In response to these challenges, we have proposed a new WLAN indoor positioning strategy, which incorporates a new preprocessing method for RSS samples, the implicit crowdsourcing sampling technique, and a semi-supervised learning algorithm. First, implicit crowdsourcing does not require users’ intervention. The acquisition program silently collects unlabeled samples, the RSS samples, without information about the position. Secondly, to cope with the heterogeneity of devices, the preprocessing method maps all the RSS values of samples to a uniform range and discretizes them. Finally, by using a large number of unlabeled samples with some labeled samples, Co-Forest, the introduced semi-supervised learning algorithm, creates and repeatedly refines a random forest ensemble classifier that performs well for location estimation. The results of experiments conducted in a real indoor environment show that the proposed strategy reduces the demand for large quantities of labeled samples and achieves good positioning accuracy.

Level-0 trigger algorithms for the ALICE PHOS detector

CERN Document Server

Wang, D; Wang, Y P; Huang, G M; Kral, J; Yin, Z B; Zhou, D C; Zhang, F; Ullaland, K; Muller, H; Liu, L J

2011-01-01

The PHOS level-0 trigger provides a minimum bias trigger for p-p collisions and information for a level-1 trigger at both p-p and Pb-Pb collisions. There are two level-0 trigger generating algorithms under consideration: the Direct Comparison algorithm and the Weighted Sum algorithm. In order to study trigger algorithms via simulation, a simplified equivalent model is extracted from the trigger electronics to derive the waveform function of the Analog-or signal as input to the trigger algorithms. Simulations shown that the Weighted Sum algorithm can achieve higher trigger efficiency and provide more precise single channel energy information than the direct compare algorithm. An energy resolution of 9.75 MeV can be achieved with the Weighted Sum algorithm at a sampling rate of 40 Msps (mega samples per second) at 1 GeV. The timing performance at a sampling rate of 40 Msps with the Weighted Sum algorithm is better than that at a sampling rate of 20 Msps with both algorithms. The level-0 trigger can be delivered...

Comparison of active and passive sampling strategies for the monitoring of pesticide contamination in streams

Science.gov (United States)

Assoumani, Azziz; Margoum, Christelle; Guillemain, Céline; Coquery, Marina

2014-05-01

The monitoring of water bodies regarding organic contaminants, and the determination of reliable estimates of concentrations are challenging issues, in particular for the implementation of the Water Framework Directive. Several strategies can be applied to collect water samples for the determination of their contamination level. Grab sampling is fast, easy, and requires little logistical and analytical needs in case of low frequency sampling campaigns. However, this technique lacks of representativeness for streams with high variations of contaminant concentrations, such as pesticides in rivers located in small agricultural watersheds. Increasing the representativeness of this sampling strategy implies greater logistical needs and higher analytical costs. Average automated sampling is therefore a solution as it allows, in a single analysis, the determination of more accurate and more relevant estimates of concentrations. Two types of automatic samplings can be performed: time-related sampling allows the assessment of average concentrations, whereas flow-dependent sampling leads to average flux concentrations. However, the purchase and the maintenance of automatic samplers are quite expensive. Passive sampling has recently been developed as an alternative to grab or average automated sampling, to obtain at lower cost, more realistic estimates of the average concentrations of contaminants in streams. These devices allow the passive accumulation of contaminants from large volumes of water, resulting in ultratrace level detection and smoothed integrative sampling over periods ranging from days to weeks. They allow the determination of time-weighted average (TWA) concentrations of the dissolved fraction of target contaminants, but they need to be calibrated in controlled conditions prior to field applications. In other words, the kinetics of the uptake of the target contaminants into the sampler must be studied in order to determine the corresponding sampling rate

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

Science.gov (United States)

Song, Ke; Li, Feiqiang; Hu, Xiao; He, Lin; Niu, Wenxu; Lu, Sihao; Zhang, Tong

2018-06-01

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

Precise fixpoint computation through strategy iteration

DEFF Research Database (Denmark)

Gawlitza, Thomas; Seidl, Helmut

2007-01-01

We present a practical algorithm for computing least solutions of systems of equations over the integers with addition, multiplication with positive constants, maximum and minimum. The algorithm is based on strategy iteration. Its run-time (w.r.t. the uniform cost measure) is independent of the s......We present a practical algorithm for computing least solutions of systems of equations over the integers with addition, multiplication with positive constants, maximum and minimum. The algorithm is based on strategy iteration. Its run-time (w.r.t. the uniform cost measure) is independent...

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

Science.gov (United States)

Fort, G.; Jourdain, B.; Lelièvre, T.; Stoltz, G.

2018-04-01

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang-Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm

Directory of Open Access Journals (Sweden)

Guang-zhou Chen

2015-01-01

Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.

The Watershed Transform : Definitions, Algorithms and Parallelization Strategies

NARCIS (Netherlands)

Roerdink, Jos B.T.M.; Meijster, Arnold

2000-01-01

The watershed transform is the method of choice for image segmentation in the field of mathematical morphology. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, and discuss various issues which often cause confusion in the

Sampling strategies to measure the prevalence of common recurrent infections in longitudinal studies

Directory of Open Access Journals (Sweden)

Luby Stephen P

2010-08-01

Full Text Available Abstract Background Measuring recurrent infections such as diarrhoea or respiratory infections in epidemiological studies is a methodological challenge. Problems in measuring the incidence of recurrent infections include the episode definition, recall error, and the logistics of close follow up. Longitudinal prevalence (LP, the proportion-of-time-ill estimated by repeated prevalence measurements, is an alternative measure to incidence of recurrent infections. In contrast to incidence which usually requires continuous sampling, LP can be measured at intervals. This study explored how many more participants are needed for infrequent sampling to achieve the same study power as frequent sampling. Methods We developed a set of four empirical simulation models representing low and high risk settings with short or long episode durations. The model was used to evaluate different sampling strategies with different assumptions on recall period and recall error. Results The model identified three major factors that influence sampling strategies: (1 the clustering of episodes in individuals; (2 the duration of episodes; (3 the positive correlation between an individual's disease incidence and episode duration. Intermittent sampling (e.g. 12 times per year often requires only a slightly larger sample size compared to continuous sampling, especially in cluster-randomized trials. The collection of period prevalence data can lead to highly biased effect estimates if the exposure variable is associated with episode duration. To maximize study power, recall periods of 3 to 7 days may be preferable over shorter periods, even if this leads to inaccuracy in the prevalence estimates. Conclusion Choosing the optimal approach to measure recurrent infections in epidemiological studies depends on the setting, the study objectives, study design and budget constraints. Sampling at intervals can contribute to making epidemiological studies and trials more efficient, valid

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

Directory of Open Access Journals (Sweden)

Hao Yin

2014-01-01

Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

Using the Perceptron Algorithm to Find Consistent Hypotheses

OpenAIRE

Anthony, M.; Shawe-Taylor, J.

1993-01-01

The perceptron learning algorithm yields quite naturally an algorithm for finding a linearly separable boolean function consistent with a sample of such a function. Using the idea of a specifying sample, we give a simple proof that this algorithm is not efficient, in general.

OUTPACE long duration stations: physical variability, context of biogeochemical sampling, and evaluation of sampling strategy

Directory of Open Access Journals (Sweden)

A. de Verneil

2018-04-01

Full Text Available Research cruises to quantify biogeochemical fluxes in the ocean require taking measurements at stations lasting at least several days. A popular experimental design is the quasi-Lagrangian drifter, often mounted with in situ incubations or sediment traps that follow the flow of water over time. After initial drifter deployment, the ship tracks the drifter for continuing measurements that are supposed to represent the same water environment. An outstanding question is how to best determine whether this is true. During the Oligotrophy to UlTra-oligotrophy PACific Experiment (OUTPACE cruise, from 18 February to 3 April 2015 in the western tropical South Pacific, three separate stations of long duration (five days over the upper 500 m were conducted in this quasi-Lagrangian sampling scheme. Here we present physical data to provide context for these three stations and to assess whether the sampling strategy worked, i.e., that a single body of water was sampled. After analyzing tracer variability and local water circulation at each station, we identify water layers and times where the drifter risks encountering another body of water. While almost no realization of this sampling scheme will be truly Lagrangian, due to the presence of vertical shear, the depth-resolved observations during the three stations show most layers sampled sufficiently homogeneous physical environments during OUTPACE. By directly addressing the concerns raised by these quasi-Lagrangian sampling platforms, a protocol of best practices can begin to be formulated so that future research campaigns include the complementary datasets and analyses presented here to verify the appropriate use of the drifter platform.

Cache-Oblivious Algorithms and Data Structures

DEFF Research Database (Denmark)

Brodal, Gerth Stølting

2004-01-01

Frigo, Leiserson, Prokop and Ramachandran in 1999 introduced the ideal-cache model as a formal model of computation for developing algorithms in environments with multiple levels of caching, and coined the terminology of cache-oblivious algorithms. Cache-oblivious algorithms are described...... as standard RAM algorithms with only one memory level, i.e. without any knowledge about memory hierarchies, but are analyzed in the two-level I/O model of Aggarwal and Vitter for an arbitrary memory and block size and an optimal off-line cache replacement strategy. The result are algorithms that automatically...... apply to multi-level memory hierarchies. This paper gives an overview of the results achieved on cache-oblivious algorithms and data structures since the seminal paper by Frigo et al....

Comparison of sampling strategies for tobacco retailer inspections to maximize coverage in vulnerable areas and minimize cost.

Science.gov (United States)

Lee, Joseph G L; Shook-Sa, Bonnie E; Bowling, J Michael; Ribisl, Kurt M

2017-06-23

In the United States, tens of thousands of inspections of tobacco retailers are conducted each year. Various sampling choices can reduce travel costs, emphasize enforcement in areas with greater non-compliance, and allow for comparability between states and over time. We sought to develop a model sampling strategy for state tobacco retailer inspections. Using a 2014 list of 10,161 North Carolina tobacco retailers, we compared results from simple random sampling; stratified, clustered at the ZIP code sampling; and, stratified, clustered at the census tract sampling. We conducted a simulation of repeated sampling and compared approaches for their comparative level of precision, coverage, and retailer dispersion. While maintaining an adequate design effect and statistical precision appropriate for a public health enforcement program, both stratified, clustered ZIP- and tract-based approaches were feasible. Both ZIP and tract strategies yielded improvements over simple random sampling, with relative improvements, respectively, of average distance between retailers (reduced 5.0% and 1.9%), percent Black residents in sampled neighborhoods (increased 17.2% and 32.6%), percent Hispanic residents in sampled neighborhoods (reduced 2.2% and increased 18.3%), percentage of sampled retailers located near schools (increased 61.3% and 37.5%), and poverty rate in sampled neighborhoods (increased 14.0% and 38.2%). States can make retailer inspections more efficient and targeted with stratified, clustered sampling. Use of statistically appropriate sampling strategies like these should be considered by states, researchers, and the Food and Drug Administration to improve program impact and allow for comparisons over time and across states. The authors present a model tobacco retailer sampling strategy for promoting compliance and reducing costs that could be used by U.S. states and the Food and Drug Administration (FDA). The design is feasible to implement in North Carolina. Use of

A novel clustering algorithm based on quantum games

International Nuclear Information System (INIS)

Li Qiang; He Yan; Jiang Jingping

2009-01-01

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

Application of epidemic algorithms for smart grids control

International Nuclear Information System (INIS)

Krkoleva, Aleksandra

2012-01-01

Smart Grids are a new concept for electricity networks development, aiming to provide economically efficient and sustainable power system by integrating effectively the actions and needs of the network users. The thesis addresses the Smart Grids concept, with emphasis on the control strategies developed on the basis of epidemic algorithms, more specifically, gossip algorithms. The thesis is developed around three Smart grid aspects: the changed role of consumers in terms of taking part in providing services within Smart Grids; the possibilities to implement decentralized control strategies based on distributed algorithms; and information exchange and benefits emerging from implementation of information and communication technologies. More specifically, the thesis presents a novel approach for providing ancillary services by implementing gossip algorithms. In a decentralized manner, by exchange of information between the consumers and by making decisions on local level, based on the received information and local parameters, the group achieves its global objective, i. e. providing ancillary services. The thesis presents an overview of the Smart Grids control strategies with emphasises on new strategies developed for the most promising Smart Grids concepts, as Micro grids and Virtual power plants. The thesis also presents the characteristics of epidemic algorithms and possibilities for their implementation in Smart Grids. Based on the research on epidemic algorithms, two applications have been developed. These applications are the main outcome of the research. The first application enables consumers, represented by their commercial aggregators, to participate in load reduction and consequently, to participate in balancing market or reduce the balancing costs of the group. In this context, the gossip algorithms are used for aggregator's message dissemination for load reduction and households and small commercial and industrial consumers to participate in maintaining

Sampling strategies for tropical forest nutrient cycling studies: a case study in São Paulo, Brazil

Directory of Open Access Journals (Sweden)

G. Sparovek

1997-12-01

Full Text Available The precise sampling of soil, biological or micro climatic attributes in tropical forests, which are characterized by a high diversity of species and complex spatial variability, is a difficult task. We found few basic studies to guide sampling procedures. The objective of this study was to define a sampling strategy and data analysis for some parameters frequently used in nutrient cycling studies, i. e., litter amount, total nutrient amounts in litter and its composition (Ca, Mg, Κ, Ν and P, and soil attributes at three depths (organic matter, Ρ content, cation exchange capacity and base saturation. A natural remnant forest in the West of São Paulo State (Brazil was selected as study area and samples were collected in July, 1989. The total amount of litter and its total nutrient amounts had a high spatial independent variance. Conversely, the variance of litter composition was lower and the spatial dependency was peculiar to each nutrient. The sampling strategy for the estimation of litter amounts and the amount of nutrient in litter should be different than the sampling strategy for nutrient composition. For the estimation of litter amounts and the amount of nutrients in litter (related to quantity a large number of randomly distributed determinations are needed. Otherwise, for the estimation of litter nutrient composition (related to quality a smaller amount of spatially located samples should be analyzed. The determination of sampling for soil attributes differed according to the depth. Overall, surface samples (0-5 cm showed high short distance spatial dependent variance, whereas, subsurface samples exhibited spatial dependency in longer distances. Short transects with sampling interval of 5-10 m are recommended for surface sampling. Subsurface samples must also be spatially located, but with transects or grids with longer distances between sampling points over the entire area. Composite soil samples would not provide a complete

ExSample. A library for sampling Sudakov-type distributions

Energy Technology Data Exchange (ETDEWEB)

Plaetzer, Simon

2011-08-15

Sudakov-type distributions are at the heart of generating radiation in parton showers as well as contemporary NLO matching algorithms along the lines of the POWHEG algorithm. In this paper, the C++ library ExSample is introduced, which implements adaptive sampling of Sudakov-type distributions for splitting kernels which are in general only known numerically. Besides the evolution variable, the splitting kernels can depend on an arbitrary number of other degrees of freedom to be sampled, and any number of further parameters which are fixed on an event-by-event basis. (orig.)

ExSample. A library for sampling Sudakov-type distributions

International Nuclear Information System (INIS)

Plaetzer, Simon

2011-08-01

Sudakov-type distributions are at the heart of generating radiation in parton showers as well as contemporary NLO matching algorithms along the lines of the POWHEG algorithm. In this paper, the C++ library ExSample is introduced, which implements adaptive sampling of Sudakov-type distributions for splitting kernels which are in general only known numerically. Besides the evolution variable, the splitting kernels can depend on an arbitrary number of other degrees of freedom to be sampled, and any number of further parameters which are fixed on an event-by-event basis. (orig.)

Equalization Algorithm for Distributed Energy Storage Systems in Islanded AC Microgrids

DEFF Research Database (Denmark)

Aldana, Nelson Leonardo Diaz; Hernández, Adriana Carolina Luna; Quintero, Juan Carlos Vasquez

2015-01-01

This paper presents a centralized strategy for equalizing the state of charge of distributed energy storage systems in an islanded ac microgrid. The strategy is based on a simple algorithm denoted as equalization algorithm, which modifies the charge or discharge ratio on the time, for distributed...

Clinical usefulness of limited sampling strategies for estimating AUC of proton pump inhibitors.

Science.gov (United States)

Niioka, Takenori

2011-03-01

Cytochrome P450 (CYP) 2C19 (CYP2C19) genotype is regarded as a useful tool to predict area under the blood concentration-time curve (AUC) of proton pump inhibitors (PPIs). In our results, however, CYP2C19 genotypes had no influence on AUC of all PPIs during fluvoxamine treatment. These findings suggest that CYP2C19 genotyping is not always a good indicator for estimating AUC of PPIs. Limited sampling strategies (LSS) were developed to estimate AUC simply and accurately. It is important to minimize the number of blood samples because of patient's acceptance. This article reviewed the usefulness of LSS for estimating AUC of three PPIs (omeprazole: OPZ, lansoprazole: LPZ and rabeprazole: RPZ). The best prediction formulas in each PPI were AUC(OPZ)=9.24 x C(6h)+2638.03, AUC(LPZ)=12.32 x C(6h)+3276.09 and AUC(RPZ)=1.39 x C(3h)+7.17 x C(6h)+344.14, respectively. In order to optimize the sampling strategy of LPZ, we tried to establish LSS for LPZ using a time point within 3 hours through the property of pharmacokinetics of its enantiomers. The best prediction formula using the fewest sampling points (one point) was AUC(racemic LPZ)=6.5 x C(3h) of (R)-LPZ+13.7 x C(3h) of (S)-LPZ-9917.3 x G1-14387.2×G2+7103.6 (G1: homozygous extensive metabolizer is 1 and the other genotypes are 0; G2: heterozygous extensive metabolizer is 1 and the other genotypes are 0). Those strategies, plasma concentration monitoring at one or two time-points, might be more suitable for AUC estimation than reference to CYP2C19 genotypes, particularly in the case of coadministration of CYP mediators.

Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

International Nuclear Information System (INIS)

Villanueva, J.F.; Sanchez, A.I.; Carlos, S.; Martorell, S.

2008-01-01

This paper presents the results of a survey to show the applicability of an approach based on a combination of distribution-free tolerance interval and genetic algorithms for testing and maintenance optimization of safety-related systems based on unavailability and cost estimation acting as uncertain decision criteria. Several strategies have been checked using a combination of Monte Carlo (simulation)--genetic algorithm (search-evolution). Tolerance intervals for the unavailability and cost estimation are obtained to be used by the genetic algorithms. Both single- and multiple-objective genetic algorithms are used. In general, it is shown that the approach is a robust, fast and powerful tool that performs very favorably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional nonlinear space in a tiny fraction of the time required for enumeration of the decision space. This approach reduces the computational effort by means of providing appropriate balance between accuracy of simulation and evolution; however, negative effects are also shown when a not well-balanced accuracy-evolution couple is used, which can be avoided or mitigated with the use of a single-objective genetic algorithm or the use of a multiple-objective genetic algorithm with additional statistical information

A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling

International Nuclear Information System (INIS)

Tong, Cao; Sun, Zhili; Zhao, Qianli; Wang, Qibin; Wang, Shuang

2015-01-01

To solve the problem of large computation when failure probability with time-consuming numerical model is calculated, we propose an improved active learning reliability method called AK-SSIS based on AK-IS algorithm. First, an improved iterative stopping criterion in active learning is presented so that iterations decrease dramatically. Second, the proposed method introduces Subset simulation importance sampling (SSIS) into the active learning reliability calculation, and then a learning function suitable for SSIS is proposed. Finally, the efficiency of AK-SSIS is proved by two academic examples from the literature. The results show that AK-SSIS requires fewer calls to the performance function than AK-IS, and the failure probability obtained from AK-SSIS is very robust and accurate. Then this method is applied on a spur gear pair for tooth contact fatigue reliability analysis.

A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling

Energy Technology Data Exchange (ETDEWEB)

Tong, Cao; Sun, Zhili; Zhao, Qianli; Wang, Qibin [Northeastern University, Shenyang (China); Wang, Shuang [Jiangxi University of Science and Technology, Ganzhou (China)

2015-08-15

To solve the problem of large computation when failure probability with time-consuming numerical model is calculated, we propose an improved active learning reliability method called AK-SSIS based on AK-IS algorithm. First, an improved iterative stopping criterion in active learning is presented so that iterations decrease dramatically. Second, the proposed method introduces Subset simulation importance sampling (SSIS) into the active learning reliability calculation, and then a learning function suitable for SSIS is proposed. Finally, the efficiency of AK-SSIS is proved by two academic examples from the literature. The results show that AK-SSIS requires fewer calls to the performance function than AK-IS, and the failure probability obtained from AK-SSIS is very robust and accurate. Then this method is applied on a spur gear pair for tooth contact fatigue reliability analysis.

Evolutionary Algorithm for Optimal Vaccination Scheme

International Nuclear Information System (INIS)

Parousis-Orthodoxou, K J; Vlachos, D S

2014-01-01

The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease

Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms

Science.gov (United States)

Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing

2017-09-01

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.

Performance of Jet Algorithms in CMS

CERN Document Server

CMS Collaboration

The CMS Combined Software and Analysis Challenge 2007 (CSA07) is well underway and expected to produce a wealth of physics analyses to be applied to the first incoming detector data in 2008. The JetMET group of CMS supports four different jet clustering algorithms for the CSA07 Monte Carlo samples, with two different parameterizations each: \\fastkt, \\siscone, \\midpoint, and \\itcone. We present several studies comparing the performance of these algorithms using QCD dijet and \\ttbar Monte Carlo samples. We specifically observe that the \\siscone algorithm performs equal to or better than the \\midpoint algorithm in all presented studies and propose that \\siscone be adopted as the preferred cone-based jet clustering algorithm in future CMS physics analyses, as it is preferred by theorists for its infrared- and collinear-safety to all orders of perturbative QCD. We furthermore encourage the use of the \\fastkt algorithm which is found to perform as good as any other algorithm under study, features dramatically reduc...

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

Science.gov (United States)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

An early-biomarker algorithm predicts lethal graft-versus-host disease and survival.

Science.gov (United States)

Hartwell, Matthew J; Özbek, Umut; Holler, Ernst; Renteria, Anne S; Major-Monfried, Hannah; Reddy, Pavan; Aziz, Mina; Hogan, William J; Ayuk, Francis; Efebera, Yvonne A; Hexner, Elizabeth O; Bunworasate, Udomsak; Qayed, Muna; Ordemann, Rainer; Wölfl, Matthias; Mielke, Stephan; Pawarode, Attaphol; Chen, Yi-Bin; Devine, Steven; Harris, Andrew C; Jagasia, Madan; Kitko, Carrie L; Litzow, Mark R; Kröger, Nicolaus; Locatelli, Franco; Morales, George; Nakamura, Ryotaro; Reshef, Ran; Rösler, Wolf; Weber, Daniela; Wudhikarn, Kitsada; Yanik, Gregory A; Levine, John E; Ferrara, James L M

2017-02-09

BACKGROUND. No laboratory test can predict the risk of nonrelapse mortality (NRM) or severe graft-versus-host disease (GVHD) after hematopoietic cellular transplantation (HCT) prior to the onset of GVHD symptoms. METHODS. Patient blood samples on day 7 after HCT were obtained from a multicenter set of 1,287 patients, and 620 samples were assigned to a training set. We measured the concentrations of 4 GVHD biomarkers (ST2, REG3α, TNFR1, and IL-2Rα) and used them to model 6-month NRM using rigorous cross-validation strategies to identify the best algorithm that defined 2 distinct risk groups. We then applied the final algorithm in an independent test set ( n = 309) and validation set ( n = 358). RESULTS. A 2-biomarker model using ST2 and REG3α concentrations identified patients with a cumulative incidence of 6-month NRM of 28% in the high-risk group and 7% in the low-risk group ( P < 0.001). The algorithm performed equally well in the test set (33% vs. 7%, P < 0.001) and the multicenter validation set (26% vs. 10%, P < 0.001). Sixteen percent, 17%, and 20% of patients were at high risk in the training, test, and validation sets, respectively. GVHD-related mortality was greater in high-risk patients (18% vs. 4%, P < 0.001), as was severe gastrointestinal GVHD (17% vs. 8%, P < 0.001). The same algorithm can be successfully adapted to define 3 distinct risk groups at GVHD onset. CONCLUSION. A biomarker algorithm based on a blood sample taken 7 days after HCT can consistently identify a group of patients at high risk for lethal GVHD and NRM. FUNDING. The National Cancer Institute, American Cancer Society, and the Doris Duke Charitable Foundation.

Sample design and gamma-ray counting strategy of neutron activation system for triton burnup measurements in KSTAR

Energy Technology Data Exchange (ETDEWEB)

Jo, Jungmin [Department of Energy System Engineering, Seoul National University, Seoul (Korea, Republic of); Cheon, Mun Seong [ITER Korea, National Fusion Research Institute, Daejeon (Korea, Republic of); Chung, Kyoung-Jae, E-mail: jkjlsh1@snu.ac.kr [Department of Energy System Engineering, Seoul National University, Seoul (Korea, Republic of); Hwang, Y.S. [Department of Energy System Engineering, Seoul National University, Seoul (Korea, Republic of)

2016-11-01

Highlights: • Sample design for triton burnup ratio measurement is carried out. • Samples for 14.1 MeV neutron measurements are selected for KSTAR. • Si and Cu are the most suitable materials for d-t neutron measurements. • Appropriate γ-ray counting strategies for each selected sample are established. - Abstract: On the purpose of triton burnup measurements in Korea Superconducting Tokamak Advanced Research (KSTAR) deuterium plasmas, appropriate neutron activation system (NAS) samples for 14.1 MeV d-t neutron measurements have been designed and gamma-ray counting strategy is established. Neutronics calculations are performed with the MCNP5 neutron transport code for the KSTAR neutral beam heated deuterium plasma discharges. Based on those calculations and the assumed d-t neutron yield, the activities induced by d-t neutrons are estimated with the inventory code FISPACT-2007 for candidate sample materials: Si, Cu, Al, Fe, Nb, Co, Ti, and Ni. It is found that Si, Cu, Al, and Fe are suitable for the KSATR NAS in terms of the minimum detectable activity (MDA) calculated based on the standard deviation of blank measurements. Considering background gamma-rays radiated from surrounding structures activated by thermalized fusion neutrons, appropriate gamma-ray counting strategy for each selected sample is established.

Females' sampling strategy to comparatively evaluate prospective mates in the peacock blenny Salaria pavo

Science.gov (United States)

Locatello, Lisa; Rasotto, Maria B.

2017-08-01

Emerging evidence suggests the occurrence of comparative decision-making processes in mate choice, questioning the traditional idea of female choice based on rules of absolute preference. In such a scenario, females are expected to use a typical best-of- n sampling strategy, being able to recall previous sampled males based on memory of their quality and location. Accordingly, the quality of preferred mate is expected to be unrelated to both the number and the sequence of female visits. We found support for these predictions in the peacock blenny, Salaria pavo, a fish where females have the opportunity to evaluate the attractiveness of many males in a short time period and in a restricted spatial range. Indeed, even considering the variability in preference among females, most of them returned to previous sampled males for further evaluations; thus, the preferred male did not represent the last one in the sequence of visited males. Moreover, there was no relationship between the attractiveness of the preferred male and the number of further visits assigned to the other males. Our results suggest the occurrence of a best-of- n mate sampling strategy in the peacock blenny.

Particle filtering with path sampling and an application to a bimodal ocean current model

International Nuclear Information System (INIS)

Weare, Jonathan

2009-01-01

This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.

An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language circuit.

Science.gov (United States)

Mishra, Arabinda; Anderson, Adam W; Wu, Xi; Gore, John C; Ding, Zhaohua

2010-08-01

The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework. To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work. The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca's to SMA and Broca's to Wernicke's) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler's) approach [P. J. Basser et al., "In vivo fiber tractography using DT-MRI data," Magn. Reson. Med. 44(4), 625-632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu's method [Y. Lu et al., "Improved fiber tractography with Bayesian

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.

Science.gov (United States)

Jaffe, Jacob D; Feeney, Caitlin M; Patel, Jinal; Lu, Xiaodong; Mani, D R

2016-11-01

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data.

Science.gov (United States)

Regier, Michael D; Moodie, Erica E M

2016-05-01

We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

Monte Carlo molecular simulations: improving the statistical efficiency of samples with the help of artificial evolution algorithms; Simulations moleculaires de Monte Carlo: amelioration de l'efficacite statistique de l'echantillonnage grace aux algorithmes d'evolution artificielle

Energy Technology Data Exchange (ETDEWEB)

Leblanc, B.

2002-03-01

Molecular simulation aims at simulating particles in interaction, describing a physico-chemical system. When considering Markov Chain Monte Carlo sampling in this context, we often meet the same problem of statistical efficiency as with Molecular Dynamics for the simulation of complex molecules (polymers for example). The search for a correct sampling of the space of possible configurations with respect to the Boltzmann-Gibbs distribution is directly related to the statistical efficiency of such algorithms (i.e. the ability of rapidly providing uncorrelated states covering all the configuration space). We investigated how to improve this efficiency with the help of Artificial Evolution (AE). AE algorithms form a class of stochastic optimization algorithms inspired by Darwinian evolution. Efficiency measures that can be turned into efficiency criteria have been first searched before identifying parameters that could be optimized. Relative frequencies for each type of Monte Carlo moves, usually empirically chosen in reasonable ranges, were first considered. We combined parallel simulations with a 'genetic server' in order to dynamically improve the quality of the sampling during the simulations progress. Our results shows that in comparison with some reference settings, it is possible to improve the quality of samples with respect to the chosen criterion. The same algorithm has been applied to improve the Parallel Tempering technique, in order to optimize in the same time the relative frequencies of Monte Carlo moves and the relative frequencies of swapping between sub-systems simulated at different temperatures. Finally, hints for further research in order to optimize the choice of additional temperatures are given. (author)

Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry

Science.gov (United States)

Wu, Dongjun

Network industries have technologies characterized by a spatial hierarchy, the "network," with capital-intensive interconnections and time-dependent, capacity-limited flows of products and services through the network to customers. This dissertation studies service pricing, investment and business operating strategies for the electric power network. First-best solutions for a variety of pricing and investment problems have been studied. The evaluation of genetic algorithms (GA, which are methods based on the idea of natural evolution) as a primary means of solving complicated network problems, both w.r.t. pricing: as well as w.r.t. investment and other operating decisions, has been conducted. New constraint-handling techniques in GAs have been studied and tested. The actual application of such constraint-handling techniques in solving practical non-linear optimization problems has been tested on several complex network design problems with encouraging initial results. Genetic algorithms provide solutions that are feasible and close to optimal when the optimal solution is know; in some instances, the near-optimal solutions for small problems by the proposed GA approach can only be tested by pushing the limits of currently available non-linear optimization software. The performance is far better than several commercially available GA programs, which are generally inadequate in solving any of the problems studied in this dissertation, primarily because of their poor handling of constraints. Genetic algorithms, if carefully designed, seem very promising in solving difficult problems which are intractable by traditional analytic methods.

Sampling informative/complex a priori probability distributions using Gibbs sampling assisted by sequential simulation

DEFF Research Database (Denmark)

Hansen, Thomas Mejer; Mosegaard, Klaus; Cordua, Knud Skou

2010-01-01

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample the solutions to non-linear inverse problems. In principle these methods allow incorporation of arbitrarily complex a priori information, but current methods allow only relatively simple...... this algorithm with the Metropolis algorithm to obtain an efficient method for sampling posterior probability densities for nonlinear inverse problems....

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

Science.gov (United States)

Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand

2018-06-01

The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ˜45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.

Local pursuit strategy-inspired cooperative trajectory planning algorithm for a class of nonlinear constrained dynamical systems

Science.gov (United States)

Xu, Yunjun; Remeikas, Charles; Pham, Khanh

2014-03-01

Cooperative trajectory planning is crucial for networked vehicles to respond rapidly in cluttered environments and has a significant impact on many applications such as air traffic or border security monitoring and assessment. One of the challenges in cooperative planning is to find a computationally efficient algorithm that can accommodate both the complexity of the environment and real hardware and configuration constraints of vehicles in the formation. Inspired by a local pursuit strategy observed in foraging ants, feasible and optimal trajectory planning algorithms are proposed in this paper for a class of nonlinear constrained cooperative vehicles in environments with densely populated obstacles. In an iterative hierarchical approach, the local behaviours, such as the formation stability, obstacle avoidance, and individual vehicle's constraints, are considered in each vehicle's (i.e. follower's) decentralised optimisation. The cooperative-level behaviours, such as the inter-vehicle collision avoidance, are considered in the virtual leader's centralised optimisation. Early termination conditions are derived to reduce the computational cost by not wasting time in the local-level optimisation if the virtual leader trajectory does not satisfy those conditions. The expected advantages of the proposed algorithms are (1) the formation can be globally asymptotically maintained in a decentralised manner; (2) each vehicle decides its local trajectory using only the virtual leader and its own information; (3) the formation convergence speed is controlled by one single parameter, which makes it attractive for many practical applications; (4) nonlinear dynamics and many realistic constraints, such as the speed limitation and obstacle avoidance, can be easily considered; (5) inter-vehicle collision avoidance can be guaranteed in both the formation transient stage and the formation steady stage; and (6) the computational cost in finding both the feasible and optimal

Limited sampling strategy models for estimating the AUC of gliclazide in Chinese healthy volunteers.

Science.gov (United States)

Huang, Ji-Han; Wang, Kun; Huang, Xiao-Hui; He, Ying-Chun; Li, Lu-Jin; Sheng, Yu-Cheng; Yang, Juan; Zheng, Qing-Shan

2013-06-01

The aim of this work is to reduce the cost of required sampling for the estimation of the area under the gliclazide plasma concentration versus time curve within 60 h (AUC0-60t ). The limited sampling strategy (LSS) models were established and validated by the multiple regression model within 4 or fewer gliclazide concentration values. Absolute prediction error (APE), root of mean square error (RMSE) and visual prediction check were used as criterion. The results of Jack-Knife validation showed that 10 (25.0 %) of the 40 LSS based on the regression analysis were not within an APE of 15 % using one concentration-time point. 90.2, 91.5 and 92.4 % of the 40 LSS models were capable of prediction using 2, 3 and 4 points, respectively. Limited sampling strategies were developed and validated for estimating AUC0-60t of gliclazide. This study indicates that the implementation of an 80 mg dosage regimen enabled accurate predictions of AUC0-60t by the LSS model. This study shows that 12, 6, 4, 2 h after administration are the key sampling times. The combination of (12, 2 h), (12, 8, 2 h) or (12, 8, 4, 2 h) can be chosen as sampling hours for predicting AUC0-60t in practical application according to requirement.

Evacuation route planning during nuclear emergency using genetic algorithm

International Nuclear Information System (INIS)

Suman, Vitisha; Sarkar, P.K.

2012-01-01

In nuclear industry the routing in case of any emergency is a cause of concern and of great importance. Even the smallest of time saved in the affected region saves a huge amount of otherwise received dose. Genetic algorithm an optimization technique has great ability to search for the optimal path from the affected region to a destination station in a spatially addressed problem. Usually heuristic algorithms are used to carry out these types of search strategy, but due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. Routing problems mainly are search problems for finding the shortest distance within a time limit to cover the required number of stations taking care of the traffics, road quality, population size etc. Lack of any formal mechanisms to help decision-makers explore the solution space of their problem and thereby challenges their assumptions about the number and range of options available. The Genetic Algorithm provides a way to optimize a multi-parameter constrained problem with an ease. Here use of Genetic Algorithm to generate a range of options available and to search a solution space and selectively focus on promising combinations of criteria makes them ideally suited to such complex spatial decision problems. The emergency response and routing can be made efficient, in accessing the closest facilities and determining the shortest route using genetic algorithm. The accuracy and care in creating database can be used to improve the result of the final output. The Genetic algorithm can be used to improve the accuracy of result on the basis of distance where other algorithm cannot be obtained. The search space can be utilized to its great extend

Sample preparation composite and replicate strategy case studies for assay of solid oral drug products.

Science.gov (United States)

Nickerson, Beverly; Harrington, Brent; Li, Fasheng; Guo, Michele Xuemei

2017-11-30

Drug product assay is one of several tests required for new drug products to ensure the quality of the product at release and throughout the life cycle of the product. Drug product assay testing is typically performed by preparing a composite sample of multiple dosage units to obtain an assay value representative of the batch. In some cases replicate composite samples may be prepared and the reportable assay value is the average value of all the replicates. In previously published work by Harrington et al. (2014) [5], a sample preparation composite and replicate strategy for assay was developed to provide a systematic approach which accounts for variability due to the analytical method and dosage form with a standard error of the potency assay criteria based on compendia and regulatory requirements. In this work, this sample preparation composite and replicate strategy for assay is applied to several case studies to demonstrate the utility of this approach and its application at various stages of pharmaceutical drug product development. Copyright © 2017 Elsevier B.V. All rights reserved.

Study on the influence of X-ray tube spectral distribution on the analysis of bulk samples and thin films: Fundamental parameters method and theoretical coefficient algorithms

International Nuclear Information System (INIS)

Sitko, Rafal

2008-01-01

Knowledge of X-ray tube spectral distribution is necessary in theoretical methods of matrix correction, i.e. in both fundamental parameter (FP) methods and theoretical influence coefficient algorithms. Thus, the influence of X-ray tube distribution on the accuracy of the analysis of thin films and bulk samples is presented. The calculations are performed using experimental X-ray tube spectra taken from the literature and theoretical X-ray tube spectra evaluated by three different algorithms proposed by Pella et al. (X-Ray Spectrom. 14 (1985) 125-135), Ebel (X-Ray Spectrom. 28 (1999) 255-266), and Finkelshtein and Pavlova (X-Ray Spectrom. 28 (1999) 27-32). In this study, Fe-Cr-Ni system is selected as an example and the calculations are performed for X-ray tubes commonly applied in X-ray fluorescence analysis (XRF), i.e., Cr, Mo, Rh and W. The influence of X-ray tube spectra on FP analysis is evaluated when quantification is performed using various types of calibration samples. FP analysis of bulk samples is performed using pure-element bulk standards and multielement bulk standards similar to the analyzed material, whereas for FP analysis of thin films, the bulk and thin pure-element standards are used. For the evaluation of the influence of X-ray tube spectra on XRF analysis performed by theoretical influence coefficient methods, two algorithms for bulk samples are selected, i.e. Claisse-Quintin (Can. Spectrosc. 12 (1967) 129-134) and COLA algorithms (G.R. Lachance, Paper Presented at the International Conference on Industrial Inorganic Elemental Analysis, Metz, France, June 3, 1981) and two algorithms (constant and linear coefficients) for thin films recently proposed by Sitko (X-Ray Spectrom. 37 (2008) 265-272)

Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA

Directory of Open Access Journals (Sweden)

Emanuele Gandola

2016-09-01

Full Text Available The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA (“ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning” (Gandola et al., 2016 [1]. This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements. Keywords: Comparing data, Filamentous cyanobacteria, Algorithm, Deoising, Natural sample

[Algorithms for treatment of complex hand injuries].

Science.gov (United States)

Pillukat, T; Prommersberger, K-J

2011-07-01

The primary treatment strongly influences the course and prognosis of hand injuries. Complex injuries which compromise functional recovery are especially challenging. Despite an apparently unlimited number of injury patterns it is possible to develop strategies which facilitate a standardized approach to operative treatment. In this situation algorithms can be important guidelines for a rational approach. The following algorithms have been proven in the treatment of complex injuries of the hand by our own experience. They were modified according to the current literature and refer to prehospital care, emergency room management, basic strategy in general and reconstruction of bone and joints, vessels, nerves, tendons and soft tissue coverage in detail. Algorithms facilitate the treatment of severe hand injuries. Applying simple yes/no decisions complex injury patterns are split into distinct partial problems which can be managed step by step.

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

Science.gov (United States)

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies

Directory of Open Access Journals (Sweden)

Rafał Dreżewski

2018-05-01

Full Text Available In this paper, the evolutionary algorithm for the optimization of Forex market trading strategies is proposed. The introduction to issues related to the financial markets and the evolutionary algorithms precedes the main part of the paper, in which the proposed trading system is presented. The system uses the evolutionary algorithm for optimization of a parameterized greedy strategy, which is then used as an investment strategy on the Forex market. In the proposed system, a model of the Forex market was developed, including all elements that are necessary for simulating realistic trading processes. The proposed evolutionary algorithm contains several novel mechanisms that were introduced to optimize the greedy strategy. The most important of the proposed techniques are the mechanisms for maintaining the population diversity, a mechanism for protecting the best individuals in the population, the mechanisms preventing the excessive growth of the population, the mechanisms of the initialization of the population after moving the time window and a mechanism of choosing the best strategies used for trading. The experiments, conducted with the use of real-world Forex market data, were aimed at testing the quality of the results obtained using the proposed algorithm and comparing them with the results obtained by the buy-and-hold strategy. By comparing our results with the results of the buy-and-hold strategy, we attempted to verify the validity of the efficient market hypothesis. The credibility of the hypothesis would have more general implications for many different areas of our lives, including future sustainable development policies.

Development of a sampling strategy and sample size calculation to estimate the distribution of mammographic breast density in Korean women.

Science.gov (United States)

Jun, Jae Kwan; Kim, Mi Jin; Choi, Kui Son; Suh, Mina; Jung, Kyu-Won

2012-01-01

Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.

Limited sampling strategy for determining metformin area under the plasma concentration-time curve

DEFF Research Database (Denmark)

Santoro, Ana Beatriz; Stage, Tore Bjerregaard; Struchiner, Claudio José

2016-01-01

AIM: The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin. METHODS: Metformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all su...

A Direct Search Algorithm for Global Optimization

Directory of Open Access Journals (Sweden)

Enrique Baeyens

2016-06-01

Full Text Available A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.

Implementation techniques and acceleration of DBPF reconstruction algorithm based on GPGPU for helical cone beam CT

International Nuclear Information System (INIS)

Shen Le; Xing Yuxiang

2010-01-01

The derivative back-projection filtered algorithm for a helical cone-beam CT is a newly developed exact reconstruction method. Due to its large computational complexity, the reconstruction is rather slow for practical use. General purpose graphic processing unit (GPGPU) is an SIMD paralleled hardware architecture with powerful float-point operation capacity. In this paper,we propose a new method for PI-line choice and sampling grid, and a paralleled PI-line reconstruction algorithm implemented on NVIDIA's Compute Unified Device Architecture (CUDA). Numerical simulation studies are carried out to validate our method. Compared with conventional CPU implementation, the CUDA accelerated method provides images of the same quality with a speedup factor of 318. Optimization strategies for the GPU acceleration are presented. Finally, influence of the parameters of the PI-line samples on the reconstruction speed and image quality is discussed. (authors)

A new algorithm for solving the inventory routing problem with direct shipment

Directory of Open Access Journals (Sweden)

Ali Hossein Mirzaei

2012-02-01

Full Text Available   In this paper a multi-commodity multi-period inventory routing problem in a two-echelon supply chain consisting of a manufacturer and a set of retailers has been studied. In addition to inventory management and distribution planning, production planning has also been considered in the above problem. The objective is to minimize total system cost that consists of production setup, inventory holding and distribution costs. The commodities are delivered to the retailers by an identical fleet of limited capacity vehicles through direct shipment strategy. Also it is assumed that production and storage capacity is limited and stockout is not allowed. Since similar problems without distribution planning are known as NP-hard, this is also an NP-hard problem. Therefore, in this paper, a new improved particle swarm optimization algorithm has been developed consisting of two distinguished phases for problem solving. First, the values of binary variables are determined using the proposed algorithm and then, the continuous variables are calculated by solving a linear programming model. Performance of the proposed algorithm has been compared with genetic and original particle swarm optimization algorithms using various samples of random problems. The findings imply significant performance of the proposed algorithm.         

Population pharmacokinetic analysis of clopidogrel in healthy Jordanian subjects with emphasis optimal sampling strategy.

Science.gov (United States)

Yousef, A M; Melhem, M; Xue, B; Arafat, T; Reynolds, D K; Van Wart, S A

2013-05-01

Clopidogrel is metabolized primarily into an inactive carboxyl metabolite (clopidogrel-IM) or to a lesser extent an active thiol metabolite. A population pharmacokinetic (PK) model was developed using NONMEM(®) to describe the time course of clopidogrel-IM in plasma and to design a sparse-sampling strategy to predict clopidogrel-IM exposures for use in characterizing anti-platelet activity. Serial blood samples from 76 healthy Jordanian subjects administered a single 75 mg oral dose of clopidogrel were collected and assayed for clopidogrel-IM using reverse phase high performance liquid chromatography. A two-compartment (2-CMT) PK model with first-order absorption and elimination plus an absorption lag-time was evaluated, as well as a variation of this model designed to mimic enterohepatic recycling (EHC). Optimal PK sampling strategies (OSS) were determined using WinPOPT based upon collection of 3-12 post-dose samples. A two-compartment model with EHC provided the best fit and reduced bias in C(max) (median prediction error (PE%) of 9.58% versus 12.2%) relative to the basic two-compartment model, AUC(0-24) was similar for both models (median PE% = 1.39%). The OSS for fitting the two-compartment model with EHC required the collection of seven samples (0.25, 1, 2, 4, 5, 6 and 12 h). Reasonably unbiased and precise exposures were obtained when re-fitting this model to a reduced dataset considering only these sampling times. A two-compartment model considering EHC best characterized the time course of clopidogrel-IM in plasma. Use of the suggested OSS will allow for the collection of fewer PK samples when assessing clopidogrel-IM exposures. Copyright © 2013 John Wiley & Sons, Ltd.

Methodology Series Module 5: Sampling Strategies.

Science.gov (United States)

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling - based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ' random sample' when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ' generalizability' of these results. In such a scenario, the researcher may want to use ' purposive sampling' for the study.

Methodology series module 5: Sampling strategies

Directory of Open Access Journals (Sweden)

Maninder Singh Setia

2016-01-01

Full Text Available Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the 'Sampling Method'. There are essentially two types of sampling methods: 1 probability sampling – based on chance events (such as random numbers, flipping a coin etc.; and 2 non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term 'random sample' when the researcher has used convenience sample. The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the 'generalizability' of these results. In such a scenario, the researcher may want to use 'purposive sampling' for the study.

Hardware architecture for projective model calculation and false match refining using random sample consensus algorithm

Science.gov (United States)

Azimi, Ehsan; Behrad, Alireza; Ghaznavi-Ghoushchi, Mohammad Bagher; Shanbehzadeh, Jamshid

2016-11-01

The projective model is an important mapping function for the calculation of global transformation between two images. However, its hardware implementation is challenging because of a large number of coefficients with different required precisions for fixed point representation. A VLSI hardware architecture is proposed for the calculation of a global projective model between input and reference images and refining false matches using random sample consensus (RANSAC) algorithm. To make the hardware implementation feasible, it is proved that the calculation of the projective model can be divided into four submodels comprising two translations, an affine model and a simpler projective mapping. This approach makes the hardware implementation feasible and considerably reduces the required number of bits for fixed point representation of model coefficients and intermediate variables. The proposed hardware architecture for the calculation of a global projective model using the RANSAC algorithm was implemented using Verilog hardware description language and the functionality of the design was validated through several experiments. The proposed architecture was synthesized by using an application-specific integrated circuit digital design flow utilizing 180-nm CMOS technology as well as a Virtex-6 field programmable gate array. Experimental results confirm the efficiency of the proposed hardware architecture in comparison with software implementation.

Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

Science.gov (United States)

Mohan, K. Aditya

2017-10-01

4D X-ray computed tomography (4D-XCT) is widely used to perform non-destructive characterization of time varying physical processes in various materials. The conventional approach to improving temporal resolution in 4D-XCT involves the development of expensive and complex instrumentation that acquire data faster with reduced noise. It is customary to acquire data with many tomographic views at a high signal to noise ratio. Instead, temporal resolution can be improved using regularized iterative algorithms that are less sensitive to noise and limited views. These algorithms benefit from optimization of other parameters such as the view sampling strategy while improving temporal resolution by reducing the total number of views or the detector exposure time. This paper presents the design principles of 4D-XCT experiments when using regularized iterative algorithms derived using the framework of model-based reconstruction. A strategy for performing 4D-XCT experiments is presented that allows for improving the temporal resolution by progressively reducing the number of views or the detector exposure time. Theoretical analysis of the effect of the data acquisition parameters on the detector signal to noise ratio, spatial reconstruction resolution, and temporal reconstruction resolution is also presented in this paper.

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

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

2014-07-01

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

[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC].

Science.gov (United States)

Lablans, Martin; Kadioglu, Dennis; Mate, Sebastian; Leb, Ines; Prokosch, Hans-Ulrich; Ückert, Frank

2016-03-01

Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage. Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies. Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity. The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty. Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.

Methodology Series Module 5: Sampling Strategies

Science.gov (United States)

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ‘ Sampling Method’. There are essentially two types of sampling methods: 1) probability sampling – based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ‘ random sample’ when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ‘ generalizability’ of these results. In such a scenario, the researcher may want to use ‘ purposive sampling’ for the study. PMID:27688438

Informational analysis for compressive sampling in radar imaging.

Science.gov (United States)

Zhang, Jingxiong; Yang, Ke

2015-03-24

Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation.

Super resolution reconstruction of μ-CT image of rock sample using neighbour embedding algorithm

Science.gov (United States)

Wang, Yuzhu; Rahman, Sheik S.; Arns, Christoph H.

2018-03-01

X-ray computed tomography (μ-CT) is considered to be the most effective way to obtain the inner structure of rock sample without destructions. However, its limited resolution hampers its ability to probe sub-micro structures which is critical for flow transportation of rock sample. In this study, we propose an innovative methodology to improve the resolution of μ-CT image using neighbour embedding algorithm where low frequency information is provided by μ-CT image itself while high frequency information is supplemented by high resolution scanning electron microscopy (SEM) image. In order to obtain prior for reconstruction, a large number of image patch pairs contain high- and low- image patches are extracted from the Gaussian image pyramid generated by SEM image. These image patch pairs contain abundant information about tomographic evolution of local porous structures under different resolution spaces. Relying on the assumption of self-similarity of porous structure, this prior information can be used to supervise the reconstruction of high resolution μ-CT image effectively. The experimental results show that the proposed method is able to achieve the state-of-the-art performance.

General Algorithm (High level)

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. General Algorithm (High level). Iteratively. Use Tightness Property to remove points of P1,..,Pi. Use random sampling to get a Random Sample (of enough points) from the next largest cluster, Pi+1. Use the Random Sampling Procedure to approximate ci+1 using the ...

Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS

Directory of Open Access Journals (Sweden)

Stephan P. Lovstedt

2008-01-01

Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

A Feed-forward Geometrical Compensation and Adaptive Feedback Control Algorithm for Hydraulic Robot Manipulators

DEFF Research Database (Denmark)

Conrad, Finn; Zhou, Jianjun; Gabacik, Andrzej

1998-01-01

Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control.......Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control....

Algorithm for computing significance levels using the Kolmogorov-Smirnov statistic and valid for both large and small samples

Energy Technology Data Exchange (ETDEWEB)

Kurtz, S.E.; Fields, D.E.

1983-10-01

The KSTEST code presented here is designed to perform the Kolmogorov-Smirnov one-sample test. The code may be used as a stand-alone program or the principal subroutines may be excerpted and used to service other programs. The Kolmogorov-Smirnov one-sample test is a nonparametric goodness-of-fit test. A number of codes to perform this test are in existence, but they suffer from the inability to provide meaningful results in the case of small sample sizes (number of values less than or equal to 80). The KSTEST code overcomes this inadequacy by using two distinct algorithms. If the sample size is greater than 80, an asymptotic series developed by Smirnov is evaluated. If the sample size is 80 or less, a table of values generated by Birnbaum is referenced. Valid results can be obtained from KSTEST when the sample contains from 3 to 300 data points. The program was developed on a Digital Equipment Corporation PDP-10 computer using the FORTRAN-10 language. The code size is approximately 450 card images and the typical CPU execution time is 0.19 s.

Model parameter estimations from residual gravity anomalies due to simple-shaped sources using Differential Evolution Algorithm

Science.gov (United States)

Ekinci, Yunus Levent; Balkaya, Çağlayan; Göktürkler, Gökhan; Turan, Seçil

2016-06-01

An efficient approach to estimate model parameters from residual gravity data based on differential evolution (DE), a stochastic vector-based metaheuristic algorithm, has been presented. We have showed the applicability and effectiveness of this algorithm on both synthetic and field anomalies. According to our knowledge, this is a first attempt of applying DE for the parameter estimations of residual gravity anomalies due to isolated causative sources embedded in the subsurface. The model parameters dealt with here are the amplitude coefficient (A), the depth and exact origin of causative source (zo and xo, respectively) and the shape factors (q and ƞ). The error energy maps generated for some parameter pairs have successfully revealed the nature of the parameter estimation problem under consideration. Noise-free and noisy synthetic single gravity anomalies have been evaluated with success via DE/best/1/bin, which is a widely used strategy in DE. Additionally some complicated gravity anomalies caused by multiple source bodies have been considered, and the results obtained have showed the efficiency of the algorithm. Then using the strategy applied in synthetic examples some field anomalies observed for various mineral explorations such as a chromite deposit (Camaguey district, Cuba), a manganese deposit (Nagpur, India) and a base metal sulphide deposit (Quebec, Canada) have been considered to estimate the model parameters of the ore bodies. Applications have exhibited that the obtained results such as the depths and shapes of the ore bodies are quite consistent with those published in the literature. Uncertainty in the solutions obtained from DE algorithm has been also investigated by Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing without cooling schedule. Based on the resulting histogram reconstructions of both synthetic and field data examples the algorithm has provided reliable parameter estimations being within the sampling limits of

Comparison of genetic algorithms with conjugate gradient methods

Science.gov (United States)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Adaptive Metropolis Sampling with Product Distributions

Science.gov (United States)

Wolpert, David H.; Lee, Chiu Fan

2005-01-01

The Metropolis-Hastings (MH) algorithm is a way to sample a provided target distribution pi(z). It works by repeatedly sampling a separate proposal distribution T(x,x') to generate a random walk {x(t)}. We consider a modification of the MH algorithm in which T is dynamically updated during the walk. The update at time t uses the {x(t' less than t)} to estimate the product distribution that has the least Kullback-Leibler distance to pi. That estimate is the information-theoretically optimal mean-field approximation to pi. We demonstrate through computer experiments that our algorithm produces samples that are superior to those of the conventional MH algorithm.

A comparison of sample preparation strategies for biological tissues and subsequent trace element analysis using LA-ICP-MS.

Science.gov (United States)

Bonta, Maximilian; Török, Szilvia; Hegedus, Balazs; Döme, Balazs; Limbeck, Andreas

2017-03-01

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is one of the most commonly applied methods for lateral trace element distribution analysis in medical studies. Many improvements of the technique regarding quantification and achievable lateral resolution have been achieved in the last years. Nevertheless, sample preparation is also of major importance and the optimal sample preparation strategy still has not been defined. While conventional histology knows a number of sample pre-treatment strategies, little is known about the effect of these approaches on the lateral distributions of elements and/or their quantities in tissues. The technique of formalin fixation and paraffin embedding (FFPE) has emerged as the gold standard in tissue preparation. However, the potential use for elemental distribution studies is questionable due to a large number of sample preparation steps. In this work, LA-ICP-MS was used to examine the applicability of the FFPE sample preparation approach for elemental distribution studies. Qualitative elemental distributions as well as quantitative concentrations in cryo-cut tissues as well as FFPE samples were compared. Results showed that some metals (especially Na and K) are severely affected by the FFPE process, whereas others (e.g., Mn, Ni) are less influenced. Based on these results, a general recommendation can be given: FFPE samples are completely unsuitable for the analysis of alkaline metals. When analyzing transition metals, FFPE samples can give comparable results to snap-frozen tissues. Graphical abstract Sample preparation strategies for biological tissues are compared with regard to the elemental distributions and average trace element concentrations.

Optimization of Region-of-Interest Sampling Strategies for Hepatic MRI Proton Density Fat Fraction Quantification

Science.gov (United States)

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z.; Schlein, Alexandra N.; Hooker, Jonathan C.; Dehkordy, Soudabeh Fazeli; Hamilton, Gavin; Reeder, Scott B.; Loomba, Rohit; Sirlin, Claude B.

2017-01-01

BACKGROUND Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. PURPOSE To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. STUDY TYPE Retrospective secondary analysis of prospectively acquired clinical research data. POPULATION A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. FIELD STRENGTH/SEQUENCE Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradientrecalled echo technique. ASSESSMENT An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. STATISTICAL TESTING Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland–Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland–Altman analyses. RESULTS The study population’s mean whole-liver PDFF was 10.1±8.9% (range: 1.1–44.1%). Although there was no significant difference in average segmental (P=0.452) or lobar (P=0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥ 4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) 0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. DATA CONCLUSION Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. Level of

Optimization of region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification.

Science.gov (United States)

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z; Schlein, Alexandra N; Hooker, Jonathan C; Fazeli Dehkordy, Soudabeh; Hamilton, Gavin; Reeder, Scott B; Loomba, Rohit; Sirlin, Claude B

2018-04-01

Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. Retrospective secondary analysis of prospectively acquired clinical research data. A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique. An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses. The study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) 0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:988-994. © 2017 International Society for Magnetic Resonance

Sampling solution traces for the problem of sorting permutations by signed reversals

Science.gov (United States)

2012-01-01

Background Traditional algorithms to solve the problem of sorting by signed reversals output just one optimal solution while the space of all optimal solutions can be huge. A so-called trace represents a group of solutions which share the same set of reversals that must be applied to sort the original permutation following a partial ordering. By using traces, we therefore can represent the set of optimal solutions in a more compact way. Algorithms for enumerating the complete set of traces of solutions were developed. However, due to their exponential complexity, their practical use is limited to small permutations. A partial enumeration of traces is a sampling of the complete set of traces and can be an alternative for the study of distinct evolutionary scenarios of big permutations. Ideally, the sampling should be done uniformly from the space of all optimal solutions. This is however conjectured to be ♯P-complete. Results We propose and evaluate three algorithms for producing a sampling of the complete set of traces that instead can be shown in practice to preserve some of the characteristics of the space of all solutions. The first algorithm (RA) performs the construction of traces through a random selection of reversals on the list of optimal 1-sequences. The second algorithm (DFALT) consists in a slight modification of an algorithm that performs the complete enumeration of traces. Finally, the third algorithm (SWA) is based on a sliding window strategy to improve the enumeration of traces. All proposed algorithms were able to enumerate traces for permutations with up to 200 elements. Conclusions We analysed the distribution of the enumerated traces with respect to their height and average reversal length. Various works indicate that the reversal length can be an important aspect in genome rearrangements. The algorithms RA and SWA show a tendency to lose traces with high average reversal length. Such traces are however rare, and qualitatively our results

Validation of Correction Algorithms for Near-IR Analysis of Human Milk in an Independent Sample Set-Effect of Pasteurization.

Science.gov (United States)

Kotrri, Gynter; Fusch, Gerhard; Kwan, Celia; Choi, Dasol; Choi, Arum; Al Kafi, Nisreen; Rochow, Niels; Fusch, Christoph

2016-02-26

Commercial infrared (IR) milk analyzers are being increasingly used in research settings for the macronutrient measurement of breast milk (BM) prior to its target fortification. These devices, however, may not provide reliable measurement if not properly calibrated. In the current study, we tested a correction algorithm for a Near-IR milk analyzer (Unity SpectraStar, Brookfield, CT, USA) for fat and protein measurements, and examined the effect of pasteurization on the IR matrix and the stability of fat, protein, and lactose. Measurement values generated through Near-IR analysis were compared against those obtained through chemical reference methods to test the correction algorithm for the Near-IR milk analyzer. Macronutrient levels were compared between unpasteurized and pasteurized milk samples to determine the effect of pasteurization on macronutrient stability. The correction algorithm generated for our device was found to be valid for unpasteurized and pasteurized BM. Pasteurization had no effect on the macronutrient levels and the IR matrix of BM. These results show that fat and protein content can be accurately measured and monitored for unpasteurized and pasteurized BM. Of additional importance is the implication that donated human milk, generally low in protein content, has the potential to be target fortified.

Particle swarm genetic algorithm and its application

International Nuclear Information System (INIS)

Liu Chengxiang; Yan Changxiang; Wang Jianjun; Liu Zhenhai

2012-01-01

To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem, a particle swarm genetic algorithm is designed. The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor, generates initial feasible group randomly, which accelerates particle swarm convergence speed, and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum Through the optimization calculation of the typical test functions, the results show that particle swarm genetic algorithm has better optimized performance. The algorithm is applied in nuclear power plant optimization, and the optimization results are significantly. (authors)

A systematic examination of a random sampling strategy for source apportionment calculations.

Science.gov (United States)

Andersson, August

2011-12-15

Estimating the relative contributions from multiple potential sources of a specific component in a mixed environmental matrix is a general challenge in diverse fields such as atmospheric, environmental and earth sciences. Perhaps the most common strategy for tackling such problems is by setting up a system of linear equations for the fractional influence of different sources. Even though an algebraic solution of this approach is possible for the common situation with N+1 sources and N source markers, such methodology introduces a bias, since it is implicitly assumed that the calculated fractions and the corresponding uncertainties are independent of the variability of the source distributions. Here, a random sampling (RS) strategy for accounting for such statistical bias is examined by investigating rationally designed synthetic data sets. This random sampling methodology is found to be robust and accurate with respect to reproducibility and predictability. This method is also compared to a numerical integration solution for a two-source situation where source variability also is included. A general observation from this examination is that the variability of the source profiles not only affects the calculated precision but also the mean/median source contributions. Copyright © 2011 Elsevier B.V. All rights reserved.

Machine learning for evolution strategies

CERN Document Server

Kramer, Oliver

2016-01-01

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

A HYBRID ALGORITHM FOR THE ROBUST GRAPH COLORING PROBLEM

Directory of Open Access Journals (Sweden)

Román Anselmo Mora Gutiérrez

2016-08-01

Full Text Available A hybridalgorithm which combines mathematical programming techniques (Kruskal’s algorithm and the strategy of maintaining arc consistency to solve constraint satisfaction problem “CSP” and heuristic methods (musical composition method and DSATUR to resolve the robust graph coloring problem (RGCP is proposed in this paper. Experimental result shows that this algorithm is better than the other algorithms presented on the literature.

Analytical sample preparation strategies for the determination of antimalarial drugs in human whole blood, plasma and urine

DEFF Research Database (Denmark)

Casas, Monica Escolà; Hansen, Martin; Krogh, Kristine A

2014-01-01

the available sample preparation strategies combined with liquid chromatographic (LC) analysis to determine antimalarials in whole blood, plasma and urine published over the last decade. Sample preparation can be done by protein precipitation, solid-phase extraction, liquid-liquid extraction or dilution. After...

A chaos-based evolutionary algorithm for general nonlinear programming problems

International Nuclear Information System (INIS)

El-Shorbagy, M.A.; Mousa, A.A.; Nasr, S.M.

2016-01-01

In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS) strategy to accelerate the optimum seeking operation and to speed the convergence to the global solution. The integration of global search represented in genetic algorithm and CLS procedures should offer the advantages of both optimization methods while offsetting their disadvantages. By this way, it is intended to enhance the global convergence and to prevent to stick on a local solution. The inherent characteristics of chaos can enhance optimization algorithms by enabling it to escape from local solutions and increase the convergence to reach to the global solution. Twelve chaotic maps have been analyzed in the proposed approach. The simulation results using the set of CEC’2005 show that the application of chaotic mapping may be an effective strategy to improve the performances of EAs.

Current advances and strategies towards fully automated sample preparation for regulated LC-MS/MS bioanalysis.

Science.gov (United States)

Zheng, Naiyu; Jiang, Hao; Zeng, Jianing

2014-09-01

Robotic liquid handlers (RLHs) have been widely used in automated sample preparation for liquid chromatography-tandem mass spectrometry (LC-MS/MS) bioanalysis. Automated sample preparation for regulated bioanalysis offers significantly higher assay efficiency, better data quality and potential bioanalytical cost-savings. For RLHs that are used for regulated bioanalysis, there are additional requirements, including 21 CFR Part 11 compliance, software validation, system qualification, calibration verification and proper maintenance. This article reviews recent advances in automated sample preparation for regulated bioanalysis in the last 5 years. Specifically, it covers the following aspects: regulated bioanalysis requirements, recent advances in automation hardware and software development, sample extraction workflow simplification, strategies towards fully automated sample extraction, and best practices in automated sample preparation for regulated bioanalysis.

On Invertible Sampling and Adaptive Security

DEFF Research Database (Denmark)

Ishai, Yuval; Kumarasubramanian, Abishek; Orlandi, Claudio

2011-01-01

functionalities was left open. We provide the first convincing evidence that the answer to this question is negative, namely that some (randomized) functionalities cannot be realized with adaptive security. We obtain this result by studying the following related invertible sampling problem: given an efficient...... sampling algorithm A, obtain another sampling algorithm B such that the output of B is computationally indistinguishable from the output of A, but B can be efficiently inverted (even if A cannot). This invertible sampling problem is independently motivated by other cryptographic applications. We show......, under strong but well studied assumptions, that there exist efficient sampling algorithms A for which invertible sampling as above is impossible. At the same time, we show that a general feasibility result for adaptively secure MPC implies that invertible sampling is possible for every A, thereby...

Novel prediction- and subblock-based algorithm for fractal image compression

International Nuclear Information System (INIS)

Chung, K.-L.; Hsu, C.-H.

2006-01-01

Fractal encoding is the most consuming part in fractal image compression. In this paper, a novel two-phase prediction- and subblock-based fractal encoding algorithm is presented. Initially the original gray image is partitioned into a set of variable-size blocks according to the S-tree- and interpolation-based decomposition principle. In the first phase, each current block of variable-size range block tries to find the best matched domain block based on the proposed prediction-based search strategy which utilizes the relevant neighboring variable-size domain blocks. The first phase leads to a significant computation-saving effect. If the domain block found within the predicted search space is unacceptable, in the second phase, a subblock strategy is employed to partition the current variable-size range block into smaller blocks to improve the image quality. Experimental results show that our proposed prediction- and subblock-based fractal encoding algorithm outperforms the conventional full search algorithm and the recently published spatial-correlation-based algorithm by Truong et al. in terms of encoding time and image quality. In addition, the performance comparison among our proposed algorithm and the other two algorithms, the no search-based algorithm and the quadtree-based algorithm, are also investigated

New sampling strategy using a Bayesian approach to assess iohexol clearance in kidney transplant recipients.

Science.gov (United States)

Benz-de Bretagne, I; Le Guellec, C; Halimi, J M; Gatault, P; Barbet, C; Alnajjar, A; Büchler, M; Lebranchu, Y; Andres, Christian Robert; Vourcʼh, P; Blasco, H

2012-06-01

Glomerular filtration rate (GFR) measurement is a major issue in kidney transplant recipients for clinicians. GFR can be determined by estimating the plasma clearance of iohexol, a nonradiolabeled compound. For practical and convenient application for patients and caregivers, it is important that a minimal number of samples are drawn. The aim of this study was to develop and validate a Bayesian model with fewer samples for reliable prediction of GFR in kidney transplant recipients. Iohexol plasma concentration-time curves from 95 patients were divided into an index (n = 63) and a validation set (n = 32). Samples (n = 4-6 per patient) were obtained during the elimination phase, that is, between 120 and 270 minutes. Individual reference values of iohexol clearance (CL(iohexol)) were calculated from k (elimination slope) and V (volume of distribution from intercept). Individual CL(iohexol) values were then introduced into the Bröchner-Mortensen equation to obtain the GFR (reference value). A population pharmacokinetic model was developed from the index set and validated using standard methods. For the validation set, we tested various combinations of 1, 2, or 3 sampling time to estimate CL(iohexol). According to the different combinations tested, a maximum a posteriori Bayesian estimation of CL(iohexol) was obtained from population parameters. Individual estimates of GFR were compared with individual reference values through analysis of bias and precision. A capability analysis allowed us to determine the best sampling strategy for Bayesian estimation. A 1-compartment model best described our data. Covariate analysis showed that uremia, serum creatinine, and age were significantly associated with k(e), and weight with V. The strategy, including samples drawn at 120 and 270 minutes, allowed accurate prediction of GFR (mean bias: -3.71%, mean imprecision: 7.77%). With this strategy, about 20% of individual predictions were outside the bounds of acceptance set at ± 10

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).

Structure optimization by heuristic algorithm in a coarse-grained off-lattice model

International Nuclear Information System (INIS)

Jing-Fa, Liu

2009-01-01

A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic (A) and hydrophilic (B) residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism are then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling (nPERMis) of the chain-growth algorithm, pruned-enriched-Rosenbluth method (PERM), to face-centered-cubic (FCC)-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature

Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.

Science.gov (United States)

Ferrari, Ulisse

2016-08-01

Maximum entropy models provide the least constrained probability distributions that reproduce statistical properties of experimental datasets. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters' space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters' dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a "rectified" data-driven algorithm that is fast and by sampling from the parameters' posterior avoids both under- and overfitting along all the directions of the parameters' space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method.

A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.

Directory of Open Access Journals (Sweden)

R Drew Carleton

Full Text Available Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools for assessing the efficiency of sequential sampling and of alternative, simpler sampling plans, using computer simulation with "pre-sampling" data. We illustrate our approach using data for balsam gall midge (Paradiplosis tumifex attack in Christmas tree farms. Paradiplosis tumifex proved recalcitrant to sequential sampling techniques. Midge distributions could not be fit by a common negative binomial distribution across sites. Local parameterization, using warm-up samples to estimate the clumping parameter k for each site, performed poorly: k estimates were unreliable even for samples of n ∼ 100 trees. These methods were further confounded by significant within-site spatial autocorrelation. Much simpler sampling schemes, involving random or belt-transect sampling to preset sample sizes, were effective and efficient for P. tumifex. Sampling via belt transects (through the longest dimension of a stand was the most efficient, with sample means converging on true mean density for sample sizes of n ∼ 25-40 trees. Pre-sampling and simulation techniques provide a simple method for assessing sampling strategies for estimating insect infestation. We suspect that many pests will resemble P. tumifex in challenging the assumptions of sequential sampling methods. Our software will allow practitioners to optimize sampling strategies before they are brought to real-world applications, while potentially avoiding the need for the cumbersome calculations required for sequential sampling methods.

A Fast and Accurate Algorithm for l1 Minimization Problems in Compressive Sampling (Preprint)

Science.gov (United States)

2013-01-22

However, updating uk+1 via the formulation of Step 2 in Algorithm 1 can be implemented through the use of the component-wise Gauss - Seidel iteration which...may accelerate the rate of convergence of the algorithm and therefore reduce the total CPU-time consumed. The efficiency of component-wise Gauss - Seidel ...Micchelli, L. Shen, and Y. Xu, A proximity algorithm accelerated by Gauss - Seidel iterations for L1/TV denoising models, Inverse Problems, 28 (2012), p

Partial multicanonical algorithm for molecular dynamics and Monte Carlo simulations.

Science.gov (United States)

Okumura, Hisashi

2008-09-28

Partial multicanonical algorithm is proposed for molecular dynamics and Monte Carlo simulations. The partial multicanonical simulation samples a wide range of a part of the potential-energy terms, which is necessary to sample the conformational space widely, whereas a wide range of total potential energy is sampled in the multicanonical algorithm. Thus, one can concentrate the effort to determine the weight factor only on the important energy terms in the partial multicanonical simulation. The partial multicanonical, multicanonical, and canonical molecular dynamics algorithms were applied to an alanine dipeptide in explicit water solvent. The canonical simulation sampled the states of P(II), C(5), alpha(R), and alpha(P). The multicanonical simulation covered the alpha(L) state as well as these states. The partial multicanonical simulation also sampled the C(7) (ax) state in addition to the states that were sampled by the multicanonical simulation. In the partial multicanonical simulation, furthermore, backbone dihedral angles phi and psi rotated more frequently than those in the multicanonical and canonical simulations. These results mean that the partial multicanonical algorithm has a higher sampling efficiency than the multicanonical and canonical algorithms.

Methodology Series Module 5: Sampling Strategies

OpenAIRE

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ? Sampling Method?. There are essentially two types of sampling methods: 1) probability sampling ? based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling ? based on researcher's choice, population that accessible & available. Some of the non-probabilit...

A Survey of Blue-Noise Sampling and Its Applications

KAUST Repository

Yan, Dongming; Guo, Jian-Wei; Wang, Bin; Zhang, Xiao-Peng; Wonka, Peter

2015-01-01

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

A Survey of Blue-Noise Sampling and Its Applications

KAUST Repository

Yan, Dongming

2015-05-05

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Directory of Open Access Journals (Sweden)

Lijin Wang

2015-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

Science.gov (United States)

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

2016-10-01

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

A Cooperative Framework for Fireworks Algorithm

OpenAIRE

Zheng, Shaoqiu; Li, Junzhi; Janecek, Andreas; Tan, Ying

2015-01-01

This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the selection strategy lead to the contribution of the firework with the best fitness (core firework) for the optimization overwhelms the contributions of the rest of fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to b...

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

Science.gov (United States)

Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

Genetic algorithm essentials

CERN Document Server

Kramer, Oliver

2017-01-01

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

An Evolutionary Perspective of the Relationship Between Corporate Strategy and Performance, Through the Use of Artificial Neural Networks and Genetic AlgorithmsHttp://Dx.Doi.Org/10.5585/Riae.V9i3.1689

Directory of Open Access Journals (Sweden)

Alexandre Teixeira Dias

2011-01-01

Full Text Available This study aims to contribute to the understanding of the relationship between Corporate Strategy and Performance, from the perspective of the Evolutionary Theory. As methods of data processing, obtained in secondary databases, we used artificial neural networks and genetic algorithms. The results of processing neural networks and genetic algorithms demonstrate the importance of corporate strategies in determining performance. The evolutionary perspective emphasizes the importance of investing in operations as a factor influencing the adequacy of the organization, in order to achieve an improved performance, in addition to establishing relationships with other organizations, through members of the board.

Dynamic traffic assignment : genetic algorithms approach

Science.gov (United States)

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Volume Ray Casting with Peak Finding and Differential Sampling

KAUST Repository

Knoll, A.

2009-11-01

Direct volume rendering and isosurfacing are ubiquitous rendering techniques in scientific visualization, commonly employed in imaging 3D data from simulation and scan sources. Conventionally, these methods have been treated as separate modalities, necessitating different sampling strategies and rendering algorithms. In reality, an isosurface is a special case of a transfer function, namely a Dirac impulse at a given isovalue. However, artifact-free rendering of discrete isosurfaces in a volume rendering framework is an elusive goal, requiring either infinite sampling or smoothing of the transfer function. While preintegration approaches solve the most obvious deficiencies in handling sharp transfer functions, artifacts can still result, limiting classification. In this paper, we introduce a method for rendering such features by explicitly solving for isovalues within the volume rendering integral. In addition, we present a sampling strategy inspired by ray differentials that automatically matches the frequency of the image plane, resulting in fewer artifacts near the eye and better overall performance. These techniques exhibit clear advantages over standard uniform ray casting with and without preintegration, and allow for high-quality interactive volume rendering with sharp C0 transfer functions. © 2009 IEEE.

Sampling strategy for estimating human exposure pathways to consumer chemicals

Directory of Open Access Journals (Sweden)

Eleni Papadopoulou

2016-03-01

Full Text Available Human exposure to consumer chemicals has become a worldwide concern. In this work, a comprehensive sampling strategy is presented, to our knowledge being the first to study all relevant exposure pathways in a single cohort using multiple methods for assessment of exposure from each exposure pathway. The selected groups of chemicals to be studied are consumer chemicals whose production and use are currently in a state of transition and are; per- and polyfluorinated alkyl substances (PFASs, traditional and “emerging” brominated flame retardants (BFRs and EBFRs, organophosphate esters (OPEs and phthalate esters (PEs. Information about human exposure to these contaminants is needed due to existing data gaps on human exposure intakes from multiple exposure pathways and relationships between internal and external exposure. Indoor environment, food and biological samples were collected from 61 participants and their households in the Oslo area (Norway on two consecutive days, during winter 2013-14. Air, dust, hand wipes, and duplicate diet (food and drink samples were collected as indicators of external exposure, and blood, urine, blood spots, hair, nails and saliva as indicators of internal exposure. A food diary, food frequency questionnaire (FFQ and indoor environment questionnaire were also implemented. Approximately 2000 samples were collected in total and participant views on their experiences of this campaign were collected via questionnaire. While 91% of our participants were positive about future participation in a similar project, some tasks were viewed as problematic. Completing the food diary and collection of duplicate food/drink portions were the tasks most frequent reported as “hard”/”very hard”. Nevertheless, a strong positive correlation between the reported total mass of food/drinks in the food record and the total weight of the food/drinks in the collection bottles was observed, being an indication of accurate performance

Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems