Novel SHM method to locate damages in substructures based on VARX models

NASA Astrophysics Data System (ADS)

Ugalde, U.; Anduaga, J.; Martínez, F.; Iturrospe, A.

2015-07-01

A novel damage localization method is proposed, which is based on a substructuring approach and makes use of Vector Auto-Regressive with eXogenous input (VARX) models. The substructuring approach aims to divide the monitored structure into several multi-DOF isolated substructures. Later, each individual substructure is modelled as a VARX model, and the health of each substructure is determined analyzing the variation of the VARX model. The method allows to detect whether the isolated substructure is damaged, and besides allows to locate and quantify the damage within the substructure. It is not necessary to have a theoretical model of the structure and only the measured displacement data is required to estimate the isolated substructure's VARX model. The proposed method is validated by simulations of a two-dimensional lattice structure.

Generation of electromagnetic energy in a magnetic cumulation generator with the use of inductively coupled circuits with a variable coupling coefficient

NASA Astrophysics Data System (ADS)

Gilev, S. D.; Prokopiev, V. S.

2017-07-01

A method of generation of electromagnetic energy and magnetic flux in a magnetic cumulation generator is proposed. The method is based on dynamic variation of the circuit coupling coefficient. This circuit is compared with other available circuits of magnetic energy generation with the help of magnetic cumulation (classical magnetic cumulation generator, generator with transformer coupling, and generator with a dynamic transformer). It is demonstrated that the proposed method allows obtaining high values of magnetic energy. The proposed circuit is found to be more effective than the known transformer circuit. Experiments on electromagnetic energy generation are performed, which demonstrate the efficiency of the proposed method.

A New Method for Control of the Efficiency of Gear Reducers

NASA Astrophysics Data System (ADS)

E Kozlov, K.; Egorov, A. V.; Belogusev, V. N.

2017-04-01

This article proposes a new method to control the energy efficiency of gear reducers. The method allows evaluating the friction losses in a drive motor, drive motor bearing assemblies, and toothing both at the stage of control of the finished product and in the course of its operation, maintenance, and repair. The proposed method, unlike currently used methods for control of the efficiency of gear reducers, allows determining the friction losses without the use of strain measurement, which requires calibration of tensometric sensors and expensive equipment. The method is based on the idea of invariability of mechanical characteristics of the induction motor at constant voltage, resistance of windings, and mains frequency, regardless of the driven inertia mass. This paper presents experimental results which verify the theoretical predictions. The proposed method can be implemented in the procedure of acceptance test at the companies that manufacture gear reducers, thereby assess their effectiveness and the level of degradation processes that significantly affect the service life of the research object. The method can be implemented both with universal and with specialized hardware and software complexes. At that, both an increment of the inertia moment and acceleration time of a gear reducer may serve as a performance criterion.

Hair and bare skin discrimination for laser-assisted hair removal systems.

PubMed

Cayir, Sercan; Yetik, Imam Samil

2017-07-01

Laser-assisted hair removal devices aim to remove body hair permanently. In most cases, these devices irradiate the whole area of the skin with a homogenous power density. Thus, a significant portion of the skin, where hair is not present, is burnt unnecessarily causing health risks. Therefore, methods that can distinguish hair regions automatically would be very helpful avoiding these unnecessary applications of laser. This study proposes a new system of algorithms to detect hair regions with the help of a digital camera. Unlike previous limited number of studies, our methods are very fast allowing for real-time application. Proposed methods are based on certain features derived from histograms of hair and skin regions. We compare our algorithm with competing methods in terms of localization performance and computation time and show that a much faster real-time accurate localization of hair regions is possible with the proposed method. Our results show that the algorithm we have developed is extremely fast (around 45 milliseconds) allowing for real-time application with high accuracy hair localization ( 96.48 %).

A novel method of the image processing on irregular triangular meshes

NASA Astrophysics Data System (ADS)

Vishnyakov, Sergey; Pekhterev, Vitaliy; Sokolova, Elizaveta

2018-04-01

The paper describes a novel method of the image processing based on irregular triangular meshes implementation. The triangular mesh is adaptive to the image content, least mean square linear approximation is proposed for the basic interpolation within the triangle. It is proposed to use triangular numbers to simplify using of the local (barycentric) coordinates for the further analysis - triangular element of the initial irregular mesh is to be represented through the set of the four equilateral triangles. This allows to use fast and simple pixels indexing in local coordinates, e.g. "for" or "while" loops for access to the pixels. Moreover, representation proposed allows to use discrete cosine transform of the simple "rectangular" symmetric form without additional pixels reordering (as it is used for shape-adaptive DCT forms). Furthermore, this approach leads to the simple form of the wavelet transform on triangular mesh. The results of the method application are presented. It is shown that advantage of the method proposed is a combination of the flexibility of the image-adaptive irregular meshes with the simple form of the pixel indexing in local triangular coordinates and the using of the common forms of the discrete transforms for triangular meshes. Method described is proposed for the image compression, pattern recognition, image quality improvement, image search and indexing. It also may be used as a part of video coding (intra-frame or inter-frame coding, motion detection).

Analog self-powered harvester achieving switching pause control to increase harvested energy

NASA Astrophysics Data System (ADS)

Makihara, Kanjuro; Asahina, Kei

2017-05-01

In this paper, we propose a self-powered analog controller circuit to increase the efficiency of electrical energy harvesting from vibrational energy using piezoelectric materials. Although the existing synchronized switch harvesting on inductor (SSHI) method is designed to produce efficient harvesting, its switching operation generates a vibration-suppression effect that reduces the harvested levels of electrical energy. To solve this problem, the authors proposed—in a previous paper—a switching method that takes this vibration-suppression effect into account. This method temporarily pauses the switching operation, allowing the recovery of the mechanical displacement and, therefore, of the piezoelectric voltage. In this paper, we propose a self-powered analog circuit to implement this switching control method. Self-powered vibration harvesting is achieved in this study by attaching a newly designed circuit to an existing analog controller for SSHI. This circuit aims to effectively implement the aforementioned new switching control strategy, where switching is paused in some vibration peaks, in order to allow motion recovery and a consequent increase in the harvested energy. Harvesting experiments performed using the proposed circuit reveal that the proposed method can increase the energy stored in the storage capacitor by a factor of 8.5 relative to the conventional SSHI circuit. This proposed technique is useful to increase the harvested energy especially for piezoelectric systems having large coupling factor.

Structural Equation Models in a Redundancy Analysis Framework With Covariates.

PubMed

Lovaglio, Pietro Giorgio; Vittadini, Giorgio

2014-01-01

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

On the evaluation of segmentation editing tools

PubMed Central

Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

2014-01-01

Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

An approach to define semantics for BPM systems interoperability

NASA Astrophysics Data System (ADS)

Rico, Mariela; Caliusco, María Laura; Chiotti, Omar; Rosa Galli, María

2015-04-01

This article proposes defining semantics for Business Process Management systems interoperability through the ontology of Electronic Business Documents (EBD) used to interchange the information required to perform cross-organizational processes. The semantic model generated allows aligning enterprise's business processes to support cross-organizational processes by matching the business ontology of each business partner with the EBD ontology. The result is a flexible software architecture that allows dynamically defining cross-organizational business processes by reusing the EBD ontology. For developing the semantic model, a method is presented, which is based on a strategy for discovering entity features whose interpretation depends on the context, and representing them for enriching the ontology. The proposed method complements ontology learning techniques that can not infer semantic features not represented in data sources. In order to improve the representation of these entity features, the method proposes using widely accepted ontologies, for representing time entities and relations, physical quantities, measurement units, official country names, and currencies and funds, among others. When the ontologies reuse is not possible, the method proposes identifying whether that feature is simple or complex, and defines a strategy to be followed. An empirical validation of the approach has been performed through a case study.

Relationships between the decoupled and coupled transfer functions: Theoretical studies and experimental validation

NASA Astrophysics Data System (ADS)

Wang, Zengwei; Zhu, Ping; Liu, Zhao

2018-01-01

A generalized method for predicting the decoupled transfer functions based on in-situ transfer functions is proposed. The method allows predicting the decoupled transfer functions using coupled transfer functions, without disassembling the system. Two ways to derive relationships between the decoupled and coupled transfer functions are presented. Issues related to immeasurability of coupled transfer functions are also discussed. The proposed method is validated by numerical and experimental case studies.

Recognition of building group patterns in topographic maps based on graph partitioning and random forest

NASA Astrophysics Data System (ADS)

He, Xianjin; Zhang, Xinchang; Xin, Qinchuan

2018-02-01

Recognition of building group patterns (i.e., the arrangement and form exhibited by a collection of buildings at a given mapping scale) is important to the understanding and modeling of geographic space and is hence essential to a wide range of downstream applications such as map generalization. Most of the existing methods develop rigid rules based on the topographic relationships between building pairs to identify building group patterns and thus their applications are often limited. This study proposes a method to identify a variety of building group patterns that allow for map generalization. The method first identifies building group patterns from potential building clusters based on a machine-learning algorithm and further partitions the building clusters with no recognized patterns based on the graph partitioning method. The proposed method is applied to the datasets of three cities that are representative of the complex urban environment in Southern China. Assessment of the results based on the reference data suggests that the proposed method is able to recognize both regular (e.g., the collinear, curvilinear, and rectangular patterns) and irregular (e.g., the L-shaped, H-shaped, and high-density patterns) building group patterns well, given that the correctness values are consistently nearly 90% and the completeness values are all above 91% for three study areas. The proposed method shows promises in automated recognition of building group patterns that allows for map generalization.

Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

PubMed

Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

2013-11-04

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

PubMed Central

Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

2013-01-01

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

Application of a Subspace-Based Fault Detection Method to Industrial Structures

NASA Astrophysics Data System (ADS)

Mevel, L.; Hermans, L.; van der Auweraer, H.

1999-11-01

Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.

Allowable SEM noise for unbiased LER measurement

NASA Astrophysics Data System (ADS)

Papavieros, George; Constantoudis, Vassilios; Gogolides, Evangelos

2018-03-01

Recently, a novel method for the calculation of unbiased Line Edge Roughness based on Power Spectral Density analysis has been proposed. In this paper first an alternative method is discussed and investigated, utilizing the Height-Height Correlation Function (HHCF) of edges. The HHCF-based method enables the unbiased determination of the whole triplet of LER parameters including besides rms the correlation length and roughness exponent. The key of both methods is the sensitivity of PSD and HHCF on noise at high frequencies and short distance respectively. Secondly, we elaborate a testbed of synthesized SEM images with controlled LER and noise to justify the effectiveness of the proposed unbiased methods. Our main objective is to find out the boundaries of the method in respect to noise levels and roughness characteristics, for which the method remains reliable, i.e the maximum amount of noise allowed, for which the output results cope with the controllable known inputs. At the same time, we will also set the extremes of roughness parameters for which the methods hold their accuracy.

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model

NASA Astrophysics Data System (ADS)

Ito, Shin-ichi; Nagao, Hiromichi; Kasuya, Tadashi; Inoue, Junya

2017-12-01

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Review of technical justification of assumptions and methods used by the Environmental Protection Agency for estimating risks avoided by implementing MCLs for radionuclides

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

Morris, S.C.; Rowe, M.D.; Holtzman, S.

1992-11-01

The Environmental Protection Agency (EPA) has proposed regulations for allowable levels of radioactive material in drinking water (40 CFR Part 141, 56 FR 33050, July 18, 1991). This review examined the assumptions and methods used by EPA in calculating risks that would be avoided by implementing the proposed Maximum Contaminant Levels for uranium, radium, and radon. Proposed limits on gross alpha and beta-gamma emitters were not included in this review.

Evaluation Framework for NASA's Educational Outreach Programs

NASA Technical Reports Server (NTRS)

Berg, Rick; Booker, Angela; Linde, Charlotte; Preston, Connie

1999-01-01

The objective of the proposed work is to develop an evaluation framework for NASA's educational outreach efforts. We focus on public (rather than technical or scientific) dissemination efforts, specifically on Internet-based outreach sites for children.The outcome of this work is to propose both methods and criteria for evaluation, which would enable NASA to do a more analytic evaluation of its outreach efforts. The proposed framework is based on IRL's ethnographic and video-based observational methods, which allow us to analyze how these sites are actually used.

Functional interaction-based nonlinear models with application to multiplatform genomics data.

PubMed

Davenport, Clemontina A; Maity, Arnab; Baladandayuthapani, Veerabhadran

2018-05-07

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example. Copyright © 2018 John Wiley & Sons, Ltd.

Investigation of methods to search for the boundaries on the image and their use on lung hardware of methods finding saliency map

NASA Astrophysics Data System (ADS)

Semenishchev, E. A.; Marchuk, V. I.; Fedosov, V. P.; Stradanchenko, S. G.; Ruslyakov, D. V.

2015-05-01

This work aimed to study computationally simple method of saliency map calculation. Research in this field received increasing interest for the use of complex techniques in portable devices. A saliency map allows increasing the speed of many subsequent algorithms and reducing the computational complexity. The proposed method of saliency map detection based on both image and frequency space analysis. Several examples of test image from the Kodak dataset with different detalisation considered in this paper demonstrate the effectiveness of the proposed approach. We present experiments which show that the proposed method providing better results than the framework Salience Toolbox in terms of accuracy and speed.

An optimization design proposal of automated guided vehicles for mixed type transportation in hospital environments.

PubMed

González, Domingo; Romero, Luis; Espinosa, María Del Mar; Domínguez, Manuel

2017-01-01

The aim of this paper is to present an optimization proposal in the automated guided vehicles design used in hospital logistics, as well as to analyze the impact of its implementation in a real environment. This proposal is based on the design of those elements that would allow the vehicles to deliver an extra cart by the towing method. So, the proposal intention is to improve the productivity and the performance of the current vehicles by using a transportation method of combined carts. The study has been developed following concurrent engineering premises from three different viewpoints. First, the sequence of operations has been described, and second, a proposal of design of the equipment has been undertaken. Finally, the impact of the proposal has been analyzed according to real data from the Hospital Universitario Rio Hortega in Valladolid (Spain). In this particular case, by the implementation of the analyzed proposal in the hospital a reduction of over 35% of the current time of use can be achieved. This result may allow adding new tasks to the vehicles, and according to this, both a new kind of vehicle and a specific module can be developed in order to get a better performance.

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

Single Wall Carbon Nanotube Alignment Mechanisms for Non-Destructive Evaluation

NASA Technical Reports Server (NTRS)

Hong, Seunghun

2002-01-01

As proposed in our original proposal, we developed a new innovative method to assemble millions of single wall carbon nanotube (SWCNT)-based circuit components as fast as conventional microfabrication processes. This method is based on surface template assembly strategy. The new method solves one of the major bottlenecks in carbon nanotube based electrical applications and, potentially, may allow us to mass produce a large number of SWCNT-based integrated devices of critical interests to NASA.

A direct method to solve optimal knots of B-spline curves: An application for non-uniform B-spline curves fitting.

PubMed

Dung, Van Than; Tjahjowidodo, Tegoeh

2017-01-01

B-spline functions are widely used in many industrial applications such as computer graphic representations, computer aided design, computer aided manufacturing, computer numerical control, etc. Recently, there exist some demands, e.g. in reverse engineering (RE) area, to employ B-spline curves for non-trivial cases that include curves with discontinuous points, cusps or turning points from the sampled data. The most challenging task in these cases is in the identification of the number of knots and their respective locations in non-uniform space in the most efficient computational cost. This paper presents a new strategy for fitting any forms of curve by B-spline functions via local algorithm. A new two-step method for fast knot calculation is proposed. In the first step, the data is split using a bisecting method with predetermined allowable error to obtain coarse knots. Secondly, the knots are optimized, for both locations and continuity levels, by employing a non-linear least squares technique. The B-spline function is, therefore, obtained by solving the ordinary least squares problem. The performance of the proposed method is validated by using various numerical experimental data, with and without simulated noise, which were generated by a B-spline function and deterministic parametric functions. This paper also discusses the benchmarking of the proposed method to the existing methods in literature. The proposed method is shown to be able to reconstruct B-spline functions from sampled data within acceptable tolerance. It is also shown that, the proposed method can be applied for fitting any types of curves ranging from smooth ones to discontinuous ones. In addition, the method does not require excessive computational cost, which allows it to be used in automatic reverse engineering applications.

Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task.

PubMed

Bonnet, Vincent; Azevedo-Coste, Christine; Robert, Thomas; Fraisse, Philippe; Venture, Gentiane

2017-07-01

This paper aims at developing and evaluating a new practical method for the real-time estimate of joint torques and external wrenches during multi-contact sit-to-stand (STS) task using kinematics data only. The proposed method allows also identifying subject specific body inertial segment parameters that are required to perform inverse dynamics. The identification phase is performed using simple and repeatable motions. Thanks to an accurately identified model the estimate of the total external wrench can be used as an input to solve an under-determined multi-contact problem. It is solved using a constrained quadratic optimization process minimizing a hybrid human-like energetic criterion. The weights of this hybrid cost function are adjusted and a sensitivity analysis is performed in order to reproduce robustly human external wrench distribution. The results showed that the proposed method could successfully estimate the external wrenches under buttocks, feet, and hands during STS tasks (RMS error lower than 20 N and 6 N.m). The simplicity and generalization abilities of the proposed method allow paving the way of future diagnosis solutions and rehabilitation applications, including in-home use.

Venting of fission products and shielding in thermionic nuclear reactor systems

NASA Technical Reports Server (NTRS)

Salmi, E. W.

1972-01-01

Most thermionic reactors are designed to allow the fission gases to escape out of the emitter. A scheme to allow the fission gases to escape is proposed. Because of the low activity of the fission products, this method should pose no radiation hazards.

Brief Report: Investigating Uncertainty in the Minimum Mortality Temperature: Methods and Application to 52 Spanish Cities.

PubMed

Tobías, Aurelio; Armstrong, Ben; Gasparrini, Antonio

2017-01-01

The minimum mortality temperature from J- or U-shaped curves varies across cities with different climates. This variation conveys information on adaptation, but ability to characterize is limited by the absence of a method to describe uncertainty in estimated minimum mortality temperatures. We propose an approximate parametric bootstrap estimator of confidence interval (CI) and standard error (SE) for the minimum mortality temperature from a temperature-mortality shape estimated by splines. The coverage of the estimated CIs was close to nominal value (95%) in the datasets simulated, although SEs were slightly high. Applying the method to 52 Spanish provincial capital cities showed larger minimum mortality temperatures in hotter cities, rising almost exactly at the same rate as annual mean temperature. The method proposed for computing CIs and SEs for minimums from spline curves allows comparing minimum mortality temperatures in different cities and investigating their associations with climate properly, allowing for estimation uncertainty.

A weakly-compressible Cartesian grid approach for hydrodynamic flows

NASA Astrophysics Data System (ADS)

Bigay, P.; Oger, G.; Guilcher, P.-M.; Le Touzé, D.

2017-11-01

The present article aims at proposing an original strategy to solve hydrodynamic flows. In introduction, the motivations for this strategy are developed. It aims at modeling viscous and turbulent flows including complex moving geometries, while avoiding meshing constraints. The proposed approach relies on a weakly-compressible formulation of the Navier-Stokes equations. Unlike most hydrodynamic CFD (Computational Fluid Dynamics) solvers usually based on implicit incompressible formulations, a fully-explicit temporal scheme is used. A purely Cartesian grid is adopted for numerical accuracy and algorithmic simplicity purposes. This characteristic allows an easy use of Adaptive Mesh Refinement (AMR) methods embedded within a massively parallel framework. Geometries are automatically immersed within the Cartesian grid with an AMR compatible treatment. The method proposed uses an Immersed Boundary Method (IBM) adapted to the weakly-compressible formalism and imposed smoothly through a regularization function, which stands as another originality of this work. All these features have been implemented within an in-house solver based on this WCCH (Weakly-Compressible Cartesian Hydrodynamic) method which meets the above requirements whilst allowing the use of high-order (> 3) spatial schemes rarely used in existing hydrodynamic solvers. The details of this WCCH method are presented and validated in this article.

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

Santos-Villalobos, Hector J; Barstow, Del R; Karakaya, Mahmut

Iris recognition has been proven to be an accurate and reliable biometric. However, the recognition of non-ideal iris images such as off angle images is still an unsolved problem. We propose a new biometric targeted eye model and a method to reconstruct the off-axis eye to its frontal view allowing for recognition using existing methods and algorithms. This allows for existing enterprise level algorithms and approaches to be largely unmodified by using our work as a pre-processor to improve performance. In addition, we describe the `Limbus effect' and its importance for an accurate segmentation of off-axis irides. Our method usesmore » an anatomically accurate human eye model and ray-tracing techniques to compute a transformation function, which reconstructs the iris to its frontal, non-refracted state. Then, the same eye model is used to render a frontal view of the reconstructed iris. The proposed method is fully described and results from synthetic data are shown to establish an upper limit on performance improvement and establish the importance of the proposed approach over traditional linear elliptical unwrapping methods. Our results with synthetic data demonstrate the ability to perform an accurate iris recognition with an image taken as much as 70 degrees off-axis.« less

An Unconditionally Stable, Positivity-Preserving Splitting Scheme for Nonlinear Black-Scholes Equation with Transaction Costs

PubMed Central

Guo, Jianqiang; Wang, Wansheng

2014-01-01

This paper deals with the numerical analysis of nonlinear Black-Scholes equation with transaction costs. An unconditionally stable and monotone splitting method, ensuring positive numerical solution and avoiding unstable oscillations, is proposed. This numerical method is based on the LOD-Backward Euler method which allows us to solve the discrete equation explicitly. The numerical results for vanilla call option and for European butterfly spread are provided. It turns out that the proposed scheme is efficient and reliable. PMID:24895653

An unconditionally stable, positivity-preserving splitting scheme for nonlinear Black-Scholes equation with transaction costs.

PubMed

Guo, Jianqiang; Wang, Wansheng

2014-01-01

This paper deals with the numerical analysis of nonlinear Black-Scholes equation with transaction costs. An unconditionally stable and monotone splitting method, ensuring positive numerical solution and avoiding unstable oscillations, is proposed. This numerical method is based on the LOD-Backward Euler method which allows us to solve the discrete equation explicitly. The numerical results for vanilla call option and for European butterfly spread are provided. It turns out that the proposed scheme is efficient and reliable.

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

Localization of synchronous cortical neural sources.

PubMed

Zerouali, Younes; Herry, Christophe L; Jemel, Boutheina; Lina, Jean-Marc

2013-03-01

Neural synchronization is a key mechanism to a wide variety of brain functions, such as cognition, perception, or memory. High temporal resolution achieved by EEG recordings allows the study of the dynamical properties of synchronous patterns of activity at a very fine temporal scale but with very low spatial resolution. Spatial resolution can be improved by retrieving the neural sources of EEG signal, thus solving the so-called inverse problem. Although many methods have been proposed to solve the inverse problem and localize brain activity, few of them target the synchronous brain regions. In this paper, we propose a novel algorithm aimed at localizing specifically synchronous brain regions and reconstructing the time course of their activity. Using multivariate wavelet ridge analysis, we extract signals capturing the synchronous events buried in the EEG and then solve the inverse problem on these signals. Using simulated data, we compare results of source reconstruction accuracy achieved by our method to a standard source reconstruction approach. We show that the proposed method performs better across a wide range of noise levels and source configurations. In addition, we applied our method on real dataset and identified successfully cortical areas involved in the functional network underlying visual face perception. We conclude that the proposed approach allows an accurate localization of synchronous brain regions and a robust estimation of their activity.

Silicon/copper dome-patterned electrodes for high-performance hybrid supercapacitors.

PubMed

Liu, Xuyan; Jung, Hun-Gi; Kim, Sang-Ok; Choi, Ho-Suk; Lee, Sangwha; Moon, Jun Hyuk; Lee, Joong Kee

2013-12-02

This study proposes a method for manufacturing high-performance electrode materials in which controlling the shape of the current collector and electrode material for a Li-ion capacitor (LIC). In particular, the proposed LIC manufacturing method maintains the high voltage of a cell by using a microdome-patterned electrode material, allowing for reversible reactions between the Li-ion and the active material for an extended period of time. As a result, the LICs exhibit initial capacities of approximately 42 F g⁻¹, even at 60 A g⁻¹. The LICs also exhibit good cycle performance up to approximately 15,000 cycles. In addition, these advancements allow for a considerably higher energy density than other existing capacitor systems. The energy density of the proposed LICs is approximately nine, two, and 1.5 times higher than those of the electrochemical double layer capacitor (EDLC), AC/LiMn₂O₄ hybrid capacitor, and intrinsic Si/AC LIC, respectively.

Silicon/copper dome-patterned electrodes for high-performance hybrid supercapacitors

NASA Astrophysics Data System (ADS)

Liu, Xuyan; Jung, Hun-Gi; Kim, Sang-Ok; Choi, Ho-Suk; Lee, Sangwha; Moon, Jun Hyuk; Lee, Joong Kee

2013-12-01

This study proposes a method for manufacturing high-performance electrode materials in which controlling the shape of the current collector and electrode material for a Li-ion capacitor (LIC). In particular, the proposed LIC manufacturing method maintains the high voltage of a cell by using a microdome-patterned electrode material, allowing for reversible reactions between the Li-ion and the active material for an extended period of time. As a result, the LICs exhibit initial capacities of approximately 42 F g-1, even at 60 A g-1. The LICs also exhibit good cycle performance up to approximately 15,000 cycles. In addition, these advancements allow for a considerably higher energy density than other existing capacitor systems. The energy density of the proposed LICs is approximately nine, two, and 1.5 times higher than those of the electrochemical double layer capacitor (EDLC), AC/LiMn2O4 hybrid capacitor, and intrinsic Si/AC LIC, respectively.

Modified Monte Carlo method for study of electron transport in degenerate electron gas in the presence of electron-electron interactions, application to graphene

NASA Astrophysics Data System (ADS)

Borowik, Piotr; Thobel, Jean-Luc; Adamowicz, Leszek

2017-07-01

Standard computational methods used to take account of the Pauli Exclusion Principle into Monte Carlo (MC) simulations of electron transport in semiconductors may give unphysical results in low field regime, where obtained electron distribution function takes values exceeding unity. Modified algorithms were already proposed and allow to correctly account for electron scattering on phonons or impurities. Present paper extends this approach and proposes improved simulation scheme allowing including Pauli exclusion principle for electron-electron (e-e) scattering into MC simulations. Simulations with significantly reduced computational cost recreate correct values of the electron distribution function. Proposed algorithm is applied to study transport properties of degenerate electrons in graphene with e-e interactions. This required adapting the treatment of e-e scattering in the case of linear band dispersion relation. Hence, this part of the simulation algorithm is described in details.

Frequency shifting approach towards textual transcription of heartbeat sounds.

PubMed

Arvin, Farshad; Doraisamy, Shyamala; Safar Khorasani, Ehsan

2011-10-04

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription.

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

76 FR 31262 - Application for Annuity or Lump Sum

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-31

... regulations in order to allow signature alternatives to the traditional pen-and-ink (``wet'') signature. The.... SUMMARY: The Railroad Retirement Board (Board) proposes to amend its regulations to allow alternative signature methods in addition to the traditional pen-and-ink or '' wet'' signature in order to implement an...

Brain MR image segmentation based on an improved active contour model

PubMed Central

Meng, Xiangrui; Gu, Wenya; Zhang, Jianwei

2017-01-01

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%. PMID:28854235

76 FR 5319 - Regulation of Fuel and Fuel Additives: Alternative Test Method for Olefins in Gasoline

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-31

... Regulation of Fuel and Fuel Additives: Alternative Test Method for Olefins in Gasoline AGENCY: Environmental... proposing to allow refiners and laboratories to use an alternative test method for olefin content in... test method for compliance measurement while maintaining environmental benefits achieved from our fuels...

77 FR 5470 - Periodicals-Recognition of Distribution of Periodicals via Electronic Copies

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-03

... Electronic Copies AGENCY: Postal Service TM . ACTION: Proposed rule. SUMMARY: The Postal Service proposes to....6, to allow publishers who use electronic distribution methods to report such circulation as paid or... publications through various electronic media channels. According to the standards that govern the Periodicals...

Wave transmission approach based on modal analysis for embedded mechanical systems

NASA Astrophysics Data System (ADS)

Cretu, Nicolae; Nita, Gelu; Ioan Pop, Mihail

2013-09-01

An experimental method for determining the phase velocity in small solid samples is proposed. The method is based on measuring the resonant frequencies of a binary or ternary solid elastic system comprising the small sample of interest and a gauge material of manageable size. The wave transmission matrix of the combined system is derived and the theoretical values of its eigenvalues are used to determine the expected eigenfrequencies that, equated with the measured values, allow for the numerical estimation of the phase velocities in both materials. The known phase velocity of the gauge material is then used to asses the accuracy of the method. Using computer simulation and the experimental values for phase velocities, the theoretical values for the eigenfrequencies of the eigenmodes of the embedded elastic system are obtained, to validate the method. We conclude that the proposed experimental method may be reliably used to determine the elastic properties of small solid samples whose geometries do not allow a direct measurement of their resonant frequencies.

Hybrid recommendation methods in complex networks.

PubMed

Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N

2015-07-01

We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

Computing diffusivities from particle models out of equilibrium

NASA Astrophysics Data System (ADS)

Embacher, Peter; Dirr, Nicolas; Zimmer, Johannes; Reina, Celia

2018-04-01

A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have Gaussian fluctuations but it is otherwise allowed to undergo arbitrary out-of-equilibrium evolutions. This could be potentially relevant for particle data obtained from experimental applications. The key idea underlying the method is that finite, yet large, particle systems formally obey stochastic partial differential equations of gradient flow type satisfying a fluctuation-dissipation relation. The strategy is here applied to three classic particle models, namely independent random walkers, a zero-range process and a symmetric simple exclusion process in one space dimension, to allow the comparison with analytic solutions.

Improved remote gaze estimation using corneal reflection-adaptive geometric transforms

NASA Astrophysics Data System (ADS)

Ma, Chunfei; Baek, Seung-Jin; Choi, Kang-A.; Ko, Sung-Jea

2014-05-01

Recently, the remote gaze estimation (RGE) technique has been widely applied to consumer devices as a more natural interface. In general, the conventional RGE method estimates a user's point of gaze using a geometric transform, which represents the relationship between several infrared (IR) light sources and their corresponding corneal reflections (CRs) in the eye image. Among various methods, the homography normalization (HN) method achieves state-of-the-art performance. However, the geometric transform of the HN method requiring four CRs is infeasible for the case when fewer than four CRs are available. To solve this problem, this paper proposes a new RGE method based on three alternative geometric transforms, which are adaptive to the number of CRs. Unlike the HN method, the proposed method not only can operate with two or three CRs, but can also provide superior accuracy. To further enhance the performance, an effective error correction method is also proposed. By combining the introduced transforms with the error-correction method, the proposed method not only provides high accuracy and robustness for gaze estimation, but also allows for a more flexible system setup with a different number of IR light sources. Experimental results demonstrate the effectiveness of the proposed method.

Topology optimization under stochastic stiffness

NASA Astrophysics Data System (ADS)

Asadpoure, Alireza

Topology optimization is a systematic computational tool for optimizing the layout of materials within a domain for engineering design problems. It allows variation of structural boundaries and connectivities. This freedom in the design space often enables discovery of new, high performance designs. However, solutions obtained by performing the optimization in a deterministic setting may be impractical or suboptimal when considering real-world engineering conditions with inherent variabilities including (for example) variabilities in fabrication processes and operating conditions. The aim of this work is to provide a computational methodology for topology optimization in the presence of uncertainties associated with structural stiffness, such as uncertain material properties and/or structural geometry. Existing methods for topology optimization under deterministic conditions are first reviewed. Modifications are then proposed to improve the numerical performance of the so-called Heaviside Projection Method (HPM) in continuum domains. Next, two approaches, perturbation and Polynomial Chaos Expansion (PCE), are proposed to account for uncertainties in the optimization procedure. These approaches are intrusive, allowing tight and efficient coupling of the uncertainty quantification with the optimization sensitivity analysis. The work herein develops a robust topology optimization framework aimed at reducing the sensitivity of optimized solutions to uncertainties. The perturbation-based approach combines deterministic topology optimization with a perturbation method for the quantification of uncertainties. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. The PCE approach combines the spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique. The resulting compact representations for the response quantities allow for efficient and accurate calculation of sensitivities of response statistics with respect to the design variables. The proposed methods are shown to be successful at generating robust optimal topologies. Examples from topology optimization in continuum and discrete domains (truss structures) under uncertainty are presented. It is also shown that proposed methods lead to significant computational savings when compared to Monte Carlo-based optimization which involve multiple formations and inversions of the global stiffness matrix and that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.

Hedged Monte-Carlo: low variance derivative pricing with objective probabilities

NASA Astrophysics Data System (ADS)

Potters, Marc; Bouchaud, Jean-Philippe; Sestovic, Dragan

2001-01-01

We propose a new ‘hedged’ Monte-Carlo ( HMC) method to price financial derivatives, which allows to determine simultaneously the optimal hedge. The inclusion of the optimal hedging strategy allows one to reduce the financial risk associated with option trading, and for the very same reason reduces considerably the variance of our HMC scheme as compared to previous methods. The explicit accounting of the hedging cost naturally converts the objective probability into the ‘risk-neutral’ one. This allows a consistent use of purely historical time series to price derivatives and obtain their residual risk. The method can be used to price a large class of exotic options, including those with path dependent and early exercise features.

Asteroid orbital inversion using uniform phase-space sampling

NASA Astrophysics Data System (ADS)

Muinonen, K.; Pentikäinen, H.; Granvik, M.; Oszkiewicz, D.; Virtanen, J.

2014-07-01

We review statistical inverse methods for asteroid orbit computation from a small number of astrometric observations and short time intervals of observations. With the help of Markov-chain Monte Carlo methods (MCMC), we present a novel inverse method that utilizes uniform sampling of the phase space for the orbital elements. The statistical orbital ranging method (Virtanen et al. 2001, Muinonen et al. 2001) was set out to resolve the long-lasting challenges in the initial computation of orbits for asteroids. The ranging method starts from the selection of a pair of astrometric observations. Thereafter, the topocentric ranges and angular deviations in R.A. and Decl. are randomly sampled. The two Cartesian positions allow for the computation of orbital elements and, subsequently, the computation of ephemerides for the observation dates. Candidate orbital elements are included in the sample of accepted elements if the χ^2-value between the observed and computed observations is within a pre-defined threshold. The sample orbital elements obtain weights based on a certain debiasing procedure. When the weights are available, the full sample of orbital elements allows the probabilistic assessments for, e.g., object classification and ephemeris computation as well as the computation of collision probabilities. The MCMC ranging method (Oszkiewicz et al. 2009; see also Granvik et al. 2009) replaces the original sampling algorithm described above with a proposal probability density function (p.d.f.), and a chain of sample orbital elements results in the phase space. MCMC ranging is based on a bivariate Gaussian p.d.f. for the topocentric ranges, and allows for the sampling to focus on the phase-space domain with most of the probability mass. In the virtual-observation MCMC method (Muinonen et al. 2012), the proposal p.d.f. for the orbital elements is chosen to mimic the a posteriori p.d.f. for the elements: first, random errors are simulated for each observation, resulting in a set of virtual observations; second, corresponding virtual least-squares orbital elements are derived using the Nelder-Mead downhill simplex method; third, repeating the procedure two times allows for a computation of a difference for two sets of virtual orbital elements; and, fourth, this orbital-element difference constitutes a symmetric proposal in a random-walk Metropolis-Hastings algorithm, avoiding the explicit computation of the proposal p.d.f. In a discrete approximation, the allowed proposals coincide with the differences that are based on a large number of pre-computed sets of virtual least-squares orbital elements. The virtual-observation MCMC method is thus based on the characterization of the relevant volume in the orbital-element phase space. Here we utilize MCMC to map the phase-space domain of acceptable solutions. We can make use of the proposal p.d.f.s from the MCMC ranging and virtual-observation methods. The present phase-space mapping produces, upon convergence, a uniform sampling of the solution space within a pre-defined χ^2-value. The weights of the sampled orbital elements are then computed on the basis of the corresponding χ^2-values. The present method resembles the original ranging method. On one hand, MCMC mapping is insensitive to local extrema in the phase space and efficiently maps the solution space. This is somewhat contrary to the MCMC methods described above. On the other hand, MCMC mapping can suffer from producing a small number of sample elements with small χ^2-values, in resemblance to the original ranging method. We apply the methods to example near-Earth, main-belt, and transneptunian objects, and highlight the utilization of the methods in the data processing and analysis pipeline of the ESA Gaia space mission.

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

Rebay, S.

This work is devoted to the description of an efficient unstructured mesh generation method entirely based on the Delaunay triangulation. The distinctive characteristic of the proposed method is that point positions and connections are computed simultaneously. This result is achieved by taking advantage of the sequential way in which the Bowyer-Watson algorithm computes the Delaunay triangulation. Two methods are proposed which have great geometrical flexibility, in that they allow us to treat domains of arbitrary shape and topology and to generate arbitrarily nonuniform meshes. The methods are computationally efficient and are applicable both in two and three dimensions. 11 refs.,more » 20 figs., 1 tab.« less

Evaluation of the deflected mode of the monolithic span pieces and preassembled slabs combined action

NASA Astrophysics Data System (ADS)

Roshchina, Svetlana; Ezzi, Hisham; Shishov, Ivan; Lukin, Mikhail; Sergeev, Michael

2017-10-01

In single-story industrial buildings, the cost of roof covering comprises 40-55% of the total cost of the buildings. Therefore, research, development and application of new structural forms of reinforced concrete rafter structures, that allow to reduce material consumption and reduce the sub-assembly weight of structures, are the main tasks in the field of improving the existing generic solutions. The article suggests a method for estimating the relieving effect in the rafter structure as the result of combined deformation of the roof slabs with the end arrises. Calculated and experimental method for determining the stress and strain state of the rafter structure upper belt and the roof slabs with regard to their rigid connection has been proposed. A model of a highly effective roof structure providing a significant reduction in the construction height of the roofing and the cubic content of the building at the same time allowing to include the end arrises and a part of the slabs shelves with the help of the monolithic concrete has been proposed. The proposed prefabricated monolithic concrete rafter structure and its rigid connection with ribbed slabs allows to reduce the consumption of the prestressed slabs reinforcement by 50%.

Reduced-order modeling of soft robots

PubMed Central

Chenevier, Jean; González, David; Aguado, J. Vicente; Chinesta, Francisco

2018-01-01

We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method. PMID:29470496

An Optimal Seed Based Compression Algorithm for DNA Sequences

PubMed Central

Gopalakrishnan, Gopakumar; Karunakaran, Muralikrishnan

2016-01-01

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms. PMID:27555868

Profile local linear estimation of generalized semiparametric regression model for longitudinal data.

PubMed

Sun, Yanqing; Sun, Liuquan; Zhou, Jie

2013-07-01

This paper studies the generalized semiparametric regression model for longitudinal data where the covariate effects are constant for some and time-varying for others. Different link functions can be used to allow more flexible modelling of longitudinal data. The nonparametric components of the model are estimated using a local linear estimating equation and the parametric components are estimated through a profile estimating function. The method automatically adjusts for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically model such dependence. A [Formula: see text]-fold cross-validation bandwidth selection is proposed as a working tool for locating an appropriate bandwidth. A criteria for selecting the link function is proposed to provide better fit of the data. Large sample properties of the proposed estimators are investigated. Large sample pointwise and simultaneous confidence intervals for the regression coefficients are constructed. Formal hypothesis testing procedures are proposed to check for the covariate effects and whether the effects are time-varying. A simulation study is conducted to examine the finite sample performances of the proposed estimation and hypothesis testing procedures. The methods are illustrated with a data example.

Proposed Method for Disaggregation of Secondary Data: The Model for External Reliance of Localities in the Coastal Management Zone (MERLIN-CMZ)

EPA Science Inventory

The Model for External Reliance of Localities In (MERLIN) Coastal Management Zones is a proposed solution to allow scaling of variables to smaller, nested geographies. Utilizing a Principal Components Analysis and data normalization techniques, smaller scale trends are linked to ...

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

LTI system order reduction approach based on asymptotical equivalence and the Co-operation of biology-related algorithms

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.

2017-02-01

A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.

A Multiple Period Problem in Distributed Energy Management Systems Considering CO2 Emissions

NASA Astrophysics Data System (ADS)

Muroda, Yuki; Miyamoto, Toshiyuki; Mori, Kazuyuki; Kitamura, Shoichi; Yamamoto, Takaya

Consider a special district (group) which is composed of multiple companies (agents), and where each agent responds to an energy demand and has a CO2 emission allowance imposed. A distributed energy management system (DEMS) optimizes energy consumption of a group through energy trading in the group. In this paper, we extended the energy distribution decision and optimal planning problem in DEMSs from a single period problem to a multiple periods one. The extension enabled us to consider more realistic constraints such as demand patterns, the start-up cost, and minimum running/outage times of equipment. At first, we extended the market-oriented programming (MOP) method for deciding energy distribution to the multiple periods problem. The bidding strategy of each agent is formulated by a 0-1 mixed non-linear programming problem. Secondly, we proposed decomposing the problem into a set of single period problems in order to solve it faster. In order to decompose the problem, we proposed a CO2 emission allowance distribution method, called an EP method. We confirmed that the proposed method was able to produce solutions whose group costs were close to lower-bound group costs by computational experiments. In addition, we verified that reduction in computational time was achieved without losing the quality of solutions by using the EP method.

Defining an additivity framework for mixture research in inducible whole-cell biosensors

NASA Astrophysics Data System (ADS)

Martin-Betancor, K.; Ritz, C.; Fernández-Piñas, F.; Leganés, F.; Rodea-Palomares, I.

2015-11-01

A novel additivity framework for mixture effect modelling in the context of whole cell inducible biosensors has been mathematically developed and implemented in R. The proposed method is a multivariate extension of the effective dose (EDp) concept. Specifically, the extension accounts for differential maximal effects among analytes and response inhibition beyond the maximum permissive concentrations. This allows a multivariate extension of Loewe additivity, enabling direct application in a biphasic dose-response framework. The proposed additivity definition was validated, and its applicability illustrated by studying the response of the cyanobacterial biosensor Synechococcus elongatus PCC 7942 pBG2120 to binary mixtures of Zn, Cu, Cd, Ag, Co and Hg. The novel method allowed by the first time to model complete dose-response profiles of an inducible whole cell biosensor to mixtures. In addition, the approach also allowed identification and quantification of departures from additivity (interactions) among analytes. The biosensor was found to respond in a near additive way to heavy metal mixtures except when Hg, Co and Ag were present, in which case strong interactions occurred. The method is a useful contribution for the whole cell biosensors discipline and related areas allowing to perform appropriate assessment of mixture effects in non-monotonic dose-response frameworks

Usability Evaluation of a Web-Based Learning System

ERIC Educational Resources Information Center

Nguyen, Thao

2012-01-01

The paper proposes a contingent, learner-centred usability evaluation method and a prototype tool of such systems. This is a new usability evaluation method for web-based learning systems using a set of empirically-supported usability factors and can be done effectively with limited resources. During the evaluation process, the method allows for…

A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.

ERIC Educational Resources Information Center

Chen, Ruey-Shun; Hu, Yi-Chung

2003-01-01

Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)

Fast single image dehazing based on image fusion

NASA Astrophysics Data System (ADS)

Liu, Haibo; Yang, Jie; Wu, Zhengping; Zhang, Qingnian

2015-01-01

Images captured in foggy weather conditions often fade the colors and reduce the contrast of the observed objects. An efficient image fusion method is proposed to remove haze from a single input image. First, the initial medium transmission is estimated based on the dark channel prior. Second, the method adopts an assumption that the degradation level affected by haze of each region is the same, which is similar to the Retinex theory, and uses a simple Gaussian filter to get the coarse medium transmission. Then, pixel-level fusion is achieved between the initial medium transmission and coarse medium transmission. The proposed method can recover a high-quality haze-free image based on the physical model, and the complexity of the proposed method is only a linear function of the number of input image pixels. Experimental results demonstrate that the proposed method can allow a very fast implementation and achieve better restoration for visibility and color fidelity compared to some state-of-the-art methods.

Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

PubMed

Suleimanov, Yury V; Green, William H

2015-09-08

We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs

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

Karagiannis, Georgios, E-mail: georgios.karagiannis@pnnl.gov; Lin, Guang, E-mail: guang.lin@pnnl.gov

2014-02-15

Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions. The number of gPC terms increases dramatically as the dimension of the random input variables increases. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs when the corresponding deterministic solver is computationally expensive, evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solutions, in both spatial and random domains, bymore » coupling Bayesian model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spatial points, via (1) the Bayesian model average (BMA) or (2) the median probability model, and their construction as spatial functions on the spatial domain via spline interpolation. The former accounts for the model uncertainty and provides Bayes-optimal predictions; while the latter provides a sparse representation of the stochastic solutions by evaluating the expansion on a subset of dominating gPC bases. Moreover, the proposed methods quantify the importance of the gPC bases in the probabilistic sense through inclusion probabilities. We design a Markov chain Monte Carlo (MCMC) sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed methods are suitable for, but not restricted to, problems whose stochastic solutions are sparse in the stochastic space with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the accuracy and performance of the proposed methods and make comparisons with other approaches on solving elliptic SPDEs with 1-, 14- and 40-random dimensions.« less

Silicon/copper dome-patterned electrodes for high-performance hybrid supercapacitors

PubMed Central

Liu, Xuyan; Jung, Hun-Gi; Kim, Sang-Ok; Choi, Ho-Suk; Lee, Sangwha; Moon, Jun Hyuk; Lee, Joong Kee

2013-01-01

This study proposes a method for manufacturing high-performance electrode materials in which controlling the shape of the current collector and electrode material for a Li-ion capacitor (LIC). In particular, the proposed LIC manufacturing method maintains the high voltage of a cell by using a microdome-patterned electrode material, allowing for reversible reactions between the Li-ion and the active material for an extended period of time. As a result, the LICs exhibit initial capacities of approximately 42 F g−1, even at 60 A g−1. The LICs also exhibit good cycle performance up to approximately 15,000 cycles. In addition, these advancements allow for a considerably higher energy density than other existing capacitor systems. The energy density of the proposed LICs is approximately nine, two, and 1.5 times higher than those of the electrochemical double layer capacitor (EDLC), AC/LiMn2O4 hybrid capacitor, and intrinsic Si/AC LIC, respectively. PMID:24292725

A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees.

PubMed

Amsuess, Sebastian; Goebel, Peter; Graimann, Bernhard; Farina, Dario

2015-09-01

Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem. However, the lack of robustness of the current methods impedes their routine clinical application. In this study, we propose a novel algorithm for controlling multiple degrees of freedom sequentially, inherently proportionally and with high robustness, allowing a good level of prosthetic hand function. The control algorithm is based on the spatial linear combinations of amplitude-related EMG signal features. The weighting coefficients in this combination are derived from the optimization criterion of the common spatial patterns filters which allow for maximal discriminability between movements. An important component of the study is the validation of the method which was performed on both able-bodied and amputee subjects who used physical prostheses with customized sockets and performed three standardized functional tests mimicking daily-life activities of varying difficulty. Moreover, the new method was compared in the same conditions with one clinical/industrial and one academic state-of-the-art method. The novel algorithm outperformed significantly the state-of-the-art techniques in both subject groups for tests that required the activation of more than one degree of freedom. Because of the evaluation in real time control on both able-bodied subjects and final users (amputees) wearing physical prostheses, the results obtained allow for the direct extrapolation of the benefits of the proposed method for the end users. In conclusion, the method proposed and validated in real-life use scenarios, allows the practical usability of multifunctional hand prostheses in an intuitive way, with significant advantages with respect to previous systems.

Wronskian Method for Bound States

ERIC Educational Resources Information Center

Fernandez, Francisco M.

2011-01-01

We propose a simple and straightforward method based on Wronskians for the calculation of bound-state energies and wavefunctions of one-dimensional quantum-mechanical problems. We explicitly discuss the asymptotic behaviour of the wavefunction and show that the allowed energies make the divergent part vanish. As illustrative examples we consider…

A method based on the Jacobi tau approximation for solving multi-term time-space fractional partial differential equations

NASA Astrophysics Data System (ADS)

Bhrawy, A. H.; Zaky, M. A.

2015-01-01

In this paper, we propose and analyze an efficient operational formulation of spectral tau method for multi-term time-space fractional differential equation with Dirichlet boundary conditions. The shifted Jacobi operational matrices of Riemann-Liouville fractional integral, left-sided and right-sided Caputo fractional derivatives are presented. By using these operational matrices, we propose a shifted Jacobi tau method for both temporal and spatial discretizations, which allows us to present an efficient spectral method for solving such problem. Furthermore, the error is estimated and the proposed method has reasonable convergence rates in spatial and temporal discretizations. In addition, some known spectral tau approximations can be derived as special cases from our algorithm if we suitably choose the corresponding special cases of Jacobi parameters θ and ϑ. Finally, in order to demonstrate its accuracy, we compare our method with those reported in the literature.

Critical Analysis of Existing Recyclability Assessment Methods for New Products in Order to Define a Reference Method

NASA Astrophysics Data System (ADS)

Maris, E.; Froelich, D.

The designers of products subject to the European regulations on waste have an obligation to improve the recyclability of their products from the very first design stages. The statutory texts refer to ISO standard 22 628, which proposes a method to calculate vehicle recyclability. There are several scientific studies that propose other calculation methods as well. Yet the feedback from the CREER club, a group of manufacturers and suppliers expert in ecodesign and recycling, is that the product recyclability calculation method proposed in this standard is not satisfactory, since only a mass indicator is used, the calculation scope is not clearly defined, and common data on the recycling industry does not exist to allow comparable calculations to be made for different products. For these reasons, it is difficult for manufacturers to have access to a method and common data for calculation purposes.

Use of derivatives to assess preservation of hydrocarbon deposits

NASA Astrophysics Data System (ADS)

Koshkin, K. A.; Melkishev, O. A.

2018-05-01

The paper considers the calculation of derivatives along the surface of a modern and paleostructure map of a Tl2-b formation top used to forecast the preservation of oil and gas deposits in traps according to 3D seismic survey via statistical methods. It also suggests a method to evaluate morphological changes of the formation top by calculating the difference between derivatives. The proposed method allows analyzing structural changes of the formation top in time towards primary migration of hydrocarbons. The comprehensive use of calculated indicators allowed ranking the prepared structures in terms of preservation of hydrocarbon deposits.

Paule‐Mandel estimators for network meta‐analysis with random inconsistency effects

PubMed Central

Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C.; Baker, Rose

2017-01-01

Network meta‐analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta‐analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between‐study heterogeneity. Models for network meta‐analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta‐analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta‐analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood‐based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods. PMID:28585257

Three-directional motion-compensation mask-based novel look-up table on graphics processing units for video-rate generation of digital holographic videos of three-dimensional scenes.

PubMed

Kwon, Min-Woo; Kim, Seung-Cheol; Kim, Eun-Soo

2016-01-20

A three-directional motion-compensation mask-based novel look-up table method is proposed and implemented on graphics processing units (GPUs) for video-rate generation of digital holographic videos of three-dimensional (3D) scenes. Since the proposed method is designed to be well matched with the software and memory structures of GPUs, the number of compute-unified-device-architecture kernel function calls can be significantly reduced. This results in a great increase of the computational speed of the proposed method, allowing video-rate generation of the computer-generated hologram (CGH) patterns of 3D scenes. Experimental results reveal that the proposed method can generate 39.8 frames of Fresnel CGH patterns with 1920×1080 pixels per second for the test 3D video scenario with 12,088 object points on dual GPU boards of NVIDIA GTX TITANs, and they confirm the feasibility of the proposed method in the practical application fields of electroholographic 3D displays.

Image-guided spatial localization of heterogeneous compartments for magnetic resonance

PubMed Central

An, Li; Shen, Jun

2015-01-01

Purpose: Image-guided localization SPectral Localization Achieved by Sensitivity Heterogeneity (SPLASH) allows rapid measurement of signals from irregularly shaped anatomical compartments without using phase encoding gradients. Here, the authors propose a novel method to address the issue of heterogeneous signal distribution within the localized compartments. Methods: Each compartment was subdivided into multiple subcompartments and their spectra were solved by Tikhonov regularization to enforce smoothness within each compartment. The spectrum of a given compartment was generated by combining the spectra of the components of that compartment. The proposed method was first tested using Monte Carlo simulations and then applied to reconstructing in vivo spectra from irregularly shaped ischemic stroke and normal tissue compartments. Results: Monte Carlo simulations demonstrate that the proposed regularized SPLASH method significantly reduces localization and metabolite quantification errors. In vivo results show that the intracompartment regularization results in ∼40% reduction of error in metabolite quantification. Conclusions: The proposed method significantly reduces localization errors and metabolite quantification errors caused by intracompartment heterogeneous signal distribution. PMID:26328977

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schroedinger equation

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

Leung Shingyu, E-mail: masyleung@ust.h; Qian Jianliang, E-mail: qian@math.msu.ed

2010-11-20

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schroedinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in . In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying themore » FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.« less

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schrödinger equation

NASA Astrophysics Data System (ADS)

Leung, Shingyu; Qian, Jianliang

2010-11-01

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schrödinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in [12]. In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying the FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.

Selection of Polynomial Chaos Bases via Bayesian Model Uncertainty Methods with Applications to Sparse Approximation of PDEs with Stochastic Inputs

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

Karagiannis, Georgios; Lin, Guang

2014-02-15

Generalized polynomial chaos (gPC) expansions allow the representation of the solution of a stochastic system as a series of polynomial terms. The number of gPC terms increases dramatically with the dimension of the random input variables. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs if the evaluations of the system are expensive, the evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solution, both in spacial and random domains, by coupling Bayesianmore » model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spacial points via (1) Bayesian model average or (2) medial probability model, and their construction as functions on the spacial domain via spline interpolation. The former accounts the model uncertainty and provides Bayes-optimal predictions; while the latter, additionally, provides a sparse representation of the solution by evaluating the expansion on a subset of dominating gPC bases when represented as a gPC expansion. Moreover, the method quantifies the importance of the gPC bases through inclusion probabilities. We design an MCMC sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed method is suitable for, but not restricted to, problems whose stochastic solution is sparse at the stochastic level with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the good performance of the proposed method and make comparisons with others on 1D, 14D and 40D in random space elliptic stochastic partial differential equations.« less

Multilevel filtering elliptic preconditioners

NASA Technical Reports Server (NTRS)

Kuo, C. C. Jay; Chan, Tony F.; Tong, Charles

1989-01-01

A class of preconditioners is presented for elliptic problems built on ideas borrowed from the digital filtering theory and implemented on a multilevel grid structure. They are designed to be both rapidly convergent and highly parallelizable. The digital filtering viewpoint allows the use of filter design techniques for constructing elliptic preconditioners and also provides an alternative framework for understanding several other recently proposed multilevel preconditioners. Numerical results are presented to assess the convergence behavior of the new methods and to compare them with other preconditioners of multilevel type, including the usual multigrid method as preconditioner, the hierarchical basis method and a recent method proposed by Bramble-Pasciak-Xu.

Pixel-based absolute surface metrology by three flat test with shifted and rotated maps

NASA Astrophysics Data System (ADS)

Zhai, Dede; Chen, Shanyong; Xue, Shuai; Yin, Ziqiang

2018-03-01

In traditional three flat test, it only provides the absolute profile along one surface diameter. In this paper, an absolute testing algorithm based on shift-rotation with three flat test has been proposed to reconstruct two-dimensional surface exactly. Pitch and yaw error during shift procedure is analyzed and compensated in our method. Compared with multi-rotation method proposed before, it only needs a 90° rotation and a shift, which is easy to carry out especially in condition of large size surface. It allows pixel level spatial resolution to be achieved without interpolation or assumption to the test surface. In addition, numerical simulations and optical tests are implemented and show the high accuracy recovery capability of the proposed method.

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PubMed

Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

2015-01-01

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

Local Feature Selection for Data Classification.

PubMed

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

2016-06-01

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

Analysis of high-throughput biological data using their rank values.

PubMed

Dembélé, Doulaye

2018-01-01

High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron-Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student's t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .

The use and generation of illustrative examples in computer-based instructional systems

NASA Technical Reports Server (NTRS)

Selig, William John; Johannes, James D.

1987-01-01

A method is proposed whereby the underlying domain knowledge is represented such that illustrative examples may be generated on demand. This method has the advantage that the generated example can follow changes in the domain in addition to allowing automatic customization of the example to the individual.

A Three-Stage Enhanced Reactive Power and Voltage Optimization Method for High Penetration of Solar

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

Ke, Xinda; Huang, Renke; Vallem, Mallikarjuna R.

This paper presents a three-stage enhanced volt/var optimization method to stabilize voltage fluctuations in transmission networks by optimizing the usage of reactive power control devices. In contrast with existing volt/var optimization algorithms, the proposed method optimizes the voltage profiles of the system, while keeping the voltage and real power output of the generators as close to the original scheduling values as possible. This allows the method to accommodate realistic power system operation and market scenarios, in which the original generation dispatch schedule will not be affected. The proposed method was tested and validated on a modified IEEE 118-bus system withmore » photovoltaic data.« less

Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics.

PubMed

Kawaguchi, Atsushi; Yamashita, Fumio

2017-10-01

This article proposes a procedure for describing the relationship between high-dimensional data sets, such as multimodal brain images and genetic data. We propose a supervised technique to incorporate the clinical outcome to determine a score, which is a linear combination of variables with hieratical structures to multimodalities. This approach is expected to obtain interpretable and predictive scores. The proposed method was applied to a study of Alzheimer's disease (AD). We propose a diagnostic method for AD that involves using whole-brain magnetic resonance imaging (MRI) and positron emission tomography (PET), and we select effective brain regions for the diagnostic probability and investigate the genome-wide association with the regions using single nucleotide polymorphisms (SNPs). The two-step dimension reduction method, which we previously introduced, was considered applicable to such a study and allows us to partially incorporate the proposed method. We show that the proposed method offers classification functions with feasibility and reasonable prediction accuracy based on the receiver operating characteristic (ROC) analysis and reasonable regions of the brain and genomes. Our simulation study based on the synthetic structured data set showed that the proposed method outperformed the original method and provided the characteristic for the supervised feature. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Ensemble of classifiers for ontology enrichment

NASA Astrophysics Data System (ADS)

Semenova, A. V.; Kureichik, V. M.

2018-05-01

A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word embedding are suggested. An experiment was conducted on open data, which allows us to conclude that the proposed classification method is promising. The implementation of the proposed classifier is performed in the Matlab using the functions of the Text Analytics Toolbox. The principal difference between the proposed ensembles of classifiers is the high quality of classification of data at acceptable time costs.

Ultrasensitive low noise voltage amplifier for spectral analysis.

PubMed

Giusi, G; Crupi, F; Pace, C

2008-08-01

Recently we have proposed several voltage noise measurement methods that allow, at least in principle, the complete elimination of the noise introduced by the measurement amplifier. The most severe drawback of these methods is that they require a multistep measurement procedure. Since environmental conditions may change in the different measurement steps, the final result could be affected by these changes. This problem is solved by the one-step voltage noise measurement methodology based on a novel amplifier topology proposed in this paper. Circuit implementations for the amplifier building blocks based on operational amplifiers are critically discussed. The proposed approach is validated through measurements performed on a prototype circuit.

Quantitative assessment of corneal vibrations during intraocular pressure measurement with the air-puff method in patients with keratoconus.

PubMed

Koprowski, Robert; Ambrósio, Renato

2015-11-01

One of the current methods for measuring intraocular pressure is the air-puff method. A tonometer which uses this method is the Corvis device. With the ultra-high-speed (UHS) Scheimpflug camera, it is also possible to observe corneal deformation during measurement. The use of modern image analysis and processing methods allows for analysis of higher harmonics of corneal deflection above 100 Hz. 493 eyes of healthy subjects and 279 eyes of patients with keratoconus were used in the measurements. For each eye, 140 corneal deformation images were recorded during intraocular pressure measurement. Each image was recorded every 230 µs and had a resolution of 200 × 576 pixels. A new, original algorithm for image analysis and processing has been proposed. It enables to separate the eyeball reaction as well as low-frequency and high-frequency corneal deformations from the eye response to an air puff. Furthermore, a method for classification of healthy subjects and patients with keratoconus based on decision trees has been proposed. The obtained results confirm the possibility to distinguish between patients with keratoconus and healthy subjects. The features used in this classification are directly related to corneal vibrations. They are only available in the proposed software and provide specificity of 98%, sensitivity-85%, and accuracy-92%. This confirms the usefulness of the proposed method in this type of classification that uses corneal vibrations during intraocular pressure measurement with the Corvis tonometer. With the new proposed algorithm for image analysis and processing allowing for the separation of individual features from a corneal deformation image, it is possible to: automatically measure corneal vibrations in a few characteristic points of the cornea, obtain fully repeatable measurement of vibrations for the same registered sequence of images and measure vibration parameters for large inter-individual variability in patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Method for stitching microbial images using a neural network

NASA Astrophysics Data System (ADS)

Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.; Tolstova, I. V.

2017-05-01

Currently an analog microscope has a wide distribution in the following fields: medicine, animal husbandry, monitoring technological objects, oceanography, agriculture and others. Automatic method is preferred because it will greatly reduce the work involved. Stepper motors are used to move the microscope slide and allow to adjust the focus in semi-automatic or automatic mode view with transfer images of microbiological objects from the eyepiece of the microscope to the computer screen. Scene analysis allows to locate regions with pronounced abnormalities for focusing specialist attention. This paper considers the method for stitching microbial images, obtained of semi-automatic microscope. The method allows to keep the boundaries of objects located in the area of capturing optical systems. Objects searching are based on the analysis of the data located in the area of the camera view. We propose to use a neural network for the boundaries searching. The stitching image boundary is held of the analysis borders of the objects. To auto focus, we use the criterion of the minimum thickness of the line boundaries of object. Analysis produced the object located in the focal axis of the camera. We use method of recovery of objects borders and projective transform for the boundary of objects which are based on shifted relative to the focal axis. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.

The integrative review: updated methodology.

PubMed

Whittemore, Robin; Knafl, Kathleen

2005-12-01

The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process. Recent evidence-based practice initiatives have increased the need for and the production of all types of reviews of the literature (integrative reviews, systematic reviews, meta-analyses, and qualitative reviews). The integrative review method is the only approach that allows for the combination of diverse methodologies (for example, experimental and non-experimental research), and has the potential to play a greater role in evidence-based practice for nursing. With respect to the integrative review method, strategies to enhance data collection and extraction have been developed; however, methods of analysis, synthesis, and conclusion drawing remain poorly formulated. A modified framework for research reviews is presented to address issues specific to the integrative review method. Issues related to specifying the review purpose, searching the literature, evaluating data from primary sources, analysing data, and presenting the results are discussed. Data analysis methods of qualitative research are proposed as strategies that enhance the rigour of combining diverse methodologies as well as empirical and theoretical sources in an integrative review. An updated integrative review method has the potential to allow for diverse primary research methods to become a greater part of evidence-based practice initiatives.

A quantitative risk analysis approach to port hydrocarbon logistics.

PubMed

Ronza, A; Carol, S; Espejo, V; Vílchez, J A; Arnaldos, J

2006-01-16

A method is presented that allows quantitative risk analysis to be performed on marine hydrocarbon terminals sited in ports. A significant gap was identified in the technical literature on QRA for the handling of hazardous materials in harbours published prior to this work. The analysis is extended to tanker navigation through port waters and loading and unloading facilities. The steps of the method are discussed, beginning with data collecting. As to accident scenario identification, an approach is proposed that takes into account minor and massive spills due to loading arm failures and tank rupture. Frequency estimation is thoroughly reviewed and a shortcut approach is proposed for frequency calculation. This allows for the two-fold possibility of a tanker colliding/grounding at/near the berth or while navigating to/from the berth. A number of probability data defining the possibility of a cargo spill after an external impact on a tanker are discussed. As to consequence and vulnerability estimates, a scheme is proposed for the use of ratios between the numbers of fatal victims, injured and evacuated people. Finally, an example application is given, based on a pilot study conducted in the Port of Barcelona, where the method was tested.

Least-squares deconvolution of evoked potentials and sequence optimization for multiple stimuli under low-jitter conditions.

PubMed

Bardy, Fabrice; Dillon, Harvey; Van Dun, Bram

2014-04-01

Rapid presentation of stimuli in an evoked response paradigm can lead to overlap of multiple responses and consequently difficulties interpreting waveform morphology. This paper presents a deconvolution method allowing overlapping multiple responses to be disentangled. The deconvolution technique uses a least-squared error approach. A methodology is proposed to optimize the stimulus sequence associated with the deconvolution technique under low-jitter conditions. It controls the condition number of the matrices involved in recovering the responses. Simulations were performed using the proposed deconvolution technique. Multiple overlapping responses can be recovered perfectly in noiseless conditions. In the presence of noise, the amount of error introduced by the technique can be controlled a priori by the condition number of the matrix associated with the used stimulus sequence. The simulation results indicate the need for a minimum amount of jitter, as well as a sufficient number of overlap combinations to obtain optimum results. An aperiodic model is recommended to improve reconstruction. We propose a deconvolution technique allowing multiple overlapping responses to be extracted and a method of choosing the stimulus sequence optimal for response recovery. This technique may allow audiologists, psychologists, and electrophysiologists to optimize their experimental designs involving rapidly presented stimuli, and to recover evoked overlapping responses. Copyright © 2013 International Federation of Clinical Neurophysiology. All rights reserved.

Experimental verification of a real-time tuning method of a model-based controller by perturbations to its poles

NASA Astrophysics Data System (ADS)

Kajiwara, Itsuro; Furuya, Keiichiro; Ishizuka, Shinichi

2018-07-01

Model-based controllers with adaptive design variables are often used to control an object with time-dependent characteristics. However, the controller's performance is influenced by many factors such as modeling accuracy and fluctuations in the object's characteristics. One method to overcome these negative factors is to tune model-based controllers. Herein we propose an online tuning method to maintain control performance for an object that exhibits time-dependent variations. The proposed method employs the poles of the controller as design variables because the poles significantly impact performance. Specifically, we use the simultaneous perturbation stochastic approximation (SPSA) to optimize a model-based controller with multiple design variables. Moreover, a vibration control experiment of an object with time-dependent characteristics as the temperature is varied demonstrates that the proposed method allows adaptive control and stably maintains the closed-loop characteristics.

The effect of filtering on the determination of lunar tides

NASA Astrophysics Data System (ADS)

Palumbo, A.; Mazzarella, A.

1980-01-01

The determination of lunar tides obtained by combination of a filtering process and the fixed lunar age technique is proposed. It is shown that such a method allows a reduction of the signal-to-noise ratio without altering the amplitude and the phase angle of the signal. It consequently allows the significant determination of the lunar semidiurnal component M2 from the series of data shorter than those required by other methods and the deduction of other interesting lunisolar components which have not previously been significantly determined in surface pressure and temperature data. The analysis of the data for Gan, Vesuvian Observatory and the Eiffel Tower have provided new determinations of L2(p) and have allowed comparison between the results obtained by the present and other methods.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

3D Segmentation with an application of level set-method using MRI volumes for image guided surgery.

PubMed

Bosnjak, A; Montilla, G; Villegas, R; Jara, I

2007-01-01

This paper proposes an innovation in the application for image guided surgery using a comparative study of three different method of segmentation. This segmentation method is faster than the manual segmentation of images, with the advantage that it allows to use the same patient as anatomical reference, which has more precision than a generic atlas. This new methodology for 3D information extraction is based on a processing chain structured of the following modules: 1) 3D Filtering: the purpose is to preserve the contours of the structures and to smooth the homogeneous areas; several filters were tested and finally an anisotropic diffusion filter was used. 2) 3D Segmentation. This module compares three different methods: Region growing Algorithm, Cubic spline hand assisted, and Level Set Method. It then proposes a Level Set-based on the front propagation method that allows the making of the reconstruction of the internal walls of the anatomical structures of the brain. 3) 3D visualization. The new contribution of this work consists on the visualization of the segmented model and its use in the pre-surgery planning.

An Analysis Method for Superconducting Resonator Parameter Extraction with Complex Baseline Removal

NASA Technical Reports Server (NTRS)

Cataldo, Giuseppe

2014-01-01

A new semi-empirical model is proposed for extracting the quality (Q) factors of arrays of superconducting microwave kinetic inductance detectors (MKIDs). The determination of the total internal and coupling Q factors enables the computation of the loss in the superconducting transmission lines. The method used allows the simultaneous analysis of multiple interacting discrete resonators with the presence of a complex spectral baseline arising from reflections in the system. The baseline removal allows an unbiased estimate of the device response as measured in a cryogenic instrumentation setting.

Cognitive-graphic method for constructing of hierarchical forms of basic functions of biquadratic finite element

NASA Astrophysics Data System (ADS)

Astionenko, I. O.; Litvinenko, O. I.; Osipova, N. V.; Tuluchenko, G. Ya.; Khomchenko, A. N.

2016-10-01

Recently the interpolation bases of the hierarchical type have been used for the problem solving of the approximation of multiple arguments functions (such as in the finite-element method). In this work the cognitive graphical method of constructing of the hierarchical form bases on the serendipity finite elements is suggested, which allowed to get the alternative bases on a biquadratic finite element from the serendipity family without internal knots' inclusion. The cognitive-graphic method allowed to improve the known interpolation procedure of Taylor and to get the modified elements with irregular arrangement of knots. The proposed procedures are universal and are spread in the area of finite-elements.

A synchronization method for wireless acquisition systems, application to brain computer interfaces.

PubMed

Foerster, M; Bonnet, S; van Langhenhove, A; Porcherot, J; Charvet, G

2013-01-01

A synchronization method for wireless acquisition systems has been developed and implemented on a wireless ECoG recording implant and on a wireless EEG recording helmet. The presented algorithm and hardware implementation allow the precise synchronization of several data streams from several sensor nodes for applications where timing is critical like in event-related potential (ERP) studies. The proposed method has been successfully applied to obtain visual evoked potentials and compared with a reference biosignal amplifier. The control over the exact sampling frequency allows reducing synchronization errors that will otherwise accumulate during a recording. The method is scalable to several sensor nodes communicating with a shared base station.

Segmentation of the pectoral muscle in breast MR images using structure tensor and deformable model

NASA Astrophysics Data System (ADS)

Lee, Myungeun; Kim, Jong Hyo

2012-02-01

Recently, breast MR images have been used in wider clinical area including diagnosis, treatment planning, and treatment response evaluation, which requests quantitative analysis and breast tissue segmentation. Although several methods have been proposed for segmenting MR images, segmenting out breast tissues robustly from surrounding structures in a wide range of anatomical diversity still remains challenging. Therefore, in this paper, we propose a practical and general-purpose approach for segmenting the pectoral muscle boundary based on the structure tensor and deformable model. The segmentation work flow comprises four key steps: preprocessing, detection of the region of interest (ROI) within the breast region, segmenting the pectoral muscle and finally extracting and refining the pectoral muscle boundary. From experimental results we show that the proposed method can segment the pectoral muscle robustly in diverse patient cases. In addition, the proposed method will allow the application of the quantification research for various breast images.

Pseudo-orthogonalization of memory patterns for associative memory.

PubMed

Oku, Makito; Makino, Takaki; Aihara, Kazuyuki

2013-11-01

A new method for improving the storage capacity of associative memory models on a neural network is proposed. The storage capacity of the network increases in proportion to the network size in the case of random patterns, but, in general, the capacity suffers from correlation among memory patterns. Numerous solutions to this problem have been proposed so far, but their high computational cost limits their scalability. In this paper, we propose a novel and simple solution that is locally computable without any iteration. Our method involves XNOR masking of the original memory patterns with random patterns, and the masked patterns and masks are concatenated. The resulting decorrelated patterns allow higher storage capacity at the cost of the pattern length. Furthermore, the increase in the pattern length can be reduced through blockwise masking, which results in a small amount of capacity loss. Movie replay and image recognition are presented as examples to demonstrate the scalability of the proposed method.

The cross-correlation analysis of multi property of stock markets based on MM-DFA

NASA Astrophysics Data System (ADS)

Yang, Yujun; Li, Jianping; Yang, Yimei

2017-09-01

In this paper, we propose a new method called DH-MXA based on distribution histograms of Hurst surface and multiscale multifractal detrended fluctuation analysis. The method allows us to investigate the cross-correlation characteristics among multiple properties of different stock time series. It may provide a new way of measuring the nonlinearity of several signals. It also can provide a more stable and faithful description of cross-correlation of multiple properties of stocks. The DH-MXA helps us to present much richer information than multifractal detrented cross-correlation analysis and allows us to assess many universal and subtle cross-correlation characteristics of stock markets. We show DH-MXA by selecting four artificial data sets and five properties of four stock time series from different countries. The results show that our proposed method can be adapted to investigate the cross-correlation of stock markets. In general, the American stock markets are more mature and less volatile than the Chinese stock markets.

Method for universal detection of two-photon polarization entanglement

NASA Astrophysics Data System (ADS)

Bartkiewicz, Karol; Horodecki, Paweł; Lemr, Karel; Miranowicz, Adam; Życzkowski, Karol

2015-03-01

Detecting and quantifying quantum entanglement of a given unknown state poses problems that are fundamentally important for quantum information processing. Surprisingly, no direct (i.e., without quantum tomography) universal experimental implementation of a necessary and sufficient test of entanglement has been designed even for a general two-qubit state. Here we propose an experimental method for detecting a collective universal witness, which is a necessary and sufficient test of two-photon polarization entanglement. It allows us to detect entanglement for any two-qubit mixed state and to establish tight upper and lower bounds on its amount. A different element of this method is the sequential character of its main components, which allows us to obtain relatively complicated information about quantum correlations with the help of simple linear-optical elements. As such, this proposal realizes a universal two-qubit entanglement test within the present state of the art of quantum optics. We show the optimality of our setup with respect to the minimal number of measured quantities.

A numerical method for osmotic water flow and solute diffusion with deformable membrane boundaries in two spatial dimension

NASA Astrophysics Data System (ADS)

Yao, Lingxing; Mori, Yoichiro

2017-12-01

Osmotic forces and solute diffusion are increasingly seen as playing a fundamental role in cell movement. Here, we present a numerical method that allows for studying the interplay between diffusive, osmotic and mechanical effects. An osmotically active solute obeys a advection-diffusion equation in a region demarcated by a deformable membrane. The interfacial membrane allows transmembrane water flow which is determined by osmotic and mechanical pressure differences across the membrane. The numerical method is based on an immersed boundary method for fluid-structure interaction and a Cartesian grid embedded boundary method for the solute. We demonstrate our numerical algorithm with the test case of an osmotic engine, a recently proposed mechanism for cell propulsion.

A proposed standard method for polarimetric calibration and calibration verification

NASA Astrophysics Data System (ADS)

Persons, Christopher M.; Jones, Michael W.; Farlow, Craig A.; Morell, L. Denise; Gulley, Michael G.; Spradley, Kevin D.

2007-09-01

Accurate calibration of polarimetric sensors is critical to reducing and analyzing phenomenology data, producing uniform polarimetric imagery for deployable sensors, and ensuring predictable performance of polarimetric algorithms. It is desirable to develop a standard calibration method, including verification reporting, in order to increase credibility with customers and foster communication and understanding within the polarimetric community. This paper seeks to facilitate discussions within the community on arriving at such standards. Both the calibration and verification methods presented here are performed easily with common polarimetric equipment, and are applicable to visible and infrared systems with either partial Stokes or full Stokes sensitivity. The calibration procedure has been used on infrared and visible polarimetric imagers over a six year period, and resulting imagery has been presented previously at conferences and workshops. The proposed calibration method involves the familiar calculation of the polarimetric data reduction matrix by measuring the polarimeter's response to a set of input Stokes vectors. With this method, however, linear combinations of Stokes vectors are used to generate highly accurate input states. This allows the direct measurement of all system effects, in contrast with fitting modeled calibration parameters to measured data. This direct measurement of the data reduction matrix allows higher order effects that are difficult to model to be discovered and corrected for in calibration. This paper begins with a detailed tutorial on the proposed calibration and verification reporting methods. Example results are then presented for a LWIR rotating half-wave retarder polarimeter.

PSF mapping-based correction of eddy-current-induced distortions in diffusion-weighted echo-planar imaging.

PubMed

In, Myung-Ho; Posnansky, Oleg; Speck, Oliver

2016-05-01

To accurately correct diffusion-encoding direction-dependent eddy-current-induced geometric distortions in diffusion-weighted echo-planar imaging (DW-EPI) and to minimize the calibration time at 7 Tesla (T). A point spread function (PSF) mapping based eddy-current calibration method is newly presented to determine eddy-current-induced geometric distortions even including nonlinear eddy-current effects within the readout acquisition window. To evaluate the temporal stability of eddy-current maps, calibration was performed four times within 3 months. Furthermore, spatial variations of measured eddy-current maps versus their linear superposition were investigated to enable correction in DW-EPIs with arbitrary diffusion directions without direct calibration. For comparison, an image-based eddy-current correction method was additionally applied. Finally, this method was combined with a PSF-based susceptibility-induced distortion correction approach proposed previously to correct both susceptibility and eddy-current-induced distortions in DW-EPIs. Very fast eddy-current calibration in a three-dimensional volume is possible with the proposed method. The measured eddy-current maps are very stable over time and very similar maps can be obtained by linear superposition of principal-axes eddy-current maps. High resolution in vivo brain results demonstrate that the proposed method allows more efficient eddy-current correction than the image-based method. The combination of both PSF-based approaches allows distortion-free images, which permit reliable analysis in diffusion tensor imaging applications at 7T. © 2015 Wiley Periodicals, Inc.

The approach to engineering tasks composition on knowledge portals

NASA Astrophysics Data System (ADS)

Novogrudska, Rina; Globa, Larysa; Schill, Alexsander; Romaniuk, Ryszard; Wójcik, Waldemar; Karnakova, Gaini; Kalizhanova, Aliya

2017-08-01

The paper presents an approach to engineering tasks composition on engineering knowledge portals. The specific features of engineering tasks are highlighted, their analysis makes the basis for partial engineering tasks integration. The formal algebraic system for engineering tasks composition is proposed, allowing to set the context-independent formal structures for engineering tasks elements' description. The method of engineering tasks composition is developed that allows to integrate partial calculation tasks into general calculation tasks on engineering portals, performed on user request demand. The real world scenario «Calculation of the strength for the power components of magnetic systems» is represented, approving the applicability and efficiency of proposed approach.

Parametric output-only identification of time-varying structures using a kernel recursive extended least squares TARMA approach

NASA Astrophysics Data System (ADS)

Ma, Zhi-Sai; Liu, Li; Zhou, Si-Da; Yu, Lei; Naets, Frank; Heylen, Ward; Desmet, Wim

2018-01-01

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.

Application of mathematical methods of analysis in selection of competing information technologies

NASA Astrophysics Data System (ADS)

Semenov, V. L.; Kadyshev, E. N.; Zakharova, A. N.; Patianova, A. O.; Dulina, G. S.

2018-05-01

The article discusses the use of qualimetry methods using the apparatus of mathematical analysis in the formation of the integral index that allows one to select the best option among competing information technology. The authors propose the use of affine space in the evaluation and selection of competing information technologies.

Integration of scheduling and discrete event simulation systems to improve production flow planning

NASA Astrophysics Data System (ADS)

Krenczyk, D.; Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.

2016-08-01

The increased availability of data and computer-aided technologies such as MRPI/II, ERP and MES system, allowing producers to be more adaptive to market dynamics and to improve production scheduling. Integration of production scheduling and computer modelling, simulation and visualization systems can be useful in the analysis of production system constraints related to the efficiency of manufacturing systems. A integration methodology based on semi-automatic model generation method for eliminating problems associated with complexity of the model and labour-intensive and time-consuming process of simulation model creation is proposed. Data mapping and data transformation techniques for the proposed method have been applied. This approach has been illustrated through examples of practical implementation of the proposed method using KbRS scheduling system and Enterprise Dynamics simulation system.

A geometric level set model for ultrasounds analysis

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

Sarti, A.; Malladi, R.

We propose a partial differential equation (PDE) for filtering and segmentation of echocardiographic images based on a geometric-driven scheme. The method allows edge-preserving image smoothing and a semi-automatic segmentation of the heart chambers, that regularizes the shapes and improves edge fidelity especially in presence of distinct gaps in the edge map as is common in ultrasound imagery. A numerical scheme for solving the proposed PDE is borrowed from level set methods. Results on human in vivo acquired 2D, 2D+time,3D, 3D+time echocardiographic images are shown.

Deformed exponentials and portfolio selection

NASA Astrophysics Data System (ADS)

Rodrigues, Ana Flávia P.; Guerreiro, Igor M.; Cavalcante, Charles Casimiro

In this paper, we present a method for portfolio selection based on the consideration on deformed exponentials in order to generalize the methods based on the gaussianity of the returns in portfolio, such as the Markowitz model. The proposed method generalizes the idea of optimizing mean-variance and mean-divergence models and allows a more accurate behavior for situations where heavy-tails distributions are necessary to describe the returns in a given time instant, such as those observed in economic crises. Numerical results show the proposed method outperforms the Markowitz portfolio for the cumulated returns with a good convergence rate of the weights for the assets which are searched by means of a natural gradient algorithm.

Low-coherence terahertz tomography based on spatially separated counterpropagating beams with allowance for probe radiation absorption in the medium

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

Mandrosov, V I

2015-10-31

This paper analyses low-coherence tomography of absorbing media with the use of spatially separated counterpropagating object and reference beams. A probe radiation source based on a broadband terahertz (THz) generator that emits sufficiently intense THz waves in the spectral range 90 – 350 μm and a prism spectroscope that separates out eight narrow intervals from this range are proposed for implementing this method. This allows media of interest to be examined by low-coherence tomography with counterpropagating beams in each interval. It is shown that, according to the Rayleigh criterion, the method is capable of resolving inhomogeneities with a size nearmore » one quarter of the coherence length of the probe radiation. In addition, the proposed tomograph configuration allows one to determine the average surface asperity slope and the refractive index and absorption coefficient of inhomogeneities 180 to 700 mm in size, and obtain spectra of such inhomogeneities in order to determine their chemical composition. (laser applications and other topics in quantum electronics)« less

A distributed algorithm for demand-side management: Selling back to the grid.

PubMed

Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Zandi, Sajad; Bazzi, Wael M

2017-11-01

Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.

A new approach to the extraction of single exponential diode model parameters

NASA Astrophysics Data System (ADS)

Ortiz-Conde, Adelmo; García-Sánchez, Francisco J.

2018-06-01

A new integration method is presented for the extraction of the parameters of a single exponential diode model with series resistance from the measured forward I-V characteristics. The extraction is performed using auxiliary functions based on the integration of the data which allow to isolate the effects of each of the model parameters. A differentiation method is also presented for data with low level of experimental noise. Measured and simulated data are used to verify the applicability of both proposed method. Physical insight about the validity of the model is also obtained by using the proposed graphical determinations of the parameters.

Efficient optimization of the quantum relative entropy

NASA Astrophysics Data System (ADS)

Fawzi, Hamza; Fawzi, Omar

2018-04-01

Many quantum information measures can be written as an optimization of the quantum relative entropy between sets of states. For example, the relative entropy of entanglement of a state is the minimum relative entropy to the set of separable states. The various capacities of quantum channels can also be written in this way. We propose a unified framework to numerically compute these quantities using off-the-shelf semidefinite programming solvers, exploiting the approximation method proposed in Fawzi, Saunderson and Parrilo (2017 arXiv: 1705.00812). As a notable application, this method allows us to provide numerical counterexamples for a proposed lower bound on the quantum conditional mutual information in terms of the relative entropy of recovery.

Wildlife and the coal waste policy debate: proposed rules for coal waste disposal ignore lessons from 45 years of wildlife poisoning

Treesearch

A. Dennis Lemly; Joseph P. Skorupa

2012-01-01

This analysis examines wildlife poisoning from coal combustion waste (CCW) in the context of EPA's proposed policy that would allow continued use of surface impoundments as a disposal method. Data from 21 confirmed damage sites were evaluated, ranging from locations where historic poisoning has led to corrective actions that have greatly improved environmental...

76 FR 4847 - Hazardous Materials: Safety Requirements for External Product Piping on Cargo Tanks Transporting...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-27

... limit of one liter or less in each pipe, but did not propose a specific method for achieving this... purged, we are proposing to allow a residue quantity of no more than one liter (0.26 gallon or 33 ounces... wetlines to the one liter residue level. For example, an operator may elect to design external loading...

Imaging of cardiac perfusion of free-breathing small animals using dynamic phase-correlated micro-CT

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

Sawall, Stefan; Kuntz, Jan; Socher, Michaela

Purpose:Mouse models of cardiac diseases have proven to be a valuable tool in preclinical research. The high cardiac and respiratory rates of free breathing mice prohibit conventional in vivo cardiac perfusion studies using computed tomography even if gating methods are applied. This makes a sacrification of the animals unavoidable and only allows for the application of ex vivo methods. Methods: To overcome this issue the authors propose a low dose scan protocol and an associated reconstruction algorithm that allows for in vivo imaging of cardiac perfusion and associated processes that are retrospectively synchronized to the respiratory and cardiac motion ofmore » the animal. The scan protocol consists of repetitive injections of contrast media within several consecutive scans while the ECG, respiratory motion, and timestamp of contrast injection are recorded and synchronized to the acquired projections. The iterative reconstruction algorithm employs a six-dimensional edge-preserving filter to provide low-noise, motion artifact-free images of the animal examined using the authors' low dose scan protocol. Results: The reconstructions obtained show that the complete temporal bolus evolution can be visualized and quantified in any desired combination of cardiac and respiratory phase including reperfusion phases. The proposed reconstruction method thereby keeps the administered radiation dose at a minimum and thus reduces metabolic inference to the animal allowing for longitudinal studies. Conclusions: The authors' low dose scan protocol and phase-correlated dynamic reconstruction algorithm allow for an easy and effective way to visualize phase-correlated perfusion processes in routine laboratory studies using free-breathing mice.« less

Made-to-measure modelling of observed galaxy dynamics

NASA Astrophysics Data System (ADS)

Bovy, Jo; Kawata, Daisuke; Hunt, Jason A. S.

2018-01-01

Amongst dynamical modelling techniques, the made-to-measure (M2M) method for modelling steady-state systems is amongst the most flexible, allowing non-parametric distribution functions in complex gravitational potentials to be modelled efficiently using N-body particles. Here, we propose and test various improvements to the standard M2M method for modelling observed data, illustrated using the simple set-up of a one-dimensional harmonic oscillator. We demonstrate that nuisance parameters describing the modelled system's orientation with respect to the observer - e.g. an external galaxy's inclination or the Sun's position in the Milky Way - as well as the parameters of an external gravitational field can be optimized simultaneously with the particle weights. We develop a method for sampling from the high-dimensional uncertainty distribution of the particle weights. We combine this in a Gibbs sampler with samplers for the nuisance and potential parameters to explore the uncertainty distribution of the full set of parameters. We illustrate our M2M improvements by modelling the vertical density and kinematics of F-type stars in Gaia DR1. The novel M2M method proposed here allows full probabilistic modelling of steady-state dynamical systems, allowing uncertainties on the non-parametric distribution function and on nuisance parameters to be taken into account when constraining the dark and baryonic masses of stellar systems.

Pressure filtration of ceramic pastes. 4: Treatment of experimental data

NASA Technical Reports Server (NTRS)

Torrecillas, A. S.; Polo, J. F.; Perez, A. A.

1984-01-01

The use of data processing method based on the algorithm proposed by Kalman and its application to the filtration process at constant pressure are described, as well as the advantages of this method. This technique is compared to the least squares method. The operation allows the precise parameter adjustment of the equation in direct relationship to the specific resistance of the cake.

SIMULTANEOUS MULTISLICE MAGNETIC RESONANCE FINGERPRINTING WITH LOW-RANK AND SUBSPACE MODELING

PubMed Central

Zhao, Bo; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A.; Wald, Lawrence L.; Setsompop, Kawin

2018-01-01

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T1, T2, and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3x speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice. PMID:29060594

Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.

PubMed

Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin

2017-07-01

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion.

PubMed

Zhao, Wenyi; Zhang, Chao

2008-07-01

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images.

PubMed

Peng, Shao-Hu; Kim, Deok-Hwan; Lee, Seok-Lyong; Lim, Myung-Kwan

2010-01-01

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). Copyright © 2010 Elsevier Ltd. All rights reserved.

[Use of liquid crystal eyeglasses for examination and recovery of binocular vision].

PubMed

Grigorian, A Iu; Avetisov, E S; Kashchenko, T P; Iachmeneva, E I

1999-01-01

A new method for diploptic treatment of strabismus is proposed, based on phase division of visual fields using liquid crystal eyeglasses --computer complex. The method is based on stereovision training (allowing stereothreshold measurements up to 150 ang. sec.). The method was tried in examinations of two groups of children: 10 controls and 74 patients with strabismus. Examinations of normal controls gave new criteria for measuring fusion reserves and stereovisual acuity by the proposed method. The therapeutic method was tried in 2 groups of patients. Time course of visual function improvement was followed up by several criteria: changes in binocular status by the color test and improvement of in-depth and stereoscopic visual acuity. The method is recommended for practice. The authors discuss the problem of small angle strabismus.

A logic-based method to build signaling networks and propose experimental plans.

PubMed

Rougny, Adrien; Gloaguen, Pauline; Langonné, Nathalie; Reiter, Eric; Crépieux, Pascale; Poupon, Anne; Froidevaux, Christine

2018-05-18

With the dramatic increase of the diversity and the sheer quantity of biological data generated, the construction of comprehensive signaling networks that include precise mechanisms cannot be carried out manually anymore. In this context, we propose a logic-based method that allows building large signaling networks automatically. Our method is based on a set of expert rules that make explicit the reasoning made by biologists when interpreting experimental results coming from a wide variety of experiment types. These rules allow formulating all the conclusions that can be inferred from a set of experimental results, and thus building all the possible networks that explain these results. Moreover, given an hypothesis, our system proposes experimental plans to carry out in order to validate or invalidate it. To evaluate the performance of our method, we applied our framework to the reconstruction of the FSHR-induced and the EGFR-induced signaling networks. The FSHR is known to induce the transactivation of the EGFR, but very little is known on the resulting FSH- and EGF-dependent network. We built a single network using data underlying both networks. This leads to a new hypothesis on the activation of MEK by p38MAPK, which we validate experimentally. These preliminary results represent a first step in the demonstration of a cross-talk between these two major MAP kinases pathways.

Topography measurements of high NA aspherical microlenses by digital holographic microscopy with spherical illumination

NASA Astrophysics Data System (ADS)

Józwik, Michal; Mikuła, Marta; Kozacki, Tomasz; Kostencka, Julianna; Gorecki, Christophe

2017-06-01

In this contribution, we propose a method of digital holographic microscopy (DHM) that enables measurement of high numerical aperture spherical and aspherical microstructures of both concave and convex shapes. The proposed method utilizes reflection of the spherical illumination beam from the object surface and the interference with a spherical reference beam of the similar curvature. In this case, the NA of DHM is fully utilized for illumination and imaging of the reflected object beam. Thus, the system allows capturing the phase coming from larger areas of the quasi-spherical object and, therefore, offers possibility of high accuracy characterization of its surface even in the areas of high inclination. The proposed measurement procedure allows determining all parameters required for the accurate shape recovery: the location of the object focus point and the positions of the illumination and reference point sources. The utility of the method is demonstrated with characterization of surface of high NA focusing objects. The accuracy is firstly verified by characterization of a known reference sphere with low error of sphericity. Then, the method is applied for shape measurement of spherical and aspheric microlenses. The results provide a full-field reconstruction of high NA topography with resolution in the nanometer range. The surface sphericity is evaluated by the deviation from the best fitted sphere or asphere, and the important parameters of the measured microlens: e.g.: radius of curvature and conic constant.

The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

PubMed

Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

2015-12-01

This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

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

Borowik, Piotr, E-mail: pborow@poczta.onet.pl; Thobel, Jean-Luc, E-mail: jean-luc.thobel@iemn.univ-lille1.fr; Adamowicz, Leszek, E-mail: adamo@if.pw.edu.pl

Standard computational methods used to take account of the Pauli Exclusion Principle into Monte Carlo (MC) simulations of electron transport in semiconductors may give unphysical results in low field regime, where obtained electron distribution function takes values exceeding unity. Modified algorithms were already proposed and allow to correctly account for electron scattering on phonons or impurities. Present paper extends this approach and proposes improved simulation scheme allowing including Pauli exclusion principle for electron–electron (e–e) scattering into MC simulations. Simulations with significantly reduced computational cost recreate correct values of the electron distribution function. Proposed algorithm is applied to study transport propertiesmore » of degenerate electrons in graphene with e–e interactions. This required adapting the treatment of e–e scattering in the case of linear band dispersion relation. Hence, this part of the simulation algorithm is described in details.« less

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

Electrically controlled pinning of Dzyaloshinskii-Moriya domain walls

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

Sato, Koji; Tretiakov, Oleg A., E-mail: olegt@imr.tohoku.ac.jp; School of Natural Sciences, Far Eastern Federal University, Vladivostok 690950

We propose a method to all-electrically control a domain-wall position in a ferromagnetic nanowire with Dzyaloshinskii-Moriya interaction. The strength of this interaction can be controlled by an external electric field, which in turn allows a fine tuning of the pinning potential of a spin-spiral domain wall. It allows to create more mobile pinning sites and can also be advantageous for ultra-low power electronics.

Modeling Adsorption-Desorption Processes at the Intermolecular Interactions Level

NASA Astrophysics Data System (ADS)

Varfolomeeva, Vera V.; Terentev, Alexey V.

2018-01-01

Modeling of the surface adsorption and desorption processes, as well as the diffusion, are of considerable interest for the physical phenomenon under study in ground tests conditions. When imitating physical processes and phenomena, it is important to choose the correct parameters to describe the adsorption of gases and the formation of films on the structural materials surface. In the present research the adsorption-desorption processes on the gas-solid interface are modeled with allowance for diffusion. Approaches are proposed to describe the adsorbate distribution on the solid body surface at the intermolecular interactions level. The potentials of the intermolecular interaction of water-water, water-methane and methane-methane were used to adequately modeling the real physical and chemical processes. The energies calculated by the B3LYP/aug-cc-pVDZ method. Computational algorithms for determining the average molecule area in a dense monolayer, are considered here. Differences in modeling approaches are also given: that of the proposed in this work and the previously approved probabilistic cellular automaton (PCA) method. It has been shown that the main difference is due to certain limitations of the PCA method. The importance of accounting the intermolecular interactions via hydrogen bonding has been indicated. Further development of the adsorption-desorption processes modeling will allow to find the conditions for of surface processes regulation by means of quantity adsorbed molecules control. The proposed approach to representing the molecular system significantly shortens the calculation time in comparison with the use of atom-atom potentials. In the future, this will allow to modeling the multilayer adsorption at a reasonable computational cost.

A motion compensation technique using sliced blocks and its application to hybrid video coding

NASA Astrophysics Data System (ADS)

Kondo, Satoshi; Sasai, Hisao

2005-07-01

This paper proposes a new motion compensation method using "sliced blocks" in DCT-based hybrid video coding. In H.264 ? MPEG-4 Advance Video Coding, a brand-new international video coding standard, motion compensation can be performed by splitting macroblocks into multiple square or rectangular regions. In the proposed method, on the other hand, macroblocks or sub-macroblocks are divided into two regions (sliced blocks) by an arbitrary line segment. The result is that the shapes of the segmented regions are not limited to squares or rectangles, allowing the shapes of the segmented regions to better match the boundaries between moving objects. Thus, the proposed method can improve the performance of the motion compensation. In addition, adaptive prediction of the shape according to the region shape of the surrounding macroblocks can reduce overheads to describe shape information in the bitstream. The proposed method also has the advantage that conventional coding techniques such as mode decision using rate-distortion optimization can be utilized, since coding processes such as frequency transform and quantization are performed on a macroblock basis, similar to the conventional coding methods. The proposed method is implemented in an H.264-based P-picture codec and an improvement in bit rate of 5% is confirmed in comparison with H.264.

A distance limited method for sampling downed coarse woody debris

Treesearch

Jeffrey H. Gove; Mark J. Ducey; Harry T. Valentine; Michael S. Williams

2012-01-01

A new sampling method for down coarse woody debris is proposed based on limiting the perpendicular distance from individual pieces to a randomly chosen sample point. Two approaches are presented that allow different protocols to be used to determine field measurements; estimators for each protocol are also developed. Both protocols are compared via simulation against...

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions.

PubMed

Robson, Philip M; Grant, Aaron K; Madhuranthakam, Ananth J; Lattanzi, Riccardo; Sodickson, Daniel K; McKenzie, Charles A

2008-10-01

Parallel imaging reconstructions result in spatially varying noise amplification characterized by the g-factor, precluding conventional measurements of noise from the final image. A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal-to-noise ratio and g-factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment. Only a simple "prescan" measurement of noise amplitude and correlation in the phased-array receiver, and a single accelerated image acquisition are required, allowing robust assessment of signal-to-noise ratio and g-factor. The "pseudo multiple replica" method has been rigorously validated in phantoms and in vivo, showing excellent agreement with true multiple replica and analytical methods. This method is universally applicable to the parallel imaging reconstruction techniques used in clinical applications and will allow pixel-by-pixel image noise measurements for all parallel imaging strategies, allowing quantitative comparison between arbitrary k-space trajectories, image reconstruction, or noise conditioning techniques. (c) 2008 Wiley-Liss, Inc.

Suicide: Issues of Prevention, Intervention, and Facilitation.

ERIC Educational Resources Information Center

Nelson, Franklyn L.

1984-01-01

Discusses the concept of suicide intervention which allows for the possibility of death facilitation as well as prevention. A proposed suicide intervention model is contrasted with the goals and methods of existing suicide prevention and crisis counseling services. (JAC)

A low-complexity attitude control method for large-angle agile maneuvers of a spacecraft with control moment gyros

NASA Astrophysics Data System (ADS)

Kawajiri, Shota; Matunaga, Saburo

2017-10-01

This study examines a low-complexity control method that satisfies mechanical constraints by using control moment gyros for an agile maneuver. The method is designed based on the fact that a simple rotation around an Euler's principal axis corresponds to a well-approximated solution of a time-optimal rest-to-rest maneuver. With respect to an agile large-angle maneuver using CMGs, it is suggested that there exists a coasting period in which all gimbal angles are constant, and a constant body angular velocity is almost along the Euler's principal axis. The gimbals are driven such that the coasting period is generated in the proposed method. This allows the problem to be converted into obtaining only a coasting time and gimbal angles such that their combination maximizes body angular velocity along the rotational axis of the maneuver. The effectiveness of the proposed method is demonstrated by using numerical simulations. The results indicate that the proposed method shortens the settling time by 20-70% when compared to that of a traditional feedback method. Additionally, a comparison with an existing path planning method shows that the proposed method achieves a low computational complexity (that is approximately 150 times faster) and a certain level of shortness in the settling time.

Pixel-based meshfree modelling of skeletal muscles.

PubMed

Chen, Jiun-Shyan; Basava, Ramya Rao; Zhang, Yantao; Csapo, Robert; Malis, Vadim; Sinha, Usha; Hodgson, John; Sinha, Shantanu

2016-01-01

This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) for 3D image-based modeling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The material properties and muscle fiber direction obtained from Diffusion Tensor Imaging (DTI) are input at each pixel point. The reproducing kernel (RK) approximation allows a representation of material heterogeneity with smooth transition. A multiphase multichannel level set based segmentation framework is adopted for individual muscle segmentation using Magnetic Resonance Images (MRI) and DTI. The application of the proposed methods for modeling the human lower leg is demonstrated.

Lattice Cleaving: A Multimaterial Tetrahedral Meshing Algorithm with Guarantees

PubMed Central

Bronson, Jonathan; Levine, Joshua A.; Whitaker, Ross

2014-01-01

We introduce a new algorithm for generating tetrahedral meshes that conform to physical boundaries in volumetric domains consisting of multiple materials. The proposed method allows for an arbitrary number of materials, produces high-quality tetrahedral meshes with upper and lower bounds on dihedral angles, and guarantees geometric fidelity. Moreover, the method is combinatoric so its implementation enables rapid mesh construction. These meshes are structured in a way that also allows grading, to reduce element counts in regions of homogeneity. Additionally, we provide proofs showing that both element quality and geometric fidelity are bounded using this approach. PMID:24356365

Using fuzzy models in machining control system and assessment of sustainability

NASA Astrophysics Data System (ADS)

Grinek, A. V.; Boychuk, I. P.; Dantsevich, I. M.

2018-03-01

Description of the complex relationship of the optimum velocity with the temperature-strength state in the cutting zone for machining a fuzzy model is proposed. The fuzzy-logical conclusion allows determining the processing speed, which ensures effective, from the point of view of ensuring the quality of the surface layer, the temperature in the cutting zone and the maximum allowable cutting force. A scheme for stabilizing the temperature-strength state in the cutting zone using a nonlinear fuzzy PD–controller is proposed. The stability of the nonlinear system is estimated with the help of grapho–analytical realization of the method of harmonic balance and by modeling in MatLab.

A Multistage Approach for Image Registration.

PubMed

Bowen, Francis; Hu, Jianghai; Du, Eliza Yingzi

2016-09-01

Successful image registration is an important step for object recognition, target detection, remote sensing, multimodal content fusion, scene blending, and disaster assessment and management. The geometric and photometric variations between images adversely affect the ability for an algorithm to estimate the transformation parameters that relate the two images. Local deformations, lighting conditions, object obstructions, and perspective differences all contribute to the challenges faced by traditional registration techniques. In this paper, a novel multistage registration approach is proposed that is resilient to view point differences, image content variations, and lighting conditions. Robust registration is realized through the utilization of a novel region descriptor which couples with the spatial and texture characteristics of invariant feature points. The proposed region descriptor is exploited in a multistage approach. A multistage process allows the utilization of the graph-based descriptor in many scenarios thus allowing the algorithm to be applied to a broader set of images. Each successive stage of the registration technique is evaluated through an effective similarity metric which determines subsequent action. The registration of aerial and street view images from pre- and post-disaster provide strong evidence that the proposed method estimates more accurate global transformation parameters than traditional feature-based methods. Experimental results show the robustness and accuracy of the proposed multistage image registration methodology.

Solving of the coefficient inverse problems for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time data

NASA Astrophysics Data System (ADS)

Lukyanenko, D. V.; Shishlenin, M. A.; Volkov, V. T.

2018-01-01

We propose the numerical method for solving coefficient inverse problem for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time observation data based on the asymptotic analysis and the gradient method. Asymptotic analysis allows us to extract a priory information about interior layer (moving front), which appears in the direct problem, and boundary layers, which appear in the conjugate problem. We describe and implement the method of constructing a dynamically adapted mesh based on this a priory information. The dynamically adapted mesh significantly reduces the complexity of the numerical calculations and improve the numerical stability in comparison with the usual approaches. Numerical example shows the effectiveness of the proposed method.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

PubMed

Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E

2015-09-29

In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

PubMed

Imamverdiyev, Yadigar; Abdullayeva, Fargana

2018-06-01

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. The form of the RBM that allows application of the continuous data is used. In this type of RBM, the probability distribution of the visible layer is replaced by a Gaussian distribution. Comparative analysis of the accuracy of the proposed method with Bernoulli-Bernoulli RBM, Gaussian-Bernoulli RBM, deep belief network type deep learning methods on DoS attack detection is provided. Detection accuracy of the methods is verified on the NSL-KDD data set. Higher accuracy from the proposed multilayer deep Gaussian-Bernoulli type RBM is obtained.

Designing scalable product families by the radial basis function-high-dimensional model representation metamodelling technique

NASA Astrophysics Data System (ADS)

Pirmoradi, Zhila; Haji Hajikolaei, Kambiz; Wang, G. Gary

2015-10-01

Product family design is cost-efficient for achieving the best trade-off between commonalization and diversification. However, for computationally intensive design functions which are viewed as black boxes, the family design would be challenging. A two-stage platform configuration method with generalized commonality is proposed for a scale-based family with unknown platform configuration. Unconventional sensitivity analysis and information on variation in the individual variants' optimal design are used for platform configuration design. Metamodelling is employed to provide the sensitivity and variable correlation information, leading to significant savings in function calls. A family of universal electric motors is designed for product performance and the efficiency of this method is studied. The impact of the employed parameters is also analysed. Then, the proposed method is modified for obtaining higher commonality. The proposed method is shown to yield design solutions with better objective function values, allowable performance loss and higher commonality than the previously developed methods in the literature.

Free vibration analysis of a robotic fish based on a continuous and non-uniform flexible backbone with distributed masses

NASA Astrophysics Data System (ADS)

Coral, W.; Rossi, C.; Curet, O. M.

2015-12-01

This paper presents a Differential Quadrature Element Method for free transverse vibration of a robotic fish based on a continuous and non-uniform flexible backbone with distributed masses (fish ribs). The proposed method is based on the theory of a Timoshenko cantilever beam. The effects of the masses (number, magnitude and position) on the value of natural frequencies are investigated. Governing equations, compatibility and boundary conditions are formulated according to the Differential Quadrature rules. The convergence, efficiency and accuracy are compared to other analytical solution proposed in the literature. Moreover, the proposed method has been validate against the physical prototype of a flexible fish backbone. The main advantages of this method, compared to the exact solutions available in the literature are twofold: first, smaller computational cost and second, it allows analysing the free vibration in beams whose section is an arbitrary function, which is normally difficult or even impossible with other analytical methods.

Calculation of recovery plasticity in multistage hot forging under isothermal conditions.

PubMed

Zhbankov, Iaroslav G; Perig, Alexander V; Aliieva, Leila I

2016-01-01

A widely used method for hot forming steels and alloys, especially heavy forging, is the process of multistage forging with pauses between stages. The well-known effect which accompanies multistage hot forging is metal plasticity recovery in comparison with monotonic deformation. A method which takes into consideration the recovery of plasticity in pauses between hot deformations of a billet under isothermal conditions is proposed. This method allows the prediction of billet forming limits as a function of deformation during the forging stage and the duration of the pause between the stages. This method takes into account the duration of pauses between deformations and the magnitude of subdivided deformations. A hot isothermal upsetting process with pauses was calculated by the proposed method. Results of the calculations have been confirmed with experimental data.

Online two-stage association method for robust multiple people tracking

NASA Astrophysics Data System (ADS)

Lv, Jingqin; Fang, Jiangxiong; Yang, Jie

2011-07-01

Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.

A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices.

PubMed

Futamure, Sumire; Bonnet, Vincent; Dumas, Raphael; Venture, Gentiane

2017-11-07

This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters. Copyright © 2017 Elsevier Ltd. All rights reserved.

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.

PubMed

Khomri, Bilal; Christodoulidis, Argyrios; Djerou, Leila; Babahenini, Mohamed Chaouki; Cheriet, Farida

2018-05-01

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Automatic generation of the non-holonomic equations of motion for vehicle stability analysis

NASA Astrophysics Data System (ADS)

Minaker, B. P.; Rieveley, R. J.

2010-09-01

The mathematical analysis of vehicle stability has been utilised as an important tool in the design, development, and evaluation of vehicle architectures and stability controls. This paper presents a novel method for automatic generation of the linearised equations of motion for mechanical systems that is well suited to vehicle stability analysis. Unlike conventional methods for generating linearised equations of motion in standard linear second order form, the proposed method allows for the analysis of systems with non-holonomic constraints. In the proposed method, the algebraic constraint equations are eliminated after linearisation and reduction to first order. The described method has been successfully applied to an assortment of classic dynamic problems of varying complexity including the classic rolling coin, the planar truck-trailer, and the bicycle, as well as in more recent problems such as a rotor-stator and a benchmark road vehicle with suspension. This method has also been applied in the design and analysis of a novel three-wheeled narrow tilting vehicle with zero roll-stiffness. An application for determining passively stable configurations using the proposed method together with a genetic search algorithm is detailed. The proposed method and software implementation has been shown to be robust and provides invaluable conceptual insight into the stability of vehicles and mechanical systems.

Chemically Patterned Inverse Opal Created by a Selective Photolysis Modification Process.

PubMed

Tian, Tian; Gao, Ning; Gu, Chen; Li, Jian; Wang, Hui; Lan, Yue; Yin, Xianpeng; Li, Guangtao

2015-09-02

Anisotropic photonic crystal materials have long been pursued for their broad applications. A novel method for creating chemically patterned inverse opals is proposed here. The patterning technique is based on selective photolysis of a photolabile polymer together with postmodification on released amine groups. The patterning method allows regioselective modification within an inverse opal structure, taking advantage of selective chemical reaction. Moreover, combined with the unique signal self-reporting feature of the photonic crystal, the fabricated structure is capable of various applications, including gradient photonic bandgap and dynamic chemical patterns. The proposed method provides the ability to extend the structural and chemical complexity of the photonic crystal, as well as its potential applications.

Steganographic embedding in containers-images

NASA Astrophysics Data System (ADS)

Nikishova, A. V.; Omelchenko, T. A.; Makedonskij, S. A.

2018-05-01

Steganography is one of the approaches to ensuring the protection of information transmitted over the network. But a steganographic method should vary depending on a used container. According to statistics, the most widely used containers are images and the most common image format is JPEG. Authors propose a method of data embedding into a frequency area of images in format JPEG 2000. It is proposed to use the method of Benham-Memon- Yeo-Yeung, in which instead of discrete cosine transform, discrete wavelet transform is used. Two requirements for images are formulated. Structure similarity is chosen to obtain quality assessment of data embedding. Experiments confirm that requirements satisfaction allows achieving high quality assessment of data embedding.

Display aids for remote control of untethered undersea vehicles

NASA Technical Reports Server (NTRS)

Verplank, W. L.

1978-01-01

A predictor display superimposed on slow-scan video or sonar data is proposed as a method to allow better remote manual control of an untethered submersible. Simulation experiments show good control under circumstances which otherwise make control practically impossible.

Input-output identification of controlled discrete manufacturing systems

NASA Astrophysics Data System (ADS)

Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques

2014-03-01

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.

An efficient gridding reconstruction method for multishot non-Cartesian imaging with correction of off-resonance artifacts.

PubMed

Meng, Yuguang; Lei, Hao

2010-06-01

An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the off-resonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multiple-shot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4.7 T. (c) 2010 Wiley-Liss, Inc.

3D-Web-GIS RFID location sensing system for construction objects.

PubMed

Ko, Chien-Ho

2013-01-01

Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency.

3D-Web-GIS RFID Location Sensing System for Construction Objects

PubMed Central

2013-01-01

Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency. PMID:23864821

A novel approach to quantify cybersecurity for electric power systems

NASA Astrophysics Data System (ADS)

Kaster, Paul R., Jr.

Electric Power grid cybersecurity is a topic gaining increased attention in academia, industry, and government circles, yet a method of quantifying and evaluating a system's security is not yet commonly accepted. In order to be useful, a quantification scheme must be able to accurately reflect the degree to which a system is secure, simply determine the level of security in a system using real-world values, model a wide variety of attacker capabilities, be useful for planning and evaluation, allow a system owner to publish information without compromising the security of the system, and compare relative levels of security between systems. Published attempts at quantifying cybersecurity fail at one or more of these criteria. This document proposes a new method of quantifying cybersecurity that meets those objectives. This dissertation evaluates the current state of cybersecurity research, discusses the criteria mentioned previously, proposes a new quantification scheme, presents an innovative method of modeling cyber attacks, demonstrates that the proposed quantification methodology meets the evaluation criteria, and proposes a line of research for future efforts.

An embedded formula of the Chebyshev collocation method for stiff problems

NASA Astrophysics Data System (ADS)

Piao, Xiangfan; Bu, Sunyoung; Kim, Dojin; Kim, Philsu

2017-12-01

In this study, we have developed an embedded formula of the Chebyshev collocation method for stiff problems, based on the zeros of the generalized Chebyshev polynomials. A new strategy for the embedded formula, using a pair of methods to estimate the local truncation error, as performed in traditional embedded Runge-Kutta schemes, is proposed. The method is performed in such a way that not only the stability region of the embedded formula can be widened, but by allowing the usage of larger time step sizes, the total computational costs can also be reduced. In terms of concrete convergence and stability analysis, the constructed algorithm turns out to have an 8th order convergence and it exhibits A-stability. Through several numerical experimental results, we have demonstrated that the proposed method is numerically more efficient, compared to several existing implicit methods.

Detection of glucose in the human brain with 1 H MRS at 7 Tesla.

PubMed

Kaiser, Lana G; Hirokazu, Kawaguchi; Fukunaga, Masaki; B Matson, Gerald

2016-12-01

A new method is proposed for noninvasive detection of glucose in vivo using proton MR spectroscopy at 7 Tesla. The proposed method utilizes J-difference editing to uncover the resonance of beta-glucose (β-glc) at 3.23 ppm, which is strongly overlapped with choline. Calculations using the density matrix formalism are used to maximize the signal-to-noise ratio of the β-glc resonance at 3.23 ppm. The calculations are verified using phantom and in vivo data collected at 7 Tesla. The proposed method allows observation of the glucose signal at 3.23 ppm in the human brain spectrum. Additional co-edited resonances of N-acetylaspartylglutamatate and glutathione are also detected in the same experiment. The proposed method does not require carbon ( 13 C)- labeled glucose injections and 13 C hardware; as such, it has a potential to provide valuable information on intrinsic glucose concentration in the human brain in vivo. Magn Reson Med 76:1653-1660, 2016. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Melancholic depression prediction by identifying representative features in metabolic and microarray profiles with missing values.

PubMed

Nie, Zhi; Yang, Tao; Liu, Yashu; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

2015-01-01

Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed method give rise to significantly improved sensitivity scores, suggesting that the learned features allow prediction with high accuracy of disease status in those who are diagnosed with melancholic depression. To our best knowledge, this is the first work that applies sparse coding to deal with high feature correlations and missing values, which are common challenges in many biomedical applications. The proposed method can be readily adapted to other biomedical applications involving incomplete and high-dimensional data.

Secure steganography designed for mobile platforms

NASA Astrophysics Data System (ADS)

Agaian, Sos S.; Cherukuri, Ravindranath; Sifuentes, Ronnie R.

2006-05-01

Adaptive steganography, an intelligent approach to message hiding, integrated with matrix encoding and pn-sequences serves as a promising resolution to recent security assurance concerns. Incorporating the above data hiding concepts with established cryptographic protocols in wireless communication would greatly increase the security and privacy of transmitting sensitive information. We present an algorithm which will address the following problems: 1) low embedding capacity in mobile devices due to fixed image dimensions and memory constraints, 2) compatibility between mobile and land based desktop computers, and 3) detection of stego images by widely available steganalysis software [1-3]. Consistent with the smaller available memory, processor capabilities, and limited resolution associated with mobile devices, we propose a more magnified approach to steganography by focusing adaptive efforts at the pixel level. This deeper method, in comparison to the block processing techniques commonly found in existing adaptive methods, allows an increase in capacity while still offering a desired level of security. Based on computer simulations using high resolution, natural imagery and mobile device captured images, comparisons show that the proposed method securely allows an increased amount of embedding capacity but still avoids detection by varying steganalysis techniques.

Development of a magnetic capsule as a drug release system for future applications in the human GI tract

NASA Astrophysics Data System (ADS)

Richert, Hendryk; Surzhenko, Oleksy; Wangemann, Sebastian; Heinrich, Jochen; Görnert, Peter

2005-05-01

A method for active drug delivery inside the human digestive system is proposed. This method allows the localisation of a magnetically marked capsule on its natural way through the digestive system and to open it at a desired position. Thus, the procedure contains two important components: the magnetic monitoring and active drug release.

Chosen-plaintext attack on a joint transform correlator encrypting system

NASA Astrophysics Data System (ADS)

Barrera, John Fredy; Vargas, Carlos; Tebaldi, Myrian; Torroba, Roberto

2010-10-01

We demonstrate that optical encryption methods based on the joint transform correlator architecture are vulnerable to chosen-plaintext attack. An unauthorized user, who introduces three chosen plaintexts in the accessible encryption machine, can obtain the security key code mask. In this contribution, we also propose an alternative method to eliminate ambiguities that allows obtaining the right decrypting key.

Increasing signal-to-noise ratio of reconstructed digital holograms by using light spatial noise portrait of camera's photosensor

NASA Astrophysics Data System (ADS)

Cheremkhin, Pavel A.; Evtikhiev, Nikolay N.; Krasnov, Vitaly V.; Rodin, Vladislav G.; Starikov, Sergey N.

2015-01-01

Digital holography is technique which includes recording of interference pattern with digital photosensor, processing of obtained holographic data and reconstruction of object wavefront. Increase of signal-to-noise ratio (SNR) of reconstructed digital holograms is especially important in such fields as image encryption, pattern recognition, static and dynamic display of 3D scenes, and etc. In this paper compensation of photosensor light spatial noise portrait (LSNP) for increase of SNR of reconstructed digital holograms is proposed. To verify the proposed method, numerical experiments with computer generated Fresnel holograms with resolution equal to 512×512 elements were performed. Simulation of shots registration with digital camera Canon EOS 400D was performed. It is shown that solo use of the averaging over frames method allows to increase SNR only up to 4 times, and further increase of SNR is limited by spatial noise. Application of the LSNP compensation method in conjunction with the averaging over frames method allows for 10 times SNR increase. This value was obtained for LSNP measured with 20 % error. In case of using more accurate LSNP, SNR can be increased up to 20 times.

Strength resistance of reinforced concrete elements of high-rise buildings under dynamic loads

NASA Astrophysics Data System (ADS)

Berlinov, Mikhail

2018-03-01

A new method for calculating reinforced concrete constructions of high-rise buildings under dynamic loads from wind, seismic, transport and equipment based on the initial assumptions of the modern phenomenological theory of a nonlinearly deformable elastic-creeping body is proposed. In the article examined the influence of reinforcement on the work of concrete in the conditions of triaxial stress-strain state, based on the compatibility of the deformation of concrete and reinforcement. Mathematical phenomenological equations have been obtained that make it possible to calculate the reinforced concrete elements working without and with cracks. A method for linearizing of these equations based on integral estimates is proposed, which provides the fixation of the vibro-creep processes in the considered period of time. Application of such a technique using the finite-difference method, step method and successive approximations will allow to find a numerical solution of the problem. Such an approach in the design of reinforced concrete constructions will allow not only more fully to take into account the real conditions of their work, revealing additional reserves of load capacity, but also to open additional opportunities for analysis and forecasting their functioning at various stages of operation.

Full cost accounting in the analysis of separated waste collection efficiency: A methodological proposal.

PubMed

D'Onza, Giuseppe; Greco, Giulio; Allegrini, Marco

2016-02-01

Recycling implies additional costs for separated municipal solid waste (MSW) collection. The aim of the present study is to propose and implement a management tool - the full cost accounting (FCA) method - to calculate the full collection costs of different types of waste. Our analysis aims for a better understanding of the difficulties of putting FCA into practice in the MSW sector. We propose a FCA methodology that uses standard cost and actual quantities to calculate the collection costs of separate and undifferentiated waste. Our methodology allows cost efficiency analysis and benchmarking, overcoming problems related to firm-specific accounting choices, earnings management policies and purchase policies. Our methodology allows benchmarking and variance analysis that can be used to identify the causes of off-standards performance and guide managers to deploy resources more efficiently. Our methodology can be implemented by companies lacking a sophisticated management accounting system. Copyright © 2015 Elsevier Ltd. All rights reserved.

Nonlinear optical interactions in silicon waveguides

NASA Astrophysics Data System (ADS)

Kuyken, B.; Leo, F.; Clemmen, S.; Dave, U.; Van Laer, R.; Ideguchi, T.; Zhao, H.; Liu, X.; Safioui, J.; Coen, S.; Gorza, S. P.; Selvaraja, S. K.; Massar, S.; Osgood, R. M.; Verheyen, P.; Van Campenhout, J.; Baets, R.; Green, W. M. J.; Roelkens, G.

2017-03-01

The strong nonlinear response of silicon photonic nanowire waveguides allows for the integration of nonlinear optical functions on a chip. However, the detrimental nonlinear optical absorption in silicon at telecom wavelengths limits the efficiency of many such experiments. In this review, several approaches are proposed and demonstrated to overcome this fundamental issue. By using the proposed methods, we demonstrate amongst others supercontinuum generation, frequency comb generation, a parametric optical amplifier, and a parametric optical oscillator.

Blood flow measurement in extracorporeal circulation using self-mixing laser diode

NASA Astrophysics Data System (ADS)

Cattini, Stefano; Norgia, Michele; Pesatori, Alessandro; Rovati, Luigi

2010-02-01

To measure blood flow rate in ex-vivo circulation, we propose an optical Doppler flowmeter based on the self-mixing effect within a laser diode (SM-LD). Advantages in adopting SM-LD techniques derive from reduced costs, ease of implementation and limited size. Moreover, the provided contactless sensing allows sensor reuse, hence further cost reduction. Preliminary measurements performed on bovine blood are reported, thus demonstrating the applicability of the proposed measurement method.

Imperial Valley's proposal to develop a guide for geothermal development within its county

NASA Technical Reports Server (NTRS)

Pierson, D. E.

1974-01-01

A plan to develop the geothermal resources of the Imperial Valley of California is presented. The plan consists of development policies and includes text and graphics setting forth the objectives, principles, standards, and proposals. The plan allows developers to know the goals of the surrounding community and provides a method for decision making to be used by county representatives. A summary impact statement for the geothermal development aspects is provided.

Automatic 3D segmentation of multiphoton images: a key step for the quantification of human skin.

PubMed

Decencière, Etienne; Tancrède-Bohin, Emmanuelle; Dokládal, Petr; Koudoro, Serge; Pena, Ana-Maria; Baldeweck, Thérèse

2013-05-01

Multiphoton microscopy has emerged in the past decade as a useful noninvasive imaging technique for in vivo human skin characterization. However, it has not been used until now in evaluation clinical trials, mainly because of the lack of specific image processing tools that would allow the investigator to extract pertinent quantitative three-dimensional (3D) information from the different skin components. We propose a 3D automatic segmentation method of multiphoton images which is a key step for epidermis and dermis quantification. This method, based on the morphological watershed and graph cuts algorithms, takes into account the real shape of the skin surface and of the dermal-epidermal junction, and allows separating in 3D the epidermis and the superficial dermis. The automatic segmentation method and the associated quantitative measurements have been developed and validated on a clinical database designed for aging characterization. The segmentation achieves its goals for epidermis-dermis separation and allows quantitative measurements inside the different skin compartments with sufficient relevance. This study shows that multiphoton microscopy associated with specific image processing tools provides access to new quantitative measurements on the various skin components. The proposed 3D automatic segmentation method will contribute to build a powerful tool for characterizing human skin condition. To our knowledge, this is the first 3D approach to the segmentation and quantification of these original images. © 2013 John Wiley & Sons A/S. Published by Blackwell Publishing Ltd.

Automatic age-related macular degeneration detection and staging

NASA Astrophysics Data System (ADS)

van Grinsven, Mark J. J. P.; Lechanteur, Yara T. E.; van de Ven, Johannes P. H.; van Ginneken, Bram; Theelen, Thomas; Sánchez, Clara I.

2013-03-01

Age-related macular degeneration (AMD) is a degenerative disorder of the central part of the retina, which mainly affects older people and leads to permanent loss of vision in advanced stages of the disease. AMD grading of non-advanced AMD patients allows risk assessment for the development of advanced AMD and enables timely treatment of patients, to prevent vision loss. AMD grading is currently performed manually on color fundus images, which is time consuming and expensive. In this paper, we propose a supervised classification method to distinguish patients at high risk to develop advanced AMD from low risk patients and provide an exact AMD stage determination. The method is based on the analysis of the number and size of drusen on color fundus images, as drusen are the early characteristics of AMD. An automatic drusen detection algorithm is used to detect all drusen. A weighted histogram of the detected drusen is constructed to summarize the drusen extension and size and fed into a random forest classifier in order to separate low risk from high risk patients and to allow exact AMD stage determination. Experiments showed that the proposed method achieved similar performance as human observers in distinguishing low risk from high risk AMD patients, obtaining areas under the Receiver Operating Characteristic curve of 0.929 and 0.934. A weighted kappa agreement of 0.641 and 0.622 versus two observers were obtained for AMD stage evaluation. Our method allows for quick and reliable AMD staging at low costs.

Locating Structural Centers: A Density-Based Clustering Method for Community Detection

PubMed Central

Liu, Gongshen; Li, Jianhua; Nees, Jan P.

2017-01-01

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods. PMID:28046030

Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations.

PubMed

Van Steenkiste, Gwendolyn; Jeurissen, Ben; Veraart, Jelle; den Dekker, Arnold J; Parizel, Paul M; Poot, Dirk H J; Sijbers, Jan

2016-01-01

Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation. Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error. The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time. © 2015 Wiley Periodicals, Inc.

The formulation and estimation of a spatial skew-normal generalized ordered-response model.

DOT National Transportation Integrated Search

2016-06-01

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...

Gradient-based interpolation method for division-of-focal-plane polarimeters.

PubMed

Gao, Shengkui; Gruev, Viktor

2013-01-14

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

Upper-limb kinematic reconstruction during stroke robot-aided therapy.

PubMed

Papaleo, E; Zollo, L; Garcia-Aracil, N; Badesa, F J; Morales, R; Mazzoleni, S; Sterzi, S; Guglielmelli, E

2015-09-01

The paper proposes a novel method for an accurate and unobtrusive reconstruction of the upper-limb kinematics of stroke patients during robot-aided rehabilitation tasks with end-effector machines. The method is based on a robust analytic procedure for inverse kinematics that simply uses, in addition to hand pose data provided by the robot, upper arm acceleration measurements for computing a constraint on elbow position; it is exploited for task space augmentation. The proposed method can enable in-depth comprehension of planning strategy of stroke patients in the joint space and, consequently, allow developing therapies tailored for their residual motor capabilities. The experimental validation has a twofold purpose: (1) a comparative analysis with an optoelectronic motion capturing system is used to assess the method capability to reconstruct joint motion; (2) the application of the method to healthy and stroke subjects during circle-drawing tasks with InMotion2 robot is used to evaluate its efficacy in discriminating stroke from healthy behavior. The experimental results have shown that arm angles are reconstructed with a RMSE of 8.3 × 10(-3) rad. Moreover, the comparison between healthy and stroke subjects has revealed different features in the joint space in terms of mean values and standard deviations, which also allow assessing inter- and intra-subject variability. The findings of this study contribute to the investigation of motor performance in the joint space and Cartesian space of stroke patients undergoing robot-aided therapy, thus allowing: (1) evaluating the outcomes of the therapeutic approach, (2) re-planning the robotic treatment based on patient needs, and (3) understanding pathology-related motor strategies.

Delay grid multiplexing: simple time-based multiplexing and readout method for silicon photomultipliers

NASA Astrophysics Data System (ADS)

Won, Jun Yeon; Ko, Guen Bae; Lee, Jae Sung

2016-10-01

In this paper, we propose a fully time-based multiplexing and readout method that uses the principle of the global positioning system. Time-based multiplexing allows simplifying the multiplexing circuits where the only innate traces that connect the signal pins of the silicon photomultiplier (SiPM) channels to the readout channels are used as the multiplexing circuit. Every SiPM channel is connected to the delay grid that consists of the traces on a printed circuit board, and the inherent transit times from each SiPM channel to the readout channels encode the position information uniquely. Thus, the position of each SiPM can be identified using the time difference of arrival (TDOA) measurements. The proposed multiplexing can also allow simplification of the readout circuit using the time-to-digital converter (TDC) implemented in a field-programmable gate array (FPGA), where the time-over-threshold (ToT) is used to extract the energy information after multiplexing. In order to verify the proposed multiplexing method, we built a positron emission tomography (PET) detector that consisted of an array of 4  ×  4 LGSO crystals, each with a dimension of 3  ×  3  ×  20 mm3, and one- to-one coupled SiPM channels. We first employed the waveform sampler as an initial study, and then replaced the waveform sampler with an FPGA-TDC to further simplify the readout circuits. The 16 crystals were clearly resolved using only the time information obtained from the four readout channels. The coincidence resolving times (CRTs) were 382 and 406 ps FWHM when using the waveform sampler and the FPGA-TDC, respectively. The proposed simple multiplexing and readout methods can be useful for time-of-flight (TOF) PET scanners.

Hybrid model of arm for analysis of regional blood oxygenation in non-invasive optical diagnostics

NASA Astrophysics Data System (ADS)

Nowocień, Sylwester; Mroczka, Janusz

2017-06-01

The paper presents a new comprehensive approach to modeling and analysis of processes occurring during the blood flow in the arm's small vessels as well as non-invasive measurement method of mixed venous oxygen saturation. During the work, a meta-analysis of available physiological data was performed and based on its result a hybrid model of forearm vascular tree was proposed. The model, in its structure, takes into account a classical nonlinear hydro-electric analogy in conjunction with light-tissue interaction. Several geometries of arm vascular tree obtained from magnetic resonance angiography (MRA) image were analyzed which allowed to proposed the structure of electrical analog network. Proposed model allows to simulate the behavior of forearm blood flow from the vascular tree mechanics point of view, as well as effects of the impact of cuff and vessel wall mechanics on the recorded photoplethysmographic signals. In particular, it allows to analyze the reaction and anatomical effects in small vessels and microcirculation caused by occlusive maneuver in selected techniques, what was of particular interest to authors and motivation to undertake research in this area. Preliminary studies using proposed model showed that inappropriate selection of occlusion maneuver parameters (e.g. occlusion time, cuff pressure etc.), cause dangerous turbulence of blood flow in the venous section of the vascular tree.

Robust Inference of Risks of Large Portfolios

PubMed Central

Fan, Jianqing; Han, Fang; Liu, Han; Vickers, Byron

2016-01-01

We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and quantile-based estimators, and can be viewed as a robust extension of the H-CLUB procedure (Fan et al., 2015). Such an extension allows us to handle possibly misspecified models and heavy-tailed data, which are stylized features in financial returns. Under mixing conditions, we analyze the proposed approach and demonstrate its advantage over H-CLUB. We further provide thorough numerical results to back up the developed theory, and also apply the proposed method to analyze a stock market dataset. PMID:27818569

Content Sharing Based on Personal Information in Virtually Secured Space

NASA Astrophysics Data System (ADS)

Sohn, Hosik; Ro, Yong Man; Plataniotis, Kostantinos N.

User generated contents (UGC) are shared in an open space like social media where users can upload and consume contents freely. Since the access of contents is not restricted, the contents could be delivered to unwanted users or misused sometimes. In this paper, we propose a method for sharing UGCs securely based on the personal information of users. With the proposed method, virtual secure space is created for contents delivery. The virtual secure space allows UGC creator to deliver contents to users who have similar personal information and they can consume the contents without any leakage of personal information. In order to verify the usefulness of the proposed method, the experiment was performed where the content was encrypted with personal information of creator, and users with similar personal information have decrypted and consumed the contents. The results showed that UGCs were securely shared among users who have similar personal information.

Modeling panel detection frequencies by queuing system theory: an application in gas chromatography olfactometry.

PubMed

Bult, Johannes H F; van Putten, Bram; Schifferstein, Hendrik N J; Roozen, Jacques P; Voragen, Alphons G J; Kroeze, Jan H A

2004-10-01

In continuous vigilance tasks, the number of coincident panel responses to stimuli provides an index of stimulus detectability. To determine whether this number is due to chance, panel noise levels have been approximated by the maximum coincidence level obtained in stimulus-free conditions. This study proposes an alternative method by which to assess noise levels, derived from queuing system theory (QST). Instead of critical coincidence levels, QST modeling estimates the duration of coinciding responses in the absence of stimuli. The proposed method has the advantage over previous approaches that it yields more reliable noise estimates and allows for statistical testing. The method was applied in an olfactory detection experiment using 16 panelists in stimulus-present and stimulus-free conditions. We propose that QST may be used as an alternative to signal detection theory for analyzing data from continuous vigilance tasks.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

PubMed

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

Bayesian Local Contamination Models for Multivariate Outliers

PubMed Central

Page, Garritt L.; Dunson, David B.

2013-01-01

In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465

Face Recognition Using Local Quantized Patterns and Gabor Filters

NASA Astrophysics Data System (ADS)

Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

Solving optimization problems by the public goods game

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2017-09-01

We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.

Safety evaluations under the proposed US Safe Cosmetics and Personal Care Products Act of 2013: animal use and cost estimates.

PubMed

Knight, Jean; Rovida, Costanca

2014-01-01

The proposed Safe Cosmetics and Personal Care Products Act of 2013 calls for a new evaluation program for cosmetic ingredients in the US, with the new assessments initially dependent on expanded animal testing. This paper considers possible testing scenarios under the proposed Act and estimates the number of test animals and cost under each scenario. It focuses on the impact for the first 10 years of testing, the period of greatest impact on animals and costs. The analysis suggests the first 10 years of testing under the Act could evaluate, at most, about 50% of ingredients used in cosmetics. Testing during this period would cost about $ 1.7-$ 9 billion and 1-11.5 million animals. By test year 10, alternative, high-throughput test methods under development are expected to be available, replacing animal testing and allowing rapid evaluation of all ingredients. Given the high cost in dollars and animal lives of the first 10 years for only about half of ingredients, a better choice may be to accelerate development of high-throughput methods. This would allow evaluation of 100% of cosmetic ingredients before year 10 at lower cost and without animal testing.

Automated detection of diagnostically relevant regions in H&E stained digital pathology slides

NASA Astrophysics Data System (ADS)

Bahlmann, Claus; Patel, Amar; Johnson, Jeffrey; Ni, Jie; Chekkoury, Andrei; Khurd, Parmeshwar; Kamen, Ali; Grady, Leo; Krupinski, Elizabeth; Graham, Anna; Weinstein, Ronald

2012-03-01

We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity, such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation error rate of 1.4%. We further show that the proposed method can prune ~90% of the area of pathological slides while maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.

Segmentation of mouse dynamic PET images using a multiphase level set method

NASA Astrophysics Data System (ADS)

Cheng-Liao, Jinxiu; Qi, Jinyi

2010-11-01

Image segmentation plays an important role in medical diagnosis. Here we propose an image segmentation method for four-dimensional mouse dynamic PET images. We consider that voxels inside each organ have similar time activity curves. The use of tracer dynamic information allows us to separate regions that have similar integrated activities in a static image but with different temporal responses. We develop a multiphase level set method that utilizes both the spatial and temporal information in a dynamic PET data set. Different weighting factors are assigned to each image frame based on the noise level and activity difference among organs of interest. We used a weighted absolute difference function in the data matching term to increase the robustness of the estimate and to avoid over-partition of regions with high contrast. We validated the proposed method using computer simulated dynamic PET data, as well as real mouse data from a microPET scanner, and compared the results with those of a dynamic clustering method. The results show that the proposed method results in smoother segments with the less number of misclassified voxels.

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Panorama parking assistant system with improved particle swarm optimization method

NASA Astrophysics Data System (ADS)

Cheng, Ruzhong; Zhao, Yong; Li, Zhichao; Jiang, Weigang; Wang, Xin'an; Xu, Yong

2013-10-01

A panorama parking assistant system (PPAS) for the automotive aftermarket together with a practical improved particle swarm optimization method (IPSO) are proposed in this paper. In the PPAS system, four fisheye cameras are installed in the vehicle with different views, and four channels of video frames captured by the cameras are processed as a 360-deg top-view image around the vehicle. Besides the embedded design of PPAS, the key problem for image distortion correction and mosaicking is the efficiency of parameter optimization in the process of camera calibration. In order to address this problem, an IPSO method is proposed. Compared with other parameter optimization methods, the proposed method allows a certain range of dynamic change for the intrinsic and extrinsic parameters, and can exploit only one reference image to complete all of the optimization; therefore, the efficiency of the whole camera calibration is increased. The PPAS is commercially available, and the IPSO method is a highly practical way to increase the efficiency of the installation and the calibration of PPAS in automobile 4S shops.

On the use of the fourier modal method for calculation of localized eigenmodes of integrated optical resonators