Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission

DEFF Research Database (Denmark)

Zibar, Darko; Winther, Ole; Franceschi, Niccolo

2012-01-01

In this paper, we show numerically and experimentally that expectation maximization (EM) algorithm is a powerful tool in combating system impairments such as fibre nonlinearities, inphase and quadrature (I/Q) modulator imperfections and laser linewidth. The EM algorithm is an iterative algorithm ...

Wobbling and LSF-based maximum likelihood expectation maximization reconstruction for wobbling PET

International Nuclear Information System (INIS)

Kim, Hang-Keun; Son, Young-Don; Kwon, Dae-Hyuk; Joo, Yohan; Cho, Zang-Hee

2016-01-01

Positron emission tomography (PET) is a widely used imaging modality; however, the PET spatial resolution is not yet satisfactory for precise anatomical localization of molecular activities. Detector size is the most important factor because it determines the intrinsic resolution, which is approximately half of the detector size and determines the ultimate PET resolution. Detector size, however, cannot be made too small because both the decreased detection efficiency and the increased septal penetration effect degrade the image quality. A wobbling and line spread function (LSF)-based maximum likelihood expectation maximization (WL-MLEM) algorithm, which combined the MLEM iterative reconstruction algorithm with wobbled sampling and LSF-based deconvolution using the system matrix, was proposed for improving the spatial resolution of PET without reducing the scintillator or detector size. The new algorithm was evaluated using a simulation, and its performance was compared with that of the existing algorithms, such as conventional MLEM and LSF-based MLEM. Simulations demonstrated that the WL-MLEM algorithm yielded higher spatial resolution and image quality than the existing algorithms. The WL-MLEM algorithm with wobbling PET yielded substantially improved resolution compared with conventional algorithms with stationary PET. The algorithm can be easily extended to other iterative reconstruction algorithms, such as maximum a priori (MAP) and ordered subset expectation maximization (OSEM). The WL-MLEM algorithm with wobbling PET may offer improvements in both sensitivity and resolution, the two most sought-after features in PET design. - Highlights: • This paper proposed WL-MLEM algorithm for PET and demonstrated its performance. • WL-MLEM algorithm effectively combined wobbling and line spread function based MLEM. • WL-MLEM provided improvements in the spatial resolution and the PET image quality. • WL-MLEM can be easily extended to the other iterative

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

International Nuclear Information System (INIS)

Han, H; Xing, L; Liang, Z; Li, L

2016-01-01

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of each tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

Energy Technology Data Exchange (ETDEWEB)

Han, H; Xing, L [Stanford University, Palo Alto, CA (United States); Liang, Z [Stony Brook University, Stony Brook, NY (United States); Li, L [City University of New York College of Staten Island, Staten Island, NY (United States)

2016-06-15

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of each tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

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Kanokmon Rujirakul

2014-01-01

Full Text Available Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

Science.gov (United States)

Rujirakul, Kanokmon; So-In, Chakchai; Arnonkijpanich, Banchar

2014-01-01

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

Classification of Ultrasonic NDE Signals Using the Expectation Maximization (EM) and Least Mean Square (LMS) Algorithms

International Nuclear Information System (INIS)

Kim, Dae Won

2005-01-01

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

Energy Technology Data Exchange (ETDEWEB)

Lee, Youngrok [Iowa State Univ., Ames, IA (United States)

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates of nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.

Image reconstruction of single photon emission computed tomography (SPECT) on a pebble bed reactor (PBR) using expectation maximization and exact inversion algorithms: Comparison study by means of numerical phantom

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Razali, Azhani Mohd, E-mail: azhani@nuclearmalaysia.gov.my; Abdullah, Jaafar, E-mail: jaafar@nuclearmalaysia.gov.my [Plant Assessment Technology (PAT) Group, Industrial Technology Division, Malaysian Nuclear Agency, Bangi, 43000 Kajang (Malaysia)

2015-04-29

Single Photon Emission Computed Tomography (SPECT) is a well-known imaging technique used in medical application, and it is part of medical imaging modalities that made the diagnosis and treatment of disease possible. However, SPECT technique is not only limited to the medical sector. Many works are carried out to adapt the same concept by using high-energy photon emission to diagnose process malfunctions in critical industrial systems such as in chemical reaction engineering research laboratories, as well as in oil and gas, petrochemical and petrochemical refining industries. Motivated by vast applications of SPECT technique, this work attempts to study the application of SPECT on a Pebble Bed Reactor (PBR) using numerical phantom of pebbles inside the PBR core. From the cross-sectional images obtained from SPECT, the behavior of pebbles inside the core can be analyzed for further improvement of the PBR design. As the quality of the reconstructed image is largely dependent on the algorithm used, this work aims to compare two image reconstruction algorithms for SPECT, namely the Expectation Maximization Algorithm and the Exact Inversion Formula. The results obtained from the Exact Inversion Formula showed better image contrast and sharpness, and shorter computational time compared to the Expectation Maximization Algorithm.

Image reconstruction of single photon emission computed tomography (SPECT) on a pebble bed reactor (PBR) using expectation maximization and exact inversion algorithms: Comparison study by means of numerical phantom

International Nuclear Information System (INIS)

Razali, Azhani Mohd; Abdullah, Jaafar

2015-01-01

Single Photon Emission Computed Tomography (SPECT) is a well-known imaging technique used in medical application, and it is part of medical imaging modalities that made the diagnosis and treatment of disease possible. However, SPECT technique is not only limited to the medical sector. Many works are carried out to adapt the same concept by using high-energy photon emission to diagnose process malfunctions in critical industrial systems such as in chemical reaction engineering research laboratories, as well as in oil and gas, petrochemical and petrochemical refining industries. Motivated by vast applications of SPECT technique, this work attempts to study the application of SPECT on a Pebble Bed Reactor (PBR) using numerical phantom of pebbles inside the PBR core. From the cross-sectional images obtained from SPECT, the behavior of pebbles inside the core can be analyzed for further improvement of the PBR design. As the quality of the reconstructed image is largely dependent on the algorithm used, this work aims to compare two image reconstruction algorithms for SPECT, namely the Expectation Maximization Algorithm and the Exact Inversion Formula. The results obtained from the Exact Inversion Formula showed better image contrast and sharpness, and shorter computational time compared to the Expectation Maximization Algorithm

Image reconstruction of single photon emission computed tomography (SPECT) on a pebble bed reactor (PBR) using expectation maximization and exact inversion algorithms: Comparison study by means of numerical phantom

Science.gov (United States)

Razali, Azhani Mohd; Abdullah, Jaafar

2015-04-01

Single Photon Emission Computed Tomography (SPECT) is a well-known imaging technique used in medical application, and it is part of medical imaging modalities that made the diagnosis and treatment of disease possible. However, SPECT technique is not only limited to the medical sector. Many works are carried out to adapt the same concept by using high-energy photon emission to diagnose process malfunctions in critical industrial systems such as in chemical reaction engineering research laboratories, as well as in oil and gas, petrochemical and petrochemical refining industries. Motivated by vast applications of SPECT technique, this work attempts to study the application of SPECT on a Pebble Bed Reactor (PBR) using numerical phantom of pebbles inside the PBR core. From the cross-sectional images obtained from SPECT, the behavior of pebbles inside the core can be analyzed for further improvement of the PBR design. As the quality of the reconstructed image is largely dependent on the algorithm used, this work aims to compare two image reconstruction algorithms for SPECT, namely the Expectation Maximization Algorithm and the Exact Inversion Formula. The results obtained from the Exact Inversion Formula showed better image contrast and sharpness, and shorter computational time compared to the Expectation Maximization Algorithm.

A New Augmentation Based Algorithm for Extracting Maximal Chordal Subgraphs.

Science.gov (United States)

Bhowmick, Sanjukta; Chen, Tzu-Yi; Halappanavar, Mahantesh

2015-02-01

A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph. Existing algorithms for computing maximal chordal subgraphs depend on dynamically ordering the vertices, which is an inherently sequential process and therefore limits the algorithms' parallelizability. In this paper we explore techniques to develop a scalable parallel algorithm for extracting a maximal chordal subgraph. We demonstrate that an earlier attempt at developing a parallel algorithm may induce a non-optimal vertex ordering and is therefore not guaranteed to terminate with a maximal chordal subgraph. We then give a new algorithm that first computes and then repeatedly augments a spanning chordal subgraph. After proving that the algorithm terminates with a maximal chordal subgraph, we then demonstrate that this algorithm is more amenable to parallelization and that the parallel version also terminates with a maximal chordal subgraph. That said, the complexity of the new algorithm is higher than that of the previous parallel algorithm, although the earlier algorithm computes a chordal subgraph which is not guaranteed to be maximal. We experimented with our augmentation-based algorithm on both synthetic and real-world graphs. We provide scalability results and also explore the effect of different choices for the initial spanning chordal subgraph on both the running time and on the number of edges in the maximal chordal subgraph.

Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

Science.gov (United States)

Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

2016-03-01

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.

Improved Algorithms OF CELF and CELF++ for Influence Maximization

Directory of Open Access Journals (Sweden)

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.

Positron emission tomographic images and expectation maximization: A VLSI architecture for multiple iterations per second

International Nuclear Information System (INIS)

Jones, W.F.; Byars, L.G.; Casey, M.E.

1988-01-01

A digital electronic architecture for parallel processing of the expectation maximization (EM) algorithm for Positron Emission tomography (PET) image reconstruction is proposed. Rapid (0.2 second) EM iterations on high resolution (256 x 256) images are supported. Arrays of two very large scale integration (VLSI) chips perform forward and back projection calculations. A description of the architecture is given, including data flow and partitioning relevant to EM and parallel processing. EM images shown are produced with software simulating the proposed hardware reconstruction algorithm. Projected cost of the system is estimated to be small in comparison to the cost of current PET scanners

An efficient community detection algorithm using greedy surprise maximization

International Nuclear Information System (INIS)

Jiang, Yawen; Jia, Caiyan; Yu, Jian

2014-01-01

Community detection is an important and crucial problem in complex network analysis. Although classical modularity function optimization approaches are widely used for identifying communities, the modularity function (Q) suffers from its resolution limit. Recently, the surprise function (S) was experimentally proved to be better than the Q function. However, up until now, there has been no algorithm available to perform searches to directly determine the maximal surprise values. In this paper, considering the superiority of the S function over the Q function, we propose an efficient community detection algorithm called AGSO (algorithm based on greedy surprise optimization) and its improved version FAGSO (fast-AGSO), which are based on greedy surprise optimization and do not suffer from the resolution limit. In addition, (F)AGSO does not need the number of communities K to be specified in advance. Tests on experimental networks show that (F)AGSO is able to detect optimal partitions in both simple and even more complex networks. Moreover, algorithms based on surprise maximization perform better than those algorithms based on modularity maximization, including Blondel–Guillaume–Lambiotte–Lefebvre (BGLL), Clauset–Newman–Moore (CNM) and the other state-of-the-art algorithms such as Infomap, order statistics local optimization method (OSLOM) and label propagation algorithm (LPA). (paper)

Generation of a statistical shape model with probabilistic point correspondences and the expectation maximization- iterative closest point algorithm

International Nuclear Information System (INIS)

Hufnagel, Heike; Pennec, Xavier; Ayache, Nicholas; Ehrhardt, Jan; Handels, Heinz

2008-01-01

Identification of point correspondences between shapes is required for statistical analysis of organ shapes differences. Since manual identification of landmarks is not a feasible option in 3D, several methods were developed to automatically find one-to-one correspondences on shape surfaces. For unstructured point sets, however, one-to-one correspondences do not exist but correspondence probabilities can be determined. A method was developed to compute a statistical shape model based on shapes which are represented by unstructured point sets with arbitrary point numbers. A fundamental problem when computing statistical shape models is the determination of correspondences between the points of the shape observations of the training data set. In the absence of landmarks, exact correspondences can only be determined between continuous surfaces, not between unstructured point sets. To overcome this problem, we introduce correspondence probabilities instead of exact correspondences. The correspondence probabilities are found by aligning the observation shapes with the affine expectation maximization-iterative closest points (EM-ICP) registration algorithm. In a second step, the correspondence probabilities are used as input to compute a mean shape (represented once again by an unstructured point set). Both steps are unified in a single optimization criterion which depe nds on the two parameters 'registration transformation' and 'mean shape'. In a last step, a variability model which best represents the variability in the training data set is computed. Experiments on synthetic data sets and in vivo brain structure data sets (MRI) are then designed to evaluate the performance of our algorithm. The new method was applied to brain MRI data sets, and the estimated point correspondences were compared to a statistical shape model built on exact correspondences. Based on established measures of ''generalization ability'' and ''specificity'', the estimates were very satisfactory

Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm.

Directory of Open Access Journals (Sweden)

Chia-Feng Lu

Full Text Available Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

A Linear Time Algorithm for the k Maximal Sums Problem

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Jørgensen, Allan Grønlund

2007-01-01

 k maximal sums problem. We use this algorithm to obtain algorithms solving the two-dimensional k maximal sums problem in O(m 2·n + k) time, where the input is an m ×n matrix with m ≤ n. We generalize this algorithm to solve the d-dimensional problem in O(n 2d − 1 + k) time. The space usage of all......Finding the sub-vector with the largest sum in a sequence of n numbers is known as the maximum sum problem. Finding the k sub-vectors with the largest sums is a natural extension of this, and is known as the k maximal sums problem. In this paper we design an optimal O(n + k) time algorithm for the...... the algorithms can be reduced to O(n d − 1 + k). This leads to the first algorithm for the k maximal sums problem in one dimension using O(n + k) time and O(k) space....

Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

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Murakami Takao

2017-10-01

Full Text Available Location privacy attacks based on a Markov chain model have been widely studied to de-anonymize or de-obfuscate mobility traces. An adversary can perform various kinds of location privacy attacks using a personalized transition matrix, which is trained for each target user. However, the amount of training data available to the adversary can be very small, since many users do not disclose much location information in their daily lives. In addition, many locations can be missing from the training traces, since many users do not disclose their locations continuously but rather sporadically. In this paper, we show that the Markov chain model can be a threat even in this realistic situation. Specifically, we focus on a training phase (i.e. mobility profile building phase and propose Expectation-Maximization Tensor Factorization (EMTF, which alternates between computing a distribution of missing locations (E-step and computing personalized transition matrices via tensor factorization (M-step. Since the time complexity of EMTF is exponential in the number of missing locations, we propose two approximate learning methods, one of which uses the Viterbi algorithm while the other uses the Forward Filtering Backward Sampling (FFBS algorithm. We apply our learning methods to a de-anonymization attack and a localization attack, and evaluate them using three real datasets. The results show that our learning methods significantly outperform a random guess, even when there is only one training trace composed of 10 locations per user, and each location is missing with probability 80% (i.e. even when users hardly disclose two temporally-continuous locations.

Direct reconstruction of the source intensity distribution of a clinical linear accelerator using a maximum likelihood expectation maximization algorithm.

Science.gov (United States)

Papaconstadopoulos, P; Levesque, I R; Maglieri, R; Seuntjens, J

2016-02-07

Direct determination of the source intensity distribution of clinical linear accelerators is still a challenging problem for small field beam modeling. Current techniques most often involve special equipment and are difficult to implement in the clinic. In this work we present a maximum-likelihood expectation-maximization (MLEM) approach to the source reconstruction problem utilizing small fields and a simple experimental set-up. The MLEM algorithm iteratively ray-traces photons from the source plane to the exit plane and extracts corrections based on photon fluence profile measurements. The photon fluence profiles were determined by dose profile film measurements in air using a high density thin foil as build-up material and an appropriate point spread function (PSF). The effect of other beam parameters and scatter sources was minimized by using the smallest field size ([Formula: see text] cm(2)). The source occlusion effect was reproduced by estimating the position of the collimating jaws during this process. The method was first benchmarked against simulations for a range of typical accelerator source sizes. The sources were reconstructed with an accuracy better than 0.12 mm in the full width at half maximum (FWHM) to the respective electron sources incident on the target. The estimated jaw positions agreed within 0.2 mm with the expected values. The reconstruction technique was also tested against measurements on a Varian Novalis Tx linear accelerator and compared to a previously commissioned Monte Carlo model. The reconstructed FWHM of the source agreed within 0.03 mm and 0.11 mm to the commissioned electron source in the crossplane and inplane orientations respectively. The impact of the jaw positioning, experimental and PSF uncertainties on the reconstructed source distribution was evaluated with the former presenting the dominant effect.

Pharmacokinetic modelling of intravenous tobramycin in adolescent and adult patients with cystic fibrosis using the nonparametric expectation maximization (NPEM) algorithm.

Science.gov (United States)

Touw, D J; Vinks, A A; Neef, C

1997-06-01

The availability of personal computer programs for individualizing drug dosage regimens has stimulated the interest in modelling population pharmacokinetics. Data from 82 adolescent and adult patients with cystic fibrosis (CF) who were treated with intravenous tobramycin because of an exacerbation of their pulmonary infection were analysed with a non-parametric expectation maximization (NPEM) algorithm. This algorithm estimates the entire discrete joint probability density of the pharmacokinetic parameters. It also provides traditional parametric statistics such as the means, standard deviation, median, covariances and correlations among the various parameters. It also provides graphic-2- and 3-dimensional representations of the marginal densities of the parameters investigated. Several models for intravenous tobramycin in adolescent and adult patients with CF were compared. Covariates were total body weight (for the volume of distribution) and creatinine clearance (for the total body clearance and elimination rate). Because of lack of data on patients with poor renal function, restricted models with non-renal clearance and the non-renal elimination rate constant fixed at literature values of 0.15 L/h and 0.01 h-1 were also included. In this population, intravenous tobramycin could be best described by median (+/-dispersion factor) volume of distribution per unit of total body weight of 0.28 +/- 0.05 L/kg, elimination rate constant of 0.25 +/- 0.10 h-1 and elimination rate constant per unit of creatinine clearance of 0.0008 +/- 0.0009 h-1/(ml/min/1.73 m2). Analysis of populations of increasing size showed that using a restricted model with a non-renal elimination rate constant fixed at 0.01 h-1, a model based on a population of only 10 to 20 patients, contained parameter values similar to those of the entire population and, using the full model, a larger population (at least 40 patients) was needed.

An application of the maximal independent set algorithm to course ...

African Journals Online (AJOL)

In this paper, we demonstrated one of the many applications of the Maximal Independent Set Algorithm in the area of course allocation. A program was developed in Pascal and used in implementing a modified version of the algorithm to assign teaching courses to available lecturers in any academic environment and it ...

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

Science.gov (United States)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

An iterative reconstruction method of complex images using expectation maximization for radial parallel MRI

International Nuclear Information System (INIS)

Choi, Joonsung; Kim, Dongchan; Oh, Changhyun; Han, Yeji; Park, HyunWook

2013-01-01

In MRI (magnetic resonance imaging), signal sampling along a radial k-space trajectory is preferred in certain applications due to its distinct advantages such as robustness to motion, and the radial sampling can be beneficial for reconstruction algorithms such as parallel MRI (pMRI) due to the incoherency. For radial MRI, the image is usually reconstructed from projection data using analytic methods such as filtered back-projection or Fourier reconstruction after gridding. However, the quality of the reconstructed image from these analytic methods can be degraded when the number of acquired projection views is insufficient. In this paper, we propose a novel reconstruction method based on the expectation maximization (EM) method, where the EM algorithm is remodeled for MRI so that complex images can be reconstructed. Then, to optimize the proposed method for radial pMRI, a reconstruction method that uses coil sensitivity information of multichannel RF coils is formulated. Experiment results from synthetic and in vivo data show that the proposed method introduces better reconstructed images than the analytic methods, even from highly subsampled data, and provides monotonic convergence properties compared to the conjugate gradient based reconstruction method. (paper)

Coding for Parallel Links to Maximize the Expected Value of Decodable Messages

Science.gov (United States)

Klimesh, Matthew A.; Chang, Christopher S.

2011-01-01

When multiple parallel communication links are available, it is useful to consider link-utilization strategies that provide tradeoffs between reliability and throughput. Interesting cases arise when there are three or more available links. Under the model considered, the links have known probabilities of being in working order, and each link has a known capacity. The sender has a number of messages to send to the receiver. Each message has a size and a value (i.e., a worth or priority). Messages may be divided into pieces arbitrarily, and the value of each piece is proportional to its size. The goal is to choose combinations of messages to send on the links so that the expected value of the messages decodable by the receiver is maximized. There are three parts to the innovation: (1) Applying coding to parallel links under the model; (2) Linear programming formulation for finding the optimal combinations of messages to send on the links; and (3) Algorithms for assisting in finding feasible combinations of messages, as support for the linear programming formulation. There are similarities between this innovation and methods developed in the field of network coding. However, network coding has generally been concerned with either maximizing throughput in a fixed network, or robust communication of a fixed volume of data. In contrast, under this model, the throughput is expected to vary depending on the state of the network. Examples of error-correcting codes that are useful under this model but which are not needed under previous models have been found. This model can represent either a one-shot communication attempt, or a stream of communications. Under the one-shot model, message sizes and link capacities are quantities of information (e.g., measured in bits), while under the communications stream model, message sizes and link capacities are information rates (e.g., measured in bits/second). This work has the potential to increase the value of data returned from

An Efficient Algorithm for Maximizing Range Sum Queries in a Road Network

Directory of Open Access Journals (Sweden)

Tien-Khoi Phan

2014-01-01

Full Text Available Given a set of positive-weighted points and a query rectangle r (specified by a client of given extents, the goal of a maximizing range sum (MaxRS query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm.

Blood detection in wireless capsule endoscopy using expectation maximization clustering

Science.gov (United States)

Hwang, Sae; Oh, JungHwan; Cox, Jay; Tang, Shou Jiang; Tibbals, Harry F.

2006-03-01

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. Other endoscopies such as colonoscopy, upper gastrointestinal endoscopy, push enteroscopy, and intraoperative enteroscopy could be used to visualize up to the stomach, duodenum, colon, and terminal ileum, but there existed no method to view most of the small intestine without surgery. With the miniaturization of wireless and camera technologies came the ability to view the entire gestational track with little effort. A tiny disposable video capsule is swallowed, transmitting two images per second to a small data receiver worn by the patient on a belt. During an approximately 8-hour course, over 55,000 images are recorded to a worn device and then downloaded to a computer for later examination. Typically, a medical clinician spends more than two hours to analyze a WCE video. Research has been attempted to automatically find abnormal regions (especially bleeding) to reduce the time needed to analyze the videos. The manufacturers also provide the software tool to detect the bleeding called Suspected Blood Indicator (SBI), but its accuracy is not high enough to replace human examination. It was reported that the sensitivity and the specificity of SBI were about 72% and 85%, respectively. To address this problem, we propose a technique to detect the bleeding regions automatically utilizing the Expectation Maximization (EM) clustering algorithm. Our experimental results indicate that the proposed bleeding detection method achieves 92% and 98% of sensitivity and specificity, respectively.

An O(n²) maximal planarization algorithm based on PQ-trees

NARCIS (Netherlands)

Kant, G.

1992-01-01

In this paper we investigate the problem how to delete a number of edges from a nonplanar graph G such that the resulting graph G’ is maximal planar, i.e., such that we cannot add an edge e E G – G’ to G’ without destroying planarity. Actually, our algorithm is a corrected and more generalized

Evaluation of tomographic image quality of extended and conventional parallel hole collimators using maximum likelihood expectation maximization algorithm by Monte Carlo simulations.

Science.gov (United States)

Moslemi, Vahid; Ashoor, Mansour

2017-10-01

One of the major problems associated with parallel hole collimators (PCs) is the trade-off between their resolution and sensitivity. To solve this problem, a novel PC - namely, extended parallel hole collimator (EPC) - was proposed, in which particular trapezoidal denticles were increased upon septa on the side of the detector. In this study, an EPC was designed and its performance was compared with that of two PCs, PC35 and PC41, with a hole size of 1.5 mm and hole lengths of 35 and 41 mm, respectively. The Monte Carlo method was used to calculate the important parameters such as resolution, sensitivity, scattering, and penetration ratio. A Jaszczak phantom was also simulated to evaluate the resolution and contrast of tomographic images, which were produced by the EPC6, PC35, and PC41 using the Monte Carlo N-particle version 5 code, and tomographic images were reconstructed by using maximum likelihood expectation maximization algorithm. Sensitivity of the EPC6 was increased by 20.3% in comparison with that of the PC41 at the identical spatial resolution and full-width at tenth of maximum here. Moreover, the penetration and scattering ratio of the EPC6 was 1.2% less than that of the PC41. The simulated phantom images show that the EPC6 increases contrast-resolution and contrast-to-noise ratio compared with those of PC41 and PC35. When compared with PC41 and PC35, EPC6 improved trade-off between resolution and sensitivity, reduced penetrating and scattering ratios, and produced images with higher quality. EPC6 can be used to increase detectability of more details in nuclear medicine images.

A multilevel search algorithm for the maximization of submodular functions applied to the quadratic cost partition problem

NARCIS (Netherlands)

Goldengorin, B.; Ghosh, D.

Maximization of submodular functions on a ground set is a NP-hard combinatorial optimization problem. Data correcting algorithms are among the several algorithms suggested for solving this problem exactly and approximately. From the point of view of Hasse diagrams data correcting algorithms use

Energy-driven scheduling algorithm for nanosatellite energy harvesting maximization

Science.gov (United States)

Slongo, L. K.; Martínez, S. V.; Eiterer, B. V. B.; Pereira, T. G.; Bezerra, E. A.; Paiva, K. V.

2018-06-01

The number of tasks that a satellite may execute in orbit is strongly related to the amount of energy its Electrical Power System (EPS) is able to harvest and to store. The manner the stored energy is distributed within the satellite has also a great impact on the CubeSat's overall efficiency. Most CubeSat's EPS do not prioritize energy constraints in their formulation. Unlike that, this work proposes an innovative energy-driven scheduling algorithm based on energy harvesting maximization policy. The energy harvesting circuit is mathematically modeled and the solar panel I-V curves are presented for different temperature and irradiance levels. Considering the models and simulations, the scheduling algorithm is designed to keep solar panels working close to their maximum power point by triggering tasks in the appropriate form. Tasks execution affects battery voltage, which is coupled to the solar panels through a protection circuit. A software based Perturb and Observe strategy allows defining the tasks to be triggered. The scheduling algorithm is tested in FloripaSat, which is an 1U CubeSat. A test apparatus is proposed to emulate solar irradiance variation, considering the satellite movement around the Earth. Tests have been conducted to show that the scheduling algorithm improves the CubeSat energy harvesting capability by 4.48% in a three orbit experiment and up to 8.46% in a single orbit cycle in comparison with the CubeSat operating without the scheduling algorithm.

Finding Maximal Quasiperiodicities in Strings

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Pedersen, Christian N. S.

2000-01-01

of length n in time O(n log n) and space O(n). Our algorithm uses the suffix tree as the fundamental data structure combined with efficient methods for merging and performing multiple searches in search trees. Besides finding all maximal quasiperiodic substrings, our algorithm also marks the nodes......Apostolico and Ehrenfeucht defined the notion of a maximal quasiperiodic substring and gave an algorithm that finds all maximal quasiperiodic substrings in a string of length n in time O(n log2 n). In this paper we give an algorithm that finds all maximal quasiperiodic substrings in a string...... in the suffix tree that have a superprimitive path-label....

Application of Expectation Maximization Method for Purchase Decision-Making Support in Welding Branch

Directory of Open Access Journals (Sweden)

Kujawińska Agnieszka

2016-06-01

Full Text Available The article presents a study of applying the proposed method of cluster analysis to support purchasing decisions in the welding industry. The authors analyze the usefulness of the non-hierarchical method, Expectation Maximization (EM, in the selection of material (212 combinations of flux and wire melt for the SAW (Submerged Arc Welding method process. The proposed approach to cluster analysis is proved as useful in supporting purchase decisions.

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

Energy Technology Data Exchange (ETDEWEB)

Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

2013-07-21

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

AUC-Maximizing Ensembles through Metalearning.

Science.gov (United States)

LeDell, Erin; van der Laan, Mark J; Petersen, Maya

2016-05-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Vilanova, Pedro

2016-01-01

reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ

Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees

Directory of Open Access Journals (Sweden)

Stadler Peter F

2010-06-01

Full Text Available Abstract Background The Maximal Pairing Problem (MPP is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights ωxy for the paths between any two pairs of leaves (x, y, what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however. Results We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP. Conclusions The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

Directory of Open Access Journals (Sweden)

Mahdi M. M. El-Arini

2013-01-01

Full Text Available In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM, then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

Modeling multiple time series annotations as noisy distortions of the ground truth: An Expectation-Maximization approach.

Science.gov (United States)

Gupta, Rahul; Audhkhasi, Kartik; Jacokes, Zach; Rozga, Agata; Narayanan, Shrikanth

2018-01-01

Studies of time-continuous human behavioral phenomena often rely on ratings from multiple annotators. Since the ground truth of the target construct is often latent, the standard practice is to use ad-hoc metrics (such as averaging annotator ratings). Despite being easy to compute, such metrics may not provide accurate representations of the underlying construct. In this paper, we present a novel method for modeling multiple time series annotations over a continuous variable that computes the ground truth by modeling annotator specific distortions. We condition the ground truth on a set of features extracted from the data and further assume that the annotators provide their ratings as modification of the ground truth, with each annotator having specific distortion tendencies. We train the model using an Expectation-Maximization based algorithm and evaluate it on a study involving natural interaction between a child and a psychologist, to predict confidence ratings of the children's smiles. We compare and analyze the model against two baselines where: (i) the ground truth in considered to be framewise mean of ratings from various annotators and, (ii) each annotator is assumed to bear a distinct time delay in annotation and their annotations are aligned before computing the framewise mean.

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

Directory of Open Access Journals (Sweden)

Ye Ping

2005-12-01

Full Text Available Abstract Background Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. Results We have developed Genetic Interaction Motif Finding (GIMF, an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast. Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. Conclusion GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic

Clustering algorithms for Stokes space modulation format recognition

DEFF Research Database (Denmark)

Boada, Ricard; Borkowski, Robert; Tafur Monroy, Idelfonso

2015-01-01

influences the performance of the detection process, particularly at low signal-to-noise ratios. This paper reports on an extensive study of six different clustering algorithms: k-means, expectation maximization, density-based DBSCAN and OPTICS, spectral clustering and maximum likelihood clustering, used...

Two Expectation-Maximization Algorithms for Boolean Factor Analysis

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.

2014-01-01

Roč. 130, 23 April (2014), s. 83-97 ISSN 0925-2312 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean Factor analysis * Binary Matrix factorization * Neural networks * Binary data model * Dimension reduction * Bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014

Application of an expectation maximization method to the reconstruction of X-ray-tube spectra from transmission data

International Nuclear Information System (INIS)

Endrizzi, M.; Delogu, P.; Oliva, P.

2014-01-01

An expectation maximization method is applied to the reconstruction of X-ray tube spectra from transmission measurements in the energy range 7–40 keV. A semiconductor single-photon counting detector, ionization chambers and a scintillator-based detector are used for the experimental measurement of the transmission. The number of iterations required to reach an approximate solution is estimated on the basis of the measurement error, according to the discrepancy principle. The effectiveness of the stopping rule is studied on simulated data and validated with experiments. The quality of the reconstruction depends on the information available on the source itself and the possibility to add this knowledge to the solution process is investigated. The method can produce good approximations provided that the amount of noise in the data can be estimated. - Highlights: • An expectation maximization method was used together with the discrepancy principle. • The discrepancy principle is a suitable criterion for stopping the iteration. • The method can be applied to a variety of detectors/experimental conditions. • The minimum information required is the amount of noise that affects the data. • Improved results are achieved by inserting more information when available

Expected Power-Utility Maximization Under Incomplete Information and with Cox-Process Observations

International Nuclear Information System (INIS)

Fujimoto, Kazufumi; Nagai, Hideo; Runggaldier, Wolfgang J.

2013-01-01

We consider the problem of maximization of expected terminal power utility (risk sensitive criterion). The underlying market model is a regime-switching diffusion model where the regime is determined by an unobservable factor process forming a finite state Markov process. The main novelty is due to the fact that prices are observed and the portfolio is rebalanced only at random times corresponding to a Cox process where the intensity is driven by the unobserved Markovian factor process as well. This leads to a more realistic modeling for many practical situations, like in markets with liquidity restrictions; on the other hand it considerably complicates the problem to the point that traditional methodologies cannot be directly applied. The approach presented here is specific to the power-utility. For log-utilities a different approach is presented in Fujimoto et al. (Preprint, 2012).

Expected Power-Utility Maximization Under Incomplete Information and with Cox-Process Observations

Energy Technology Data Exchange (ETDEWEB)

Fujimoto, Kazufumi, E-mail: m_fuji@kvj.biglobe.ne.jp [Bank of Tokyo-Mitsubishi UFJ, Ltd., Corporate Risk Management Division (Japan); Nagai, Hideo, E-mail: nagai@sigmath.es.osaka-u.ac.jp [Osaka University, Division of Mathematical Science for Social Systems, Graduate School of Engineering Science (Japan); Runggaldier, Wolfgang J., E-mail: runggal@math.unipd.it [Universita di Padova, Dipartimento di Matematica Pura ed Applicata (Italy)

2013-02-15

We consider the problem of maximization of expected terminal power utility (risk sensitive criterion). The underlying market model is a regime-switching diffusion model where the regime is determined by an unobservable factor process forming a finite state Markov process. The main novelty is due to the fact that prices are observed and the portfolio is rebalanced only at random times corresponding to a Cox process where the intensity is driven by the unobserved Markovian factor process as well. This leads to a more realistic modeling for many practical situations, like in markets with liquidity restrictions; on the other hand it considerably complicates the problem to the point that traditional methodologies cannot be directly applied. The approach presented here is specific to the power-utility. For log-utilities a different approach is presented in Fujimoto et al. (Preprint, 2012).

PedMine – A simulated annealing algorithm to identify maximally unrelated individuals in population isolates

OpenAIRE

Douglas, Julie A.; Sandefur, Conner I.

2008-01-01

In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a ge...

Simulated annealing algorithm for optimal capital growth

Science.gov (United States)

Luo, Yong; Zhu, Bo; Tang, Yong

2014-08-01

We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.

A Local Scalable Distributed EM Algorithm for Large P2P Networks

Data.gov (United States)

National Aeronautics and Space Administration — his paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...

A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

Science.gov (United States)

Obulesu, O.; Rama Mohan Reddy, A., Dr; Mahendra, M.

2017-08-01

Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

Science.gov (United States)

Weissman, Alexander

2013-01-01

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

Maximizing and customer loyalty: Are maximizers less loyal?

Directory of Open Access Journals (Sweden)

Linda Lai

2011-06-01

Full Text Available Despite their efforts to choose the best of all available solutions, maximizers seem to be more inclined than satisficers to regret their choices and to experience post-decisional dissonance. Maximizers may therefore be expected to change their decisions more frequently and hence exhibit lower customer loyalty to providers of products and services compared to satisficers. Findings from the study reported here (N = 1978 support this prediction. Maximizers reported significantly higher intentions to switch to another service provider (television provider than satisficers. Maximizers' intentions to switch appear to be intensified and mediated by higher proneness to regret, increased desire to discuss relevant choices with others, higher levels of perceived knowledge of alternatives, and higher ego involvement in the end product, compared to satisficers. Opportunities for future research are suggested.

Evaluation of list-mode ordered subset expectation maximization image reconstruction for pixelated solid-state compton gamma camera with large number of channels

Science.gov (United States)

Kolstein, M.; De Lorenzo, G.; Chmeissani, M.

2014-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For Compton camera, especially with a large number of readout channels, image reconstruction presents a big challenge. In this work, results are presented for the List-Mode Ordered Subset Expectation Maximization (LM-OSEM) image reconstruction algorithm on simulated data with the VIP Compton camera design. For the simulation, all realistic contributions to the spatial resolution are taken into account, including the Doppler broadening effect. The results show that even with a straightforward implementation of LM-OSEM, good images can be obtained for the proposed Compton camera design. Results are shown for various phantoms, including extended sources and with a distance between the field of view and the first detector plane equal to 100 mm which corresponds to a realistic nuclear medicine environment.

Optimal topology of urban buildings for maximization of annual solar irradiation availability using a genetic algorithm

International Nuclear Information System (INIS)

Conceição António, Carlos A.; Monteiro, João Brasileiro; Afonso, Clito Félix

2014-01-01

An approach based on the optimal placement of buildings that favors the use of solar energy is proposed. By maximizing the area of exposure to incident solar irradiation on roofs and facades of buildings, improvements on the energy performance of the urban matrix are reached, contributing decisively to reduce dependence on other less environmentally friendly energy options. A mathematical model is proposed to optimize the annual solar irradiation availability where the placement of the buildings in urban environment favors the use of solar energy resource. Improvements on the solar energy potential of the urban grid are reached by maximizing the exposure of incident solar irradiation on roofs and facades of buildings. The proposed model considers predominant, the amount of direct solar radiation, omitting the components of the solar irradiation diffused and reflected. The dynamic interaction of buildings on exposure to sunlight is simulated aiming to evaluate the shadowing zones. The incident solar irradiation simulation and the dynamic shading model were integrated in an optimization approach implemented numerically. The search for optimal topological solutions for urban grid is based on a Genetic Algorithm. The objective is to generate optimal scenarios for the placement of buildings into the urban grid in the pre-design phase, which enhances the use of solar irradiation. - Highlights: • A mathematical model is proposed to optimize annual solar irradiation availability. • Maximization of incident solar irradiation on roofs and facades of buildings. • Dynamic interaction of buildings is simulated aiming to evaluate shadowing zones. • Search for optimal topological solutions for urban grid based on genetic algorithm. • Solutions are compared with the conventional configurations for urban grid

COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

Directory of Open Access Journals (Sweden)

A.A. Haseena Thasneem

2015-05-01

Full Text Available This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive, Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging, Contour models (Active Contour Model and Chan - Vese Model and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.

Quantum speedup in solving the maximal-clique problem

Science.gov (United States)

Chang, Weng-Long; Yu, Qi; Li, Zhaokai; Chen, Jiahui; Peng, Xinhua; Feng, Mang

2018-03-01

The maximal-clique problem, to find the maximally sized clique in a given graph, is classically an NP-complete computational problem, which has potential applications ranging from electrical engineering, computational chemistry, and bioinformatics to social networks. Here we develop a quantum algorithm to solve the maximal-clique problem for any graph G with n vertices with quadratic speedup over its classical counterparts, where the time and spatial complexities are reduced to, respectively, O (√{2n}) and O (n2) . With respect to oracle-related quantum algorithms for the NP-complete problems, we identify our algorithm as optimal. To justify the feasibility of the proposed quantum algorithm, we successfully solve a typical clique problem for a graph G with two vertices and one edge by carrying out a nuclear magnetic resonance experiment involving four qubits.

Maximal lattice free bodies, test sets and the Frobenius problem

DEFF Research Database (Denmark)

Jensen, Anders Nedergaard; Lauritzen, Niels; Roune, Bjarke Hammersholt

Maximal lattice free bodies are maximal polytopes without interior integral points. Scarf initiated the study of maximal lattice free bodies relative to the facet normals in a fixed matrix. In this paper we give an efficient algorithm for computing the maximal lattice free bodies of an integral m...... method is inspired by the novel algorithm by Einstein, Lichtblau, Strzebonski and Wagon and the Groebner basis approach by Roune....

The Large Margin Mechanism for Differentially Private Maximization

OpenAIRE

Chaudhuri, Kamalika; Hsu, Daniel; Song, Shuang

2014-01-01

A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy. This problem has been used as a sub-routine in many privacy-preserving algorithms for statistics and machine-learning. Previous algorithms for this problem are either range-dependent---i.e., their utility diminishes with the size of the universe...

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Science.gov (United States)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Trust regions in Kriging-based optimization with expected improvement

Science.gov (United States)

Regis, Rommel G.

2016-06-01

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

A maximum-likelihood reconstruction algorithm for tomographic gamma-ray nondestructive assay

International Nuclear Information System (INIS)

Prettyman, T.H.; Estep, R.J.; Cole, R.A.; Sheppard, G.A.

1994-01-01

A new tomographic reconstruction algorithm for nondestructive assay with high resolution gamma-ray spectroscopy (HRGS) is presented. The reconstruction problem is formulated using a maximum-likelihood approach in which the statistical structure of both the gross and continuum measurements used to determine the full-energy response in HRGS is precisely modeled. An accelerated expectation-maximization algorithm is used to determine the optimal solution. The algorithm is applied to safeguards and environmental assays of large samples (for example, 55-gal. drums) in which high continuum levels caused by Compton scattering are routinely encountered. Details of the implementation of the algorithm and a comparative study of the algorithm's performance are presented

Formula I(1 and I(2: Race Tracks for Likelihood Maximization Algorithms of I(1 and I(2 Cointegrated VAR Models

Directory of Open Access Journals (Sweden)

Jurgen A. Doornik

2017-11-01

Full Text Available This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1 and I(2 models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research.

Dragonfly: an implementation of the expand-maximize-compress algorithm for single-particle imaging.

Science.gov (United States)

Ayyer, Kartik; Lan, Ti-Yen; Elser, Veit; Loh, N Duane

2016-08-01

Single-particle imaging (SPI) with X-ray free-electron lasers has the potential to change fundamentally how biomacromolecules are imaged. The structure would be derived from millions of diffraction patterns, each from a different copy of the macromolecule before it is torn apart by radiation damage. The challenges posed by the resultant data stream are staggering: millions of incomplete, noisy and un-oriented patterns have to be computationally assembled into a three-dimensional intensity map and then phase reconstructed. In this paper, the Dragonfly software package is described, based on a parallel implementation of the expand-maximize-compress reconstruction algorithm that is well suited for this task. Auxiliary modules to simulate SPI data streams are also included to assess the feasibility of proposed SPI experiments at the Linac Coherent Light Source, Stanford, California, USA.

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.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

Science.gov (United States)

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Algorithmic detectability threshold of the stochastic block model

Science.gov (United States)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation

International Nuclear Information System (INIS)

Takeda, Koujin; Kabashima, Yoshiyuki

2013-01-01

We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of ℓ 1 -norm minimization using a standard linear programming algorithm is O(N 3 ). We show that this cost can be reduced to O(N 2 ) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach

On maximal massive 3D supergravity

OpenAIRE

Bergshoeff , Eric A; Hohm , Olaf; Rosseel , Jan; Townsend , Paul K

2010-01-01

ABSTRACT We construct, at the linearized level, the three-dimensional (3D) N = 4 supersymmetric " general massive supergravity " and the maximally supersymmetric N = 8 " new massive supergravity ". We also construct the maximally supersymmetric linearized N = 7 topologically massive supergravity, although we expect N = 6 to be maximal at the non-linear level. (Bergshoeff, Eric A) (Hohm, Olaf) (Rosseel, Jan) P.K.Townsend@da...

Expectation-maximization of the potential of mean force and diffusion coefficient in Langevin dynamics from single molecule FRET data photon by photon.

Science.gov (United States)

Haas, Kevin R; Yang, Haw; Chu, Jhih-Wei

2013-12-12

The dynamics of a protein along a well-defined coordinate can be formally projected onto the form of an overdamped Lagevin equation. Here, we present a comprehensive statistical-learning framework for simultaneously quantifying the deterministic force (the potential of mean force, PMF) and the stochastic force (characterized by the diffusion coefficient, D) from single-molecule Förster-type resonance energy transfer (smFRET) experiments. The likelihood functional of the Langevin parameters, PMF and D, is expressed by a path integral of the latent smFRET distance that follows Langevin dynamics and realized by the donor and the acceptor photon emissions. The solution is made possible by an eigen decomposition of the time-symmetrized form of the corresponding Fokker-Planck equation coupled with photon statistics. To extract the Langevin parameters from photon arrival time data, we advance the expectation-maximization algorithm in statistical learning, originally developed for and mostly used in discrete-state systems, to a general form in the continuous space that allows for a variational calculus on the continuous PMF function. We also introduce the regularization of the solution space in this Bayesian inference based on a maximum trajectory-entropy principle. We use a highly nontrivial example with realistically simulated smFRET data to illustrate the application of this new method.

An analytical study of composite laminate lay-up using search algorithms for maximization of flexural stiffness and minimization of springback angle

Science.gov (United States)

Singh, Ranjan Kumar; Rinawa, Moti Lal

2018-04-01

The residual stresses arising in fiber-reinforced laminates during their curing in closed molds lead to changes in the composites after their removal from the molds and cooling. One of these dimensional changes of angle sections is called springback. The parameters such as lay-up, stacking sequence, material system, cure temperature, thickness etc play important role in it. In present work, it is attempted to optimize lay-up and stacking sequence for maximization of flexural stiffness and minimization of springback angle. The search algorithms are employed to obtain best sequence through repair strategy such as swap. A new search algorithm, termed as lay-up search algorithm (LSA) is also proposed, which is an extension of permutation search algorithm (PSA). The efficacy of PSA and LSA is tested on the laminates with a range of lay-ups. A computer code is developed on MATLAB implementing the above schemes. Also, the strategies for multi objective optimization using search algorithms are suggested and tested.

Planning the FUSE Mission Using the SOVA Algorithm

Science.gov (United States)

Lanzi, James; Heatwole, Scott; Ward, Philip R.; Civeit, Thomas; Calvani, Humberto; Kruk, Jeffrey W.; Suchkov, Anatoly

2011-01-01

Three documents discuss the Sustainable Objective Valuation and Attainability (SOVA) algorithm and software as used to plan tasks (principally, scientific observations and associated maneuvers) for the Far Ultraviolet Spectroscopic Explorer (FUSE) satellite. SOVA is a means of managing risk in a complex system, based on a concept of computing the expected return value of a candidate ordered set of tasks as a product of pre-assigned task values and assessments of attainability made against qualitatively defined strategic objectives. For the FUSE mission, SOVA autonomously assembles a week-long schedule of target observations and associated maneuvers so as to maximize the expected scientific return value while keeping the satellite stable, managing the angular momentum of spacecraft attitude- control reaction wheels, and striving for other strategic objectives. A six-degree-of-freedom model of the spacecraft is used in simulating the tasks, and the attainability of a task is calculated at each step by use of strategic objectives as defined by use of fuzzy inference systems. SOVA utilizes a variant of a graph-search algorithm known as the A* search algorithm to assemble the tasks into a week-long target schedule, using the expected scientific return value to guide the search.

High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models. Research Report. ETS RR-16-34

Science.gov (United States)

von Davier, Matthias

2016-01-01

This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

Real-time topic-aware influence maximization using preprocessing.

Science.gov (United States)

Chen, Wei; Lin, Tian; Yang, Cheng

2016-01-01

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

Science.gov (United States)

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Liu Yang

2015-12-01

Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

Maximum Simulated Likelihood and Expectation-Maximization Methods to Estimate Random Coefficients Logit with Panel Data

DEFF Research Database (Denmark)

Cherchi, Elisabetta; Guevara, Cristian

2012-01-01

with cross-sectional or with panel data, and (d) EM systematically attained more efficient estimators than the MSL method. The results imply that if the purpose of the estimation is only to determine the ratios of the model parameters (e.g., the value of time), the EM method should be preferred. For all......The random coefficients logit model allows a more realistic representation of agents' behavior. However, the estimation of that model may involve simulation, which may become impractical with many random coefficients because of the curse of dimensionality. In this paper, the traditional maximum...... simulated likelihood (MSL) method is compared with the alternative expectation- maximization (EM) method, which does not require simulation. Previous literature had shown that for cross-sectional data, MSL outperforms the EM method in the ability to recover the true parameters and estimation time...

Deriving the largest expected number of elementary particles in the standard model from the maximal compact subgroup H of the exceptional Lie group E7(-5)

International Nuclear Information System (INIS)

El Naschie, M.S.

2008-01-01

The maximal number of elementary particles which could be expected to be found within a modestly extended energy scale of the standard model was found using various methods to be N = 69. In particular using E-infinity theory the present Author found the exact transfinite expectation value to be =α-bar o /2≅69 where α-bar o =137.082039325 is the exact inverse fine structure constant. In the present work we show among other things how to derive the exact integer value 69 from the exceptional Lie symmetry groups hierarchy. It is found that the relevant number is given by dim H = 69 where H is the maximal compact subspace of E 7(-5) so that N = dim H = 69 while dim E 7 = 133

A Case Study on Maximizing Aqua Feed Pellet Properties Using Response Surface Methodology and Genetic Algorithm

Energy Technology Data Exchange (ETDEWEB)

Tumuluru, Jaya

2013-01-10

Aims: The present case study is on maximizing the aqua feed properties using response surface methodology and genetic algorithm. Study Design: Effect of extrusion process variables like screw speed, L/D ratio, barrel temperature, and feed moisture content were analyzed to maximize the aqua feed properties like water stability, true density, and expansion ratio. Place and Duration of Study: This study was carried out in the Department of Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, India. Methodology: A variable length single screw extruder was used in the study. The process variables selected were screw speed (rpm), length-to-diameter (L/D) ratio, barrel temperature (degrees C), and feed moisture content (%). The pelletized aqua feed was analyzed for physical properties like water stability (WS), true density (TD), and expansion ratio (ER). Extrusion experimental data was collected by based on central composite design. The experimental data was further analyzed using response surface methodology (RSM) and genetic algorithm (GA) for maximizing feed properties. Results: Regression equations developed for the experimental data has adequately described the effect of process variables on the physical properties with coefficient of determination values (R2) of > 0.95. RSM analysis indicated WS, ER, and TD were maximized at L/D ratio of 12-13, screw speed of 60-80 rpm, feed moisture content of 30-40%, and barrel temperature of = 80 degrees C for ER and TD and > 90 degrees C for WS. Based on GA analysis, a maxium WS of 98.10% was predicted at a screw speed of 96.71 rpm, L/D radio of 13.67, barrel temperature of 96.26 degrees C, and feed moisture content of 33.55%. Maximum ER and TD of 0.99 and 1346.9 kg/m3 was also predicted at screw speed of 60.37 and 90.24 rpm, L/D ratio of 12.18 and 13.52, barrel temperature of 68.50 and 64.88 degrees C, and medium feed moisture content of 33.61 and 38.36%. Conclusion: The present data analysis indicated

Optimization of Surplus Reinsurance Treaty using the Conditional Tail Expectation

Directory of Open Access Journals (Sweden)

Abderrahim El Attar

2018-03-01

Full Text Available In this work, we propose a new optimization strategy for reinsurance using the genetic algorithms. This approach is to determine an optimal structure of a "surplus" reinsurance contract by finding the optimal cession rates through an optimization model which is based on the minimization of the Conditional Tail Expectation (CTE risk measure under the constraint of technical benefit. This approach can be seen as a decision support tool that can be used by managers to minimize the actuarial risk and maximize the technical benefit in the insurance company.

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

Science.gov (United States)

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Continuous Analog of Accelerated OS-EM Algorithm for Computed Tomography

Directory of Open Access Journals (Sweden)

Kiyoko Tateishi

2017-01-01

Full Text Available The maximum-likelihood expectation-maximization (ML-EM algorithm is used for an iterative image reconstruction (IIR method and performs well with respect to the inverse problem as cross-entropy minimization in computed tomography. For accelerating the convergence rate of the ML-EM, the ordered-subsets expectation-maximization (OS-EM with a power factor is effective. In this paper, we propose a continuous analog to the power-based accelerated OS-EM algorithm. The continuous-time image reconstruction (CIR system is described by nonlinear differential equations with piecewise smooth vector fields by a cyclic switching process. A numerical discretization of the differential equation by using the geometric multiplicative first-order expansion of the nonlinear vector field leads to an exact equivalent iterative formula of the power-based OS-EM. The convergence of nonnegatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem for consistent inverse problems. We illustrate through numerical experiments that the convergence characteristics of the continuous system have the highest quality compared with that of discretization methods. We clarify how important the discretization method approximates the solution of the CIR to design a better IIR method.

Models and Algorithms for Tracking Target with Coordinated Turn Motion

Directory of Open Access Journals (Sweden)

Xianghui Yuan

2014-01-01

Full Text Available Tracking target with coordinated turn (CT motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT model with known turn rate, augmented coordinated turn (ACT model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM framework, the algorithm based on expectation maximization (EM algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM algorithm, the EM algorithm shows its effectiveness.

Efficiency maximization and performance evaluation of hybrid dual channel semitransparent photovoltaic thermal module using fuzzyfied genetic algorithm

International Nuclear Information System (INIS)

Singh, Sonveer; Agrawal, Sanjay

2016-01-01

Highlights: • Thermal modeling of novel dual channel semitransparent photovoltaic thermal hybrid module. • Efficiency maximization and performance evaluation of dual channel photovoltaic thermal module. • Annual performance has been evaluated for Srinagar, Jodhpur, Bangalore and New Delhi (India). • There are improvements in results for optimized system as compared to un-optimized system. - Abstract: The work has been carried out in two steps; firstly the parameters of hybrid dual channel semitransparent photovoltaic thermal module has been optimized using a fuzzyfied genetic algorithm. During the course of optimization, overall exergy efficiency is considered as an objective function and different design parameters of the proposed module have been optimized. Fuzzy controller is used to improve the performance of genetic algorithms and the approach is called as a fuzzyfied genetic algorithm. In the second step, the performance of the module has been analyzed for four cities of India such as Srinagar, Bangalore, Jodhpur and New Delhi. The performance of the module has been evaluated for daytime 08:00 AM to 05:00 PM and annually from January to December. It is to be noted that, an average improvement occurs in electrical efficiency of the optimized module, simultaneously there is also a reduction in solar cell temperature as compared to un-optimized module.

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.

Non-negative matrix factorization by maximizing correntropy for cancer clustering

KAUST Repository

Wang, Jim Jing-Yan; Wang, Xiaolei; Gao, Xin

2013-01-01

Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.

Non-negative matrix factorization by maximizing correntropy for cancer clustering

KAUST Repository

Wang, Jim Jing-Yan

2013-03-24

Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.

A quantitative performance evaluation of the EM algorithm applied to radiographic images

International Nuclear Information System (INIS)

Brailean, J.C.; Sullivan, B.J.; Giger, M.L.; Chen, C.T.

1991-01-01

In this paper, the authors quantitatively evaluate the performance of the Expectation Maximization (EM) algorithm as a restoration technique for radiographic images. The perceived signal-to-noise ratio (SNR), of simple radiographic patterns processed by the EM algorithm are calculated on the basis of a statistical decision theory model that includes both the observer's visual response function and a noise component internal to the eye-brain system. The relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to quantitatively compare the effects of the EM algorithm to two popular image enhancement techniques: contrast enhancement (windowing) and unsharp mask filtering

Calculation of core axial power shapes using alternating conditional expectation algorithm

International Nuclear Information System (INIS)

Lee, Eun Ki; Kim, Yong Hee; Cha, Kune Ho; Park, Moon Kyu

1998-01-01

We have introduced the alternating conditional expectation (ACE) algorithm in the method of reconstructing 20 node axial power shapes from five level detector powers. The ACE algorithm was used to find the optimal relationships between each plane power and normalized five detector powers. The obtained all optimal transformations had simple forms to be represented with polynomials. The reference axial power shapes and simulated detector powers were drawn out of the 3-dimensional results of Reactor Operation and Control Simulation (ROCS) code for various core states. By the ACE algorithm, we obtained the optimal relationship between dependent variable plane power, y, and independent variable detector powers, {Di, i=1,...,5 without any preprocessing, where a total of ≅3490 data sets per each cycle of YongGwang Nuclear (YGN) Power Plant units 3 and 4 are used. To test the validity and accuracy of the new method, about 21,200 cases of reconstructed axial power shapes are compared to original ROCS axial power shapes, and they are also contrasted with those obtained by Fourier fitting method (FFM). The average error of root mean square (rms), axial peak (DFZ), and axial shape index (DASI) of our new method for total 21204 data cases are 0.81%, 0.51% and 0.00204, while FFM 2.29%, 2.37% and 0.00264, respectively. The evaluation results for the data sets not used in the ACE transformations also show that the accuracy of new method is much better than that of FFM

Estimating model error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximisation algorithm

KAUST Repository

Dreano, Denis; Tandeo, P.; Pulido, M.; Ait-El-Fquih, Boujemaa; Chonavel, T.; Hoteit, Ibrahim

2017-01-01

Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation-maximisation (EM) algorithm to estimate the model error covariances using classical extended

Algorithms over partially ordered sets

DEFF Research Database (Denmark)

Baer, Robert M.; Østerby, Ole

1969-01-01

in partially ordered sets, answer the combinatorial question of how many maximal chains might exist in a partially ordered set withn elements, and we give an algorithm for enumerating all maximal chains. We give (in § 3) algorithms which decide whether a partially ordered set is a (lower or upper) semi......-lattice, and whether a lattice has distributive, modular, and Boolean properties. Finally (in § 4) we give Algol realizations of the various algorithms....

On the use of successive data in the ML-EM algorithm in Positron Emission Tomography

Energy Technology Data Exchange (ETDEWEB)

Desmedt, P; Lemahieu, I [University of Ghent, ELIS Department, SInt-Pietersnieuwstraat 41, B-9000 Gent, (Belgium)

1994-12-31

The Maximum Likelihood-Expectation Maximization (ML-EM) algorithm is the most popular statistical reconstruction technique for Positron Emission Tomography (PET). The ML-EM algorithm is however also renowned for its long reconstruction times. An acceleration technique for this algorithm is studied in this paper. The proposed technique starts the ML-EM algorithm before the measurement process is completed. Since the reconstruction is initiated during the scan of the patient, the time elapsed before a reconstruction becomes available is reduced. Experiments with software phantoms indicate that the quality of the reconstructed image using successive data is comparable to the quality of the reconstruction with the normal ML-EM algorithm. (authors). 7 refs, 3 figs.

Profit maximization with customer satisfaction control for electric vehicle charging in smart grids

Directory of Open Access Journals (Sweden)

Edwin Collado

2017-05-01

Full Text Available As the market of electric vehicles is gaining popularity, large-scale commercialized or privately-operated charging stations are expected to play a key role as a technology enabler. In this paper, we study the problem of charging electric vehicles at stations with limited charging machines and power resources. The purpose of this study is to develop a novel profit maximization framework for station operation in both offline and online charging scenarios, under certain customer satisfaction constraints. The main goal is to maximize the profit obtained by the station owner and provide a satisfactory charging service to the customers. The framework includes not only the vehicle scheduling and charging power control, but also the managing of user satisfaction factors, which are defined as the percentages of finished charging targets. The profit maximization problem is proved to be NPcomplete in both scenarios (NP refers to “nondeterministic polynomial time”, for which two-stage charging strategies are proposed to obtain efficient suboptimal solutions. Competitive analysis is also provided to analyze the performance of the proposed online two-stage charging algorithm against the offline counterpart under non-congested and congested charging scenarios. Finally, the simulation results show that the proposed two-stage charging strategies achieve performance close to that with exhaustive search. Also, the proposed algorithms provide remarkable performance gains compared to the other conventional charging strategies with respect to not only the unified profit, but also other practical interests, such as the computational time, the user satisfaction factor, the power consumption, and the competitive ratio.

Natural maximal νμ-ντ mixing

International Nuclear Information System (INIS)

Wetterich, C.

1999-01-01

The naturalness of maximal mixing between myon- and tau-neutrinos is investigated. A spontaneously broken nonabelian generation symmetry can explain a small parameter which governs the deviation from maximal mixing. In many cases all three neutrino masses are almost degenerate. Maximal ν μ -ν τ -mixing suggests that the leading contribution to the light neutrino masses arises from the expectation value of a heavy weak triplet rather than from the seesaw mechanism. In this scenario the deviation from maximal mixing is predicted to be less than about 1%. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

Directory of Open Access Journals (Sweden)

Seyedeh Elham Eftekharian

2017-11-01

Full Text Available Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA. In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.

Maximizing Entropy over Markov Processes

DEFF Research Database (Denmark)

Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis

2013-01-01

The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of an system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code....

Maximizing entropy over Markov processes

DEFF Research Database (Denmark)

Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis

2014-01-01

The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of a system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code. © 2014 Elsevier...

Application of the EM algorithm to radiographic images.

Science.gov (United States)

Brailean, J C; Little, D; Giger, M L; Chen, C T; Sullivan, B J

1992-01-01

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.

Maximization of Tsallis entropy in the combinatorial formulation

International Nuclear Information System (INIS)

Suyari, Hiroki

2010-01-01

This paper presents the mathematical reformulation for maximization of Tsallis entropy S q in the combinatorial sense. More concretely, we generalize the original derivation of Maxwell-Boltzmann distribution law to Tsallis statistics by means of the corresponding generalized multinomial coefficient. Our results reveal that maximization of S 2-q under the usual expectation or S q under q-average using the escort expectation are naturally derived from the combinatorial formulations for Tsallis statistics with respective combinatorial dualities, that is, one for additive duality and the other for multiplicative duality.

Robot Mapping With Real-Time Incremental Localization Using Expectation Maximization

National Research Council Canada - National Science Library

Owens, Kevin L

2005-01-01

This research effort explores and develops a real-time sonar-based robot mapping and localization algorithm that provides pose correction within the context of a singe room, to be combined with pre...

Estimating model error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximisation algorithm

KAUST Repository

Dreano, Denis

2017-04-05

Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation-maximisation (EM) algorithm to estimate the model error covariances using classical extended and ensemble versions of the Kalman smoother. We show that, for additive model errors, the estimate of the error covariance converges. We also investigate other forms of model error, such as parametric or multiplicative errors. We show that additive Gaussian model error is able to compensate for non additive sources of error in the algorithms we propose. We also demonstrate the limitations of the extended version of the algorithm and recommend the use of the more robust and flexible ensemble version. This article is a proof of concept of the methodology with the Lorenz-63 attractor. We developed an open-source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.

Sparse Variational Bayesian SAGE Algorithm With Application to the Estimation of Multipath Wireless Channels

DEFF Research Database (Denmark)

Shutin, Dmitriy; Fleury, Bernard Henri

2011-01-01

In this paper, we develop a sparse variational Bayesian (VB) extension of the space-alternating generalized expectation-maximization (SAGE) algorithm for the high resolution estimation of the parameters of relevant multipath components in the response of frequency and spatially selective wireless...... channels. The application context of the algorithm considered in this contribution is parameter estimation from channel sounding measurements for radio channel modeling purpose. The new sparse VB-SAGE algorithm extends the classical SAGE algorithm in two respects: i) by monotonically minimizing...... parametric sparsity priors for the weights of the multipath components. We revisit the Gaussian sparsity priors within the sparse VB-SAGE framework and extend the results by considering Laplace priors. The structure of the VB-SAGE algorithm allows for an analytical stability analysis of the update expression...

Distributed interference alignment iterative algorithms in symmetric wireless network

Directory of Open Access Journals (Sweden)

YANG Jingwen

2015-02-01

Full Text Available Interference alignment is a novel interference alignment way,which is widely noted all of the world.Interference alignment overlaps interference in the same signal space at receiving terminal by precoding so as to thoroughly eliminate the influence of interference impacted on expected signals,thus making the desire user achieve the maximum degree of freedom.In this paper we research three typical algorithms for realizing interference alignment,including minimizing the leakage interference,maximizing Signal to Interference plus Noise Ratio (SINR and minimizing mean square error(MSE.All of these algorithms utilize the reciprocity of wireless network,and iterate the precoders between original network and the reverse network so as to achieve interference alignment.We use the uplink transmit rate to analyze the performance of these three algorithms.Numerical simulation results show the advantages of these algorithms.which is the foundation for the further study in the future.The feasibility and future of interference alignment are also discussed at last.

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.

Science.gov (United States)

Wunderlich, Adam; Goossens, Bart; Abbey, Craig K

2016-09-01

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.

Optimization of cascade hydropower system operation by genetic algorithm to maximize clean energy output

Directory of Open Access Journals (Sweden)

Aida Tayebiyan

2016-06-01

Full Text Available Background: Several reservoir systems have been constructed for hydropower generation around the world. Hydropower offers an economical source of electricity with reduce carbon emissions. Therefore, it is such a clean and renewable source of energy. Reservoirs that generate hydropower are typically operated with the goal of maximizing energy revenue. Yet, reservoir systems are inefficiently operated and manage according to policies determined at the construction time. It is worth noting that with little enhancement in operation of reservoir system, there could be an increase in efficiency of the scheme for many consumers. Methods: This research develops simulation-optimization models that reflect discrete hedging policy (DHP to manage and operate hydropower reservoir system and analyse it in both single and multireservoir system. Accordingly, three operational models (2 single reservoir systems and 1 multi-reservoir system were constructed and optimized by genetic algorithm (GA. Maximizing the total power generation in horizontal time is chosen as an objective function in order to improve the functional efficiency in hydropower production with consideration to operational and physical limitations. The constructed models, which is a cascade hydropower reservoirs system have been tested and evaluated in the Cameron Highland and Batang Padang in Malaysia. Results: According to the given results, usage of DHP for hydropower reservoir system operation could increase the power generation output to nearly 13% in the studied reservoir system compared to present operating policy (TNB operation. This substantial increase in power production will enhance economic development. Moreover, the given results of single and multi-reservoir systems affirmed that hedging policy could manage the single system much better than operation of the multi-reservoir system. Conclusion: It can be summarized that DHP is an efficient and feasible policy, which could be used

New Maximal Two-distance Sets

DEFF Research Database (Denmark)

Lisonek, Petr

1996-01-01

A two-distance set in E^d is a point set X inthe d-dimensional Euclidean spacesuch that the distances between distinct points in Xassume only two different non-zero values. Based on results from classical distance geometry, we developan algorithm to classify, for a given dimension, all maximal...... (largest possible)two-distance sets in E^d.Using this algorithm we have completed the full classificationfor all dimensions less than or equal to 7, andwe have found one set in E^8 whosemaximality follows from Blokhuis' upper bound on sizes of s-distance sets.While in the dimensions less than or equal to 6...

Quantitative validation of a new coregistration algorithm

International Nuclear Information System (INIS)

Pickar, R.D.; Esser, P.D.; Pozniakoff, T.A.; Van Heertum, R.L.; Stoddart, H.A. Jr.

1995-01-01

A new coregistration software package, Neuro9OO Image Coregistration software, has been developed specifically for nuclear medicine. With this algorithm, the correlation coefficient is maximized between volumes generated from sets of transaxial slices. No localization markers or segmented surfaces are needed. The coregistration program was evaluated for translational and rotational registration accuracy. A Tc-99m HM-PAO split-dose study (0.53 mCi low dose, L, and 1.01 mCi high dose, H) was simulated with a Hoffman Brain Phantom with five fiducial markers. Translation error was determined by a shift in image centroid, and rotation error was determined by a simplified two-axis approach. Changes in registration accuracy were measured with respect to: (1) slice spacing, using the four different combinations LL, LH, HL, HH, (2) translational and rotational misalignment before coregistration, (3) changes in the step size of the iterative parameters. In all the cases the algorithm converged with only small difference in translation offset, 0 and 0. At 6 nun slice spacing, translational efforts ranged from 0.9 to 2.8 mm (system resolution at 100 mm, 6.8 mm). The converged parameters showed little sensitivity to count density. In addition the correlation coefficient increased with decreasing iterative step size, as expected. From these experiments, the authors found that this algorithm based on the maximization of the correlation coefficient between studies was an accurate way to coregister SPECT brain images

Super-Relaxed ( -Proximal Point Algorithms, Relaxed ( -Proximal Point Algorithms, Linear Convergence Analysis, and Nonlinear Variational Inclusions

Directory of Open Access Journals (Sweden)

Agarwal RaviP

2009-01-01

Full Text Available We glance at recent advances to the general theory of maximal (set-valued monotone mappings and their role demonstrated to examine the convex programming and closely related field of nonlinear variational inequalities. We focus mostly on applications of the super-relaxed ( -proximal point algorithm to the context of solving a class of nonlinear variational inclusion problems, based on the notion of maximal ( -monotonicity. Investigations highlighted in this communication are greatly influenced by the celebrated work of Rockafellar (1976, while others have played a significant part as well in generalizing the proximal point algorithm considered by Rockafellar (1976 to the case of the relaxed proximal point algorithm by Eckstein and Bertsekas (1992. Even for the linear convergence analysis for the overrelaxed (or super-relaxed ( -proximal point algorithm, the fundamental model for Rockafellar's case does the job. Furthermore, we attempt to explore possibilities of generalizing the Yosida regularization/approximation in light of maximal ( -monotonicity, and then applying to first-order evolution equations/inclusions.

Distributed-Memory Fast Maximal Independent Set

Energy Technology Data Exchange (ETDEWEB)

Kanewala Appuhamilage, Thejaka Amila J.; Zalewski, Marcin J.; Lumsdaine, Andrew

2017-09-13

The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those algorithms are designed focusing on shared-memory machines and are analyzed using the PRAM model. These algorithms do not have direct efficient distributed-memory implementations. In this paper, we extend two of Luby’s seminal MIS algorithms, “Luby(A)” and “Luby(B),” to distributed-memory execution, and we evaluate their performance. We compare our results with the “Filtered MIS” implementation in the Combinatorial BLAS library for two types of synthetic graph inputs.

Performance evaluation of the EM algorithm applied to radiographic images

International Nuclear Information System (INIS)

Brailean, J.C.; Giger, M.L.; Chen, C.T.; Sullivan, B.J.

1990-01-01

In this paper the authors evaluate the expectation maximization (EM) algorithm, both qualitatively and quantitatively, as a technique for enhancing radiographic images. Previous studies have qualitatively shown the usefulness of the EM algorithm but have failed to quantify and compare its performance with those of other image processing techniques. Recent studies by Loo et al, Ishida et al, and Giger et al, have explained improvements in image quality quantitatively in terms of a signal-to-noise ratio (SNR) derived from signal detection theory. In this study, we take a similar approach in quantifying the effect of the EM algorithm on detection of simulated low-contrast square objects superimposed on radiographic mottle. The SNRs of the original and processed images are calculated taking into account both the human visual system response and the screen-film transfer function as well as a noise component internal to the eye-brain system. The EM algorithm was also implemented on digital screen-film images of test patterns and clinical mammograms

A new iterative algorithm to reconstruct the refractive index.

Science.gov (United States)

Liu, Y J; Zhu, P P; Chen, B; Wang, J Y; Yuan, Q X; Huang, W X; Shu, H; Li, E R; Liu, X S; Zhang, K; Ming, H; Wu, Z Y

2007-06-21

The latest developments in x-ray imaging are associated with techniques based on the phase contrast. However, the image reconstruction procedures demand significant improvements of the traditional methods, and/or new algorithms have to be introduced to take advantage of the high contrast and sensitivity of the new experimental techniques. In this letter, an improved iterative reconstruction algorithm based on the maximum likelihood expectation maximization technique is presented and discussed in order to reconstruct the distribution of the refractive index from data collected by an analyzer-based imaging setup. The technique considered probes the partial derivative of the refractive index with respect to an axis lying in the meridional plane and perpendicular to the propagation direction. Computer simulations confirm the reliability of the proposed algorithm. In addition, the comparison between an analytical reconstruction algorithm and the iterative method has been also discussed together with the convergent characteristic of this latter algorithm. Finally, we will show how the proposed algorithm may be applied to reconstruct the distribution of the refractive index of an epoxy cylinder containing small air bubbles of about 300 micro of diameter.

EM algorithm for one-shot device testing with competing risks under exponential distribution

International Nuclear Information System (INIS)

Balakrishnan, N.; So, H.Y.; Ling, M.H.

2015-01-01

This paper provides an extension of the work of Balakrishnan and Ling [1] by introducing a competing risks model into a one-shot device testing analysis under an accelerated life test setting. An Expectation Maximization (EM) algorithm is then developed for the estimation of the model parameters. An extensive Monte Carlo simulation study is carried out to assess the performance of the EM algorithm and then compare the obtained results with the initial estimates obtained by the Inequality Constrained Least Squares (ICLS) method of estimation. Finally, we apply the EM algorithm to a clinical data, ED01, to illustrate the method of inference developed here. - Highlights: • ALT data analysis for one-shot devices with competing risks is considered. • EM algorithm is developed for the determination of the MLEs. • The estimations of lifetime under normal operating conditions are presented. • The EM algorithm improves the convergence rate

Sum Rate Maximization using Linear Precoding and Decoding in the Multiuser MIMO Downlink

OpenAIRE

Tenenbaum, Adam J.; Adve, Raviraj S.

2008-01-01

We propose an algorithm to maximize the instantaneous sum data rate transmitted by a base station in the downlink of a multiuser multiple-input, multiple-output system. The transmitter and the receivers may each be equipped with multiple antennas and each user may receive more than one data stream. We show that maximizing the sum rate is closely linked to minimizing the product of mean squared errors (PMSE). The algorithm employs an uplink/downlink duality to iteratively design transmit-recei...

Determination of Pavement Rehabilitation Activities through a Permutation Algorithm

Directory of Open Access Journals (Sweden)

Sangyum Lee

2013-01-01

Full Text Available This paper presents a mathematical programming model for optimal pavement rehabilitation planning. The model maximized the rehabilitation area through a newly developed permutation algorithm, based on the procedures outlined in the harmony search (HS algorithm. Additionally, the proposed algorithm was based on an optimal solution method for the problem of multilocation rehabilitation activities on pavement structure, using empirical deterioration and rehabilitation effectiveness models, according to a limited maintenance budget. Thus, nonlinear pavement performance and rehabilitation activity decision models were used to maximize the objective functions of the rehabilitation area within a limited budget, through the permutation algorithm. Our results showed that the heuristic permutation algorithm provided a good optimum in terms of maximizing the rehabilitation area, compared with a method of the worst-first maintenance currently used in Seoul.

Multi-objective optimal design of sandwich panels using a genetic algorithm

Science.gov (United States)

Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

2017-10-01

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

Dopaminergic balance between reward maximization and policy complexity

Directory of Open Access Journals (Sweden)

Naama eParush

2011-05-01

Full Text Available Previous reinforcement-learning models of the basal ganglia network have highlighted the role of dopamine in encoding the mismatch between prediction and reality. Far less attention has been paid to the computational goals and algorithms of the main-axis (actor. Here, we construct a top-down model of the basal ganglia with emphasis on the role of dopamine as both a reinforcement learning signal and as a pseudo-temperature signal controlling the general level of basal ganglia excitability and motor vigilance of the acting agent. We argue that the basal ganglia endow the thalamic-cortical networks with the optimal dynamic tradeoff between two constraints: minimizing the policy complexity (cost and maximizing the expected future reward (gain. We show that this multi-dimensional optimization processes results in an experience-modulated version of the softmax behavioral policy. Thus, as in classical softmax behavioral policies, probability of actions are selected according to their estimated values and the pseudo-temperature, but in addition also vary according to the frequency of previous choices of these actions. We conclude that the computational goal of the basal ganglia is not to maximize cumulative (positive and negative reward. Rather, the basal ganglia aim at optimization of independent gain and cost functions. Unlike previously suggested single-variable maximization processes, this multi-dimensional optimization process leads naturally to a softmax-like behavioral policy. We suggest that beyond its role in the modulation of the efficacy of the cortico-striatal synapses, dopamine directly affects striatal excitability and thus provides a pseudo-temperature signal that modulates the trade-off between gain and cost. The resulting experience and dopamine modulated softmax policy can then serve as a theoretical framework to account for the broad range of behaviors and clinical states governed by the basal ganglia and dopamine systems.

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

International Nuclear Information System (INIS)

Viana, R.S.; Yoriyaz, H.; Santos, A.

2011-01-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

Energy Technology Data Exchange (ETDEWEB)

Viana, R.S.; Yoriyaz, H.; Santos, A., E-mail: rodrigossviana@gmail.com, E-mail: hyoriyaz@ipen.br, E-mail: asantos@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Coupling graph perturbation theory with scalable parallel algorithms for large-scale enumeration of maximal cliques in biological graphs

International Nuclear Information System (INIS)

Samatova, N F; Schmidt, M C; Hendrix, W; Breimyer, P; Thomas, K; Park, B-H

2008-01-01

Data-driven construction of predictive models for biological systems faces challenges from data intensity, uncertainty, and computational complexity. Data-driven model inference is often considered a combinatorial graph problem where an enumeration of all feasible models is sought. The data-intensive and the NP-hard nature of such problems, however, challenges existing methods to meet the required scale of data size and uncertainty, even on modern supercomputers. Maximal clique enumeration (MCE) in a graph derived from such biological data is often a rate-limiting step in detecting protein complexes in protein interaction data, finding clusters of co-expressed genes in microarray data, or identifying clusters of orthologous genes in protein sequence data. We report two key advances that address this challenge. We designed and implemented the first (to the best of our knowledge) parallel MCE algorithm that scales linearly on thousands of processors running MCE on real-world biological networks with thousands and hundreds of thousands of vertices. In addition, we proposed and developed the Graph Perturbation Theory (GPT) that establishes a foundation for efficiently solving the MCE problem in perturbed graphs, which model the uncertainty in the data. GPT formulates necessary and sufficient conditions for detecting the differences between the sets of maximal cliques in the original and perturbed graphs and reduces the enumeration time by more than 80% compared to complete recomputation

Learning to maximize reward rate: a model based on semi-Markov decision processes.

Science.gov (United States)

Khodadadi, Arash; Fakhari, Pegah; Busemeyer, Jerome R

2014-01-01

WHEN ANIMALS HAVE TO MAKE A NUMBER OF DECISIONS DURING A LIMITED TIME INTERVAL, THEY FACE A FUNDAMENTAL PROBLEM: how much time they should spend on each decision in order to achieve the maximum possible total outcome. Deliberating more on one decision usually leads to more outcome but less time will remain for other decisions. In the framework of sequential sampling models, the question is how animals learn to set their decision threshold such that the total expected outcome achieved during a limited time is maximized. The aim of this paper is to provide a theoretical framework for answering this question. To this end, we consider an experimental design in which each trial can come from one of the several possible "conditions." A condition specifies the difficulty of the trial, the reward, the penalty and so on. We show that to maximize the expected reward during a limited time, the subject should set a separate value of decision threshold for each condition. We propose a model of learning the optimal value of decision thresholds based on the theory of semi-Markov decision processes (SMDP). In our model, the experimental environment is modeled as an SMDP with each "condition" being a "state" and the value of decision thresholds being the "actions" taken in those states. The problem of finding the optimal decision thresholds then is cast as the stochastic optimal control problem of taking actions in each state in the corresponding SMDP such that the average reward rate is maximized. Our model utilizes a biologically plausible learning algorithm to solve this problem. The simulation results show that at the beginning of learning the model choses high values of decision threshold which lead to sub-optimal performance. With experience, however, the model learns to lower the value of decision thresholds till finally it finds the optimal values.

Siting Samplers to Minimize Expected Time to Detection

Energy Technology Data Exchange (ETDEWEB)

Walter, Travis; Lorenzetti, David M.; Sohn, Michael D.

2012-05-02

We present a probabilistic approach to designing an indoor sampler network for detecting an accidental or intentional chemical or biological release, and demonstrate it for a real building. In an earlier paper, Sohn and Lorenzetti(1) developed a proof of concept algorithm that assumed samplers could return measurements only slowly (on the order of hours). This led to optimal detect to treat architectures, which maximize the probability of detecting a release. This paper develops a more general approach, and applies it to samplers that can return measurements relatively quickly (in minutes). This leads to optimal detect to warn architectures, which minimize the expected time to detection. Using a model of a real, large, commercial building, we demonstrate the approach by optimizing networks against uncertain release locations, source terms, and sampler characteristics. Finally, we speculate on rules of thumb for general sampler placement.

Expected Net Present Value, Expected Net Future Value, and the Ramsey Rule

OpenAIRE

Gollier, Christian

2010-01-01

Weitzman (1998) showed that when future interest rates are uncertain, using the expected net present value implies a term structure of discount rates that is decreasing to the smallest possible interest rate. On the contrary, using the expected net future value criteria implies an increasing term structure of discount rates up to the largest possible interest rate. We reconcile the two approaches by introducing risk aversion and utility maximization. We show that if the aggregate consumption ...

Polarity related influence maximization in signed social networks.

Directory of Open Access Journals (Sweden)

Dong Li

Full Text Available Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.

The index-based subgraph matching algorithm (ISMA: fast subgraph enumeration in large networks using optimized search trees.

Directory of Open Access Journals (Sweden)

Sofie Demeyer

Full Text Available Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA, a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/.

The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

Science.gov (United States)

Demeyer, Sofie; Michoel, Tom; Fostier, Jan; Audenaert, Pieter; Pickavet, Mario; Demeester, Piet

2013-01-01

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/. PMID:23620730

Application and performance of an ML-EM algorithm in NEXT

Science.gov (United States)

Simón, A.; Lerche, C.; Monrabal, F.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Benlloch-Rodríguez, J. M.; Borges, F. I. G. M.; Botas, A.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Escada, J.; Esteve, R.; Felkai, R.; Fernandes, L. M. P.; Ferrario, P.; Ferreira, A. L.; Freitas, E. D. C.; Goldschmidt, A.; González-Díaz, D.; Gutiérrez, R. M.; Hauptman, J.; Henriques, C. A. O.; Hernandez, A. I.; Hernando Morata, J. A.; Herrero, V.; Jones, B. J. P.; Labarga, L.; Laing, A.; Lebrun, P.; Liubarsky, I.; López-March, N.; Losada, M.; Martín-Albo, J.; Martínez-Lema, G.; Martínez, A.; McDonald, A. D.; Monteiro, C. M. B.; Mora, F. J.; Moutinho, L. M.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Nygren, D. R.; Palmeiro, B.; Para, A.; Pérez, J.; Querol, M.; Renner, J.; Ripoll, L.; Rodríguez, J.; Rogers, L.; Santos, F. P.; dos Santos, J. M. F.; Sofka, C.; Sorel, M.; Stiegler, T.; Toledo, J. F.; Torrent, J.; Tsamalaidze, Z.; Veloso, J. F. C. A.; Webb, R.; White, J. T.; Yahlali, N.

2017-08-01

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in 136Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.

No Mikheyev-Smirnov-Wolfenstein Effect in Maximal Mixing

OpenAIRE

Harrison, P. F.; Perkins, D. H.; Scott, W. G.

1996-01-01

We investigate the possible influence of the MSW effect on the expectations for the solar neutrino experiments in the maximal mixing scenario suggested by the atmospheric neutrino data. A direct numerical calculation of matter induced effects in the Sun shows that the naive vacuum predictions are left completely undisturbed in the particular case of maximal mixing, so that the MSW effect turns out to be unobservable. We give a qualitative explanation of this result.

Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner

Science.gov (United States)

Ram Yu, A.; Kim, Jin Su

2015-10-01

Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data. Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.

Acceleration of the direct reconstruction of linear parametric images using nested algorithms

International Nuclear Information System (INIS)

Wang Guobao; Qi Jinyi

2010-01-01

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Gap processing for adaptive maximal poisson-disk sampling

KAUST Repository

Yan, Dongming; Wonka, Peter

2013-01-01

In this article, we study the generation of maximal Poisson-disk sets with varying radii. First, we present a geometric analysis of gaps in such disk sets. This analysis is the basis for maximal and adaptive sampling in Euclidean space and on manifolds. Second, we propose efficient algorithms and data structures to detect gaps and update gaps when disks are inserted, deleted, moved, or when their radii are changed.We build on the concepts of regular triangulations and the power diagram. Third, we show how our analysis contributes to the state-of-the-art in surface remeshing. © 2013 ACM.

Gap processing for adaptive maximal poisson-disk sampling

KAUST Repository

Yan, Dongming

2013-10-17

In this article, we study the generation of maximal Poisson-disk sets with varying radii. First, we present a geometric analysis of gaps in such disk sets. This analysis is the basis for maximal and adaptive sampling in Euclidean space and on manifolds. Second, we propose efficient algorithms and data structures to detect gaps and update gaps when disks are inserted, deleted, moved, or when their radii are changed.We build on the concepts of regular triangulations and the power diagram. Third, we show how our analysis contributes to the state-of-the-art in surface remeshing. © 2013 ACM.

A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses

International Nuclear Information System (INIS)

Aman, M.M.; Jasmon, G.B.; Bakar, A.H.A.; Mokhlis, H.

2013-01-01

Highlights: • A new algorithm is proposed for optimum DG placement and sizing.• I 2 R losses minimization and voltage stability maximization is considered in fitness function.• Bus voltage stability and line stability is considered in voltage stability maximization.• Multi-objective PSO is used to solve the problem.• Proposed method is compared with analytical and grid search algorithm. - Abstract: Distributed Generation (DG) placement on the basis of minimization of losses and maximization of system voltage stability are two different approaches, discussed in research. In the new proposed algorithm, a multi-objective approach is used to combine the both approaches together. Minimization of power losses and maximization of voltage stability due to finding weakest voltage bus as well as due to weakest link in the system are considered in the fitness function. Particle Swarm Optimization (PSO) algorithm is used in this paper to solve the multi-objective problem. This paper will also compare the propose method with existing DG placement methods. From results, the proposed method is found more advantageous than the previous work in terms of voltage profile improvement, maximization of system loadability, reduction in power system losses and maximization of bus and line voltage stability. The results are validated on 12-bus, 30-bus, 33-bus and 69-bus radial distribution networks and also discussed in detailed

Statistical trajectory of an approximate EM algorithm for probabilistic image processing

International Nuclear Information System (INIS)

Tanaka, Kazuyuki; Titterington, D M

2007-01-01

We calculate analytically a statistical average of trajectories of an approximate expectation-maximization (EM) algorithm with generalized belief propagation (GBP) and a Gaussian graphical model for the estimation of hyperparameters from observable data in probabilistic image processing. A statistical average with respect to observed data corresponds to a configuration average for the random-field Ising model in spin glass theory. In the present paper, hyperparameters which correspond to interactions and external fields of spin systems are estimated by an approximate EM algorithm. A practical algorithm is described for gray-level image restoration based on a Gaussian graphical model and GBP. The GBP approach corresponds to the cluster variation method in statistical mechanics. Our main result in the present paper is to obtain the statistical average of the trajectory in the approximate EM algorithm by using loopy belief propagation and GBP with respect to degraded images generated from a probability density function with true values of hyperparameters. The statistical average of the trajectory can be expressed in terms of recursion formulas derived from some analytical calculations

Disk Density Tuning of a Maximal Random Packing.

Science.gov (United States)

Ebeida, Mohamed S; Rushdi, Ahmad A; Awad, Muhammad A; Mahmoud, Ahmed H; Yan, Dong-Ming; English, Shawn A; Owens, John D; Bajaj, Chandrajit L; Mitchell, Scott A

2016-08-01

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

Science.gov (United States)

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

2018-04-01

Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

Subjective expected utility with non-increasing risk aversion

NARCIS (Netherlands)

Wakker, P.P.

1989-01-01

It is shown that assumptions about risk aversion, usually studied under the presupposition of expected utility maximization, have a surprising extra merit at an earlier stage of the measurement work: together with the sure-thing principle, these assumptions imply subjective expected utility

Tri-maximal vs. bi-maximal neutrino mixing

International Nuclear Information System (INIS)

Scott, W.G

2000-01-01

It is argued that data from atmospheric and solar neutrino experiments point strongly to tri-maximal or bi-maximal lepton mixing. While ('optimised') bi-maximal mixing gives an excellent a posteriori fit to the data, tri-maximal mixing is an a priori hypothesis, which is not excluded, taking account of terrestrial matter effects

Modeling the ultrasonic testing echoes by a combination of particle swarm optimization and Levenberg–Marquardt algorithms

International Nuclear Information System (INIS)

Gholami, Ali; Honarvar, Farhang; Moghaddam, Hamid Abrishami

2017-01-01

This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg–Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm. (paper)

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

Science.gov (United States)

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Optimization, evaluation, and comparison of standard algorithms for image reconstruction with the VIP-PET.

Science.gov (United States)

Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M

2014-07-01

A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm 3 ) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.

Mining the National Career Assessment Examination Result Using Clustering Algorithm

Science.gov (United States)

Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

2018-03-01

Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

Bilateral Cochlear Implants: Maximizing Expected Outcomes.

Science.gov (United States)

Wallis, Kate E; Blum, Nathan J; Waryasz, Stephanie A; Augustyn, Marilyn

Sonia is a 4 years 1 month-year-old girl with Waardenburg syndrome and bilateral sensorineural hearing loss who had bilateral cochlear implants at 2 years 7 months years of age. She is referred to Developmental-Behavioral Pediatrics by her speech/language pathologist because of concerns that her language skills are not progressing as expected after the cochlear implant. At the time of the implant, she communicated using approximately 20 signs and 1 spoken word (mama). At the time of the evaluation (18 months after the implant) she had approximately 70 spoken words (English and Spanish) and innumerable signs that she used to communicate. She could follow 1-step directions in English but had more difficulty after 2-step directions.Sonia was born in Puerto Rico at 40 weeks gestation after an uncomplicated pregnancy. She failed her newborn hearing test and was given hearing aids that did not seem to help.At age 2 years, Sonia, her mother, and younger sister moved to the United States where she was diagnosed with bilateral severe-to-profound hearing loss. Genetic testing led to a diagnosis of Waardenburg syndrome (group of genetic conditions that can cause hearing loss and changes in coloring [pigmentation] of the hair, skin, and eyes). She received bilateral cochlear implants 6 months later.Sonia's mother is primarily Spanish-speaking and mostly communicates with her in Spanish or with gestures but has recently begun to learn American Sign Language (ASL). In a preschool program at a specialized school for the deaf, Sonia is learning both English and ASL. Sonia seems to prefer to use ASL to communicate.Sonia receives speech and language therapy (SLT) 3 times per week (90 minutes total) individually in school and once per week within a group. She is also receiving outpatient SLT once per week. Therapy sessions are completed in English, with the aid of an ASL interpreter. Sonia's language scores remain low, with her receptive skills in the first percentile, and her

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.

Reconstruction of core axial power shapes using the alternating conditional expectation algorithm

International Nuclear Information System (INIS)

Lee, Eun Ki; Kim, Yong Hee; Cha, Kune Ho; Park, Moon Ghu

1999-01-01

We have introduced the alternating conditional expectation (ACE) algorithm in reconstructing 20-node axial core power shapes from five-level in-core detector powers. The core design code, Reactor Operation and Control Simulation (ROCS), calculates 3-dimensional power distributions for various core states, and the reference core-averaged axial power shapes and corresponding simulated detector powers are utilized to synthesize the axial power shape. By using the ACE algorithm, the optimal relationship between a dependent variable, the plane power, and independent variables, five detector powers, is determined without any preprocessing. A total of ∼3490 data sets per each cycle of YongGwang Nuclear (YGN) power plant units 3 and 4 is used for the regression. Continuous analytic function corresponding to each optimal transformation is calculated by simple regression model. The reconstructed axial power shapes of ∼21,200 cases are compared to the original ROCS axial power shapes. Also, to test the validity and accuracy of the new method, its performance is compared with that of the Fourier fitting method (FFM), a typical method of the deterministic approach. For a total of 21,204 data cases, the averages of root mean square (rms) error, axial peak error (ΔF z ), and axial shape index error (ΔASI) of new method are calculated as 0.81%, 0.51% and 0.00204, while those of FFM are 2.29%, 2.37% and 0.00264, respectively. We also evaluated the wide range of axial power profiles from the xenon-oscillation. The results show that the newly developed method is far superior to FFM; average rms and axial peak error are just ∼35 and ∼20% of those of FFM, respectively

Unsupervised classification of multivariate geostatistical data: Two algorithms

Science.gov (United States)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

Science.gov (United States)

Kreitler, Jason R.; Stoms, David M.; Davis, Frank W.

2014-01-01

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

Change detection algorithms for surveillance in visual iot: a comparative study

International Nuclear Information System (INIS)

Akram, B.A.; Zafar, A.; Akbar, A.H.; Chaudhry, A.

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule’s Coefficient) and JC (Jaccard’s Coefficient), execution time and memory consumption. Experimental results showed that Kapur’s algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes. (author)

Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction

International Nuclear Information System (INIS)

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-01-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constraint involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the PAPA. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. (paper)

A Receiver for Differential Space-Time -Shifted BPSK Modulation Based on Scalar-MSDD and the EM Algorithm

Directory of Open Access Journals (Sweden)

Kim Jae H

2005-01-01

Full Text Available In this paper, we consider the issue of blind detection of Alamouti-type differential space-time (ST modulation in static Rayleigh fading channels. We focus our attention on a -shifted BPSK constellation, introducing a novel transformation to the received signal such that this binary ST modulation, which has a second-order transmit diversity, is equivalent to QPSK modulation with second-order receive diversity. This equivalent representation allows us to apply a low-complexity detection technique specifically designed for receive diversity, namely, scalar multiple-symbol differential detection (MSDD. To further increase receiver performance, we apply an iterative expectation-maximization (EM algorithm which performs joint channel estimation and sequence detection. This algorithm uses minimum mean square estimation to obtain channel estimates and the maximum-likelihood principle to detect the transmitted sequence, followed by differential decoding. With receiver complexity proportional to the observation window length, our receiver can achieve the performance of a coherent maximal ratio combining receiver (with differential decoding in as few as a single EM receiver iteration, provided that the window size of the initial MSDD is sufficiently long. To further demonstrate that the MSDD is a vital part of this receiver setup, we show that an initial ST conventional differential detector would lead to strange convergence behavior in the EM algorithm.

Maximal Conflict Set Enumeration Algorithm Based on Locality of Petri Nets%基于Pe tri网局部性的极大冲突集枚举算法

Institute of Scientific and Technical Information of China (English)

潘理; 郑红; 刘显明; 杨勃

2016-01-01

冲突是Petri网研究的重要主题。目前Petri网冲突研究主要集中于冲突建模和冲突消解策略，而对冲突问题本身的计算复杂性却很少关注。提出Petri网的冲突集问题，并证明冲突集问题是NP（Non-deterministic Polyno-mial）完全的。提出极大冲突集动态枚举算法，该算法基于当前标识的所有极大冲突集，利用Petri网实施局部性，仅计算下一标识中受局部性影响的极大冲突集，从而避免重新枚举所有极大冲突集。该算法时间复杂度为O（m2 n），m是当前标识的极大冲突集数目，n是变迁数。最后证明自由选择网、非对称选择网的极大冲突集枚举算法复杂度可降至O（n2）。极大冲突集枚举算法研究将为Petri网冲突问题的算法求解提供理论参考。%Conflict is an essential concept in Petri net theory.The existing research focuses on the modelling and resolu-tion strategies of conflict problems,but less on the computational complexity of the problems theirselves.In this paper,we pro-pose the conflict set problem for Petri nets,and prove that the conflict set problem is NP-complete.Furthermore,we present a dynamic algorithm for the maximal conflict set enumeration.Our algorithm only computes those conflict sets that are affected by local firing,which avoids enumerating all maximal conflict sets at each marking.The algorithm needs time O(m2n)where m is the number of maximal conflict sets at the current marking and n is the number of transitions.Finally,we show that the maximal conflict set enumeration problem can be solved in O(n2)for free-choice nets and asymmetric choice nets.The results on complexity of thel conflict set problem provide a theoretical reference for solving conflict problems of Petri nets.

Pseudo-deterministic Algorithms

OpenAIRE

Goldwasser , Shafi

2012-01-01

International audience; In this talk we describe a new type of probabilistic algorithm which we call Bellagio Algorithms: a randomized algorithm which is guaranteed to run in expected polynomial time, and to produce a correct and unique solution with high probability. These algorithms are pseudo-deterministic: they can not be distinguished from deterministic algorithms in polynomial time by a probabilistic polynomial time observer with black box access to the algorithm. We show a necessary an...

Nonlinear spatio-temporal filtering of dynamic PET data using a four-dimensional Gaussian filter and expectation-maximization deconvolution

International Nuclear Information System (INIS)

Floberg, J M; Holden, J E

2013-01-01

We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering with EM deconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications. (paper)

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

Science.gov (United States)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

Interval Solution for Nonlinear Programming of Maximizing the Fatigue Life of V-Belt under Polymorphic Uncertain Environment

Directory of Open Access Journals (Sweden)

Zhong Wan

2013-01-01

Full Text Available In accord with the practical engineering design conditions, a nonlinear programming model is constructed for maximizing the fatigue life of V-belt drive in which some polymorphic uncertainties are incorporated. For a given satisfaction level and a confidence level, an equivalent formulation of this uncertain optimization model is obtained where only interval parameters are involved. Based on the concepts of maximal and minimal range inequalities for describing interval inequality, the interval parameter model is decomposed into two standard nonlinear programming problems, and an algorithm, called two-step based sampling algorithm, is developed to find an interval optimal solution for the original problem. Case study is employed to demonstrate the validity and practicability of the constructed model and the algorithm.

Can monkeys make investments based on maximized pay-off?

Directory of Open Access Journals (Sweden)

Sophie Steelandt

2011-03-01

Full Text Available Animals can maximize benefits but it is not known if they adjust their investment according to expected pay-offs. We investigated whether monkeys can use different investment strategies in an exchange task. We tested eight capuchin monkeys (Cebus apella and thirteen macaques (Macaca fascicularis, Macaca tonkeana in an experiment where they could adapt their investment to the food amounts proposed by two different experimenters. One, the doubling partner, returned a reward that was twice the amount given by the subject, whereas the other, the fixed partner, always returned a constant amount regardless of the amount given. To maximize pay-offs, subjects should invest a maximal amount with the first partner and a minimal amount with the second. When tested with the fixed partner only, one third of monkeys learned to remove a maximal amount of food for immediate consumption before investing a minimal one. With both partners, most subjects failed to maximize pay-offs by using different decision rules with each partner' quality. A single Tonkean macaque succeeded in investing a maximal amount to one experimenter and a minimal amount to the other. The fact that only one of over 21 subjects learned to maximize benefits in adapting investment according to experimenters' quality indicates that such a task is difficult for monkeys, albeit not impossible.

Expecting the unexpected: applying the Develop-Distort Dilemma to maximize positive market impacts in health.

Science.gov (United States)

Peters, David H; Paina, Ligia; Bennett, Sara

2012-10-01

Although health interventions start with good intentions to develop services for disadvantaged populations, they often distort the health market, making the delivery or financing of services difficult once the intervention is over: a condition called the 'Develop-Distort Dilemma' (DDD). In this paper, we describe how to examine whether a proposed intervention may develop or distort the health market. Our goal is to produce a tool that facilitates meaningful and systematic dialogue for practitioners and researchers to ensure that well-intentioned health interventions lead to productive health systems while reducing the undesirable distortions of such efforts. We apply the DDD tool to plan for development rather than distortions in health markets, using intervention research being conducted under the Future Health Systems consortium in Bangladesh, China and Uganda. Through a review of research proposals and interviews with principal investigators, we use the DDD tool to systematically understand how a project fits within the broader health market system, and to identify gaps in planning for sustainability. We found that while current stakeholders and funding sources for activities were easily identified, future ones were not. The implication is that the projects could raise community expectations that future services will be available and paid for, despite this actually being uncertain. Each project addressed the 'rules' of the health market system differently. The China research assesses changes in the formal financing rules, whereas Bangladesh and Uganda's projects involve influencing community level providers, where informal rules are more important. In each case, we recognize the importance of building trust between providers, communities and government officials. Each project could both develop and distort local health markets. Anyone intervening in the health market must recognize the main market perturbations, whether positive or negative, and manage them so

Analysing Music with Point-Set Compression Algorithms

DEFF Research Database (Denmark)

Meredith, David

2016-01-01

Several point-set pattern-discovery and compression algorithms designed for analysing music are reviewed and evaluated. Each algorithm takes as input a point-set representation of a score in which each note is represented as a point in pitch-time space. Each algorithm computes the maximal...... and sections in pieces of classical music. On the first task, the best-performing algorithms achieved success rates of around 84%. In the second task, the best algorithms achieved mean F1 scores of around 0.49, with scores for individual pieces rising as high as 0.71....

Phenomenology of maximal and near-maximal lepton mixing

International Nuclear Information System (INIS)

Gonzalez-Garcia, M. C.; Pena-Garay, Carlos; Nir, Yosef; Smirnov, Alexei Yu.

2001-01-01

The possible existence of maximal or near-maximal lepton mixing constitutes an intriguing challenge for fundamental theories of flavor. We study the phenomenological consequences of maximal and near-maximal mixing of the electron neutrino with other (x=tau and/or muon) neutrinos. We describe the deviations from maximal mixing in terms of a parameter ε(equivalent to)1-2sin 2 θ ex and quantify the present experimental status for |ε| e mixing comes from solar neutrino experiments. We find that the global analysis of solar neutrino data allows maximal mixing with confidence level better than 99% for 10 -8 eV 2 ∼ 2 ∼ -7 eV 2 . In the mass ranges Δm 2 ∼>1.5x10 -5 eV 2 and 4x10 -10 eV 2 ∼ 2 ∼ -7 eV 2 the full interval |ε| e mixing in atmospheric neutrinos, supernova neutrinos, and neutrinoless double beta decay

Enumerating all maximal frequent subtrees in collections of phylogenetic trees.

Science.gov (United States)

Deepak, Akshay; Fernández-Baca, David

2014-01-01

A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees.

The geometry of entanglement and Grover's algorithm

International Nuclear Information System (INIS)

Iwai, Toshihiro; Hayashi, Naoki; Mizobe, Kimitake

2008-01-01

A measure of entanglement with respect to a bipartite partition of n-qubit has been defined and studied from the viewpoint of Riemannian geometry (Iwai 2007 J. Phys. A: Math. Theor. 40 12161). This paper has two aims. One is to study further the geometry of entanglement, and the other is to investigate Grover's search algorithms, both the original and the fixed-point ones, in reference with entanglement. As the distance between the maximally entangled states and the separable states is known already in the previous paper, this paper determines the set of maximally entangled states nearest to a typical separable state which is used as an initial state in Grover's search algorithms, and to find geodesic segments which realize the above-mentioned distance. As for Grover's algorithms, it is already known that while the initial and the target states are separable, the algorithms generate sequences of entangled states. This fact is confirmed also in the entanglement measure proposed in the previous paper, and then a split Grover algorithm is proposed which generates sequences of separable states only with respect to the bipartite partition

Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering

KAUST Repository

Fonarev, Alexander; Mikhalev, Alexander; Serdyukov, Pavel; Gusev, Gleb; Oseledets, Ivan

2017-01-01

preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation - a seed set of a

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

Science.gov (United States)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

Assessment of two aerosol optical thickness retrieval algorithms applied to MODIS Aqua and Terra measurements in Europe

Directory of Open Access Journals (Sweden)

P. Glantz

2012-07-01

Full Text Available The aim of the present study is to validate AOT (aerosol optical thickness and Ångström exponent (α, obtained from MODIS (MODerate resolution Imaging Spectroradiometer Aqua and Terra calibrated level 1 data (1 km horizontal resolution at ground with the SAER (Satellite AErosol Retrieval algorithm and with MODIS Collection 5 (c005 standard product retrievals (10 km horizontal resolution, against AERONET (AErosol RObotic NETwork sun photometer observations over land surfaces in Europe. An inter-comparison of AOT at 0.469 nm obtained with the two algorithms has also been performed. The time periods investigated were chosen to enable a validation of the findings of the two algorithms for a maximal possible variation in sun elevation. The satellite retrievals were also performed with a significant variation in the satellite-viewing geometry, since Aqua and Terra passed the investigation area twice a day for several of the cases analyzed. The validation with AERONET shows that the AOT at 0.469 and 0.555 nm obtained with MODIS c005 is within the expected uncertainty of one standard deviation of the MODIS c005 retrievals (ΔAOT = ± 0.05 ± 0.15 · AOT. The AOT at 0.443 nm retrieved with SAER, but with a much finer spatial resolution, also agreed reasonably well with AERONET measurements. The majority of the SAER AOT values are within the MODIS c005 expected uncertainty range, although somewhat larger average absolute deviation occurs compared to the results obtained with the MODIS c005 algorithm. The discrepancy between AOT from SAER and AERONET is, however, substantially larger for the wavelength 488 nm. This means that the values are, to a larger extent, outside of the expected MODIS uncertainty range. In addition, both satellite retrieval algorithms are unable to estimate α accurately, although the MODIS c005 algorithm performs better. Based on the inter-comparison of the SAER and MODIS c005 algorithms, it was found that SAER on the whole is

Aging and loss decision making: increased risk aversion and decreased use of maximizing information, with correlated rationality and value maximization.

Science.gov (United States)

Kurnianingsih, Yoanna A; Sim, Sam K Y; Chee, Michael W L; Mullette-Gillman, O'Dhaniel A

2015-01-01

We investigated how adult aging specifically alters economic decision-making, focusing on examining alterations in uncertainty preferences (willingness to gamble) and choice strategies (what gamble information influences choices) within both the gains and losses domains. Within each domain, participants chose between certain monetary outcomes and gambles with uncertain outcomes. We examined preferences by quantifying how uncertainty modulates choice behavior as if altering the subjective valuation of gambles. We explored age-related preferences for two types of uncertainty, risk, and ambiguity. Additionally, we explored how aging may alter what information participants utilize to make their choices by comparing the relative utilization of maximizing and satisficing information types through a choice strategy metric. Maximizing information was the ratio of the expected value of the two options, while satisficing information was the probability of winning. We found age-related alterations of economic preferences within the losses domain, but no alterations within the gains domain. Older adults (OA; 61-80 years old) were significantly more uncertainty averse for both risky and ambiguous choices. OA also exhibited choice strategies with decreased use of maximizing information. Within OA, we found a significant correlation between risk preferences and choice strategy. This linkage between preferences and strategy appears to derive from a convergence to risk neutrality driven by greater use of the effortful maximizing strategy. As utility maximization and value maximization intersect at risk neutrality, this result suggests that OA are exhibiting a relationship between enhanced rationality and enhanced value maximization. While there was variability in economic decision-making measures within OA, these individual differences were unrelated to variability within examined measures of cognitive ability. Our results demonstrate that aging alters economic decision-making for

Aging and loss decision making: increased risk aversion and decreased use of maximizing information, with correlated rationality and value maximization

Directory of Open Access Journals (Sweden)

Yoanna Arlina Kurnianingsih

2015-05-01

Full Text Available We investigated how adult aging specifically alters economic decision-making, focusing on examining alterations in uncertainty preferences (willingness to gamble and choice strategies (what gamble information influences choices within both the gains and losses domains. Within each domain, participants chose between certain monetary outcomes and gambles with uncertain outcomes. We examined preferences by quantifying how uncertainty modulates choice behavior as if altering the subjective valuation of gambles. We explored age-related preferences for two types of uncertainty, risk and ambiguity. Additionally, we explored how aging may alter what information participants utilize to make their choices by comparing the relative utilization of maximizing and satisficing information types through a choice strategy metric. Maximizing information was the ratio of the expected value of the two options, while satisficing information was the probability of winning.We found age-related alterations of economic preferences within the losses domain, but no alterations within the gains domain. Older adults (OA; 61 to 80 years old were significantly more uncertainty averse for both risky and ambiguous choices. OA also exhibited choice strategies with decreased use of maximizing information. Within OA, we found a significant correlation between risk preferences and choice strategy. This linkage between preferences and strategy appears to derive from a convergence to risk neutrality driven by greater use of the effortful maximizing strategy. As utility maximization and value maximization intersect at risk neutrality, this result suggests that OA are exhibiting a relationship between enhanced rationality and enhanced value maximization. While there was variability in economic decision-making measures within OA, these individual differences were unrelated to variability within examined measures of cognitive ability. Our results demonstrate that aging alters economic

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.

Enumerating all maximal frequent subtrees in collections of phylogenetic trees

Science.gov (United States)

2014-01-01

Background A common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events. Results We give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/. Conclusions Our computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees. PMID:25061474

Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing

Directory of Open Access Journals (Sweden)

Martorella Marco

2006-01-01

Full Text Available Image contrast maximization and entropy minimization are two commonly used techniques for ISAR image autofocusing. When the signal phase history due to the target radial motion has to be approximated with high order polynomial models, classic optimization techniques fail when attempting to either maximize the image contrast or minimize the image entropy. In this paper a solution of this problem is proposed by using genetic algorithms. The performances of the new algorithms that make use of genetic algorithms overcome the problem with previous implementations based on deterministic approaches. Tests on real data of airplanes and ships confirm the insight.

Contributions of leaf photosynthetic capacity, leaf angle and self-shading to the maximization of net photosynthesis in Acer saccharum: a modelling assessment.

Science.gov (United States)

Posada, Juan M; Sievänen, Risto; Messier, Christian; Perttunen, Jari; Nikinmaa, Eero; Lechowicz, Martin J

2012-08-01

Plants are expected to maximize their net photosynthetic gains and efficiently use available resources, but the fundamental principles governing trade-offs in suites of traits related to resource-use optimization remain uncertain. This study investigated whether Acer saccharum (sugar maple) saplings could maximize their net photosynthetic gains through a combination of crown structure and foliar characteristics that let all leaves maximize their photosynthetic light-use efficiency (ε). A functional-structural model, LIGNUM, was used to simulate individuals of different leaf area index (LAI(ind)) together with a genetic algorithm to find distributions of leaf angle (L(A)) and leaf photosynthetic capacity (A(max)) that maximized net carbon gain at the whole-plant level. Saplings grown in either the open or in a forest gap were simulated with A(max) either unconstrained or constrained to an upper value consistent with reported values for A(max) in A. saccharum. It was found that total net photosynthetic gain was highest when whole-plant PPFD absorption and leaf ε were simultaneously maximized. Maximization of ε required simultaneous adjustments in L(A) and A(max) along gradients of PPFD in the plants. When A(max) was constrained to a maximum, plants growing in the open maximized their PPFD absorption but not ε because PPFD incident on leaves was higher than the PPFD at which ε(max) was attainable. Average leaf ε in constrained plants nonetheless improved with increasing LAI(ind) because of an increase in self-shading. It is concluded that there are selective pressures for plants to simultaneously maximize both PPFD absorption at the scale of the whole individual and ε at the scale of leaves, which requires a highly integrated response between L(A), A(max) and LAI(ind). The results also suggest that to maximize ε plants have evolved mechanisms that co-ordinate the L(A) and A(max) of individual leaves with PPFD availability.

Autism spectrum disorders and fetal hypoxia in a population-based cohort: Accounting for missing exposures via Estimation-Maximization algorithm

Directory of Open Access Journals (Sweden)

Yasui Yutaka

2011-01-01

Full Text Available Abstract Background Autism spectrum disorders (ASD are associated with complications of pregnancy that implicate fetal hypoxia (FH; the excess of ASD in male gender is poorly understood. We tested the hypothesis that risk of ASD is related to fetal hypoxia and investigated whether this effect is greater among males. Methods Provincial delivery records (PDR identified the cohort of all 218,890 singleton live births in the province of Alberta, Canada, between 01-01-98 and 12-31-04. These were followed-up for ASD via ICD-9 diagnostic codes assigned by physician billing until 03-31-08. Maternal and obstetric risk factors, including FH determined from blood tests of acidity (pH, were extracted from PDR. The binary FH status was missing in approximately half of subjects. Assuming that characteristics of mothers and pregnancies would be correlated with FH, we used an Estimation-Maximization algorithm to estimate HF-ASD association, allowing for both missing-at-random (MAR and specific not-missing-at-random (NMAR mechanisms. Results Data indicated that there was excess risk of ASD among males who were hypoxic at birth, not materially affected by adjustment for potential confounding due to birth year and socio-economic status: OR 1.13, 95%CI: 0.96, 1.33 (MAR assumption. Limiting analysis to full-term males, the adjusted OR under specific NMAR assumptions spanned 95%CI of 1.0 to 1.6. Conclusion Our results are consistent with a weak effect of fetal hypoxia on risk of ASD among males. E-M algorithm is an efficient and flexible tool for modeling missing data in the studied setting.

Maximization of Energy Efficiency in Wireless ad hoc and Sensor Networks With SERENA

Directory of Open Access Journals (Sweden)

Saoucene Mahfoudh

2009-01-01

Full Text Available In wireless ad hoc and sensor networks, an analysis of the node energy consumption distribution shows that the largest part is due to the time spent in the idle state. This result is at the origin of SERENA, an algorithm to SchEdule RoutEr Nodes Activity. SERENA allows router nodes to sleep, while ensuring end-to-end communication in the wireless network. It is a localized and decentralized algorithm assigning time slots to nodes. Any node stays awake only during its slot and the slots assigned to its neighbors, it sleeps the remaining time. Simulation results show that SERENA enables us to maximize network lifetime while increasing the number of user messages delivered. SERENA is based on a two-hop coloring algorithm, whose complexity in terms of colors and rounds is evaluated. We then quantify the slot reuse. Finally, we show how SERENA improves the node energy consumption distribution and maximizes the energy efficiency of wireless ad hoc and sensor networks. We compare SERENA with classical TDMA and optimized variants such as USAP in wireless ad hoc and sensor networks.

On Maximizing the Throughput of Packet Transmission under Energy Constraints.

Science.gov (United States)

Wu, Weiwei; Dai, Guangli; Li, Yan; Shan, Feng

2018-06-23

More and more Internet of Things (IoT) wireless devices have been providing ubiquitous services over the recent years. Since most of these devices are powered by batteries, a fundamental trade-off to be addressed is the depleted energy and the achieved data throughput in wireless data transmission. By exploiting the rate-adaptive capacities of wireless devices, most existing works on energy-efficient data transmission try to design rate-adaptive transmission policies to maximize the amount of transmitted data bits under the energy constraints of devices. Such solutions, however, cannot apply to scenarios where data packets have respective deadlines and only integrally transmitted data packets contribute. Thus, this paper introduces a notion of weighted throughput, which measures how much total value of data packets are successfully and integrally transmitted before their own deadlines. By designing efficient rate-adaptive transmission policies, this paper aims to make the best use of the energy and maximize the weighted throughput. What is more challenging but with practical significance, we consider the fading effect of wireless channels in both offline and online scenarios. In the offline scenario, we develop an optimal algorithm that computes the optimal solution in pseudo-polynomial time, which is the best possible solution as the problem undertaken is NP-hard. In the online scenario, we propose an efficient heuristic algorithm based on optimal properties derived for the optimal offline solution. Simulation results validate the efficiency of the proposed algorithm.

Maximizing Expected Achievable Rates for Block-Fading Buffer-Aided Relay Channels

KAUST Repository

Shaqfeh, Mohammad

2016-05-25

In this paper, the long-term average achievable rate over block-fading buffer-aided relay channels is maximized using a hybrid scheme that combines three essential transmission strategies, which are decode-and-forward, compress-and-forward, and direct transmission. The proposed hybrid scheme is dynamically adapted based on the channel state information. The integration and optimization of these three strategies provide a more generic and fundamental solution and give better achievable rates than the known schemes in the literature. Despite the large number of optimization variables, the proposed hybrid scheme can be optimized using simple closed-form formulas that are easy to apply in practical relay systems. This includes adjusting the transmission rate and compression when compress-and-forward is the selected strategy based on the channel conditions. Furthermore, in this paper, the hybrid scheme is applied to three different models of the Gaussian block-fading buffer-aided relay channels, depending on whether the relay is half or full duplex and whether the source and the relay have orthogonal or non-orthogonal channel access. Several numerical examples are provided to demonstrate the achievable rate results and compare them to the upper bounds of the ergodic capacity for each one of the three channel models under consideration.

Maximizing Expected Achievable Rates for Block-Fading Buffer-Aided Relay Channels

KAUST Repository

Shaqfeh, Mohammad; Zafar, Ammar; Alnuweiri, Hussein; Alouini, Mohamed-Slim

2016-01-01

In this paper, the long-term average achievable rate over block-fading buffer-aided relay channels is maximized using a hybrid scheme that combines three essential transmission strategies, which are decode-and-forward, compress-and-forward, and direct transmission. The proposed hybrid scheme is dynamically adapted based on the channel state information. The integration and optimization of these three strategies provide a more generic and fundamental solution and give better achievable rates than the known schemes in the literature. Despite the large number of optimization variables, the proposed hybrid scheme can be optimized using simple closed-form formulas that are easy to apply in practical relay systems. This includes adjusting the transmission rate and compression when compress-and-forward is the selected strategy based on the channel conditions. Furthermore, in this paper, the hybrid scheme is applied to three different models of the Gaussian block-fading buffer-aided relay channels, depending on whether the relay is half or full duplex and whether the source and the relay have orthogonal or non-orthogonal channel access. Several numerical examples are provided to demonstrate the achievable rate results and compare them to the upper bounds of the ergodic capacity for each one of the three channel models under consideration.

Algorithms for Reinforcement Learning

CERN Document Server

Szepesvari, Csaba

2010-01-01

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters.

Directory of Open Access Journals (Sweden)

Kaarina Matilainen

Full Text Available Estimation of variance components by Monte Carlo (MC expectation maximization (EM restricted maximum likelihood (REML is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR, where the information matrix was generated via sampling; MC average information(AI, where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

Maximal Bell's inequality violation for non-maximal entanglement

International Nuclear Information System (INIS)

Kobayashi, M.; Khanna, F.; Mann, A.; Revzen, M.; Santana, A.

2004-01-01

Bell's inequality violation (BIQV) for correlations of polarization is studied for a product state of two two-mode squeezed vacuum (TMSV) states. The violation allowed is shown to attain its maximal limit for all values of the squeezing parameter, ζ. We show via an explicit example that a state whose entanglement is not maximal allow maximal BIQV. The Wigner function of the state is non-negative and the average value of either polarization is nil

Primal Decomposition-Based Method for Weighted Sum-Rate Maximization in Downlink OFDMA Systems

Directory of Open Access Journals (Sweden)

Weeraddana Chathuranga

2010-01-01

Full Text Available We consider the weighted sum-rate maximization problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA systems. Motivated by the increasing popularity of OFDMA in future wireless technologies, a low complexity suboptimal resource allocation algorithm is obtained for joint optimization of multiuser subcarrier assignment and power allocation. The algorithm is based on an approximated primal decomposition-based method, which is inspired from exact primal decomposition techniques. The original nonconvex optimization problem is divided into two subproblems which can be solved independently. Numerical results are provided to compare the performance of the proposed algorithm to Lagrange relaxation based suboptimal methods as well as to optimal exhaustive search-based method. Despite its reduced computational complexity, the proposed algorithm provides close-to-optimal performance.

Maximizing carbon storage in the Appalachians: A method for considering the risk of disturbance events

Science.gov (United States)

Michael R. Vanderberg; Kevin Boston; John. Bailey

2011-01-01

Accounting for the probability of loss due to disturbance events can influence the prediction of carbon flux over a planning horizon, and can affect the determination of optimal silvicultural regimes to maximize terrestrial carbon storage. A preliminary model that includes forest disturbance-related carbon loss was developed to maximize expected values of carbon stocks...

An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve

Generation of Referring Expressions: Assessing the Incremental Algorithm

Science.gov (United States)

van Deemter, Kees; Gatt, Albert; van der Sluis, Ielka; Power, Richard

2012-01-01

A substantial amount of recent work in natural language generation has focused on the generation of "one-shot" referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We…

Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

Science.gov (United States)

Latos, Dorota; Kolanowski, Bogdan; Pachelski, Wojciech; Sołoducha, Ryszard

2017-12-01

Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object's behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).

Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

Directory of Open Access Journals (Sweden)

Latos Dorota

2017-12-01

Full Text Available Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar.

Power maximization of a point absorber wave energy converter using improved model predictive control

Science.gov (United States)

Milani, Farideh; Moghaddam, Reihaneh Kardehi

2017-08-01

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Randomized Filtering Algorithms

DEFF Research Database (Denmark)

Katriel, Irit; Van Hentenryck, Pascal

2008-01-01

of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...

Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering

KAUST Repository

Fonarev, Alexander

2017-02-07

Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation - a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method\\'s error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.

A Smoothing-Type Algorithm for Solving Linear Complementarity Problems with Strong Convergence Properties

International Nuclear Information System (INIS)

Huang Zhenghai; Gu Weizhe

2008-01-01

In this paper, we construct an augmented system of the standard monotone linear complementarity problem (LCP), and establish the relations between the augmented system and the LCP. We present a smoothing-type algorithm for solving the augmented system. The algorithm is shown to be globally convergent without assuming any prior knowledge of feasibility/infeasibility of the problem. In particular, if the LCP has a solution, then the algorithm either generates a maximal complementary solution of the LCP or detects correctly solvability of the LCP, and in the latter case, an existing smoothing-type algorithm can be directly applied to solve the LCP without any additional assumption and it generates a maximal complementary solution of the LCP; and that if the LCP is infeasible, then the algorithm detect correctly infeasibility of the LCP. To the best of our knowledge, such properties have not appeared in the existing literature for smoothing-type algorithms

Do Speakers and Listeners Observe the Gricean Maxim of Quantity?

Science.gov (United States)

Engelhardt, Paul E.; Bailey, Karl G. D.; Ferreira, Fernanda

2006-01-01

The Gricean Maxim of Quantity is believed to govern linguistic performance. Speakers are assumed to provide as much information as required for referent identification and no more, and listeners are believed to expect unambiguous but concise descriptions. In three experiments we examined the extent to which naive participants are sensitive to the…

Automatic control algorithm effects on energy production

Science.gov (United States)

Mcnerney, G. M.

1981-01-01

A computer model was developed using actual wind time series and turbine performance data to simulate the power produced by the Sandia 17-m VAWT operating in automatic control. The model was used to investigate the influence of starting algorithms on annual energy production. The results indicate that, depending on turbine and local wind characteristics, a bad choice of a control algorithm can significantly reduce overall energy production. The model can be used to select control algorithms and threshold parameters that maximize long term energy production. The results from local site and turbine characteristics were generalized to obtain general guidelines for control algorithm design.

Maximal sfermion flavour violation in super-GUTs

CERN Document Server

AUTHOR|(CDS)2108556; Velasco-Sevilla, Liliana

2016-01-01

We consider supersymmetric grand unified theories with soft supersymmetry-breaking scalar masses $m_0$ specified above the GUT scale (super-GUTs) and patterns of Yukawa couplings motivated by upper limits on flavour-changing interactions beyond the Standard Model. If the scalar masses are smaller than the gaugino masses $m_{1/2}$, as is expected in no-scale models, the dominant effects of renormalization between the input scale and the GUT scale are generally expected to be those due to the gauge couplings, which are proportional to $m_{1/2}$ and generation-independent. In this case, the input scalar masses $m_0$ may violate flavour maximally, a scenario we call MaxFV, and there is no supersymmetric flavour problem. We illustrate this possibility within various specific super-GUT scenarios that are deformations of no-scale gravity.

Performance in population models for count data, part II: a new SAEM algorithm

Science.gov (United States)

Savic, Radojka; Lavielle, Marc

2009-01-01

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (1). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13 % for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7 % for all explored scenarios. The longest CPU time was 95s for parameter estimation and 56s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009). PMID:19680795

A Lyapunov based approach to energy maximization in renewable energy technologies

Science.gov (United States)

Iyasere, Erhun

This dissertation describes the design and implementation of Lyapunov-based control strategies for the maximization of the power captured by renewable energy harnessing technologies such as (i) a variable speed, variable pitch wind turbine, (ii) a variable speed wind turbine coupled to a doubly fed induction generator, and (iii) a solar power generating system charging a constant voltage battery. First, a torque control strategy is presented to maximize wind energy captured in variable speed, variable pitch wind turbines at low to medium wind speeds. The proposed strategy applies control torque to the wind turbine pitch and rotor subsystems to simultaneously control the blade pitch and tip speed ratio, via the rotor angular speed, to an optimum point at which the capture efficiency is maximum. The control method allows for aerodynamic rotor power maximization without exact knowledge of the wind turbine model. A series of numerical results show that the wind turbine can be controlled to achieve maximum energy capture. Next, a control strategy is proposed to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximal for a particular blade pitch angle and wind speed by using the generator rotor voltage as a control input. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge. Representative numerical results demonstrate that the wind turbine can be controlled to achieve near maximum energy capture. Finally, a power system consisting of a photovoltaic (PV) array panel, dc-to-dc switching converter, charging a battery is considered wherein the environmental conditions are time-varying. A backstepping PWM controller is developed to maximize the power of the solar generating

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

Science.gov (United States)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

Directory of Open Access Journals (Sweden)

Christian L Barrett

2006-05-01

Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

Principles of maximally classical and maximally realistic quantum ...

Indian Academy of Sciences (India)

Principles of maximally classical and maximally realistic quantum mechanics. S M ROY. Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India. Abstract. Recently Auberson, Mahoux, Roy and Singh have proved a long standing conjecture of Roy and Singh: In 2N-dimensional phase space, ...

Clinical evaluation of iterative reconstruction (ordered-subset expectation maximization) in dynamic positron emission tomography: quantitative effects on kinetic modeling with N-13 ammonia in healthy subjects

DEFF Research Database (Denmark)

Hove, Jens D; Rasmussen, Rune; Freiberg, Jacob

2008-01-01

BACKGROUND: The purpose of this study was to investigate the quantitative properties of ordered-subset expectation maximization (OSEM) on kinetic modeling with nitrogen 13 ammonia compared with filtered backprojection (FBP) in healthy subjects. METHODS AND RESULTS: Cardiac N-13 ammonia positron...... emission tomography (PET) studies from 20 normal volunteers at rest and during dipyridamole stimulation were analyzed. Image data were reconstructed with either FBP or OSEM. FBP- and OSEM-derived input functions and tissue curves were compared together with the myocardial blood flow and spillover values...... and OSEM flow values were observed with a flow underestimation of 45% (rest/dipyridamole) in the septum and of 5% (rest) and 15% (dipyridamole) in the lateral myocardial wall. CONCLUSIONS: OSEM reconstruction of myocardial perfusion images with N-13 ammonia and PET produces high-quality images for visual...

SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation

Energy Technology Data Exchange (ETDEWEB)

Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D [Washington University School of Medicine, St Louis, MO (United States)

2016-06-15

Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and

SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation

International Nuclear Information System (INIS)

Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D

2016-01-01

Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and

Modeling Multioperator Multi-UAV Operator Attention Allocation Problem Based on Maximizing the Global Reward

Directory of Open Access Journals (Sweden)

Yuhang Wu

2016-01-01

Full Text Available This paper focuses on the attention allocation problem (AAP in modeling multioperator multi-UAV (MOMU, with the operator model and task properties taken into consideration. The model of MOMU operator AAP based on maximizing the global reward is established and used to allocate tasks to all operators as well as set work time and rest time to each task simultaneously for operators. The proposed model is validated in Matlab simulation environment, using the immune algorithm and dynamic programming algorithm to evaluate the performance of the model in terms of the reward value with regard to the work time, rest time, and task allocation. The result shows that the total reward of the proposed model is larger than the one obtained from previously published methods using local maximization and the total reward of our method has an exponent-like relation with the task arrival rate. The proposed model can improve the operators’ task processing efficiency in the MOMU command and control scenarios.

Profit maximization mitigates competition

DEFF Research Database (Denmark)

Dierker, Egbert; Grodal, Birgit

1996-01-01

We consider oligopolistic markets in which the notion of shareholders' utility is well-defined and compare the Bertrand-Nash equilibria in case of utility maximization with those under the usual profit maximization hypothesis. Our main result states that profit maximization leads to less price...... competition than utility maximization. Since profit maximization tends to raise prices, it may be regarded as beneficial for the owners as a whole. Moreover, if profit maximization is a good proxy for utility maximization, then there is no need for a general equilibrium analysis that takes the distribution...... of profits among consumers fully into account and partial equilibrium analysis suffices...

Implications of maximal Jarlskog invariant and maximal CP violation

International Nuclear Information System (INIS)

Rodriguez-Jauregui, E.; Universidad Nacional Autonoma de Mexico

2001-04-01

We argue here why CP violating phase Φ in the quark mixing matrix is maximal, that is, Φ=90 . In the Standard Model CP violation is related to the Jarlskog invariant J, which can be obtained from non commuting Hermitian mass matrices. In this article we derive the conditions to have Hermitian mass matrices which give maximal Jarlskog invariant J and maximal CP violating phase Φ. We find that all squared moduli of the quark mixing elements have a singular point when the CP violation phase Φ takes the value Φ=90 . This special feature of the Jarlskog invariant J and the quark mixing matrix is a clear and precise indication that CP violating Phase Φ is maximal in order to let nature treat democratically all of the quark mixing matrix moduli. (orig.)

Memetic algorithms for de novo motif-finding in biomedical sequences.

Science.gov (United States)

Bi, Chengpeng

2012-09-01

The objectives of this study are to design and implement a new memetic algorithm for de novo motif discovery, which is then applied to detect important signals hidden in various biomedical molecular sequences. In this paper, memetic algorithms are developed and tested in de novo motif-finding problems. Several strategies in the algorithm design are employed that are to not only efficiently explore the multiple sequence local alignment space, but also effectively uncover the molecular signals. As a result, there are a number of key features in the implementation of the memetic motif-finding algorithm (MaMotif), including a chromosome replacement operator, a chromosome alteration-aware local search operator, a truncated local search strategy, and a stochastic operation of local search imposed on individual learning. To test the new algorithm, we compare MaMotif with a few of other similar algorithms using simulated and experimental data including genomic DNA, primary microRNA sequences (let-7 family), and transmembrane protein sequences. The new memetic motif-finding algorithm is successfully implemented in C++, and exhaustively tested with various simulated and real biological sequences. In the simulation, it shows that MaMotif is the most time-efficient algorithm compared with others, that is, it runs 2 times faster than the expectation maximization (EM) method and 16 times faster than the genetic algorithm-based EM hybrid. In both simulated and experimental testing, results show that the new algorithm is compared favorably or superior to other algorithms. Notably, MaMotif is able to successfully discover the transcription factors' binding sites in the chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) data, correctly uncover the RNA splicing signals in gene expression, and precisely find the highly conserved helix motif in the transmembrane protein sequences, as well as rightly detect the palindromic segments in the primary micro

SU(2) gauge theory in the maximally Abelian gauge without monopoles

International Nuclear Information System (INIS)

Shmakov, S.Yu.; Zadorozhnyj, A.M.

1995-01-01

We present an algorithm for simulation of SU(2) lattice gauge theory under the maximally Abelian (MA) gauge and first numerical results for the theory without Abelian monopoles. The results support the idea that nonperturbative interaction arises between monopoles and residual Abelian field and the other interactions are perturbative. It is shown that the Gribov region for the theory with the MA gauge fixed is non-connected. 12 refs., 1 tab

Maximal sfermion flavour violation in super-GUTs

Energy Technology Data Exchange (ETDEWEB)

Ellis, John [King' s College London, Theoretical Particle Physics and Cosmology Group, Department of Physics, London (United Kingdom); Olive, Keith A. [CERN, Theoretical Physics Department, Geneva (Switzerland); University of Minnesota, William I. Fine Theoretical Physics Institute, School of Physics and Astronomy, Minneapolis, MN (United States); Velasco-Sevilla, L. [University of Bergen, Department of Physics and Technology, PO Box 7803, Bergen (Norway)

2016-10-15

We consider supersymmetric grand unified theories with soft supersymmetry-breaking scalar masses m{sub 0} specified above the GUT scale (super-GUTs) and patterns of Yukawa couplings motivated by upper limits on flavour-changing interactions beyond the Standard Model. If the scalar masses are smaller than the gaugino masses m{sub 1/2}, as is expected in no-scale models, the dominant effects of renormalisation between the input scale and the GUT scale are generally expected to be those due to the gauge couplings, which are proportional to m{sub 1/2} and generation independent. In this case, the input scalar masses m{sub 0} may violate flavour maximally, a scenario we call MaxSFV, and there is no supersymmetric flavour problem. We illustrate this possibility within various specific super-GUT scenarios that are deformations of no-scale gravity. (orig.)

MAXIMIZING HYDROPOWER PRODUCTION FROM RESERVOIRS:THE CASE STUDY OF MARKABA

International Nuclear Information System (INIS)

Jaafar, H.

2014-01-01

Hydropower is a form of renewable energy that is clean and cheap. Under uncertain climatic conditions, maximization of hydropower production becomes a challenging task.Stochastic Dynamic programming (SDP) is a promising optimization algorithm that is usedfor complex non-linear reservoir operational policies and strategies.In this research, a combined simulation-SDPoptimization model isdeveloped andverified for maximizing large-scale hydropower production in a monthly time step. The model isdeveloped to generate optimal operational policies for the Qarawn reservoir in Lebanon and test these policies in real time conditions. The model isused to derive operational regimes for the Qarawn reservoirunder varying flows using transitional probability matrices. Simulating the derived rules and the generated operational policies proved effective in maximizingthe hydropower production from the Markaba power plant. The model could be successfully applied to other hydropower dams in the region. (author)

Maximizers versus satisficers

Directory of Open Access Journals (Sweden)

Andrew M. Parker

2007-12-01

Full Text Available Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007. Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002, we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Contrary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables.

Coverage maximization under resource constraints using a nonuniform proliferating random walk.

Science.gov (United States)

Saha, Sudipta; Ganguly, Niloy

2013-02-01

Information management services on networks, such as search and dissemination, play a key role in any large-scale distributed system. One of the most desirable features of these services is the maximization of the coverage, i.e., the number of distinctly visited nodes under constraints of network resources as well as time. However, redundant visits of nodes by different message packets (modeled, e.g., as walkers) initiated by the underlying algorithms for these services cause wastage of network resources. In this work, using results from analytical studies done in the past on a K-random-walk-based algorithm, we identify that redundancy quickly increases with an increase in the density of the walkers. Based on this postulate, we design a very simple distributed algorithm which dynamically estimates the density of the walkers and thereby carefully proliferates walkers in sparse regions. We use extensive computer simulations to test our algorithm in various kinds of network topologies whereby we find it to be performing particularly well in networks that are highly clustered as well as sparse.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System.

Science.gov (United States)

Chinnadurai, Sunil; Selvaprabhu, Poongundran; Jeong, Yongchae; Jiang, Xueqin; Lee, Moon Ho

2017-09-18

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach's algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System

Directory of Open Access Journals (Sweden)

Sunil Chinnadurai

2017-09-01

Full Text Available In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE maximization problem in a 5G massive multiple-input multiple-output (MIMO-non-orthogonal multiple access (NOMA downlink system with imperfect channel state information (CSI at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM. A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach’s algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA scheme.

Portfolios of quantum algorithms.

Science.gov (United States)

Maurer, S M; Hogg, T; Huberman, B A

2001-12-17

Quantum computation holds promise for the solution of many intractable problems. However, since many quantum algorithms are stochastic in nature they can find the solution of hard problems only probabilistically. Thus the efficiency of the algorithms has to be characterized by both the expected time to completion and the associated variance. In order to minimize both the running time and its uncertainty, we show that portfolios of quantum algorithms analogous to those of finance can outperform single algorithms when applied to the NP-complete problems such as 3-satisfiability.

Constructing Optimal Coarse-Grained Sites of Huge Biomolecules by Fluctuation Maximization.

Science.gov (United States)

Li, Min; Zhang, John Zenghui; Xia, Fei

2016-04-12

Coarse-grained (CG) models are valuable tools for the study of functions of large biomolecules on large length and time scales. The definition of CG representations for huge biomolecules is always a formidable challenge. In this work, we propose a new method called fluctuation maximization coarse-graining (FM-CG) to construct the CG sites of biomolecules. The defined residual in FM-CG converges to a maximal value as the number of CG sites increases, allowing an optimal CG model to be rigorously defined on the basis of the maximum. More importantly, we developed a robust algorithm called stepwise local iterative optimization (SLIO) to accelerate the process of coarse-graining large biomolecules. By means of the efficient SLIO algorithm, the computational cost of coarse-graining large biomolecules is reduced to within the time scale of seconds, which is far lower than that of conventional simulated annealing. The coarse-graining of two huge systems, chaperonin GroEL and lengsin, indicates that our new methods can coarse-grain huge biomolecular systems with up to 10,000 residues within the time scale of minutes. The further parametrization of CG sites derived from FM-CG allows us to construct the corresponding CG models for studies of the functions of huge biomolecular systems.

Evaluation of Origin Ensemble algorithm for image reconstruction for pixelated solid-state detectors with large number of channels

Science.gov (United States)

Kolstein, M.; De Lorenzo, G.; Mikhaylova, E.; Chmeissani, M.; Ariño, G.; Calderón, Y.; Ozsahin, I.; Uzun, D.

2013-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For PET scanners, conventional algorithms like Filtered Back-Projection (FBP) and Ordered Subset Expectation Maximization (OSEM) are straightforward to use and give good results. However, FBP presents difficulties for detectors with limited angular coverage like PEM and Compton gamma cameras, whereas OSEM has an impractically large time and memory consumption for a Compton gamma camera with a large number of channels. In this article, the Origin Ensemble (OE) algorithm is evaluated as an alternative algorithm for image reconstruction. Monte Carlo simulations of the PET design are used to compare the performance of OE, FBP and OSEM in terms of the bias, variance and average mean squared error (MSE) image quality metrics. For the PEM and Compton camera designs, results obtained with OE are presented.

Developing maximal neuromuscular power: Part 1--biological basis of maximal power production.

Science.gov (United States)

Cormie, Prue; McGuigan, Michael R; Newton, Robert U

2011-01-01

This series of reviews focuses on the most important neuromuscular function in many sport performances, the ability to generate maximal muscular power. Part 1 focuses on the factors that affect maximal power production, while part 2, which will follow in a forthcoming edition of Sports Medicine, explores the practical application of these findings by reviewing the scientific literature relevant to the development of training programmes that most effectively enhance maximal power production. The ability of the neuromuscular system to generate maximal power is affected by a range of interrelated factors. Maximal muscular power is defined and limited by the force-velocity relationship and affected by the length-tension relationship. The ability to generate maximal power is influenced by the type of muscle action involved and, in particular, the time available to develop force, storage and utilization of elastic energy, interactions of contractile and elastic elements, potentiation of contractile and elastic filaments as well as stretch reflexes. Furthermore, maximal power production is influenced by morphological factors including fibre type contribution to whole muscle area, muscle architectural features and tendon properties as well as neural factors including motor unit recruitment, firing frequency, synchronization and inter-muscular coordination. In addition, acute changes in the muscle environment (i.e. alterations resulting from fatigue, changes in hormone milieu and muscle temperature) impact the ability to generate maximal power. Resistance training has been shown to impact each of these neuromuscular factors in quite specific ways. Therefore, an understanding of the biological basis of maximal power production is essential for developing training programmes that effectively enhance maximal power production in the human.

Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

International Nuclear Information System (INIS)

Roche-Lima, Abiel; Thulasiram, Ruppa K

2012-01-01

Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

Development of an image reconstruction algorithm for a few number of projection data

International Nuclear Information System (INIS)

Vieira, Wilson S.; Brandao, Luiz E.; Braz, Delson

2007-01-01

An image reconstruction algorithm was developed for specific cases of radiotracer applications in industry (rotating cylindrical mixers), involving a very few number of projection data. The algorithm was planned for imaging radioactive isotope distributions around the center of circular planes. The method consists of adapting the original expectation maximization algorithm (EM) to solve the ill-posed emission tomography inverse problem in order to reconstruct transversal 2D images of an object with only four projections. To achieve this aim, counts of photons emitted by selected radioactive sources in the plane, after they had been simulated using the commercial software MICROSHIELD 5.05, constitutes the projections and a computational code (SPECTEM) was developed to generate activity vectors or images related to those sources. SPECTEM is flexible to support simultaneous changes of the detectors's geometry, the medium under investigation and the properties of the gamma radiation. As a consequence of the code had been followed correctly the proposed method, good results were obtained and they encouraged us to continue the next step of the research: the validation of SPECTEM utilizing experimental data to check its real performance. We aim this code will improve considerably radiotracer methodology, making easier the diagnosis of fails in industrial processes. (author)

Development of an image reconstruction algorithm for a few number of projection data

Energy Technology Data Exchange (ETDEWEB)

Vieira, Wilson S.; Brandao, Luiz E. [Instituto de Engenharia Nuclear (IEN-CNEN/RJ), Rio de Janeiro , RJ (Brazil)]. E-mails: wilson@ien.gov.br; brandao@ien.gov.br; Braz, Delson [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programa de Pos-graduacao de Engenharia (COPPE). Lab. de Instrumentacao Nuclear]. E-mail: delson@mailhost.lin.ufrj.br

2007-07-01

An image reconstruction algorithm was developed for specific cases of radiotracer applications in industry (rotating cylindrical mixers), involving a very few number of projection data. The algorithm was planned for imaging radioactive isotope distributions around the center of circular planes. The method consists of adapting the original expectation maximization algorithm (EM) to solve the ill-posed emission tomography inverse problem in order to reconstruct transversal 2D images of an object with only four projections. To achieve this aim, counts of photons emitted by selected radioactive sources in the plane, after they had been simulated using the commercial software MICROSHIELD 5.05, constitutes the projections and a computational code (SPECTEM) was developed to generate activity vectors or images related to those sources. SPECTEM is flexible to support simultaneous changes of the detectors's geometry, the medium under investigation and the properties of the gamma radiation. As a consequence of the code had been followed correctly the proposed method, good results were obtained and they encouraged us to continue the next step of the research: the validation of SPECTEM utilizing experimental data to check its real performance. We aim this code will improve considerably radiotracer methodology, making easier the diagnosis of fails in industrial processes. (author)

Link adaptation algorithm for distributed coded transmissions in cooperative OFDMA systems

DEFF Research Database (Denmark)

Varga, Mihaly; Badiu, Mihai Alin; Bota, Vasile

2015-01-01

This paper proposes a link adaptation algorithm for cooperative transmissions in the down-link connection of an OFDMA-based wireless system. The algorithm aims at maximizing the spectral efficiency of a relay-aided communication link, while satisfying the block error rate constraints at both...... adaptation algorithm has linear complexity with the number of available resource blocks, while still provides a very good performance, as shown by simulation results....

A comparison of algorithms for inference and learning in probabilistic graphical models.

Science.gov (United States)

Frey, Brendan J; Jojic, Nebojsa

2005-09-01

Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

Science.gov (United States)

Chemali, Jessica; Ching, ShiNung; Purdon, Patrick L.; Solt, Ken; Brown, Emery N.

2013-10-01

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.

Investor regret: The role of expectation in comparing what is to what might have been

Directory of Open Access Journals (Sweden)

Wen-Hsien Huang

2012-07-01

Full Text Available Investors, like any decision maker, feel regret when they compare the outcome of an investment with what the outcome would have been had they invested differently. We argue and show that this counterfactual comparison process is most likely to take place when the decision maker's expectations are violated. Across five scenario experiments we found that decision makers were influenced only by forgone investment outcomes when the realized investment fell short of the expected result. However, when their investments exceeded prior expectations, the effect of foregone investment on regret disappeared. In addition, Experiment 4 found that individual differences in the need to maximize further moderated the effects of their expectations, such that maximizers always take into account the forgone investment. The final experiment found that when probed to make counterfactual comparisons, also investments that exceed expectations may lead to regret. Together these experiments reveal insights into the comparative processes leading to decision regret.

Teaching learning based optimization algorithm and its engineering applications

CERN Document Server

Rao, R Venkata

2016-01-01

Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.

Super-Encryption Implementation Using Monoalphabetic Algorithm and XOR Algorithm for Data Security

Science.gov (United States)

Rachmawati, Dian; Andri Budiman, Mohammad; Aulia, Indra

2018-03-01

The exchange of data that occurs offline and online is very vulnerable to the threat of data theft. In general, cryptography is a science and art to maintain data secrecy. An encryption is a cryptography algorithm in which data is transformed into cipher text, which is something that is unreadable and meaningless so it cannot be read or understood by other parties. In super-encryption, two or more encryption algorithms are combined to make it more secure. In this work, Monoalphabetic algorithm and XOR algorithm are combined to form a super- encryption. Monoalphabetic algorithm works by changing a particular letter into a new letter based on existing keywords while the XOR algorithm works by using logic operation XOR Since Monoalphabetic algorithm is a classical cryptographic algorithm and XOR algorithm is a modern cryptographic algorithm, this scheme is expected to be both easy-to-implement and more secure. The combination of the two algorithms is capable of securing the data and restoring it back to its original form (plaintext), so the data integrity is still ensured.

An Ordering Linear Unification Algorithm

Institute of Scientific and Technical Information of China (English)

胡运发

1989-01-01

In this paper,we present an ordering linear unification algorithm(OLU).A new idea on substituteion of the binding terms is introduced to the algorithm,which is able to overcome some drawbacks of other algorithms,e.g.,MM algorithm[1],RG1 and RG2 algorithms[2],Particularly,if we use the directed eyclie graphs,the algoritm needs not check the binding order,then the OLU algorithm can also be aplied to the infinite tree data struceture,and a higher efficiency can be expected.The paper focuses upon the discussion of OLU algorithm and a partial order structure with respect to the unification algorithm.This algorithm has been implemented in the GKD-PROLOG/VAX 780 interpreting system.Experimental results have shown that the algorithm is very simple and efficient.

Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method

Directory of Open Access Journals (Sweden)

Jinhyun Park

2015-08-01

Full Text Available The in-wheel electric vehicle is expected to be a popular next-generation vehicle because an in-wheel system can simplify the powertrain and improve driving performance. In addition, it also has an advantage in that it maximizes driving efficiency through independent torque control considering the motor efficiency. However, there is an instability problem if only the driving torque is controlled in consideration of only the motor efficiency. In this paper, integrated torque distribution strategies are proposed to overcome these problems. The control algorithm consists of various strategies for optimizing driving efficiency, satisfying driver demands, and considering tire slip and vehicle cornering. Fuzzy logic is used to determine the appropriate timing of intervention for each distribution strategy. A performance simulator for in-wheel electric vehicles was developed by using MATLAB/Simulink and CarSim to validate the control strategies. From simulation results under complex driving conditions, the proposed algorithm was verified to improve both the driving stability and fuel economy of the in-wheel vehicle.

Maximizers versus satisficers

OpenAIRE

Andrew M. Parker; Wandi Bruine de Bruin; Baruch Fischhoff

2007-01-01

Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions...

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer.

Science.gov (United States)

Yu, Hongyan; Zhang, Yongqiang; Guo, Songtao; Yang, Yuanyuan; Ji, Luyue

2017-08-18

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively.

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer

Science.gov (United States)

Yu, Hongyan; Zhang, Yongqiang; Yang, Yuanyuan; Ji, Luyue

2017-01-01

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively. PMID:28820496

Optimal dynamic premium control in non-life insurance. Maximizing dividend pay-outs

DEFF Research Database (Denmark)

Højgaard, Bjarne

2002-01-01

In this paper we consider the problem of finding optimal dynamic premium policies in non-life insurance. The reserve of a company is modeled using the classical Cramér-Lundberg model with premium rates calculated via the expected value principle. The company controls dynamically the relative safety...... loading with the possibility of gaining or loosing customers. It distributes dividends according to a 'barrier strategy' and the objective of the company is to find an optimal premium policy and dividend barrier maximizing the expected total, discounted pay-out of dividends. In the case of exponential...

An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

Directory of Open Access Journals (Sweden)

Md. Rezaul Karim

2012-03-01

Full Text Available Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

Evaluation of 3D reconstruction algorithms for a small animal PET camera

International Nuclear Information System (INIS)

Johnson, C.A.; Gandler, W.R.; Seidel, J.

1996-01-01

The use of paired, opposing position-sensitive phototube scintillation cameras (SCs) operating in coincidence for small animal imaging with positron emitters is currently under study. Because of the low sensitivity of the system even in 3D mode and the need to produce images with high resolution, it was postulated that a 3D expectation maximization (EM) reconstruction algorithm might be well suited for this application. We investigated four reconstruction algorithms for the 3D SC PET camera: 2D filtered back-projection (FBP), 2D ordered subset EM (OSEM), 3D reprojection (3DRP), and 3D OSEM. Noise was assessed for all slices by the coefficient of variation in a simulated uniform cylinder. Resolution was assessed from a simulation of 15 point sources in the warm background of the uniform cylinder. At comparable noise levels, the resolution achieved with OSEM (0.9-mm to 1.2-mm) is significantly better than that obtained with FBP or 3DRP (1.5-mm to 2.0-mm.) Images of a rat skull labeled with 18 F-fluoride suggest that 3D OSEM can improve image quality of a small animal PET camera

Runway Operations Planning: A Two-Stage Heuristic Algorithm

Science.gov (United States)

Anagnostakis, Ioannis; Clarke, John-Paul

2003-01-01

The airport runway is a scarce resource that must be shared by different runway operations (arrivals, departures and runway crossings). Given the possible sequences of runway events, careful Runway Operations Planning (ROP) is required if runway utilization is to be maximized. From the perspective of departures, ROP solutions are aircraft departure schedules developed by optimally allocating runway time for departures given the time required for arrivals and crossings. In addition to the obvious objective of maximizing throughput, other objectives, such as guaranteeing fairness and minimizing environmental impact, can also be incorporated into the ROP solution subject to constraints introduced by Air Traffic Control (ATC) procedures. This paper introduces a two stage heuristic algorithm for solving the Runway Operations Planning (ROP) problem. In the first stage, sequences of departure class slots and runway crossings slots are generated and ranked based on departure runway throughput under stochastic conditions. In the second stage, the departure class slots are populated with specific flights from the pool of available aircraft, by solving an integer program with a Branch & Bound algorithm implementation. Preliminary results from this implementation of the two-stage algorithm on real-world traffic data are presented.

A Faster Algorithm for Computing Motorcycle Graphs

KAUST Repository

Vigneron, Antoine E.; Yan, Lie

2014-01-01

We present a new algorithm for computing motorcycle graphs that runs in (Formula presented.) time for any (Formula presented.), improving on all previously known algorithms. The main application of this result is to computing the straight skeleton of a polygon. It allows us to compute the straight skeleton of a non-degenerate polygon with (Formula presented.) holes in (Formula presented.) expected time. If all input coordinates are (Formula presented.)-bit rational numbers, we can compute the straight skeleton of a (possibly degenerate) polygon with (Formula presented.) holes in (Formula presented.) expected time. In particular, it means that we can compute the straight skeleton of a simple polygon in (Formula presented.) expected time if all input coordinates are (Formula presented.)-bit rationals, while all previously known algorithms have worst-case running time (Formula presented.). © 2014 Springer Science+Business Media New York.

A Faster Algorithm for Computing Motorcycle Graphs

KAUST Repository

Vigneron, Antoine E.

2014-08-29

We present a new algorithm for computing motorcycle graphs that runs in (Formula presented.) time for any (Formula presented.), improving on all previously known algorithms. The main application of this result is to computing the straight skeleton of a polygon. It allows us to compute the straight skeleton of a non-degenerate polygon with (Formula presented.) holes in (Formula presented.) expected time. If all input coordinates are (Formula presented.)-bit rational numbers, we can compute the straight skeleton of a (possibly degenerate) polygon with (Formula presented.) holes in (Formula presented.) expected time. In particular, it means that we can compute the straight skeleton of a simple polygon in (Formula presented.) expected time if all input coordinates are (Formula presented.)-bit rationals, while all previously known algorithms have worst-case running time (Formula presented.). © 2014 Springer Science+Business Media New York.

Fast algorithm of adaptive Fourier series

Science.gov (United States)

Gao, You; Ku, Min; Qian, Tao

2018-05-01

Adaptive Fourier decomposition (AFD, precisely 1-D AFD or Core-AFD) was originated for the goal of positive frequency representations of signals. It achieved the goal and at the same time offered fast decompositions of signals. There then arose several types of AFDs. AFD merged with the greedy algorithm idea, and in particular, motivated the so-called pre-orthogonal greedy algorithm (Pre-OGA) that was proven to be the most efficient greedy algorithm. The cost of the advantages of the AFD type decompositions is, however, the high computational complexity due to the involvement of maximal selections of the dictionary parameters. The present paper offers one formulation of the 1-D AFD algorithm by building the FFT algorithm into it. Accordingly, the algorithm complexity is reduced, from the original $\\mathcal{O}(M N^2)$ to $\\mathcal{O}(M N\\log_2 N)$, where $N$ denotes the number of the discretization points on the unit circle and $M$ denotes the number of points in $[0,1)$. This greatly enhances the applicability of AFD. Experiments are carried out to show the high efficiency of the proposed algorithm.

A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

Science.gov (United States)

Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

2017-02-01

Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

Interval-based reconstruction for uncertainty quantification in PET

Science.gov (United States)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

Research on Improved NSGA-II Algorithm and Its Application in Emergency Management

Directory of Open Access Journals (Sweden)

Xi Fang

2018-01-01

Full Text Available This paper constructs a dynamic multiobjective location model; three objectives are considered: the first objective maximizes the total utility of relief supplies, the second objective minimizes the number of temporary facilities needed to operate, and the third objective maximizes the satisfaction for all demand points. We propose an improved NSGA-II to solve the optimization problem. The computational experiments are divided into two sections: In the first procedure, the numerical experiment is constructed by the classical functions ZDT1, ZDT2, and DTLZ2; the results show that the proposed algorithm generates the exact Pareto front, and the convergence and uniformity of the proposed algorithm are better than the NSGA-II and MOEA/D. In the second procedure, the simulation experiment is constructed by a case in emergency management; the results show that the proposed algorithm is more reasonable than the traditional algorithms NSGA-II and MOEA/D in terms of the three objectives. It is proved that the improved NSGA-II algorithm, which is proposed in this paper, has high precision application for the sudden disaster crisis and emergency management.

Entropy maximization

Indian Academy of Sciences (India)

Abstract. It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf f that satisfy. ∫ fhi dμ = λi for i = 1, 2,...,...k the maximizer of entropy is an f0 that is pro- portional to exp(. ∑ ci hi ) for some choice of ci . An extension of this to a continuum of.

Algorithm comparison for schedule optimization in MR fingerprinting.

Science.gov (United States)

Cohen, Ouri; Rosen, Matthew S

2017-09-01

In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

Statistical iterative reconstruction for streak artefact reduction when using multidetector CT to image the dento-alveolar structures.

Science.gov (United States)

Dong, J; Hayakawa, Y; Kober, C

2014-01-01

When metallic prosthetic appliances and dental fillings exist in the oral cavity, the appearance of metal-induced streak artefacts is not avoidable in CT images. The aim of this study was to develop a method for artefact reduction using the statistical reconstruction on multidetector row CT images. Adjacent CT images often depict similar anatomical structures. Therefore, reconstructed images with weak artefacts were attempted using projection data of an artefact-free image in a neighbouring thin slice. Images with moderate and strong artefacts were continuously processed in sequence by successive iterative restoration where the projection data was generated from the adjacent reconstructed slice. First, the basic maximum likelihood-expectation maximization algorithm was applied. Next, the ordered subset-expectation maximization algorithm was examined. Alternatively, a small region of interest setting was designated. Finally, the general purpose graphic processing unit machine was applied in both situations. The algorithms reduced the metal-induced streak artefacts on multidetector row CT images when the sequential processing method was applied. The ordered subset-expectation maximization and small region of interest reduced the processing duration without apparent detriments. A general-purpose graphic processing unit realized the high performance. A statistical reconstruction method was applied for the streak artefact reduction. The alternative algorithms applied were effective. Both software and hardware tools, such as ordered subset-expectation maximization, small region of interest and general-purpose graphic processing unit achieved fast artefact correction.

An Evolutionary Algorithm to Mine High-Utility Itemsets

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

A polynomial time algorithm for checking regularity of totally normed process algebra

NARCIS (Netherlands)

Yang, F.; Huang, H.

2015-01-01

A polynomial algorithm for the regularity problem of weak and branching bisimilarity on totally normed process algebra (PA) processes is given. Its time complexity is O(n 3 +mn) O(n3+mn), where n is the number of transition rules and m is the maximal length of the rules. The algorithm works for

Multiuser TOA Estimation Algorithm in DS-CDMA Sparse Channel for Radiolocation

Science.gov (United States)

Kim, Sunwoo

This letter considers multiuser time delay estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) algorithm is considered, and the continuous time delay of each path is estimated without requiring a priori known data sequences.

Entropy Maximization

Indian Academy of Sciences (India)

It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf that satisfy ∫ f h i d = i for i = 1 , 2 , … , … k the maximizer of entropy is an f 0 that is proportional to exp ⁡ ( ∑ c i h i ) for some choice of c i . An extension of this to a continuum of ...

Algorithmic test design using classical item parameters

NARCIS (Netherlands)

van der Linden, Willem J.; Adema, Jos J.

Two optimalization models for the construction of tests with a maximal value of coefficient alpha are given. Both models have a linear form and can be solved by using a branch-and-bound algorithm. The first model assumes an item bank calibrated under the Rasch model and can be used, for instance,

基于距离最大化和缺失数据聚类的填充算法%Missing value imputation algorithm based on distance maximization and missing data clustering

Institute of Scientific and Technical Information of China (English)

赵星; 王逊; 黄树成

2018-01-01

Through the improvement of the missing value filling algorithm based on K-means clustering,a filling algorithm based on distance maximization and missing data clustering is proposed in this paper.First of all,the original filling algorithm needs to enter the number of clusters in advance.To solve this problem,an improved K-means clustering algorithm is designed.It determines the cluster centers by the maximum distance between the data.It will automatically generate the number of clusters,and improve the efficiency of clustering.Secondly,the clustering distance function is improved.The improved algorithm can be used to cluster the missing value records,thus simplifying the steps of the original filling algorithm.Through experiments on STUDENT ALCOHOL CONSUMPTION data set,experimental results show that the proposed algorithm can improve efficiency and effectively fill the missing data at the same time.%通过对基于K-means聚类的缺失值填充算法的改进,文中提出了基于距离最大化和缺失数据聚类的填充算法.首先,针对原填充算法需要提前输入聚类个数这一缺点,设计了改进的K-means聚类算法:使用数据间的最大距离确定聚类中心,自动产生聚类个数,提高聚类效果;其次,对聚类的距离函数进行改进,采用部分距离度量方式,改进后的算法可以对含有缺失值的记录进行聚类,简化原填充算法步骤.通过对STUDENT ALCOHOL CONSUMPTION数据集的实验,结果证明了该算法能够在提高效率的同时,有效地填充缺失数据.

Recursive expectation-maximization clustering: A method for identifying buffering mechanisms composed of phenomic modules

Science.gov (United States)

Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.

2010-06-01

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance

A multicenter evaluation of seven commercial ML-EM algorithms for SPECT image reconstruction using simulation data

International Nuclear Information System (INIS)

Matsumoto, Keiichi; Ohnishi, Hideo; Niida, Hideharu; Nishimura, Yoshihiro; Wada, Yasuhiro; Kida, Tetsuo

2003-01-01

The maximum likelihood expectation maximization (ML-EM) algorithm has become available as an alternative to filtered back projection in SPECT. The actual physical performance may be different depending on the manufacturer and model, because of differences in computational details. The purpose of this study was to investigate the characteristics of seven different types of ML-EM algorithms using simple simulation data. Seven ML-EM algorithm programs were used: Genie (GE), esoft (Siemens), HARP-III (Hitachi), GMS-5500UI (Toshiba), Pegasys (ADAC), ODYSSEY-FX (Marconi), and Windows-PC (original software). Projection data of a 2-pixel-wide line source in the center of the field of view were simulated without attenuation or scatter. Images were reconstructed with ML-EM by changing the number of iterations from 1 to 45 for each algorithm. Image quality was evaluated after a reconstruction using full width at half maximum (FWHM), full width at tenth maximum (FWTM), and the total counts of the reconstructed images. In the maximum number of iterations, the difference in the FWHM value was up to 1.5 pixels, and that of FWTM, no less than 2.0 pixels. The total counts of the reconstructed images in the initial few iterations were larger or smaller than the converged value depending on the initial values. Our results for the simplest simulation data suggest that each ML-EM algorithm itself provides a simulation image. We should keep in mind which algorithm is being used and its computational details, when physical and clinical usefulness are compared. (author)

First results with two light flavours of quarks with maximally twisted mass

International Nuclear Information System (INIS)

Jansen, K.; Urbach, C.

2006-10-01

We report on first results of an ongoing effort to simulate lattice QCD with two degenerate flavours of quarks by means of the twisted mass formulation tuned to maximal twist. By utilising recent improvements of the HMC algorithm, pseudo-scalar masses well below 300 MeV are simulated on volumes L 3 .T with T=2L and L>2 fm and at values of the lattice spacing a f =2+1+1 flavours are discussed. (orig.)

Work Placement in UK Undergraduate Programmes. Student Expectations and Experiences.

Science.gov (United States)

Leslie, David; Richardson, Anne

1999-01-01

A survey of 189 pre- and 106 post-sandwich work-experience students in tourism suggested that potential benefits were not being maximized. Students needed better preparation for the work experience, especially in terms of their expectations. The work experience needed better design, and the role of industry tutors needed clarification. (SK)

Maximally incompatible quantum observables

Energy Technology Data Exchange (ETDEWEB)

Heinosaari, Teiko, E-mail: teiko.heinosaari@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Schultz, Jussi, E-mail: jussi.schultz@gmail.com [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Toigo, Alessandro, E-mail: alessandro.toigo@polimi.it [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Celoria 16, I-20133 Milano (Italy); Ziman, Mario, E-mail: ziman@savba.sk [RCQI, Institute of Physics, Slovak Academy of Sciences, Dúbravská cesta 9, 84511 Bratislava (Slovakia); Faculty of Informatics, Masaryk University, Botanická 68a, 60200 Brno (Czech Republic)

2014-05-01

The existence of maximally incompatible quantum observables in the sense of a minimal joint measurability region is investigated. Employing the universal quantum cloning device it is argued that only infinite dimensional quantum systems can accommodate maximal incompatibility. It is then shown that two of the most common pairs of complementary observables (position and momentum; number and phase) are maximally incompatible.

Maximally incompatible quantum observables

International Nuclear Information System (INIS)

Heinosaari, Teiko; Schultz, Jussi; Toigo, Alessandro; Ziman, Mario

2014-01-01

The existence of maximally incompatible quantum observables in the sense of a minimal joint measurability region is investigated. Employing the universal quantum cloning device it is argued that only infinite dimensional quantum systems can accommodate maximal incompatibility. It is then shown that two of the most common pairs of complementary observables (position and momentum; number and phase) are maximally incompatible.

Loss Modification Incentives for Insurers Under Expected Utility and Loss Aversion

NARCIS (Netherlands)

Soetevent, Adriaan R.; Zhou, Liting

We investigate whether a profit-maximizing insurer with the opportunity to modify the loss probability will engage in loss prevention or instead spend effort to increase the loss probability. First we study this question within a traditional expected utility framework; then we apply Koszegi and

Loss-minimal Algorithmic Trading Based on Levy Processes

Directory of Open Access Journals (Sweden)

Farhad Kia

2014-08-01

Full Text Available In this paper we optimize portfolios assuming that the value of the portfolio follows a Lévy process. First we identify the parameters of the underlying Lévy process and then portfolio optimization is performed by maximizing the probability of positive return. The method has been tested by extensive performance analysis on Forex and SP 500 historical time series. The proposed trading algorithm has achieved 4.9\\% percent yearly return on average without leverage which proves its applicability to algorithmic trading.

Resolution recovery for Compton camera using origin ensemble algorithm.

Science.gov (United States)

Andreyev, A; Celler, A; Ozsahin, I; Sitek, A

2016-08-01

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions

Resolution recovery for Compton camera using origin ensemble algorithm

Energy Technology Data Exchange (ETDEWEB)

Andreyev, A. [Philips Healthcare, Highland Heights, Ohio 44143 (United States); Celler, A. [Medical Imaging Research Group, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC V5Z 1M9 (Canada); Ozsahin, I.; Sitek, A., E-mail: sarkadiu@gmail.com [Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115 (United States)

2016-08-15

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image

Resolution recovery for Compton camera using origin ensemble algorithm

International Nuclear Information System (INIS)

Andreyev, A.; Celler, A.; Ozsahin, I.; Sitek, A.

2016-01-01

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image

Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm.

Science.gov (United States)

Goolsby, Eric W

2017-04-01

Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars

Survival associated pathway identification with group Lp penalized global AUC maximization

Directory of Open Access Journals (Sweden)

Liu Zhenqiu

2010-08-01

Full Text Available Abstract It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS. This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.

Genetic algorithm based on qubits and quantum gates

International Nuclear Information System (INIS)

Silva, Joao Batista Rosa; Ramos, Rubens Viana

2003-01-01

Full text: Genetic algorithm, a computational technique based on the evolution of the species, in which a possible solution of the problem is coded in a binary string, called chromosome, has been used successfully in several kinds of problems, where the search of a minimal or a maximal value is necessary, even when local minima are present. A natural generalization of a binary string is a qubit string. Hence, it is possible to use the structure of a genetic algorithm having a sequence of qubits as a chromosome and using quantum operations in the reproduction in order to find the best solution in some problems of quantum information. For example, given a unitary matrix U what is the pair of qubits that, when applied at the input, provides the output state with maximal entanglement? In order to solve this problem, a population of chromosomes of two qubits was created. The crossover was performed applying the quantum gates CNOT and SWAP at the pair of qubits, while the mutation was performed applying the quantum gates Hadamard, Z and Not in a single qubit. The result was compared with a classical genetic algorithm used to solve the same problem. A hundred simulations using the same U matrix was performed. Both algorithms, hereafter named by CGA (classical) and QGA (using qu bits), reached good results close to 1 however, the number of generations needed to find the best result was lower for the QGA. Another problem where the QGA can be useful is in the calculation of the relative entropy of entanglement. We have tested our algorithm using 100 pure states chosen randomly. The stop criterion used was the error lower than 0.01. The main advantages of QGA are its good precision, robustness and very easy implementation. The main disadvantage is its low velocity, as happen for all kind of genetic algorithms. (author)

Fisher's method of scoring in statistical image reconstruction: comparison of Jacobi and Gauss-Seidel iterative schemes.

Science.gov (United States)

Hudson, H M; Ma, J; Green, P

1994-01-01

Many algorithms for medical image reconstruction adopt versions of the expectation-maximization (EM) algorithm. In this approach, parameter estimates are obtained which maximize a complete data likelihood or penalized likelihood, in each iteration. Implicitly (and sometimes explicitly) penalized algorithms require smoothing of the current reconstruction in the image domain as part of their iteration scheme. In this paper, we discuss alternatives to EM which adapt Fisher's method of scoring (FS) and other methods for direct maximization of the incomplete data likelihood. Jacobi and Gauss-Seidel methods for non-linear optimization provide efficient algorithms applying FS in tomography. One approach uses smoothed projection data in its iterations. We investigate the convergence of Jacobi and Gauss-Seidel algorithms with clinical tomographic projection data.

Maximal combustion temperature estimation

International Nuclear Information System (INIS)

Golodova, E; Shchepakina, E

2006-01-01

This work is concerned with the phenomenon of delayed loss of stability and the estimation of the maximal temperature of safe combustion. Using the qualitative theory of singular perturbations and canard techniques we determine the maximal temperature on the trajectories located in the transition region between the slow combustion regime and the explosive one. This approach is used to estimate the maximal temperature of safe combustion in multi-phase combustion models

Developing maximal neuromuscular power: part 2 - training considerations for improving maximal power production.

Science.gov (United States)

Cormie, Prue; McGuigan, Michael R; Newton, Robert U

2011-02-01

This series of reviews focuses on the most important neuromuscular function in many sport performances: the ability to generate maximal muscular power. Part 1, published in an earlier issue of Sports Medicine, focused on the factors that affect maximal power production while part 2 explores the practical application of these findings by reviewing the scientific literature relevant to the development of training programmes that most effectively enhance maximal power production. The ability to generate maximal power during complex motor skills is of paramount importance to successful athletic performance across many sports. A crucial issue faced by scientists and coaches is the development of effective and efficient training programmes that improve maximal power production in dynamic, multi-joint movements. Such training is referred to as 'power training' for the purposes of this review. Although further research is required in order to gain a deeper understanding of the optimal training techniques for maximizing power in complex, sports-specific movements and the precise mechanisms underlying adaptation, several key conclusions can be drawn from this review. First, a fundamental relationship exists between strength and power, which dictates that an individual cannot possess a high level of power without first being relatively strong. Thus, enhancing and maintaining maximal strength is essential when considering the long-term development of power. Second, consideration of movement pattern, load and velocity specificity is essential when designing power training programmes. Ballistic, plyometric and weightlifting exercises can be used effectively as primary exercises within a power training programme that enhances maximal power. The loads applied to these exercises will depend on the specific requirements of each particular sport and the type of movement being trained. The use of ballistic exercises with loads ranging from 0% to 50% of one-repetition maximum (1RM) and

RNA folding kinetics using Monte Carlo and Gillespie algorithms.

Science.gov (United States)

Clote, Peter; Bayegan, Amir H

2018-04-01

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny ([Formula: see text]20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm. Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to [Formula: see text] times that of the Gillespie algorithm, where [Formula: see text] denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by [Formula: see text]; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated. Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence-see http://bioinformatics.bc.edu/clote/RNAexpNumNbors .

The vortex-finding property of maximal center (and other) gauges

International Nuclear Information System (INIS)

Faber, M.; Greensite, J.; Olejnik, S.; Yamada, D.

1999-01-01

The authors argue that the vortex-finding property of maximal center gauge, i.e. the ability of this gauge to locate center vortices inserted by hand on any given lattice, is the key to its success in extracting the vortex content of thermalized lattice configurations. The authors explain how this property comes about, and why it is expected not only in maximal center gauge, but also in an infinite class of gauge conditions based on adjoint-representation link variables. In principle, the vortex-finding property can be foiled by Gribov copies. This fact is relevant to a gauge-fixing procedure devised by Kovacs and Tomboulis, where they show that the loss of center dominance, found in their procedure, is explained by a corresponding loss of the vortex-finding property. The dependence of center dominance on the vortex-finding property is demonstrated numerically in a number of other gauges

Energy Aware Clustering Algorithms for Wireless Sensor Networks

Science.gov (United States)

Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

2011-09-01

The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

Techniques to maximize software reliability in radiation fields

International Nuclear Information System (INIS)

Eichhorn, G.; Piercey, R.B.

1986-01-01

Microprocessor system failures due to memory corruption by single event upsets (SEUs) and/or latch-up in RAM or ROM memory are common in environments where there is high radiation flux. Traditional methods to harden microcomputer systems against SEUs and memory latch-up have usually involved expensive large scale hardware redundancy. Such systems offer higher reliability, but they tend to be more complex and non-standard. At the Space Astronomy Laboratory the authors have developed general programming techniques for producing software which is resistant to such memory failures. These techniques, which may be applied to standard off-the-shelf hardware, as well as custom designs, include an implementation of Maximally Redundant Software (MRS) model, error detection algorithms and memory verification and management

A Simulated Annealing method to solve a generalized maximal covering location problem

Directory of Open Access Journals (Sweden)

M. Saeed Jabalameli

2011-04-01

Full Text Available The maximal covering location problem (MCLP seeks to locate a predefined number of facilities in order to maximize the number of covered demand points. In a classical sense, MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations are extended: the gradual cover models, the cooperative cover models, and the variable radius models. In this paper, we develop a special form of MCLP which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed problem has many applications such as locating cell phone towers. The model is formulated as a mixed integer non-linear programming (MINLP. In addition, a simulated annealing algorithm is used to solve the resulted problem and the performance of the proposed method is evaluated with a set of randomly generated problems.

Identifying multiple influential spreaders by a heuristic clustering algorithm

International Nuclear Information System (INIS)

Bao, Zhong-Kui; Liu, Jian-Guo; Zhang, Hai-Feng

2017-01-01

The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

Identifying multiple influential spreaders by a heuristic clustering algorithm

Energy Technology Data Exchange (ETDEWEB)

Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)

2017-03-18

The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

Optimization of magnet sorting in a storage ring using genetic algorithms

International Nuclear Information System (INIS)

Chen Jia; Wang Lin; Li Weimin; Gao Weiwei

2013-01-01

In this paper, the genetic algorithms are applied to the optimization problem of magnet sorting in an electron storage ring, according to which the objectives are set so that the closed orbit distortion and beta beating can be minimized and the dynamic aperture maximized. The sorting of dipole, quadrupole and sextupole magnets is optimized while the optimization results show the power of the application of genetic algorithms in magnet sorting. (authors)

Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm

Science.gov (United States)

Jain, N. K.; Nangia, Uma; Singh, Iqbal

2017-10-01

This paper presents the application of genetic algorithm to Multiobjective Economic Load Dispatch (MELD) problem considering fuel cost, transmission losses and environmental pollution as objective functions. The MELD problem has been formulated using constraint method. The non-inferior set for IEEE 5, 14 and 30-bus system has been generated by using genetic algorithm and the target point has been obtained by using maximization of minimum relative attainments.

Auctioning access to networks: evidence and expectations

International Nuclear Information System (INIS)

McDaniel, T.

2003-01-01

Under very strict assumptions the outcomes that make common auction designs theoretically attractive can be expected to emerge: goods will go to those who value them most and the allocation will maximize revenue for the auctioneer. In network industries such as electricity, gas, telecommunications, rail, and airlines where agents are of different sizes and incumbency these assumptions do not always apply for a number of reasons. For example, the auctioned product may be an intermediate good, bidders are not symmetric with respect to values and information, markets are mixed, and property rights are hard to define. We discuss why it is not straightforward to make theoretical predictions about expected outcomes for these industries and use the British gas industry to discuss the importance of competition and scarcity for auctions of network capacity. (Author)

Sort-Mid tasks scheduling algorithm in grid computing.

Science.gov (United States)

Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M

2015-11-01

Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

Science.gov (United States)

Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

2016-05-04

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in

Contributions: SAGE

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. Contributions: SAGE. Space Alternating Generalized Expectation (SAGE) Maximization algorithm provides an iterative approach to parameter estimation when direct maximization of the likelihood function may be infeasible. Complexity is less in those applications ...

Uplink transmit beamforming design for SINR maximization with full multiuser channel state information

Science.gov (United States)

Xi, Songnan; Zoltowski, Michael D.

2008-04-01

Multiuser multiple-input multiple-output (MIMO) systems are considered in this paper. We continue our research on uplink transmit beamforming design for multiple users under the assumption that the full multiuser channel state information, which is the collection of the channel state information between each of the users and the base station, is known not only to the receiver but also to all the transmitters. We propose an algorithm for designing optimal beamforming weights in terms of maximizing the signal-to-interference-plus-noise ratio (SINR). Through statistical modeling, we decouple the original mathematically intractable optimization problem and achieved a closed-form solution. As in our previous work, the minimum mean-squared error (MMSE) receiver with successive interference cancellation (SIC) is adopted for multiuser detection. The proposed scheme is compared with an existing jointly optimized transceiver design, referred to as the joint transceiver in this paper, and our previously proposed eigen-beamforming algorithm. Simulation results demonstrate that our algorithm, with much less computational burden, accomplishes almost the same performance as the joint transceiver for spatially independent MIMO channel and even better performance for spatially correlated MIMO channels. And it always works better than our previously proposed eigen beamforming algorithm.

A Comparison of Heuristics with Modularity Maximization Objective using Biological Data Sets

Directory of Open Access Journals (Sweden)

Pirim Harun

2016-01-01

Full Text Available Finding groups of objects exhibiting similar patterns is an important data analytics task. Many disciplines have their own terminologies such as cluster, group, clique, community etc. defining the similar objects in a set. Adopting the term community, many exact and heuristic algorithms are developed to find the communities of interest in available data sets. Here, three heuristic algorithms to find communities are compared using five gene expression data sets. The heuristics have a common objective function of maximizing the modularity that is a quality measure of a partition and a reflection of objects’ relevance in communities. Partitions generated by the heuristics are compared with the real ones using the adjusted rand index, one of the most commonly used external validation measures. The paper discusses the results of the partitions on the mentioned biological data sets.

Blind Multiuser Detection by Kurtosis Maximization for Asynchronous Multirate DS/CDMA Systems

Directory of Open Access Journals (Sweden)

Peng Chun-Hsien

2006-01-01

Full Text Available Chi et al. proposed a fast kurtosis maximization algorithm (FKMA for blind equalization/deconvolution of multiple-input multiple-output (MIMO linear time-invariant systems. This algorithm has been applied to blind multiuser detection of single-rate direct-sequence/code-division multiple-access (DS/CDMA systems and blind source separation (or independent component analysis. In this paper, the FKMA is further applied to blind multiuser detection for multirate DS/CDMA systems. The ideas are to properly formulate discrete-time MIMO signal models by converting real multirate users into single-rate virtual users, followed by the use of FKMA for extraction of virtual users' data sequences associated with the desired user, and recovery of the data sequence of the desired user from estimated virtual users' data sequences. Assuming that all the users' spreading sequences are given a priori, two multirate blind multiuser detection algorithms (with either a single receive antenna or multiple antennas, which also enjoy the merits of superexponential convergence rate and guaranteed convergence of the FKMA, are proposed in the paper, one based on a convolutional MIMO signal model and the other based on an instantaneous MIMO signal model. Some simulation results are then presented to demonstrate their effectiveness and to provide a performance comparison with some existing algorithms.

Hybrid Multi-Objective Optimization of Folsom Reservoir Operation to Maximize Storage in Whole Watershed

Science.gov (United States)

Goharian, E.; Gailey, R.; Maples, S.; Azizipour, M.; Sandoval Solis, S.; Fogg, G. E.

2017-12-01

The drought incidents and growing water scarcity in California have a profound effect on human, agricultural, and environmental water needs. California experienced multi-year droughts, which have caused groundwater overdraft and dropping groundwater levels, and dwindling of major reservoirs. These concerns call for a stringent evaluation of future water resources sustainability and security in the state. To answer to this call, Sustainable Groundwater Management Act (SGMA) was passed in 2014 to promise a sustainable groundwater management in California by 2042. SGMA refers to managed aquifer recharge (MAR) as a key management option, especially in areas with high variation in water availability intra- and inter-annually, to secure the refill of underground water storage and return of groundwater quality to a desirable condition. The hybrid optimization of an integrated water resources system provides an opportunity to adapt surface reservoir operations for enhancement in groundwater recharge. Here, to re-operate Folsom Reservoir, objectives are maximizing the storage in the whole American-Cosumnes watershed and maximizing hydropower generation from Folsom Reservoir. While a linear programing (LP) module tends to maximize the total groundwater recharge by distributing and spreading water over suitable lands in basin, a genetic based algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), layer above it controls releases from the reservoir to secure the hydropower generation, carry-over storage in reservoir, available water for replenishment, and downstream water requirements. The preliminary results show additional releases from the reservoir for groundwater recharge during high flow seasons. Moreover, tradeoffs between the objectives describe that new operation performs satisfactorily to increase the storage in the basin, with nonsignificant effects on other objectives.

Algorithm for Controlling a Centrifugal Compressor

Science.gov (United States)

Benedict, Scott M.

2004-01-01

An algorithm has been developed for controlling a centrifugal compressor that serves as the prime mover in a heatpump system. Experimental studies have shown that the operating conditions for maximum compressor efficiency are close to the boundary beyond which surge occurs. Compressor surge is a destructive condition in which there are instantaneous reversals of flow associated with a high outlet-to-inlet pressure differential. For a given cooling load, the algorithm sets the compressor speed at the lowest possible value while adjusting the inlet guide vane angle and diffuser vane angle to maximize efficiency, subject to an overriding requirement to prevent surge. The onset of surge is detected via the onset of oscillations of the electric current supplied to the compressor motor, associated with surge-induced oscillations of the torque exerted by and on the compressor rotor. The algorithm can be implemented in any of several computer languages.

Documenting and explaining the common AAB pattern in music and humor: establishing and breaking expectations.

Science.gov (United States)

Rozin, Paul; Rozin, Alexander; Appel, Brian; Wachtel, Charles

2006-08-01

The AAB pattern consists of two similar events followed by a third dissimilar event. The prevalence of this pattern in the aesthetic domain may be explained as violation of expectation: A minimum of two iterations is required to establish a repetitive pattern; once established, it is most efficient to promptly violate the expected continuance of the pattern to produce the maximal aesthetic effect. We demonstrate the prevalence of this pattern (in comparison to AB or AAAB) in a representative sample of a variety of musical genres and in a representative sample of repetitive genre of jokes. We also provide experimental evidence that the AAB pattern in jokes is maximally effective in producing a humor response in participants. (c) 2006 APA, all rights reserved

A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

Science.gov (United States)

Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

2010-10-30

Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. Copyright © 2010 Elsevier B.V. All rights reserved.

Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET.

Science.gov (United States)

Rapisarda, E; Bettinardi, V; Thielemans, K; Gilardi, M C

2010-07-21

The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

Is CP violation maximal

International Nuclear Information System (INIS)

Gronau, M.

1984-01-01

Two ambiguities are noted in the definition of the concept of maximal CP violation. The phase convention ambiguity is overcome by introducing a CP violating phase in the quark mixing matrix U which is invariant under rephasing transformations. The second ambiguity, related to the parametrization of U, is resolved by finding a single empirically viable definition of maximal CP violation when assuming that U does not single out one generation. Considerable improvement in the calculation of nonleptonic weak amplitudes is required to test the conjecture of maximal CP violation. 21 references

A Faster Algorithm for Computing Straight Skeletons

KAUST Repository

Mencel, Liam A.

2014-01-01

computation in O(n (log n) log r) time. It improves on the previously best known algorithm for this reduction, which is randomised, and runs in expected O(n √(h+1) log² n) time for a polygon with h holes. Using known motorcycle graph algorithms, our result

Shareholder, stakeholder-owner or broad stakeholder maximization

OpenAIRE

Mygind, Niels

2004-01-01

With reference to the discussion about shareholder versus stakeholder maximization it is argued that the normal type of maximization is in fact stakeholder-owner maxi-mization. This means maximization of the sum of the value of the shares and stake-holder benefits belonging to the dominating stakeholder-owner. Maximization of shareholder value is a special case of owner-maximization, and only under quite re-strictive assumptions shareholder maximization is larger or equal to stakeholder-owner...

M-Theory and Maximally Supersymmetric Gauge Theories

CERN Document Server

Lambert, Neil

2012-01-01

In this informal review for non-specalists we discuss the construction of maximally supersymmetric gauge theories that arise on the worldvolumes branes in String Theory and M-Theory. Particular focus is made on the relatively recent construction of M2-brane worldvolume theories. In a formal sense, the existence of these quantum field theories can be viewed as predictions of M-Theory. Their construction is therefore a reinforcement of the ideas underlying String Theory and M-Theory. We also briefly discuss the six-dimensional conformal field theory that is expected to arise on M5-branes. The construction of this theory is not only an important open problem for M-Theory but also a significant challenge to our current understanding of quantum field theory more generally.

Task-oriented maximally entangled states

International Nuclear Information System (INIS)

Agrawal, Pankaj; Pradhan, B

2010-01-01

We introduce the notion of a task-oriented maximally entangled state (TMES). This notion depends on the task for which a quantum state is used as the resource. TMESs are the states that can be used to carry out the task maximally. This concept may be more useful than that of a general maximally entangled state in the case of a multipartite system. We illustrate this idea by giving an operational definition of maximally entangled states on the basis of communication tasks of teleportation and superdense coding. We also give examples and a procedure to obtain such TMESs for n-qubit systems.

Exponential Lower Bounds for the PPSZ k-SAT Algorithm

DEFF Research Database (Denmark)

Chen, Shiteng; Scheder, Dominik Alban; Talebanfard, Navid

2013-01-01

In 1998, Paturi, Pudl´ak, Saks, and Zane presented PPSZ, an elegant randomized algorithm for k-SAT. Fourteen years on, this algorithm is still the fastest known worst-case algorithm. They proved that its expected running time on k-CNF formulas with n variables is at most 2(1−k)n, where k 2 (1/k).......). So far, no exponential lower bounds at all have been known. In this paper, we construct hard instances for PPSZ. That is, we construct satisfiable k-CNF formulas over n variables on which the expected running time is at least 2(1−k)n, for k 2 O(log2 k/k)....

Iterative Mixture Component Pruning Algorithm for Gaussian Mixture PHD Filter

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Xiaoxi Yan

2014-01-01

Full Text Available As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the posterior probability density of the mixture weights. The entropy distribution of the mixture weights is adopted as the prior distribution of mixture component parameters. The iterative update formulations of the mixture weights are derived by Lagrange multiplier and Lambert W function. Mixture components, whose weights become negative during iterative procedure, are pruned by setting corresponding mixture weights to zeros. In addition, multiple mixture components with similar parameters describing the same PHD peak can be merged into one mixture component in the algorithm. Simulation results show that the proposed iterative mixture component pruning algorithm is superior to the typical pruning algorithm based on thresholds.

Treating Chronically Ill Diabetic Patients with Limited Life Expectancy: Implications for Performance Measurement

Science.gov (United States)

Woodard, LeChauncy D.; Landrum, Cassie R.; Urech, Tracy H.; Profit, Jochen; Virani, Salim S.; Petersen, Laura A.

2012-01-01

Background/Objectives To validly assess quality-of-care differences among providers, performance measurement programs must reliably identify and exclude patients for whom the quality indicator may not be desirable, including those with limited life expectancy. We developed an algorithm to identify patients with limited life expectancy and examined the impact of limited life expectancy on glycemic control and treatment intensification among diabetic patients. Design We identified diabetic patients with coexisting congestive heart failure, chronic obstructive pulmonary disease, dementia, end-stage liver disease, and/or primary/metastatic cancers with limited life expectancy. To validate our algorithm, we assessed 5-year mortality among patients identified as having limited life expectancy. We compared rates of meeting performance measures for glycemic control between patients with and without limited life expectancy. Among uncontrolled patients, we examined the impact of limited life expectancy on treatment intensification within 90 days. Setting 110 Veterans Administration facilities; October 2006 – September 2007 Participants 888,628 diabetic patients Measurements Hemoglobin A1c (HbA1c) Quality measurement and performance-based reimbursement systems should acknowledge the different needs of this population. PMID:22260627

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

Development of Automatic Cluster Algorithm for Microcalcification in Digital Mammography

International Nuclear Information System (INIS)

Choi, Seok Yoon; Kim, Chang Soo

2009-01-01

Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.

Optimal data replication: A new approach to optimizing parallel EM algorithms on a mesh-connected multiprocessor for 3D PET image reconstruction

International Nuclear Information System (INIS)

Chen, C.M.; Lee, S.Y.

1995-01-01

The EM algorithm promises an estimated image with the maximal likelihood for 3D PET image reconstruction. However, due to its long computation time, the EM algorithm has not been widely used in practice. While several parallel implementations of the EM algorithm have been developed to make the EM algorithm feasible, they do not guarantee an optimal parallelization efficiency. In this paper, the authors propose a new parallel EM algorithm which maximizes the performance by optimizing data replication on a mesh-connected message-passing multiprocessor. To optimize data replication, the authors have formally derived the optimal allocation of shared data, group sizes, integration and broadcasting of replicated data as well as the scheduling of shared data accesses. The proposed parallel EM algorithm has been implemented on an iPSC/860 with 16 PEs. The experimental and theoretical results, which are consistent with each other, have shown that the proposed parallel EM algorithm could improve performance substantially over those using unoptimized data replication

FLOUTING MAXIMS IN INDONESIA LAWAK KLUB CONVERSATION

Directory of Open Access Journals (Sweden)

Rahmawati Sukmaningrum

2017-04-01

Full Text Available This study aims to identify the types of maxims flouted in the conversation in famous comedy show, Indonesia Lawak Club. Likewise, it also tries to reveal the speakers‘ intention of flouting the maxim in the conversation during the show. The writers use descriptive qualitative method in conducting this research. The data is taken from the dialogue of Indonesia Lawak club and then analyzed based on Grice‘s cooperative principles. The researchers read the dialogue‘s transcripts, identify the maxims, and interpret the data to find the speakers‘ intention for flouting the maxims in the communication. The results show that there are four types of maxims flouted in the dialogue. Those are maxim of quality (23%, maxim of quantity (11%, maxim of manner (31%, and maxim of relevance (35. Flouting the maxims in the conversations is intended to make the speakers feel uncomfortable with the conversation, show arrogances, show disagreement or agreement, and ridicule other speakers.

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

OpenAIRE

Luo, Zihan; Armour, Simon; McGeehan, Joe

2015-01-01

This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...

VIOLATION OF CONVERSATION MAXIM ON TV ADVERTISEMENTS

Directory of Open Access Journals (Sweden)

Desak Putu Eka Pratiwi

2015-07-01

Full Text Available Maxim is a principle that must be obeyed by all participants textually and interpersonally in order to have a smooth communication process. Conversation maxim is divided into four namely maxim of quality, maxim of quantity, maxim of relevance, and maxim of manner of speaking. Violation of the maxim may occur in a conversation in which the information the speaker has is not delivered well to his speaking partner. Violation of the maxim in a conversation will result in an awkward impression. The example of violation is the given information that is redundant, untrue, irrelevant, or convoluted. Advertisers often deliberately violate the maxim to create unique and controversial advertisements. This study aims to examine the violation of maxims in conversations of TV ads. The source of data in this research is food advertisements aired on TV media. Documentation and observation methods are applied to obtain qualitative data. The theory used in this study is a maxim theory proposed by Grice (1975. The results of the data analysis are presented with informal method. The results of this study show an interesting fact that the violation of maxim in a conversation found in the advertisement exactly makes the advertisements very attractive and have a high value.

The maximization of the efficiency in the energy conversion in isolated photovoltaic systems; Tecnicas de maxima transferencia de potencia em sistemas fotovoltaicos isolados

Energy Technology Data Exchange (ETDEWEB)

Machado-Neto, L. V. B.; Cabral, C. V. T.; Diniz, A. S. A. C.; Cortizo, P. C.; Oliveira-Filho, D.

2004-07-01

The maximization of the efficiency in the energy conversion is essential into the developing of technical and economic sustainability of photovoltaic solar energy systems. In this paper is realized the study of a power maximization technique for photovoltaic generators. The power maximization technique explored in this paper is the Maximum Power Point Tracking (MPPT). There are different strategies being studied currently; this work consists of the development of an electronic converter prototype for MPPT, including the developing of the tracking algorithm implemented in a microcontroller. It is also realized a simulation of the system and a prototype was assembled and the first results are presented here. (Author)

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

Directory of Open Access Journals (Sweden)

Apisit Eiumnoh

2013-10-01

Full Text Available Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF model to simultaneously align two or more images and obtain a land cover map (LCM of the scene. The expectation maximization (EM algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm

Directory of Open Access Journals (Sweden)

Baljit Singh Khehra

2015-03-01

Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

International Nuclear Information System (INIS)

Guerrero, Rubén D.; Arango, Carlos A.; Reyes, Andrés

2015-01-01

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions

Analytical optimal pulse shapes obtained with the aid of genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Guerrero, Rubén D., E-mail: rdguerrerom@unal.edu.co [Department of Physics, Universidad Nacional de Colombia, Bogota (Colombia); Arango, Carlos A. [Department of Chemical Sciences, Universidad Icesi, Cali (Colombia); Reyes, Andrés [Department of Chemistry, Universidad Nacional de Colombia, Bogota (Colombia)

2015-09-28

We propose a methodology to design optimal pulses for achieving quantum optimal control on molecular systems. Our approach constrains pulse shapes to linear combinations of a fixed number of experimentally relevant pulse functions. Quantum optimal control is obtained by maximizing a multi-target fitness function using genetic algorithms. As a first application of the methodology, we generated an optimal pulse that successfully maximized the yield on a selected dissociation channel of a diatomic molecule. Our pulse is obtained as a linear combination of linearly chirped pulse functions. Data recorded along the evolution of the genetic algorithm contained important information regarding the interplay between radiative and diabatic processes. We performed a principal component analysis on these data to retrieve the most relevant processes along the optimal path. Our proposed methodology could be useful for performing quantum optimal control on more complex systems by employing a wider variety of pulse shape functions.

ITEM-QM solutions for EM problems in image reconstruction exemplary for the Compton Camera

CERN Document Server

Pauli, Josef; Anton, G

2002-01-01

Imaginary time expectation maximation (ITEM), a new algorithm for expectation maximization problems based on the quantum mechanics energy minimalization via imaginary (euclidian) time evolution is presented. Both (the algorithm as well as the implementation (http://www.johannes-pauli.de/item/index.html) are published under the terms of General GNU public License (http://www.gnu.org/copyleft/gpl.html). Due to its generality ITEM is applicable to various image reconstruction problems like CT, PET, SPECT, NMR, Compton Camera, tomosynthesis as well as any other energy minimization problem. The choice of the optimal ITEM Hamiltonian is discussed and numerical results are presented for the Compton Camera.

Optimization Shape of Variable Capacitance Micromotor Using Differential Evolution Algorithm

Directory of Open Access Journals (Sweden)

A. Ketabi

2010-01-01

Full Text Available A new method for optimum shape design of variable capacitance micromotor (VCM using Differential Evolution (DE, a stochastic search algorithm, is presented. In this optimization exercise, the objective function aims to maximize torque value and minimize the torque ripple, where the geometric parameters are considered to be the variables. The optimization process is carried out using a combination of DE algorithm and FEM analysis. Fitness value is calculated by FEM analysis using COMSOL3.4, and the DE algorithm is realized by MATLAB7.4. The proposed method is applied to a VCM with 8 poles at the stator and 6 poles at the rotor. The results show that the optimized micromotor using DE algorithm had higher torque value and lower torque ripple, indicating the validity of this methodology for VCM design.

Sort-Mid tasks scheduling algorithm in grid computing

Directory of Open Access Journals (Sweden)

Naglaa M. Reda

2015-11-01

Full Text Available Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

Science.gov (United States)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

A new hybrid metaheuristic algorithm for wind farm micrositing

International Nuclear Information System (INIS)

Massan, S.U.R.; Wagan, A.I.; Shaikh, M.M.

2017-01-01

This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic Algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (Differential Evolution Algorithm) and the FA (Firefly Algorithm). The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic Algorithm) used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together. (author)

A New Hybrid Metaheuristic Algorithm for Wind Farm Micrositing

Directory of Open Access Journals (Sweden)

SHAFIQ-UR-REHMAN MASSAN

2017-07-01

Full Text Available This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic Algorithm for the solution of the WTO (Wind Turbine Optimization problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (Differential Evolution Algorithm and the FA (Firefly Algorithm. The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic Algorithm used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together.

Matrix and position correction of shuffler assays by application of the alternating conditional expectation algorithm to shuffler data

International Nuclear Information System (INIS)

Pickrell, M.M.; Rinard, P.M.

1992-01-01

The 252 Cf shuffler assays fissile uranium and plutonium using active neutron interrogation and then counting the induced delayed neutrons. Using the shuffler, we conducted over 1700 assays of 55-gal. drums with 28 different matrices and several different fissionable materials. We measured the drums to dispose the matrix and position effects on 252 Cf shuffler assays. We used several neutron flux monitors during irradiation and kept statistics on the count rates of individual detector banks. The intent of these measurements was to gauge the effect of the matrix independently from the uranium assay. Although shufflers have previously been equipped neutron monitors, the functional relationship between the flux monitor sepals and the matrix-induced perturbation has been unknown. There are several flux monitors so the problem is multivariate, and the response is complicated. Conventional regression techniques cannot address complicated multivariate problems unless the underlying functional form and approximate parameter values are known in advance. Neither was available in this case. To address this problem, we used a new technique called alternating conditional expectations (ACE), which requires neither the functional relationship nor the initial parameters. The ACE algorithm develops the functional form and performs a numerical regression from only the empirical data. We applied the ACE algorithm to the shuffler-assay and flux-monitor data and developed an analytic function for the matrix correction. This function was optimized using conventional multivariate techniques. We were able to reduce the matrix-induced-bias error for homogeneous samples to 12.7%. The bias error for inhomogeneous samples was reduced to 13.5%. These results used only a few adjustable parameters compared to the number of available data points; the data were not ''over fit,'' but rather the results are general and robust

PARAMETRIZATION OF INNER STRUCTURE OF AGRICULTURAL SYSTEMS ON THE BASIS OF MAXIMAL YIELDS ISOLINES (ISOCARPS

Directory of Open Access Journals (Sweden)

K KUDRNA

2004-07-01

Full Text Available On the basis of analysis of yield time series from a ten-year period, isolines of maximal yields of crops (isocarps have been constructed, homogenized yield zones have been determined, and inner structures of the agricultural system have been calculated. The algorithm of a normal and an optimal structure calculation have been used, and differences in the structure of the agricultural system have been determined for every defi ned zone.

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

Science.gov (United States)

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low

Algorithms of maximum likelihood data clustering with applications

Science.gov (United States)

Giada, Lorenzo; Marsili, Matteo

2002-12-01

We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

Shareholder, stakeholder-owner or broad stakeholder maximization

DEFF Research Database (Denmark)

Mygind, Niels

2004-01-01

With reference to the discussion about shareholder versus stakeholder maximization it is argued that the normal type of maximization is in fact stakeholder-owner maxi-mization. This means maximization of the sum of the value of the shares and stake-holder benefits belonging to the dominating...... including the shareholders of a company. Although it may be the ultimate goal for Corporate Social Responsibility to achieve this kind of maximization, broad stakeholder maximization is quite difficult to give a precise definition. There is no one-dimensional measure to add different stakeholder benefits...... not traded on the mar-ket, and therefore there is no possibility for practical application. Broad stakeholder maximization instead in practical applications becomes satisfying certain stakeholder demands, so that the practical application will be stakeholder-owner maximization un-der constraints defined...

On the maximal superalgebras of supersymmetric backgrounds

International Nuclear Information System (INIS)

Figueroa-O'Farrill, Jose; Hackett-Jones, Emily; Moutsopoulos, George; Simon, Joan

2009-01-01

In this paper we give a precise definition of the notion of a maximal superalgebra of certain types of supersymmetric supergravity backgrounds, including the Freund-Rubin backgrounds, and propose a geometric construction extending the well-known construction of its Killing superalgebra. We determine the structure of maximal Lie superalgebras and show that there is a finite number of isomorphism classes, all related via contractions from an orthosymplectic Lie superalgebra. We use the structure theory to show that maximally supersymmetric waves do not possess such a maximal superalgebra, but that the maximally supersymmetric Freund-Rubin backgrounds do. We perform the explicit geometric construction of the maximal superalgebra of AdS 4 X S 7 and find that it is isomorphic to osp(1|32). We propose an algebraic construction of the maximal superalgebra of any background asymptotic to AdS 4 X S 7 and we test this proposal by computing the maximal superalgebra of the M2-brane in its two maximally supersymmetric limits, finding agreement.

Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach

Directory of Open Access Journals (Sweden)

Aidin Delgoshaei

2016-09-01

Full Text Available Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.

Research reactor loading pattern optimization using estimation of distribution algorithms

International Nuclear Information System (INIS)

Jiang, S.; Ziver, K.; Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H.; Franklin, S. J.; Phillips, H. J.

2006-01-01

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K eff ) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K eff with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Pareto optimization of an industrial ecosystem: sustainability maximization

Directory of Open Access Journals (Sweden)

J. G. M.-S. Monteiro

2010-09-01

Full Text Available This work investigates a procedure to design an Industrial Ecosystem for sequestrating CO2 and consuming glycerol in a Chemical Complex with 15 integrated processes. The Complex is responsible for the production of methanol, ethylene oxide, ammonia, urea, dimethyl carbonate, ethylene glycol, glycerol carbonate, β-carotene, 1,2-propanediol and olefins, and is simulated using UNISIM Design (Honeywell. The process environmental impact (EI is calculated using the Waste Reduction Algorithm, while Profit (P is estimated using classic cost correlations. MATLAB (The Mathworks Inc is connected to UNISIM to enable optimization. The objective is granting maximum process sustainability, which involves finding a compromise between high profitability and low environmental impact. Sustainability maximization is therefore understood as a multi-criteria optimization problem, addressed by means of the Pareto optimization methodology for trading off P vs. EI.

Algorithmic Trading with Developmental and Linear Genetic Programming

Science.gov (United States)

Wilson, Garnett; Banzhaf, Wolfgang

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Maximally multipartite entangled states

Science.gov (United States)

Facchi, Paolo; Florio, Giuseppe; Parisi, Giorgio; Pascazio, Saverio

2008-06-01

We introduce the notion of maximally multipartite entangled states of n qubits as a generalization of the bipartite case. These pure states have a bipartite entanglement that does not depend on the bipartition and is maximal for all possible bipartitions. They are solutions of a minimization problem. Examples for small n are investigated, both analytically and numerically.

From outcomes to acts : a non-standard axiomatization of the expected utility principle

NARCIS (Netherlands)

Peterson, M.B.

2004-01-01

This paper presents an axiomatization of the principle of maximizing expected utility that does not rely on the independence axiom or sure-thing principle. Perhaps more importantly the new axiomatization is based on an ex ante approach, instead of the standard ex post approach. An ex post approach

Maximally Symmetric Composite Higgs Models.

Science.gov (United States)

Csáki, Csaba; Ma, Teng; Shu, Jing

2017-09-29

Maximal symmetry is a novel tool for composite pseudo Goldstone boson Higgs models: it is a remnant of an enhanced global symmetry of the composite fermion sector involving a twisting with the Higgs field. Maximal symmetry has far-reaching consequences: it ensures that the Higgs potential is finite and fully calculable, and also minimizes the tuning. We present a detailed analysis of the maximally symmetric SO(5)/SO(4) model and comment on its observational consequences.

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

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N. Sri Madhava Raja

2014-01-01

Full Text Available Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD guided firefly algorithm (FA. A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR, structural similarity (SSIM index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

Tools to identify linear combination of prognostic factors which maximizes area under receiver operator curve.

Science.gov (United States)

Todor, Nicolae; Todor, Irina; Săplăcan, Gavril

2014-01-01

The linear combination of variables is an attractive method in many medical analyses targeting a score to classify patients. In the case of ROC curves the most popular problem is to identify the linear combination which maximizes area under curve (AUC). This problem is complete closed when normality assumptions are met. With no assumption of normality search algorithm are avoided because it is accepted that we have to evaluate AUC n(d) times where n is the number of distinct observation and d is the number of variables. For d = 2, using particularities of AUC formula, we described an algorithm which lowered the number of evaluations of AUC from n(2) to n(n-1) + 1. For d > 2 our proposed solution is an approximate method by considering equidistant points on the unit sphere in R(d) where we evaluate AUC. The algorithms were applied to data from our lab to predict response of treatment by a set of molecular markers in cervical cancers patients. In order to evaluate the strength of our algorithms a simulation was added. In the case of no normality presented algorithms are feasible. For many variables computation time could be increased but acceptable.

A Study on GPU-based Iterative ML-EM Reconstruction Algorithm for Emission Computed Tomographic Imaging Systems

Energy Technology Data Exchange (ETDEWEB)

Ha, Woo Seok; Kim, Soo Mee; Park, Min Jae; Lee, Dong Soo; Lee, Jae Sung [Seoul National University, Seoul (Korea, Republic of)

2009-10-15

The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 sec, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 sec, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries

A Study on GPU-based Iterative ML-EM Reconstruction Algorithm for Emission Computed Tomographic Imaging Systems

International Nuclear Information System (INIS)

Ha, Woo Seok; Kim, Soo Mee; Park, Min Jae; Lee, Dong Soo; Lee, Jae Sung

2009-01-01

The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 sec, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 sec, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries

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...

Maximizing Total Profit in Two-agent Problem of Order Acceptance and Scheduling

Directory of Open Access Journals (Sweden)

Mohammad Reisi-Nafchi

2017-03-01

Full Text Available In competitive markets, attracting potential customers and keeping current customers is a survival condition for each company. So, paying attention to the requests of customers is important and vital. In this paper, the problem of order acceptance and scheduling has been studied, in which two types of customers or agents compete in a single machine environment. The objective is maximizing sum of the total profit of first agent's accepted orders and the total revenue of second agent. Therefore, only the first agent has penalty and its penalty function is lateness and the second agent's orders have a common due date and this agent does not accept any tardy order. To solve the problem, a mathematical programming, a heuristic algorithm and a pseudo-polynomial dynamic programming algorithm are proposed. Computational results confirm the ability of solving all problem instances up to 70 orders size optimally and also 93.12% of problem instances up to 150 orders size by dynamic programming.

Performance Analysis of Binary Search Algorithm in RFID

Directory of Open Access Journals (Sweden)

Xiangmei SONG

2014-12-01

Full Text Available Binary search algorithm (BS is a kind of important anti-collision algorithm in the Radio Frequency Identification (RFID, is also one of the key technologies which determine whether the information in the tag is identified by the reader-writer fast and reliably. The performance of BS directly affects the quality of service in Internet of Things. This paper adopts an automated formal technology: probabilistic model checking to analyze the performance of BS algorithm formally. Firstly, according to the working principle of BS algorithm, its dynamic behavior is abstracted into a Discrete Time Markov Chains which can describe deterministic, discrete time and the probability selection. And then on the model we calculate the probability of the data sent successfully and the expected time of tags completing the data transmission. Compared to the another typical anti-collision protocol S-ALOHA in RFID, experimental results show that with an increase in the number of tags the BS algorithm has a less space and time consumption, the average number of conflicts increases slower than the S-ALOHA protocol standard, BS algorithm needs fewer expected time to complete the data transmission, and the average speed of the data transmission in BS is as 1.6 times as the S-ALOHA protocol.

Maximal quantum Fisher information matrix

International Nuclear Information System (INIS)

Chen, Yu; Yuan, Haidong

2017-01-01

We study the existence of the maximal quantum Fisher information matrix in the multi-parameter quantum estimation, which bounds the ultimate precision limit. We show that when the maximal quantum Fisher information matrix exists, it can be directly obtained from the underlying dynamics. Examples are then provided to demonstrate the usefulness of the maximal quantum Fisher information matrix by deriving various trade-off relations in multi-parameter quantum estimation and obtaining the bounds for the scalings of the precision limit. (paper)

AC-600 reactor reloading pattern optimization by using genetic algorithms

International Nuclear Information System (INIS)

Wu Hongchun; Xie Zhongsheng; Yao Dong; Li Dongsheng; Zhang Zongyao

2000-01-01

The use of genetic algorithms to optimize reloading pattern of the nuclear power plant reactor is proposed. And a new encoding and translating method is given. Optimization results of minimizing core power peak and maximizing cycle length for both low-leakage and out-in loading pattern of AC-600 reactor are obtained

Understanding Violations of Gricean Maxims in Preschoolers and Adults

Directory of Open Access Journals (Sweden)

Mako eOkanda

2015-07-01

Full Text Available This study used a revised Conversational Violations Test to examine Gricean maxim violations in 4- to 6-year-old Japanese children and adults. Participants’ understanding of the following maxims was assessed: be informative (first maxim of quantity, avoid redundancy (second maxim of quantity, be truthful (maxim of quality, be relevant (maxim of relation, avoid ambiguity (second maxim of manner, and be polite (maxim of politeness. Sensitivity to violations of Gricean maxims increased with age: 4-year-olds’ understanding of maxims was near chance, 5-year-olds understood some maxims (first maxim of quantity and maxims of quality, relation, and manner, and 6-year-olds and adults understood all maxims. Preschoolers acquired the maxim of relation first and had the greatest difficulty understanding the second maxim of quantity. Children and adults differed in their comprehension of the maxim of politeness. The development of the pragmatic understanding of Gricean maxims and implications for the construction of developmental tasks from early childhood to adulthood are discussed.

Image reconstruction using three-dimensional compound Gauss-Markov random field in emission computed tomography

International Nuclear Information System (INIS)

Watanabe, Shuichi; Kudo, Hiroyuki; Saito, Tsuneo

1993-01-01

In this paper, we propose a new reconstruction algorithm based on MAP (maximum a posteriori probability) estimation principle for emission tomography. To improve noise suppression properties of the conventional ML-EM (maximum likelihood expectation maximization) algorithm, direct three-dimensional reconstruction that utilizes intensity correlations between adjacent transaxial slices is introduced. Moreover, to avoid oversmoothing of edges, a priori knowledge of RI (radioisotope) distribution is represented by using a doubly-stochastic image model called the compound Gauss-Markov random field. The a posteriori probability is maximized by using the iterative GEM (generalized EM) algorithm. Computer simulation results are shown to demonstrate validity of the proposed algorithm. (author)

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

General conditions for maximal violation of non-contextuality in discrete and continuous variables

International Nuclear Information System (INIS)

Laversanne-Finot, A; Ketterer, A; Coudreau, T; Keller, A; Milman, P; Barros, M R; Walborn, S P

2017-01-01

The contextuality of quantum mechanics can be shown by the violation of inequalities based on measurements of well chosen observables. An important property of such observables is that their expectation value can be expressed in terms of probabilities for obtaining two exclusive outcomes. Examples of such inequalities have been constructed using either observables with a dichotomic spectrum or using periodic functions obtained from displacement operators in phase space. Here we identify the general conditions on the spectral decomposition of observables demonstrating state independent contextuality of quantum mechanics. Our results not only unify existing strategies for maximal violation of state independent non-contextuality inequalities but also lead to new scenarios enabling such violations. Among the consequences of our results is the impossibility of having a state independent maximal violation of non-contextuality in the Peres–Mermin scenario with discrete observables of odd dimensions. (paper)

Combinatorial optimization theory and algorithms

CERN Document Server

Korte, Bernhard

2018-01-01

This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the fundamentals, covers the classical topics (paths, flows, matching, matroids, NP-completeness, approximation algorithms) in detail, and proceeds to advanced and recent topics, some of which have not appeared in a textbook before. Throughout, it contains complete but concise proofs, and also provides numerous exercises and references. This sixth edition has again been updated, revised, and significantly extended. Among other additions, there are new sections on shallow-light trees, submodular function maximization, smoothed analysis of the knapsack problem, the (ln 4+ɛ)-approximation for Steiner trees, and the VPN theorem. Thus, this book continues to represent the state of the art of combinatorial opti...

Formal Darwinism, the individual-as-maximizing-agent analogy and bet-hedging

Science.gov (United States)

Grafen, A.

1999-01-01

The central argument of The origin of species was that mechanical processes (inheritance of features and the differential reproduction they cause) can give rise to the appearance of design. The 'mechanical processes' are now mathematically represented by the dynamic systems of population genetics, and the appearance of design by optimization and game theory in which the individual plays the part of the maximizing agent. Establishing a precise individual-as-maximizing-agent (IMA) analogy for a population-genetics system justifies optimization approaches, and so provides a modern formal representation of the core of Darwinism. It is a hitherto unnoticed implication of recent population-genetics models that, contrary to a decades-long consensus, an IMA analogy can be found in models with stochastic environments (subject to a convexity assumption), in which individuals maximize expected reproductive value. The key is that the total reproductive value of a species must be considered as constant, so therefore reproductive value should always be calculated in relative terms. This result removes a major obstacle from the theoretical challenge to find a unifying framework which establishes the IMA analogy for all of Darwinian biology, including as special cases inclusive fitness, evolutionarily stable strategies, evolutionary life-history theory, age-structured models and sex ratio theory. This would provide a formal, mathematical justification of fruitful and widespread but 'intentional' terms in evolutionary biology, such as 'selfish', 'altruism' and 'conflict'.

DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Francisco José Estévez

2016-06-01

Full Text Available The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities.

Research reactor loading pattern optimization using estimation of distribution algorithms

Energy Technology Data Exchange (ETDEWEB)

Jiang, S. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Ziver, K. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); AMCG Group, RM Consultants, Abingdon (United Kingdom); Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Franklin, S. J.; Phillips, H. J. [Imperial College, Reactor Centre, Silwood Park, Buckhurst Road, Ascot, Berkshire, SL5 7TE (United Kingdom)

2006-07-01

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K{sub eff}) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K{sub eff} with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Designing lattice structures with maximal nearest-neighbor entanglement

Energy Technology Data Exchange (ETDEWEB)

Navarro-Munoz, J C; Lopez-Sandoval, R [Instituto Potosino de Investigacion CientIfica y Tecnologica, Camino a la presa San Jose 2055, 78216 San Luis Potosi (Mexico); Garcia, M E [Theoretische Physik, FB 18, Universitaet Kassel and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Str.40, 34132 Kassel (Germany)

2009-08-07

In this paper, we study the numerical optimization of nearest-neighbor concurrence of bipartite one- and two-dimensional lattices, as well as non-bipartite two-dimensional lattices. These systems are described in the framework of a tight-binding Hamiltonian while the optimization of concurrence was performed using genetic algorithms. Our results show that the concurrence of the optimized lattice structures is considerably higher than that of non-optimized systems. In the case of one-dimensional chains, the concurrence increases dramatically when the system begins to dimerize, i.e., it undergoes a structural phase transition (Peierls distortion). This result is consistent with the idea that entanglement is maximal or shows a singularity near quantum phase transitions. Moreover, the optimization of concurrence in two-dimensional bipartite and non-bipartite lattices is achieved when the structures break into smaller subsystems, which are arranged in geometrically distinguishable configurations.

On Time with Minimal Expected Cost!

DEFF Research Database (Denmark)

David, Alexandre; Jensen, Peter Gjøl; Larsen, Kim Guldstrand

2014-01-01

(Priced) timed games are two-player quantitative games involving an environment assumed to be completely antogonistic. Classical analysis consists in the synthesis of strategies ensuring safety, time-bounded or cost-bounded reachability objectives. Assuming a randomized environment, the (priced......) timed game essentially defines an infinite-state Markov (reward) decision proces. In this setting the objective is classically to find a strategy that will minimize the expected reachability cost, but with no guarantees on worst-case behaviour. In this paper, we provide efficient methods for computing...... reachability strategies that will both ensure worst case time-bounds as well as provide (near-) minimal expected cost. Our method extends the synthesis algorithms of the synthesis tool Uppaal-Tiga with suitable adapted reinforcement learning techniques, that exhibits several orders of magnitude improvements w...

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

Directory of Open Access Journals (Sweden)

Chemmangattuvalappil Nishanth

2012-09-01

Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

A Fast Detection Algorithm for the X-Ray Pulsar Signal

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Hao Liang

2017-01-01

Full Text Available The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

Sum Rate Maximization of D2D Communications in Cognitive Radio Network Using Cheating Strategy

Directory of Open Access Journals (Sweden)

Yanjing Sun

2018-01-01

Full Text Available This paper focuses on the cheating algorithm for device-to-device (D2D pairs that reuse the uplink channels of cellular users. We are concerned about the way how D2D pairs are matched with cellular users (CUs to maximize their sum rate. In contrast with Munkres’ algorithm which gives the optimal matching in terms of the maximum throughput, Gale-Shapley algorithm ensures the stability of the system on the same time and achieves a men-optimal stable matching. In our system, D2D pairs play the role of “men,” so that each D2D pair could be matched to the CU that ranks as high as possible in the D2D pair’s preference list. It is found by previous studies that, by unilaterally falsifying preference lists in a particular way, some men can get better partners, while no men get worse off. We utilize this theory to exploit the best cheating strategy for D2D pairs. We find out that to acquire such a cheating strategy, we need to seek as many and as large cabals as possible. To this end, we develop a cabal finding algorithm named RHSTLC, and also we prove that it reaches the Pareto optimality. In comparison with other algorithms proposed by related works, the results show that our algorithm can considerably improve the sum rate of D2D pairs.

The Scatter Search Based Algorithm to Revenue Management Problem in Broadcasting Companies

Science.gov (United States)

Pishdad, Arezoo; Sharifyazdi, Mehdi; Karimpour, Reza

2009-09-01

The problem under question in this paper which is faced by broadcasting companies is how to benefit from a limited advertising space. This problem is due to the stochastic behavior of customers (advertiser) in different fare classes. To address this issue we propose a mathematical constrained nonlinear multi period model which incorporates cancellation and overbooking. The objective function is to maximize the total expected revenue and our numerical method performs it by determining the sales limits for each class of customer to present the revenue management control policy. Scheduling the advertising spots in breaks is another area of concern and we consider it as a constraint in our model. In this paper an algorithm based on Scatter search is developed to acquire a good feasible solution. This method uses simulation over customer arrival and in a continuous finite time horizon [0, T]. Several sensitivity analyses are conducted in computational result for depicting the effectiveness of proposed method. It also provides insight into better results of considering revenue management (control policy) compared to "no sales limit" policy in which sooner demand will served first.

MAX-SLNR Precoding Algorithm for Massive MIMO System

Directory of Open Access Journals (Sweden)

Jiang Jing

2016-01-01

Full Text Available Pilot Contamination obviously degrades the system performance of Massive MIMO systems. In this paper, a downlink precoding algorithm based on the Signal-to- Leakage-plus-Noise-Ratio (SLNR criterion is put forward. First, the impact of Pilot Contamination on SLNR is analyzed，then the precoding matrix is calculated with the eigenvalues decomposition of SLNR, which not only maximize the array gains of the target user, but also minimize the impact of Pilot Contamination and the leak to the users of other cells. Further, a simplified solution is derived, in which the impact of Pilot Contamination can be suppressed only with the large-scale fading coefficients. Simulation results reveal that: in the scenario of the serious pilot contamination, the proposed algorithm can avoid the performance loss caused by the pilot contamination compared with the conventional Massive MIMO precoding algorithm. Thus the proposed algorithm can acquire the perfect performance gains of Massive MIMO system and has better practical value since the large-scale fading coefficients are easy to measure and feedback.

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.

Inclusive fitness maximization: An axiomatic approach.

Science.gov (United States)

Okasha, Samir; Weymark, John A; Bossert, Walter

2014-06-07

Kin selection theorists argue that evolution in social contexts will lead organisms to behave as if maximizing their inclusive, as opposed to personal, fitness. The inclusive fitness concept allows biologists to treat organisms as akin to rational agents seeking to maximize a utility function. Here we develop this idea and place it on a firm footing by employing a standard decision-theoretic methodology. We show how the principle of inclusive fitness maximization and a related principle of quasi-inclusive fitness maximization can be derived from axioms on an individual׳s 'as if preferences' (binary choices) for the case in which phenotypic effects are additive. Our results help integrate evolutionary theory and rational choice theory, help draw out the behavioural implications of inclusive fitness maximization, and point to a possible way in which evolution could lead organisms to implement it. Copyright © 2014 Elsevier Ltd. All rights reserved.

Toward efficient computation of the expected relative entropy for nonlinear experimental design

International Nuclear Information System (INIS)

Coles, Darrell; Prange, Michael

2012-01-01

The expected relative entropy between prior and posterior model-parameter distributions is a Bayesian objective function in experimental design theory that quantifies the expected gain in information of an experiment relative to a previous state of knowledge. The expected relative entropy is a preferred measure of experimental quality because it can handle nonlinear data-model relationships, an important fact due to the ubiquity of nonlinearity in science and engineering and its effects on post-inversion parameter uncertainty. This objective function does not necessarily yield experiments that mediate well-determined systems, but, being a Bayesian quality measure, it rigorously accounts for prior information which constrains model parameters that may be only weakly constrained by the optimized dataset. Historically, use of the expected relative entropy has been limited by the computing and storage requirements associated with high-dimensional numerical integration. Herein, a bifocal algorithm is developed that makes these computations more efficient. The algorithm is demonstrated on a medium-sized problem of sampling relaxation phenomena and on a large problem of source–receiver selection for a 2D vertical seismic profile. The method is memory intensive but workarounds are discussed. (paper)

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

International Nuclear Information System (INIS)

Yang, Yu; Fritzsching, Keith J.; Hong, Mei

2013-01-01

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm.

Science.gov (United States)

Yang, Yu; Fritzsching, Keith J; Hong, Mei

2013-11-01

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a

Effects of visualization on algorithm comprehension

Science.gov (United States)

Mulvey, Matthew

Computer science students are expected to learn and apply a variety of core algorithms which are an essential part of the field. Any one of these algorithms by itself is not necessarily extremely complex, but remembering the large variety of algorithms and the differences between them is challenging. To address this challenge, we present a novel algorithm visualization tool designed to enhance students understanding of Dijkstra's algorithm by allowing them to discover the rules of the algorithm for themselves. It is hoped that a deeper understanding of the algorithm will help students correctly select, adapt and apply the appropriate algorithm when presented with a problem to solve, and that what is learned here will be applicable to the design of other visualization tools designed to teach different algorithms. Our visualization tool is currently in the prototype stage, and this thesis will discuss the pedagogical approach that informs its design, as well as the results of some initial usability testing. Finally, to clarify the direction for further development of the tool, four different variations of the prototype were implemented, and the instructional effectiveness of each was assessed by having a small sample participants use the different versions of the prototype and then take a quiz to assess their comprehension of the algorithm.

Dividend Maximization when Cash Reserves Follow a Jump-diffusion Process

Institute of Scientific and Technical Information of China (English)

LI LI-LI; FENG JIN-GHAI; SONG LI-XIN

2009-01-01

This paper deals with the dividend optimization problem for an insur-ance company, whose surplus follows a jump-diffusion process. The objective of the company is to maximize the expected total discounted dividends paid out until the time of ruin. Under concavity assumption on the optimal value function, the paper states some general properties and, in particular, smoothness results on the optimal value function, whose analysis mainly relies on viscosity solutions of the associated Hamilton-Jacobi-Bellman (HJB) equations. Based on these properties, the explicit expression of the optimal value function is obtained. And some numerical calculations are presented as the application of the results.

Robust optimization model and algorithm for railway freight center location problem in uncertain environment.

Science.gov (United States)

Liu, Xing-Cai; He, Shi-Wei; Song, Rui; Sun, Yang; Li, Hao-Dong

2014-01-01

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

Robust Optimization Model and Algorithm for Railway Freight Center Location Problem in Uncertain Environment

Directory of Open Access Journals (Sweden)

Xing-cai Liu

2014-01-01

Full Text Available Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

A new home energy management algorithm with voltage control in a smart home environment

International Nuclear Information System (INIS)

Elma, Onur; Selamogullari, Ugur Savas

2015-01-01

Energy management in electrical systems is one of the important issues for energy efficiency and future grid systems. Energy management is defined as a HEM (home energy management) on the residential consumer side. The HEM system plays a key role in residential demand response applications. In this study, a new HEM algorithm is proposed for smart home environments to reduce peak demand and increase the energy efficiency. The proposed algorithm includes VC (voltage control) methodology to reduce the power consumption of residential appliances so that the shifting of appliances is minimized. The results of the survey are used to produce representative load profiles for a weekday and for a weekend. Then, case studies are completed to test the proposed HEM algorithm in reducing the peak demand in the house. The main aim of the proposed HEM algorithm is to minimize the number of turned-off appliances to decrease demand so that the customer comfort is maximized. The smart home laboratory at Yildiz Technical University, Istanbul, Turkey is used in case studies. Experimental results show that the proposed HEM algorithm reduces the peak demand by 17.5% with the voltage control and by 38% with both the voltage control and the appliance shifting. - Highlights: • A new HEM (home energy management) algorithm is proposed. • Voltage control in the HEM is introduced as a solution for peak load reduction. • Customer comfort is maximized by minimizing the number of turned-off appliances. • The proposed HEM algorithm is experimentally validated at a smart home laboratory. • A survey is completed to produce typical load profiles of a Turkish family.

Algorithm for the Stochastic Generalized Transportation Problem

Directory of Open Access Journals (Sweden)

Marcin Anholcer

2012-01-01

Full Text Available The equalization method for the stochastic generalized transportation problem has been presented. The algorithm allows us to find the optimal solution to the problem of minimizing the expected total cost in the generalized transportation problem with random demand. After a short introduction and literature review, the algorithm is presented. It is a version of the method proposed by the author for the nonlinear generalized transportation problem. It is shown that this version of the method generates a sequence of solutions convergent to the KKT point. This guarantees the global optimality of the obtained solution, as the expected cost functions are convex and twice differentiable. The computational experiments performed for test problems of reasonable size show that the method is fast. (original abstract

Maximal Entanglement in High Energy Physics

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Alba Cervera-Lierta, José I. Latorre, Juan Rojo, Luca Rottoli

2017-11-01

Full Text Available We analyze how maximal entanglement is generated at the fundamental level in QED by studying correlations between helicity states in tree-level scattering processes at high energy. We demonstrate that two mechanisms for the generation of maximal entanglement are at work: i $s$-channel processes where the virtual photon carries equal overlaps of the helicities of the final state particles, and ii the indistinguishable superposition between $t$- and $u$-channels. We then study whether requiring maximal entanglement constrains the coupling structure of QED and the weak interactions. In the case of photon-electron interactions unconstrained by gauge symmetry, we show how this requirement allows reproducing QED. For $Z$-mediated weak scattering, the maximal entanglement principle leads to non-trivial predictions for the value of the weak mixing angle $\\theta_W$. Our results are a first step towards understanding the connections between maximal entanglement and the fundamental symmetries of high-energy physics.

Maximal Inequalities for Dependent Random Variables

DEFF Research Database (Denmark)

Hoffmann-Jorgensen, Jorgen

2016-01-01

Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X-k. Then a......Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X......-k. Then a maximal inequality gives conditions ensuring that the maximal partial sum M-n = max(1) (...

An ethical justification of profit maximization

DEFF Research Database (Denmark)

Koch, Carsten Allan

2010-01-01

In much of the literature on business ethics and corporate social responsibility, it is more or less taken for granted that attempts to maximize profits are inherently unethical. The purpose of this paper is to investigate whether an ethical argument can be given in support of profit maximizing...... behaviour. It is argued that some form of consequential ethics must be applied, and that both profit seeking and profit maximization can be defended from a rule-consequential point of view. It is noted, however, that the result does not apply unconditionally, but requires that certain form of profit (and...... utility) maximizing actions are ruled out, e.g., by behavioural norms or formal institutions....

Scheduling algorithms for saving energy and balancing load

Energy Technology Data Exchange (ETDEWEB)

Antoniadis, Antonios

2012-08-03

-processor result the competitive factor increases by an additive constant of 1. With respect to load balancing, we consider offline load balancing on identical machines, with the objective of minimizing the current load, for temporary unit-weight jobs. The problem can be seen as coloring n intervals with k colors, such that for each point on the line, the maximal difference between the number of intervals of any two colors is minimal. We prove that a coloring with maximal difference at most one is always possible, and develop a fast polynomial-time algorithm for generating such a coloring. Regarding the online version of the problem, we show that the maximal difference in the size of color classes can become arbitrary high for any online algorithm. Lastly, we prove that two generalizations of the problem are NP-hard. In the first we generalize from intervals to d-dimensional boxes while in the second we consider multiple-intervals, i.e., specific subsets of disjoint intervals must receive the same color.

Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutations

DEFF Research Database (Denmark)

Kratsch, Stefan; Lehre, Per Kristian; Neumann, Frank

2011-01-01

Evolutionary algorithms have been shown to be very successful for a wide range of NP-hard combinatorial optimization problems. We investigate the NP-hard problem of computing a spanning tree that has a maximal number of leaves by evolutionary algorithms in the context of fixed parameter tractabil...... two common mutation operators, we show that an operator related to spanning tree problems leads to an FPT running time in contrast to a general mutation operator that does not have this property....

A probabilistic multi objective CLSC model with Genetic algorithm-ε_Constraint approach

Directory of Open Access Journals (Sweden)

Alireza TaheriMoghadam

2014-05-01

Full Text Available In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

Science.gov (United States)

Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

2014-12-01

As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a

Maximum entropy algorithm and its implementation for the neutral beam profile measurement

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung Wook; Cho, Gyu Seong [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Cho, Yong Sub [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1998-12-31

A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI (Korea Superconducting Tokamak Advanced Research Neutral Beam Injector), which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography. 8 refs., 3 figs. (Author)

Maximum entropy algorithm and its implementation for the neutral beam profile measurement

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung Wook; Cho, Gyu Seong [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Cho, Yong Sub [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1997-12-31

A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI (Korea Superconducting Tokamak Advanced Research Neutral Beam Injector), which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography. 8 refs., 3 figs. (Author)

Inclusive Fitness Maximization:An Axiomatic Approach

OpenAIRE

Okasha, Samir; Weymark, John; Bossert, Walter

2014-01-01

Kin selection theorists argue that evolution in social contexts will lead organisms to behave as if maximizing their inclusive, as opposed to personal, fitness. The inclusive fitness concept allows biologists to treat organisms as akin to rational agents seeking to maximize a utility function. Here we develop this idea and place it on a firm footing by employing a standard decision-theoretic methodology. We show how the principle of inclusive fitness maximization and a related principle of qu...

Does mental exertion alter maximal muscle activation?

Directory of Open Access Journals (Sweden)

Vianney eRozand

2014-09-01

Full Text Available Mental exertion is known to impair endurance performance, but its effects on neuromuscular function remain unclear. The purpose of this study was to test the hypothesis that mental exertion reduces torque and muscle activation during intermittent maximal voluntary contractions of the knee extensors. Ten subjects performed in a randomized order three separate mental exertion conditions lasting 27 minutes each: i high mental exertion (incongruent Stroop task, ii moderate mental exertion (congruent Stroop task, iii low mental exertion (watching a movie. In each condition, mental exertion was combined with ten intermittent maximal voluntary contractions of the knee extensor muscles (one maximal voluntary contraction every 3 minutes. Neuromuscular function was assessed using electrical nerve stimulation. Maximal voluntary torque, maximal muscle activation and other neuromuscular parameters were similar across mental exertion conditions and did not change over time. These findings suggest that mental exertion does not affect neuromuscular function during intermittent maximal voluntary contractions of the knee extensors.

Improved core protection calculator system algorithm

International Nuclear Information System (INIS)

Yoon, Tae Young; Park, Young Ho; In, Wang Kee; Bae, Jong Sik; Baeg, Seung Yeob

2009-01-01

Core Protection Calculator System (CPCS) is a digitized core protection system which provides core protection functions based on two reactor core operation parameters, Departure from Nucleate Boiling Ratio (DNBR) and Local Power Density (LPD). It generates a reactor trip signal when the core condition exceeds the DNBR or LPD design limit. It consists of four independent channels which adapted a two out of four trip logic. CPCS algorithm improvement for the newly designed core protection calculator system, RCOPS (Reactor COre Protection System), is described in this paper. New features include the improvement of DNBR algorithm for thermal margin, the addition of pre trip alarm generation for auxiliary trip function, VOPT (Variable Over Power Trip) prevention during RPCS (Reactor Power Cutback System) actuation and the improvement of CEA (Control Element Assembly) signal checking algorithm. To verify the improved CPCS algorithm, CPCS algorithm verification tests, 'Module Test' and 'Unit Test', would be performed on RCOPS single channel facility. It is expected that the improved CPCS algorithm will increase DNBR margin and enhance the plant availability by reducing unnecessary reactor trips

Hybrid Proximal-Point Methods for Zeros of Maximal Monotone Operators, Variational Inequalities and Mixed Equilibrium Problems

Directory of Open Access Journals (Sweden)

Kriengsak Wattanawitoon

2011-01-01

Full Text Available We prove strong and weak convergence theorems of modified hybrid proximal-point algorithms for finding a common element of the zero point of a maximal monotone operator, the set of solutions of equilibrium problems, and the set of solution of the variational inequality operators of an inverse strongly monotone in a Banach space under different conditions. Moreover, applications to complementarity problems are given. Our results modify and improve the recently announced ones by Li and Song (2008 and many authors.

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Directory of Open Access Journals (Sweden)

Kai Yang

2016-01-01

Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

Traffic sharing algorithms for hybrid mobile networks

Science.gov (United States)

Arcand, S.; Murthy, K. M. S.; Hafez, R.

1995-01-01

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

A comparative study on using meta-heuristic algorithms for road maintenance planning: Insights from field study in a developing country

Directory of Open Access Journals (Sweden)

Ali Gerami Matin

2017-10-01

Full Text Available Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at proposing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms (GA, particle swarm optimization (PSO, and combination of genetic algorithm and particle swarm optimization (GAPSO as single-objective techniques are used, while the non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization (MOPSO which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.

On maximal surfaces in asymptotically flat space-times

International Nuclear Information System (INIS)

Bartnik, R.; Chrusciel, P.T.; O Murchadha, N.

1990-01-01

Existence of maximal and 'almost maximal' hypersurfaces in asymptotically flat space-times is established under boundary conditions weaker than those considered previously. We show in particular that every vacuum evolution of asymptotically flat data for Einstein equations can be foliated by slices maximal outside a spatially compact set and that every (strictly) stationary asymptotically flat space-time can be foliated by maximal hypersurfaces. Amongst other uniqueness results, we show that maximal hypersurface can be used to 'partially fix' an asymptotic Poincare group. (orig.)

Insulin resistance and maximal oxygen uptake

DEFF Research Database (Denmark)

Seibaek, Marie; Vestergaard, Henrik; Burchardt, Hans

2003-01-01

BACKGROUND: Type 2 diabetes, coronary atherosclerosis, and physical fitness all correlate with insulin resistance, but the relative importance of each component is unknown. HYPOTHESIS: This study was undertaken to determine the relationship between insulin resistance, maximal oxygen uptake......, and the presence of either diabetes or ischemic heart disease. METHODS: The study population comprised 33 patients with and without diabetes and ischemic heart disease. Insulin resistance was measured by a hyperinsulinemic euglycemic clamp; maximal oxygen uptake was measured during a bicycle exercise test. RESULTS......: There was a strong correlation between maximal oxygen uptake and insulin-stimulated glucose uptake (r = 0.7, p = 0.001), and maximal oxygen uptake was the only factor of importance for determining insulin sensitivity in a model, which also included the presence of diabetes and ischemic heart disease. CONCLUSION...

Low-energy restoration of parity and maximal symmetry

International Nuclear Information System (INIS)

Raychaudhuri, A.; Sarkar, U.

1982-01-01

The maximal symmetry of fermions of one generation, SU(16), which includes the left-right-symmetric Pati-Salam group, SU(4)/sub c/ x SU(2) /sub L/ x SU(2)/sub R/, as a subgroup, allows the possibility of a low-energy (M/sub R/approx.100 GeV) breaking of the left-right symmetry. It is known that such a low-energy restoration of parity can be consistent with weak-interaction phenomenology. We examine different chains of descent of SU(16) that admit a low value of M/sub R/ and determine the other intermediate symmetry-breaking mass scales associated with each of these chains. These additional mass scales provide an alternative to the ''great desert'' expected in some grand unifying models. The contributions of the Higgs fields in the renormalization-group equations are retained and are found to be important

Theoretical maximal storage of hydrogen in zeolitic frameworks.

Science.gov (United States)

Vitillo, Jenny G; Ricchiardi, Gabriele; Spoto, Giuseppe; Zecchina, Adriano

2005-12-07

Physisorption and encapsulation of molecular hydrogen in tailored microporous materials are two of the options for hydrogen storage. Among these materials, zeolites have been widely investigated. In these materials, the attained storage capacities vary widely with structure and composition, leading to the expectation that materials with improved binding sites, together with lighter frameworks, may represent efficient storage materials. In this work, we address the problem of the determination of the maximum amount of molecular hydrogen which could, in principle, be stored in a given zeolitic framework, as limited by the size, structure and flexibility of its pore system. To this end, the progressive filling with H2 of 12 purely siliceous models of common zeolite frameworks has been simulated by means of classical molecular mechanics. By monitoring the variation of cell parameters upon progressive filling of the pores, conclusions are drawn regarding the maximum storage capacity of each framework and, more generally, on framework flexibility. The flexible non-pentasils RHO, FAU, KFI, LTA and CHA display the highest maximal capacities, ranging between 2.86-2.65 mass%, well below the targets set for automotive applications but still in an interesting range. The predicted maximal storage capacities correlate well with experimental results obtained at low temperature. The technique is easily extendable to any other microporous structure, and it can provide a method for the screening of hypothetical new materials for hydrogen storage applications.

Maximize Minimum Utility Function of Fractional Cloud Computing System Based on Search Algorithm Utilizing the Mittag-Leffler Sum

Directory of Open Access Journals (Sweden)

Rabha W. Ibrahim

2018-01-01

Full Text Available The maximum min utility function (MMUF problem is an important representative of a large class of cloud computing systems (CCS. Having numerous applications in practice, especially in economy and industry. This paper introduces an effective solution-based search (SBS algorithm for solving the problem MMUF. First, we suggest a new formula of the utility function in term of the capacity of the cloud. We formulate the capacity in CCS, by using a fractional diffeo-integral equation. This equation usually describes the flow of CCS. The new formula of the utility function is modified recent active utility functions. The suggested technique first creates a high-quality initial solution by eliminating the less promising components, and then develops the quality of the achieved solution by the summation search solution (SSS. This method is considered by the Mittag-Leffler sum as hash functions to determine the position of the agent. Experimental results commonly utilized in the literature demonstrate that the proposed algorithm competes approvingly with the state-of-the-art algorithms both in terms of solution quality and computational efficiency.

Maximal network reliability for a stochastic power transmission network

International Nuclear Information System (INIS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2011-01-01

Many studies regarded a power transmission network as a binary-state network and constructed it with several arcs and vertices to evaluate network reliability. In practice, the power transmission network should be stochastic because each arc (transmission line) combined with several physical lines is multistate. Network reliability is the probability that the network can transmit d units of electric power from a power plant (source) to a high voltage substation at a specific area (sink). This study focuses on searching for the optimal transmission line assignment to the power transmission network such that network reliability is maximized. A genetic algorithm based method integrating the minimal paths and the Recursive Sum of Disjoint Products is developed to solve this assignment problem. A real power transmission network is adopted to demonstrate the computational efficiency of the proposed method while comparing with the random solution generation approach.

On Maximal Hard-Core Thinnings of Stationary Particle Processes

Science.gov (United States)

Hirsch, Christian; Last, Günter

2018-02-01

The present paper studies existence and distributional uniqueness of subclasses of stationary hard-core particle systems arising as thinnings of stationary particle processes. These subclasses are defined by natural maximality criteria. We investigate two specific criteria, one related to the intensity of the hard-core particle process, the other one being a local optimality criterion on the level of realizations. In fact, the criteria are equivalent under suitable moment conditions. We show that stationary hard-core thinnings satisfying such criteria exist and are frequently distributionally unique. More precisely, distributional uniqueness holds in subcritical and barely supercritical regimes of continuum percolation. Additionally, based on the analysis of a specific example, we argue that fluctuations in grain sizes can play an important role for establishing distributional uniqueness at high intensities. Finally, we provide a family of algorithmically constructible approximations whose volume fractions are arbitrarily close to the maximum.

A method to derive fixed budget results from expected optimisation times

DEFF Research Database (Denmark)

Doerr, Benjamin; Jansen, Thomas; Witt, Carsten

2013-01-01

At last year's GECCO a novel perspective for theoretical performance analysis of evolutionary algorithms and other randomised search heuristics was introduced that concentrates on the expected function value after a pre-defined number of steps, called budget. This is significantly different from...... the common perspective where the expected optimisation time is analysed. While there is a huge body of work and a large collection of tools for the analysis of the expected optimisation time the new fixed budget perspective introduces new analytical challenges. Here it is shown how results on the expected...... optimisation time that are strengthened by deviation bounds can be systematically turned into fixed budget results. We demonstrate our approach by considering the (1+1) EA on LeadingOnes and significantly improving previous results. We prove that deviating from the expected time by an additive term of ω(n3...

Power maximization method for land-transportable fully passive lead–bismuth cooled small modular reactor systems

Energy Technology Data Exchange (ETDEWEB)

Cho, Jaehyun, E-mail: chojh@kaeri.re.kr [Korea Atomic Energy Research Institute, 1405 Daedeok-daero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Shin, Yong-Hoon; Hwang, Il Soon [Seoul National University, Sillim-dong, Gwanak-gu, Seoul 151-742 (Korea, Republic of)

2015-08-15

Highlights: • The power maximization method for LBE natural circulation cooled SMRs was developed. • The two powers in view of neutronics and thermal-hydraulics were considered. • The limitations for designing of LBE natural circulation cooled SMRs were summarized. • The necessary conditions for safety shutdown in accidents were developed. • The maximized power in the case study is 206 MW thermal. - Abstract: Although current pressurized water reactors (PWRs) have significantly contributed to global energy supply, PWR technology has not been considered a trustworthy energy solution owing to its problems of spent nuclear fuels (SNFs), nuclear safety, and nuclear economy. In order to overcome these problems, a lead–bismuth eutectic (LBE) fully passive cooling small modular reactor (SMR) system is suggested. This technology can not only provide the solution for the problems of SNFs through the transmutation feature of the LBE coolant, but also strengthen safety and economy through the concept of natural circulation cooling SMRs. It is necessary to maximize the advantages, namely safety and economy, of this type of nuclear power plants for broader applications in the future. Accordingly, the objective of this study is to maximize the reactor core power while satisfying the limitations of shipping size, materials endurance, and criticality of a long-burning core as well as safety under beyond design basis events. To achieve these objectives, the design limitations of natural circulating LBE-cooling SMRs are derived. Then, the power maximization method is developed based on obtaining the design limitations. The results of this study are expected to contribute to the effectiveness of the reactor design stage by providing insights to designers, as well as by formulating methods for the power maximization of other types of SMRs.

POLITENESS MAXIM OF MAIN CHARACTER IN SECRET FORGIVEN

Directory of Open Access Journals (Sweden)

Sang Ayu Isnu Maharani

2017-06-01

Full Text Available Maxim of Politeness is an interesting subject to be discussed, since politeness has been criticized from our childhood. We are obliques to be polite to anyone either in speaking or in acting. Somehow we are manage to show politeness in our spoken expression though our intention might be not so polite. For example we must appriciate others opinion although we feel objection toward the opinion. In this article the analysis of politeness is based on maxim proposes by Leech. He proposed six types of politeness maxim. The discussion shows that the main character (Kristen and Kami use all types of maxim in their conversation. The most commonly used are approbation maxim and agreement maxim

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

Science.gov (United States)

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Directory of Open Access Journals (Sweden)

Nadeem Javaid

2017-03-01

Full Text Available In recent years, demand side management (DSM techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA, the binary particle swarm optimization (BPSO algorithm, the bacterial foraging optimization algorithm (BFOA, the wind-driven optimization (WDO algorithm and our proposed hybrid genetic wind-driven (GWD algorithm are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs and off-peak hours (OPHs in a real-time pricing (RTP environment while maximizing user comfort (UC and minimizing both electricity cost and the peak to average ratio (PAR. Moreover, these algorithms are tested in two scenarios: (i scheduling the load of a single home and (ii scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

Directory of Open Access Journals (Sweden)

Liping Liu

2018-01-01

Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

Steam condenser optimization using Real-parameter Genetic Algorithm for Prototype Fast Breeder Reactor

Energy Technology Data Exchange (ETDEWEB)

Jayalal, M.L., E-mail: jayalal@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Kumar, L. Satish, E-mail: satish@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Jehadeesan, R., E-mail: jeha@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Rajeswari, S., E-mail: raj@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Satya Murty, S.A.V., E-mail: satya@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Balasubramaniyan, V.; Chetal, S.C. [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India)

2011-10-15

Highlights: > We model design optimization of a vital reactor component using Genetic Algorithm. > Real-parameter Genetic Algorithm is used for steam condenser optimization study. > Comparison analysis done with various Genetic Algorithm related mechanisms. > The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.

Gaussian maximally multipartite-entangled states

Science.gov (United States)

Facchi, Paolo; Florio, Giuseppe; Lupo, Cosmo; Mancini, Stefano; Pascazio, Saverio

2009-12-01

We study maximally multipartite-entangled states in the context of Gaussian continuous variable quantum systems. By considering multimode Gaussian states with constrained energy, we show that perfect maximally multipartite-entangled states, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of these states and their frustration for n≤7 .

Gaussian maximally multipartite-entangled states

International Nuclear Information System (INIS)

Facchi, Paolo; Florio, Giuseppe; Pascazio, Saverio; Lupo, Cosmo; Mancini, Stefano

2009-01-01

We study maximally multipartite-entangled states in the context of Gaussian continuous variable quantum systems. By considering multimode Gaussian states with constrained energy, we show that perfect maximally multipartite-entangled states, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of these states and their frustration for n≤7.

2D evaluation of spectral LIBS data derived from heterogeneous materials using cluster algorithm

Science.gov (United States)

Gottlieb, C.; Millar, S.; Grothe, S.; Wilsch, G.

2017-08-01

Laser-induced Breakdown Spectroscopy (LIBS) is capable of providing spatially resolved element maps in regard to the chemical composition of the sample. The evaluation of heterogeneous materials is often a challenging task, especially in the case of phase boundaries. In order to determine information about a certain phase of a material, the need for a method that offers an objective evaluation is necessary. This paper will introduce a cluster algorithm in the case of heterogeneous building materials (concrete) to separate the spectral information of non-relevant aggregates and cement matrix. In civil engineering, the information about the quantitative ingress of harmful species like Cl-, Na+ and SO42- is of great interest in the evaluation of the remaining lifetime of structures (Millar et al., 2015; Wilsch et al., 2005). These species trigger different damage processes such as the alkali-silica reaction (ASR) or the chloride-induced corrosion of the reinforcement. Therefore, a discrimination between the different phases, mainly cement matrix and aggregates, is highly important (Weritz et al., 2006). For the 2D evaluation, the expectation-maximization-algorithm (EM algorithm; Ester and Sander, 2000) has been tested for the application presented in this work. The method has been introduced and different figures of merit have been presented according to recommendations given in Haddad et al. (2014). Advantages of this method will be highlighted. After phase separation, non-relevant information can be excluded and only the wanted phase displayed. Using a set of samples with known and unknown composition, the EM-clustering method has been validated regarding to Gustavo González and Ángeles Herrador (2007).

Utility maximization and mode of payment

NARCIS (Netherlands)

Koning, R.H.; Ridder, G.; Heijmans, R.D.H.; Pollock, D.S.G.; Satorra, A.

2000-01-01

The implications of stochastic utility maximization in a model of choice of payment are examined. Three types of compatibility with utility maximization are distinguished: global compatibility, local compatibility on an interval, and local compatibility on a finite set of points. Keywords:

Modeling and Design of MPPT Controller Using Stepped P&O Algorithm in Solar Photovoltaic System

OpenAIRE

R. Prakash; B. Meenakshipriya; R. Kumaravelan

2014-01-01

This paper presents modeling and simulation of Grid Connected Photovoltaic (PV) system by using improved mathematical model. The model is used to study different parameter variations and effects on the PV array including operating temperature and solar irradiation level. In this paper stepped P&O algorithm is proposed for MPPT control. This algorithm will identify the suitable duty ratio in which the DC-DC converter should be operated to maximize the power output. Photo voltaic array with pro...

The power of belief and expectancy in understanding and management of depression.

Science.gov (United States)

Alladin, Assen

2013-01-01

This article examines how beliefs can influence the definition, classification, understanding, and treatment of depression. It is organized in five parts: The first part critically reviews the definition of depression; the second part explores the medicalization of depression; the third part examines the role of the pharmaceutical industry in the promotion and marketing of antidepressant medications; the fourth part surveys the psychological therapies for depression and examines the role of expectancy in outcome; and the last part looks at the mechanisms involved in the placebo effect. A list of evidence-based strategies, including hypnosis, are discussed in the context of cognitive hypnotherapy for depression to illustrate how expectancy effect can be maximized in psychotherapy.

Closed-form expressions for flip angle variation that maximize total signal in T1-weighted rapid gradient echo MRI.

Science.gov (United States)

Drobnitzky, Matthias; Klose, Uwe

2017-03-01

Magnetization-prepared rapid gradient-echo (MPRAGE) sequences are commonly employed for T1-weighted structural brain imaging. Following a contrast preparation radiofrequency (RF) pulse, the data acquisition proceeds under nonequilibrium conditions of the relaxing longitudinal magnetization. Variation of the flip angle can be used to maximize total available signal. Simulated annealing or greedy algorithms have so far been published to numerically solve this problem, with signal-to-noise ratios optimized for clinical imaging scenarios by adhering to a predefined shape of the signal evolution. We propose an unconstrained optimization of the MPRAGE experiment that employs techniques from resource allocation theory. A new dynamic programming solution is introduced that yields closed-form expressions for optimal flip angle variation. Flip angle series are proposed that maximize total transverse magnetization (Mxy) for a range of physiologic T1 values. A 3D MPRAGE sequence is modified to allow for a controlled variation of the excitation angle. Experiments employing a T1 contrast phantom are performed at 3T. 1D acquisitions without phase encoding permit measurement of the temporal development of Mxy. Image mean signal and standard deviation for reference flip angle trains are compared in 2D measurements. Signal profiles at sharp phantom edges are acquired to access image blurring related to nonuniform Mxy development. A novel closed-form expression for flip angle variation is found that constitutes the optimal policy to reach maximum total signal. It numerically equals previously published results of other authors when evaluated under their simplifying assumptions. Longitudinal magnetization (Mz) is exhaustively used without causing abrupt changes in the measured MR signal, which is a prerequisite for artifact free images. Phantom experiments at 3T verify the expected benefit for total accumulated k-space signal when compared with published flip angle series. Describing

CISO's guide to penetration testing a framework to plan, manage, and maximize benefits

CERN Document Server

Tiller, James S

2011-01-01

CISO's Guide to Penetration Testing: A Framework to Plan, Manage, and Maximize Benefits details the methodologies, framework, and unwritten conventions penetration tests should cover to provide the most value to your organization and your customers. Discussing the process from both a consultative and technical perspective, it provides an overview of the common tools and exploits used by attackers along with the rationale for why they are used. From the first meeting to accepting the deliverables and knowing what to do with the results, James Tiller explains what to expect from all phases of th

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

Science.gov (United States)

Pan, Wei; Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

CryoSat-2 science algorithm status, expected future improvements and impacts concerning Sentinel-3 and Jason-CS missions

Science.gov (United States)

Cullen, R.; Wingham, D.; Francis, R.; Parrinello, T.

2011-12-01

With CryoSat-2 soon to enter its second year of post commissioning operations there is now sufficient experience and evidence showing improvements of the SIRAL's (Synthetic interferometric radar altimeter) SAR and SARIn modes over conventional pulse-width limited altimeters for both the targeted marine/land ice fields but also for non mission relevant surfaces such as the ocean, for example. In the process of understanding the CryoSat data some side effects of the end-to-end platform measurement and ground retrieval system have been identified and whilst those key to mission success are understood and are being handled others, remain open and pave the way to longer term fine-tuning. Of interest to the session will be a summary of the manditory changes made during 2011 to all the modes of CryoSat-2 science processing with a view to longer term algorithm improvements that could benefit the planned mid-to-late nominal operations re-processing. Since some of the science processor improvements have direct implication to the SAR mode processing of Sentinel-3 and Jason-CS science then these will also be highlighted. Finally a summary of the CryoSat-2 in-orbit platform and payload performances and their stability will also be provided. Expectations of the longer term uses of CryoSat's primary sensor (SIRAL) and its successors will be discussed.

Algorithm for image retrieval based on edge gradient orientation statistical code.

Science.gov (United States)

Zeng, Jiexian; Zhao, Yonggang; Li, Weiye; Fu, Xiang

2014-01-01

Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.

SU-E-T-304: Dosimetric Comparison of Cavernous Sinus Tumors: Heterogeneity Corrected Pencil Beam (PB-Hete) Vs. X-Ray Voxel Monte Carlo (XVMC) Algorithms for Stereotactic Radiotherapy (SRT)

Energy Technology Data Exchange (ETDEWEB)

Pokhrel, D; Sood, S; Badkul, R; Jiang, H; Saleh, H; Wang, F [University of Kansas Hospital, Kansas City, KS (United States)

2015-06-15

Purpose: To compare dose distributions calculated using PB-hete vs. XVMC algorithms for SRT treatments of cavernous sinus tumors. Methods: Using PB-hete SRT, five patients with cavernous sinus tumors received the prescription dose of 25 Gy in 5 fractions for planning target volume PTV(V100%)=95%. Gross tumor volume (GTV) and organs at risk (OARs) were delineated on T1/T2 MRI-CT-fused images. PTV (range 2.1–84.3cc, mean=21.7cc) was generated using a 5mm uniform-margin around GTV. PB-hete SRT plans included a combination of non-coplanar conformal arcs/static beams delivered by Novalis-TX consisting of HD-MLCs and a 6MV-SRS(1000 MU/min) beam. Plans were re-optimized using XVMC algorithm with identical beam geometry and MLC positions. Comparison of plan specific PTV(V99%), maximal, mean, isocenter doses, and total monitor units(MUs) were evaluated. Maximal dose to OARs such as brainstem, optic-pathway, spinal cord, and lenses as well as normal tissue volume receiving 12Gy(V12) were compared between two algorithms. All analysis was performed using two-tailed paired t-tests of an upper-bound p-value of <0.05. Results: Using either algorithm, no dosimetrically significant differences in PTV coverage (PTVV99%,maximal, mean, isocenter doses) and total number of MUs were observed (all p-values >0.05, mean ratios within 2%). However, maximal doses to optic-chiasm and nerves were significantly under-predicted using PB-hete (p=0.04). Maximal brainstem, spinal cord, lens dose and V12 were all comparable between two algorithms, with exception of one patient with the largest PTV who exhibited 11% higher V12 with XVMC. Conclusion: Unlike lung tumors, XVMC and PB-hete treatment plans provided similar PTV coverage for cavernous sinus tumors. Majority of OARs doses were comparable between two algorithms, except for small structures such as optic chiasm/nerves which could potentially receive higher doses when using XVMC algorithm. Special attention may need to be paid on a case

Activity versus outcome maximization in time management.

Science.gov (United States)

Malkoc, Selin A; Tonietto, Gabriela N

2018-04-30

Feeling time-pressed has become ubiquitous. Time management strategies have emerged to help individuals fit in more of their desired and necessary activities. We provide a review of these strategies. In doing so, we distinguish between two, often competing, motives people have in managing their time: activity maximization and outcome maximization. The emerging literature points to an important dilemma: a given strategy that maximizes the number of activities might be detrimental to outcome maximization. We discuss such factors that might hinder performance in work tasks and enjoyment in leisure tasks. Finally, we provide theoretically grounded recommendations that can help balance these two important goals in time management. Published by Elsevier Ltd.

The predictive validity of prospect theory versus expected utility in health utility measurement.

Science.gov (United States)

Abellan-Perpiñan, Jose Maria; Bleichrodt, Han; Pinto-Prades, Jose Luis

2009-12-01

Most health care evaluations today still assume expected utility even though the descriptive deficiencies of expected utility are well known. Prospect theory is the dominant descriptive alternative for expected utility. This paper tests whether prospect theory leads to better health evaluations than expected utility. The approach is purely descriptive: we explore how simple measurements together with prospect theory and expected utility predict choices and rankings between more complex stimuli. For decisions involving risk prospect theory is significantly more consistent with rankings and choices than expected utility. This conclusion no longer holds when we use prospect theory utilities and expected utilities to predict intertemporal decisions. The latter finding cautions against the common assumption in health economics that health state utilities are transferable across decision contexts. Our results suggest that the standard gamble and algorithms based on, should not be used to value health.

An intelligent identification algorithm for the monoclonal picking instrument

Science.gov (United States)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

Science.gov (United States)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

Hitting times of local and global optima in genetic algorithms with very high selection pressure

Directory of Open Access Journals (Sweden)

Eremeev Anton V.

2017-01-01

Full Text Available The paper is devoted to upper bounds on the expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure. The results of this paper extend the range of situations where the upper bounds on the expected runtime are known for genetic algorithms and apply, in particular, to the Canonical Genetic Algorithm. The obtained bounds do not require the probability of fitness-decreasing mutation to be bounded by a constant which is less than one.

Submaximal Riemann-Roch expected curves and symplectic packing.

Directory of Open Access Journals (Sweden)

Wioletta Syzdek

2007-06-01

Full Text Available We study Riemann-Roch expected curves on $mathbb{P}^1 imes mathbb{P}^1$ in the context of the Nagata-Biran conjecture. This conjecture predicts that for sufficiently large number of points multiple points Seshadri constants of an ample line bundle on algebraic surface are maximal. Biran gives an effective lower bound $N_0$. We construct examples verifying to the effect that the assertions of the Nagata-Biran conjecture can not hold for small number of points. We discuss cases where our construction fails. We observe also that there exists a strong relation between Riemann-Roch expected curves on $mathbb{P}^1 imes mathbb{P}^1$ and the symplectic packing problem. Biran relates the packing problem to the existence of solutions of certain Diophantine equations. We construct such solutions for any ample line bundle on $mathbb{P}^1 imes mathbb{P}^1$ and a relatively smallnumber of points. The solutions geometrically correspond to Riemann-Roch expected curves. Finally we discuss in how far the Biran number $N_0$ is optimal in the case of mathbb{P}^1 imes mathbb{P}^1. In fact we conjecture that it can be replaced by a lower number and we provide evidence justifying this conjecture.

Maximizing the productivity of the microalgae Scenedesmus AMDD cultivated in a continuous photobioreactor using an online flow rate control.

Science.gov (United States)

McGinn, Patrick J; MacQuarrie, Scott P; Choi, Jerome; Tartakovsky, Boris

2017-01-01

In this study, production of the microalga Scenedesmus AMDD in a 300 L continuous flow photobioreactor was maximized using an online flow (dilution rate) control algorithm. To enable online control, biomass concentration was estimated in real time by measuring chlorophyll-related culture fluorescence. A simple microalgae growth model was developed and used to solve the optimization problem aimed at maximizing the photobioreactor productivity. When optimally controlled, Scenedesmus AMDD culture demonstrated an average volumetric biomass productivity of 0.11 g L -1  d -1 over a 25 day cultivation period, equivalent to a 70 % performance improvement compared to the same photobioreactor operated as a turbidostat. The proposed approach for optimizing photobioreactor flow can be adapted to a broad range of microalgae cultivation systems.

Optimal Wind Turbines Micrositing in Onshore Wind Farms Using Fuzzy Genetic Algorithm

Directory of Open Access Journals (Sweden)

Jun Yang

2015-01-01

Full Text Available With the fast growth in the number and size of installed wind farms (WFs around the world, optimal wind turbines (WTs micrositing has become a challenge from both technological and mathematical points of view. An appropriate layout of wind turbines is crucial to obtain adequate performance with respect to the development and operation of the wind power plant during its life span. This work presents a fuzzy genetic algorithm (FGA for maximizing the economic profitability of the project. The algorithm considers a new WF model including several important factors to the design of the layout. The model consists of wake loss, terrain effect, and economic benefits, which can be calculated by locations of wind turbines. The results demonstrate that the algorithm performs better than genetic algorithm, in terms of maximum values of net annual value of wind power plants and computational burden.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

NARCIS (Netherlands)

Hamdy, M.; Nguyen, A.T. (Anh Tuan); Hensen, J.L.M.

2016-01-01

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design

Optimal Investment Under Transaction Costs: A Threshold Rebalanced Portfolio Approach

Science.gov (United States)

Tunc, Sait; Donmez, Mehmet Ali; Kozat, Suleyman Serdar

2013-06-01

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.

HEALTH INSURANCE: CONTRIBUTIONS AND REIMBURSEMENT MAXIMAL

CERN Document Server

HR Division

2000-01-01

Affected by both the salary adjustment index on 1.1.2000 and the evolution of the staff members and fellows population, the average reference salary, which is used as an index for fixed contributions and reimbursement maximal, has changed significantly. An adjustment of the amounts of the reimbursement maximal and the fixed contributions is therefore necessary, as from 1 January 2000.Reimbursement maximalThe revised reimbursement maximal will appear on the leaflet summarising the benefits for the year 2000, which will soon be available from the divisional secretariats and from the AUSTRIA office at CERN.Fixed contributionsThe fixed contributions, applicable to some categories of voluntarily insured persons, are set as follows (amounts in CHF for monthly contributions):voluntarily insured member of the personnel, with complete coverage:815,- (was 803,- in 1999)voluntarily insured member of the personnel, with reduced coverage:407,- (was 402,- in 1999)voluntarily insured no longer dependent child:326,- (was 321...

On the maximal diphoton width

CERN Document Server

Salvio, Alberto; Strumia, Alessandro; Urbano, Alfredo

2016-01-01

Motivated by the 750 GeV diphoton excess found at LHC, we compute the maximal width into $\\gamma\\gamma$ that a neutral scalar can acquire through a loop of charged fermions or scalars as function of the maximal scale at which the theory holds, taking into account vacuum (meta)stability bounds. We show how an extra gauge symmetry can qualitatively weaken such bounds, and explore collider probes and connections with Dark Matter.

An efficient quantum algorithm for spectral estimation

Science.gov (United States)

Steffens, Adrian; Rebentrost, Patrick; Marvian, Iman; Eisert, Jens; Lloyd, Seth

2017-03-01

We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies and damping factors of signals consisting of finite sums of exponentially damped sinusoids. Our algorithm provides a quantum speedup in a natural regime where the sampling rate is much higher than the number of sinusoid components. Along the way, we develop techniques that are expected to be useful for other quantum algorithms as well—consecutive phase estimations to efficiently make products of asymmetric low rank matrices classically accessible and an alternative method to efficiently exponentiate non-Hermitian matrices. Our algorithm features an efficient quantum-classical division of labor: the time-critical steps are implemented in quantum superposition, while an interjacent step, requiring much fewer parameters, can operate classically. We show that frequencies and damping factors can be obtained in time logarithmic in the number of sampling points, exponentially faster than known classical algorithms.

Steam condenser optimization using Real-parameter Genetic Algorithm for Prototype Fast Breeder Reactor

International Nuclear Information System (INIS)

Jayalal, M.L.; Kumar, L. Satish; Jehadeesan, R.; Rajeswari, S.; Satya Murty, S.A.V.; Balasubramaniyan, V.; Chetal, S.C.

2011-01-01

Highlights: → We model design optimization of a vital reactor component using Genetic Algorithm. → Real-parameter Genetic Algorithm is used for steam condenser optimization study. → Comparison analysis done with various Genetic Algorithm related mechanisms. → The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.

Algorithms for Monte Carlo calculations with fermions

International Nuclear Information System (INIS)

Weingarten, D.

1985-01-01

We describe a fermion Monte Carlo algorithm due to Petcher and the present author and another due to Fucito, Marinari, Parisi and Rebbi. For the first algorithm we estimate the number of arithmetic operations required to evaluate a vacuum expectation value grows as N 11 /msub(q) on an N 4 lattice with fixed periodicity in physical units and renormalized quark mass msub(q). For the second algorithm the rate of growth is estimated to be N 8 /msub(q) 2 . Numerical experiments are presented comparing the two algorithms on a lattice of size 2 4 . With a hopping constant K of 0.15 and β of 4.0 we find the number of operations for the second algorithm is about 2.7 times larger than for the first and about 13 000 times larger than for corresponding Monte Carlo calculations with a pure gauge theory. An estimate is given for the number of operations required for more realistic calculations by each algorithm on a larger lattice. (orig.)

Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?

Science.gov (United States)

Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight. PMID:29702665

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

Directory of Open Access Journals (Sweden)

Wei Pan

Full Text Available Conventional decision theory suggests that under risk, people choose option(s by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

Science.gov (United States)

Anzalone, Evan

2018-01-01

both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

A Partial Backlogging Inventory Model for Deteriorating Item under Fuzzy Inflation and Discounting over Random Planning Horizon: A Fuzzy Genetic Algorithm Approach

Directory of Open Access Journals (Sweden)

Dipak Kumar Jana

2013-01-01

Full Text Available An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA and fuzzy simulation-based genetic algorithm (FSGA are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.

Parallel optimization of IDW interpolation algorithm on multicore platform

Science.gov (United States)

Guan, Xuefeng; Wu, Huayi

2009-10-01

Due to increasing power consumption, heat dissipation, and other physical issues, the architecture of central processing unit (CPU) has been turning to multicore rapidly in recent years. Multicore processor is packaged with multiple processor cores in the same chip, which not only offers increased performance, but also presents significant challenges to application developers. As a matter of fact, in GIS field most of current GIS algorithms were implemented serially and could not best exploit the parallelism potential on such multicore platforms. In this paper, we choose Inverse Distance Weighted spatial interpolation algorithm (IDW) as an example to study how to optimize current serial GIS algorithms on multicore platform in order to maximize performance speedup. With the help of OpenMP, threading methodology is introduced to split and share the whole interpolation work among processor cores. After parallel optimization, execution time of interpolation algorithm is greatly reduced and good performance speedup is achieved. For example, performance speedup on Intel Xeon 5310 is 1.943 with 2 execution threads and 3.695 with 4 execution threads respectively. An additional output comparison between pre-optimization and post-optimization is carried out and shows that parallel optimization does to affect final interpolation result.

An approach for maximizing the smallest eigenfrequency of structure vibration based on piecewise constant level set method

Science.gov (United States)

Zhang, Zhengfang; Chen, Weifeng

2018-05-01

Maximization of the smallest eigenfrequency of the linearized elasticity system with area constraint is investigated. The elasticity system is extended into a large background domain, but the void is vacuum and not filled with ersatz material. The piecewise constant level set (PCLS) method is applied to present two regions, the original material region and the void region. A quadratic PCLS function is proposed to represent the characteristic function. Consequently, the functional derivative of the smallest eigenfrequency with respect to PCLS function takes nonzero value in the original material region and zero in the void region. A penalty gradient algorithm is proposed, which initializes the whole background domain with the original material and decreases the area of original material region till the area constraint is satisfied. 2D and 3D numerical examples are presented, illustrating the validity of the proposed algorithm.

The role of multisensory interplay in enabling temporal expectations.

Science.gov (United States)

Ball, Felix; Michels, Lara E; Thiele, Carsten; Noesselt, Toemme

2018-01-01

Temporal regularities can guide our attention to focus on a particular moment in time and to be especially vigilant just then. Previous research provided evidence for the influence of temporal expectation on perceptual processing in unisensory auditory, visual, and tactile contexts. However, in real life we are often exposed to a complex and continuous stream of multisensory events. Here we tested - in a series of experiments - whether temporal expectations can enhance perception in multisensory contexts and whether this enhancement differs from enhancements in unisensory contexts. Our discrimination paradigm contained near-threshold targets (subject-specific 75% discrimination accuracy) embedded in a sequence of distractors. The likelihood of target occurrence (early or late) was manipulated block-wise. Furthermore, we tested whether spatial and modality-specific target uncertainty (i.e. predictable vs. unpredictable target position or modality) would affect temporal expectation (TE) measured with perceptual sensitivity (d ' ) and response times (RT). In all our experiments, hidden temporal regularities improved performance for expected multisensory targets. Moreover, multisensory performance was unaffected by spatial and modality-specific uncertainty, whereas unisensory TE effects on d ' but not RT were modulated by spatial and modality-specific uncertainty. Additionally, the size of the temporal expectation effect, i.e. the increase in perceptual sensitivity and decrease of RT, scaled linearly with the likelihood of expected targets. Finally, temporal expectation effects were unaffected by varying target position within the stream. Together, our results strongly suggest that participants quickly adapt to novel temporal contexts, that they benefit from multisensory (relative to unisensory) stimulation and that multisensory benefits are maximal if the stimulus-driven uncertainty is highest. We propose that enhanced informational content (i.e. multisensory

Fast algorithm for automatically computing Strahler stream order

Science.gov (United States)

Lanfear, Kenneth J.

1990-01-01

An efficient algorithm was developed to determine Strahler stream order for segments of stream networks represented in a Geographic Information System (GIS). The algorithm correctly assigns Strahler stream order in topologically complex situations such as braided streams and multiple drainage outlets. Execution time varies nearly linearly with the number of stream segments in the network. This technique is expected to be particularly useful for studying the topology of dense stream networks derived from digital elevation model data.

A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

International Nuclear Information System (INIS)

Barbee, David L; Holden, James E; Nickles, Robert J; Jeraj, Robert; Flynn, Ryan T

2010-01-01

Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised by partial volume effects which may affect treatment prognosis, assessment or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discovery LS at positions of increasing radii from the scanner's center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method's correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three-dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of ±30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated

Two Light Sterile Neutrinos that Mix Maximally with Each Other and Moderately with Three Active Neutrinos

International Nuclear Information System (INIS)

Krolikowski, W.

2004-01-01

Since the 3+1 neutrino models with one light sterile neutrino turn out to be not very effective, at least two light sterile neutrinos may be needed to reconcile the solar and atmospheric neutrino experiments with the LSND result, if this is confirmed by the ongoing MiniBooNE experiment (and when the CPT invariance is assumed to hold for neutrino oscillations). We present an attractive 3+2 neutrino model, where two light sterile neutrinos mix maximally with each other, in analogy to the observed maximal mixing of muon and tauon active neutrinos. But, while the mixing of ν e and (ν μ - ν τ )/√2 is observed as large (though not maximal), the mixing of ν e with the corresponding combination of two light sterile neutrinos is expected to be only moderate because of the reported smallness of LSND oscillation amplitude. The presented model turns out, however, not to be more effective in explaining the hypothetic LSND result than the simplest 3+1 neutrino model. On the other hand, in the considered 3+2 model, the deviations from conventional oscillations of three active neutrinos appear to be minimal within a larger class of 3+2 models. (author)

Contracting under asymmetric holding cost information in a serial supply chain with a nearly profit maximizing buyer

OpenAIRE

Guido Voigt

2012-01-01

Screening contracts (or non-linear "menu of contracts") are frequently used for aligning the incentives in supply chains with private information. In this context, it is assumed that all supply chain parties are strictly (expected) profit maximizing and, therefore, sensible to even arbitrarily small pay-off differences between contract alternatives. However, previous behavioral work on contracting under asymmetric information in supply chains shows that agents (buyers) are not always strictly...

Alien Noise Cancellation

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. Full FEXT Cancellation. Expectation Maximization based Algorithms. Partial Cancellation. Optimal Choice of what to Cancel and what not to! Alien Noise Cancellation. Efficient Crosstalk channel estimation. In addition:

A New Adaptive Hungarian Mating Scheme in Genetic Algorithms

Directory of Open Access Journals (Sweden)

Chanju Jung

2016-01-01

Full Text Available In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.

A General Constant Power Generation Algorithm for Photovoltaic Systems

DEFF Research Database (Denmark)

Tafti, Hossein Dehghani; Maswood, Ali Iftekhar; Konstantinou, Georgios

2018-01-01

Photovoltaic power plants (PVPPs) typically operate by tracking the maximum power point in order to maximize conversion efficiency. However, with the continuous increase of installed grid-connected PVPPs, power system operators have been experiencing new challenges, like overloading, overvoltages...... on a hysteresis band controller in order to obtain fast dynamic response under transients and low power oscillation during steady-state operation. The performance of the proposed algorithm for both single- and two-stage PVPPs is examined on a 50-kVA simulation setup of these topologies. Moreover, experimental...... and operation during grid voltage disturbances. Consequently, constant power generation (CPG) is imposed by grid codes. An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from the PVPP, is introduced in this paper. The key novelty of the proposed...

Exergetic optimization of turbofan engine with genetic algorithm method

Energy Technology Data Exchange (ETDEWEB)

Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr

2011-07-01

With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.

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

Faster algorithms for RNA-folding using the Four-Russians method.

Science.gov (United States)

Venkatachalam, Balaji; Gusfield, Dan; Frid, Yelena

2014-03-06

The secondary structure that maximizes the number of non-crossing matchings between complimentary bases of an RNA sequence of length n can be computed in O(n3) time using Nussinov's dynamic programming algorithm. The Four-Russians method is a technique that reduces the running time for certain dynamic programming algorithms by a multiplicative factor after a preprocessing step where solutions to all smaller subproblems of a fixed size are exhaustively enumerated and solved. Frid and Gusfield designed an O(n3logn) algorithm for RNA folding using the Four-Russians technique. In their algorithm the preprocessing is interleaved with the algorithm computation. We simplify the algorithm and the analysis by doing the preprocessing once prior to the algorithm computation. We call this the two-vector method. We also show variants where instead of exhaustive preprocessing, we only solve the subproblems encountered in the main algorithm once and memoize the results. We give a simple proof of correctness and explore the practical advantages over the earlier method.The Nussinov algorithm admits an O(n2) time parallel algorithm. We show a parallel algorithm using the two-vector idea that improves the time bound to O(n2logn). We have implemented the parallel algorithm on graphics processing units using the CUDA platform. We discuss the organization of the data structures to exploit coalesced memory access for fast running times. The ideas to organize the data structures also help in improving the running time of the serial algorithms. For sequences of length up to 6000 bases the parallel algorithm takes only about 2.5 seconds and the two-vector serial method takes about 57 seconds on a desktop and 15 seconds on a server. Among the serial algorithms, the two-vector and memoized versions are faster than the Frid-Gusfield algorithm by a factor of 3, and are faster than Nussinov by up to a factor of 20. The source-code for the algorithms is available at http://github.com/ijalabv/FourRussiansRNAFolding.

A Faster Algorithm for Computing Straight Skeletons

KAUST Repository

Cheng, Siu-Wing

2014-09-01

We present a new algorithm for computing the straight skeleton of a polygon. For a polygon with n vertices, among which r are reflex vertices, we give a deterministic algorithm that reduces the straight skeleton computation to a motorcycle graph computation in O(n (logn)logr) time. It improves on the previously best known algorithm for this reduction, which is randomized, and runs in expected O(n√h+1log2n) time for a polygon with h holes. Using known motorcycle graph algorithms, our result yields improved time bounds for computing straight skeletons. In particular, we can compute the straight skeleton of a non-degenerate polygon in O(n (logn) logr + r 4/3 + ε ) time for any ε > 0. On degenerate input, our time bound increases to O(n (logn) logr + r 17/11 + ε ).

A Faster Algorithm for Computing Straight Skeletons

KAUST Repository

Mencel, Liam A.

2014-05-06

We present a new algorithm for computing the straight skeleton of a polygon. For a polygon with n vertices, among which r are reflex vertices, we give a deterministic algorithm that reduces the straight skeleton computation to a motorcycle graph computation in O(n (log n) log r) time. It improves on the previously best known algorithm for this reduction, which is randomised, and runs in expected O(n √(h+1) log² n) time for a polygon with h holes. Using known motorcycle graph algorithms, our result yields improved time bounds for computing straight skeletons. In particular, we can compute the straight skeleton of a non-degenerate polygon in O(n (log n) log r + r^(4/3 + ε)) time for any ε > 0. On degenerate input, our time bound increases to O(n (log n) log r + r^(17/11 + ε))

A Faster Algorithm for Computing Straight Skeletons

KAUST Repository

Cheng, Siu-Wing; Mencel, Liam A.; Vigneron, Antoine E.

2014-01-01

We present a new algorithm for computing the straight skeleton of a polygon. For a polygon with n vertices, among which r are reflex vertices, we give a deterministic algorithm that reduces the straight skeleton computation to a motorcycle graph computation in O(n (logn)logr) time. It improves on the previously best known algorithm for this reduction, which is randomized, and runs in expected O(n√h+1log2n) time for a polygon with h holes. Using known motorcycle graph algorithms, our result yields improved time bounds for computing straight skeletons. In particular, we can compute the straight skeleton of a non-degenerate polygon in O(n (logn) logr + r 4/3 + ε ) time for any ε > 0. On degenerate input, our time bound increases to O(n (logn) logr + r 17/11 + ε ).

A Criterion to Identify Maximally Entangled Four-Qubit State

International Nuclear Information System (INIS)

Zha Xinwei; Song Haiyang; Feng Feng

2011-01-01

Paolo Facchi, et al. [Phys. Rev. A 77 (2008) 060304(R)] presented a maximally multipartite entangled state (MMES). Here, we give a criterion for the identification of maximally entangled four-qubit states. Using this criterion, we not only identify some existing maximally entangled four-qubit states in the literature, but also find several new maximally entangled four-qubit states as well. (general)

A theoretical analysis of the median LMF adaptive algorithm

DEFF Research Database (Denmark)

Bysted, Tommy Kristensen; Rusu, C.

1999-01-01

Higher order adaptive algorithms are sensitive to impulse interference. In the case of the LMF (Least Mean Fourth), an easy and effective way to reduce this is to median filter the instantaneous gradient of the LMF algorithm. Although previous published simulations have indicated that this reduces...... the speed of convergence, no analytical studies have yet been made to prove this. In order to enhance the usability, this paper presents a convergence and steady-state analysis of the median LMF adaptive algorithm. As expected this proves that the median LMF has a slower convergence and a lower steady...

Effect of stage development of mining operations on maximization the net present value in long-term planning of open pits

OpenAIRE

Kržanović, Daniel; Rajković, Radmilo; Mikić, Miomir; Ljubojev, Milenko

2014-01-01

Long-term planning in the mining industry has one main goal: maximizing the value that is realized by excavation and processing of mineral resources. When designing the open pits, determining the stages of development the mining operations, (eng. Pushback) is one of the important factors in the process of long-term production planning. Using the different scientific methods and mathematical algorithms underlying the operation of modern software for strategic planning of production, it is poss...

Online Planning Algorithm

Science.gov (United States)

Rabideau, Gregg R.; Chien, Steve A.

2010-01-01

AVA v2 software selects goals for execution from a set of goals that oversubscribe shared resources. The term goal refers to a science or engineering request to execute a possibly complex command sequence, such as image targets or ground-station downlinks. Developed as an extension to the Virtual Machine Language (VML) execution system, the software enables onboard and remote goal triggering through the use of an embedded, dynamic goal set that can oversubscribe resources. From the set of conflicting goals, a subset must be chosen that maximizes a given quality metric, which in this case is strict priority selection. A goal can never be pre-empted by a lower priority goal, and high-level goals can be added, removed, or updated at any time, and the "best" goals will be selected for execution. The software addresses the issue of re-planning that must be performed in a short time frame by the embedded system where computational resources are constrained. In particular, the algorithm addresses problems with well-defined goal requests without temporal flexibility that oversubscribes available resources. By using a fast, incremental algorithm, goal selection can be postponed in a "just-in-time" fashion allowing requests to be changed or added at the last minute. Thereby enabling shorter response times and greater autonomy for the system under control.

Stokes Space-Based Optical Modulation Format Recognition for Digital Coherent Receivers