Three dimensional steady subsonic Euler flows in bounded nozzles

Science.gov (United States)

Chen, Chao; Xie, Chunjing

The existence and uniqueness of three dimensional steady subsonic Euler flows in rectangular nozzles were obtained when prescribing normal component of momentum at both the entrance and exit. If, in addition, the normal component of the voriticity and the variation of Bernoulli's function at the entrance are both zero, then there exists a unique subsonic potential flow when the magnitude of the normal component of the momentum is less than a critical number. As the magnitude of the normal component of the momentum approaches the critical number, the associated flows converge to a subsonic-sonic flow. Furthermore, when the normal component of vorticity and the variation of Bernoulli function are both small, the existence and uniqueness of subsonic Euler flows with non-zero vorticity are established. The proof of these results is based on a new formulation for the Euler system, a priori estimate for nonlinear elliptic equations with nonlinear boundary conditions, detailed study for a linear div-curl system, and delicate estimate for the transport equations.

Wing aeroelasticity analysis based on an integral boundary-layer method coupled with Euler solver

Directory of Open Access Journals (Sweden)

Ma Yanfeng

2016-10-01

Full Text Available An interactive boundary-layer method, which solves the unsteady flow, is developed for aeroelastic computation in the time domain. The coupled method combines the Euler solver with the integral boundary-layer solver (Euler/BL in a “semi-inverse” manner to compute flows with the inviscid and viscous interaction. Unsteady boundary conditions on moving surfaces are taken into account by utilizing the approximate small-perturbation method without moving the computational grids. The steady and unsteady flow calculations for the LANN wing are presented. The wing tip displacement of high Reynolds number aero-structural dynamics (HIRENASD Project is simulated under different angles of attack. The flutter-boundary predictions for the AGARD 445.6 wing are provided. The results of the interactive boundary-layer method are compared with those of the Euler method and experimental data. The study shows that viscous effects are significant for these cases and the further data analysis confirms the validity and practicability of the coupled method.

Improving Euler computations at low Mach numbers

NARCIS (Netherlands)

Koren, B.; Leer, van B.; Deconinck, H.; Koren, B.

1997-01-01

The paper consists of two parts, both dealing with conditioning techniques for lowMach-number Euler-flow computations, in which a multigrid technique is applied. In the first part, for subsonic flows and upwind-discretized, linearized 1-D Euler equations, the smoothing behavior of

Improving Euler computations at low Mach numbers

NARCIS (Netherlands)

Koren, B.

1996-01-01

This paper consists of two parts, both dealing with conditioning techniques for low-Mach-number Euler-flow computations, in which a multigrid technique is applied. In the first part, for subsonic flows and upwind-discretized linearized 1-D Euler equations, the smoothing behavior of

Analogues of Euler and Poisson Summation Formulae

Indian Academy of Sciences (India)

... f ( n ) have been obtained in a unified manner, where (()) is a periodic complex sequence; () is the divisor function and () is a sufficiently smooth function on [, ]. We also state a generalised Abel's summation formula, generalised Euler's summation formula and Euler's summation formula in several variables.

Variational problems with fractional derivatives: Euler-Lagrange equations

International Nuclear Information System (INIS)

Atanackovic, T M; Konjik, S; Pilipovic, S

2008-01-01

We generalize the fractional variational problem by allowing the possibility that the lower bound in the fractional derivative does not coincide with the lower bound of the integral that is minimized. Also, for the standard case when these two bounds coincide, we derive a new form of Euler-Lagrange equations. We use approximations for fractional derivatives in the Lagrangian and obtain the Euler-Lagrange equations which approximate the initial Euler-Lagrange equations in a weak sense

Development of Euler's ideas at the Moscow State Regional University

Science.gov (United States)

Vysikaylo, P. I.; Belyaev, V. V.

2018-03-01

In honor of the 250th anniversary of Euler's discovery of three libration points in Russia in 1767 in the area of two rotating gravitational attractors in 2017 an International Interdisciplinary Conference “Euler Readings MRSU 2017” was held in Moscow Region State University (MRSU). The Conference demonstrated that the Euler's ideas continue to remain relevant at the present time. This paper summarizes the main achievements on the basis of Leonard Euler's ideas presented at the Conference.

Spike neural models (part I: The Hodgkin-Huxley model

Directory of Open Access Journals (Sweden)

Johnson, Melissa G.

2017-05-01

Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

Combinatorial Aspects of the Generalized Euler's Totient

Directory of Open Access Journals (Sweden)

Nittiya Pabhapote

2010-01-01

Full Text Available A generalized Euler's totient is defined as a Dirichlet convolution of a power function and a product of the Souriau-Hsu-Möbius function with a completely multiplicative function. Two combinatorial aspects of the generalized Euler's totient, namely, its connections to other totients and its relations with counting formulae, are investigated.

Conservative numerical schemes for Euler-Lagrange equations

Energy Technology Data Exchange (ETDEWEB)

Vazquez, L. [Universidad Complutense, Madrid (Spain). Dept. de Matematica Aplicada; Jimenez, S. [Universidad Alfonso X El Sabio, Madrid (Spain). Dept. de Matematica Aplicada

1999-05-01

As a preliminary step to study magnetic field lines, the authors seek numerical schemes that reproduce at discrete level the significant feature of the continuous model, based on an underling Lagrangian structure. The resulting scheme give discrete counterparts of the variation law for the energy as well of as the Euler-Lagrange equations and their symmetries.

Conservation of energy for the Euler-Korteweg equations

KAUST Repository

Dębiec, Tomasz

2017-12-30

In this article we study the principle of energy conservation for the Euler-Korteweg system. We formulate an Onsager-type sufficient regularity condition for weak solutions of the Euler-Korteweg system to conserve the total energy. The result applies to the system of Quantum Hydrodynamics.

Conservation of energy for the Euler-Korteweg equations

KAUST Repository

Dębiec, Tomasz; Gwiazda, Piotr; Świerczewska-Gwiazda, Agnieszka; Tzavaras, Athanasios

2017-01-01

In this article we study the principle of energy conservation for the Euler-Korteweg system. We formulate an Onsager-type sufficient regularity condition for weak solutions of the Euler-Korteweg system to conserve the total energy. The result applies to the system of Quantum Hydrodynamics.

Euler European Libraries and Electronic Resources in Mathematical Sciences

CERN Document Server

The Euler Project. Karlsruhe

The European Libraries and Electronic Resources (EULER) Project in Mathematical Sciences provides the EulerService site for searching out "mathematical resources such as books, pre-prints, web-pages, abstracts, proceedings, serials, technical reports preprints) and NetLab (for Internet resources), this outstanding engine is capable of simple, full, and refined searches. It also offers a browse option, which responds to entries in the author, keyword, and title fields. Further information about the Project is provided at the EULER homepage.

Euler deconvolution and spectral analysis of regional aeromagnetic ...

African Journals Online (AJOL)

Existing regional aeromagnetic data from the south-central Zimbabwe craton has been analysed using 3D Euler deconvolution and spectral analysis to obtain quantitative information on the geological units and structures for depth constraints on the geotectonic interpretation of the region. The Euler solution maps confirm ...

Leonhard Euler and the mechanics of rigid bodies

Science.gov (United States)

Marquina, J. E.; Marquina, M. L.; Marquina, V.; Hernández-Gómez, J. J.

2017-01-01

In this work we present the original ideas and the construction of the rigid bodies theory realised by Leonhard Euler between 1738 and 1775. The number of treatises written by Euler on this subject is enormous, including the most notorious Scientia Navalis (1749), Decouverte d’un noveau principe de mecanique (1752), Du mouvement de rotation des corps solides autour d’un axe variable (1765), Theoria motus corporum solidorum seu rigidorum (1765) and Nova methodus motu corporum rigidorum determinandi (1776), in which he developed the ideas of the instantaneous rotation axis, the so-called Euler equations and angles, the components of what is now known as the inertia tensor, the principal axes of inertia, and, finally, the generalisation of the translation and rotation movement equations for any system. Euler, the man who ‘put most of mechanics into its modern form’ (Truesdell 1968 Essays in the History of Mechanics (Berlin: Springer) p 106).

An improved front tracking method for the Euler equations

NARCIS (Netherlands)

Witteveen, J.A.S.; Koren, B.; Bakker, P.G.

2007-01-01

An improved front tracking method for hyperbolic conservation laws is presented. The improved method accurately resolves discontinuities as well as continuous phenomena. The method is based on an improved front interaction model for a physically more accurate modeling of the Euler equations, as

Remarks on Heisenberg-Euler-type electrodynamics

Science.gov (United States)

Kruglov, S. I.

2017-05-01

We consider Heisenberg-Euler-type model of nonlinear electrodynamics with two parameters. Heisenberg-Euler electrodynamics is a particular case of this model. Corrections to Coulomb’s law at r →∞ are obtained and energy conditions are studied. The total electrostatic energy of charged particles is finite. The charged black hole solution in the framework of nonlinear electrodynamics is investigated. We find the asymptotic of the metric and mass functions at r →∞. Corrections to the Reissner-Nordström solution are obtained.

Dr. Euler's fabulous formula Cures many mathematical ills

CERN Document Server

Nahin, Paul J

2006-01-01

I used to think math was no fun'Cause I couldn't see how it was doneNow Euler's my heroFor I now see why zeroEquals e[pi] i+1--Paul Nahin, electrical engineer In the mid-eighteenth century, Swiss-born mathematician Leonhard Euler developed a formula so innovative and complex that it continues to inspire research, discussion, and even the occasional limerick. Dr. Euler's Fabulous Formula shares the fascinating story of this groundbreaking formula--long regarded as the gold standard for mathematical beauty--and shows why it still lies at the heart of complex number theory. This book is the seque

Euler angles for G2

International Nuclear Information System (INIS)

Cacciatori, Sergio L.; Cerchiai, Bianca L.; Della Vedova, Alberto; Ortenzi, Giovanni; Scotti, Antonio

2005-01-01

We provide a simple coordinatization for the group G 2 , which is analogous to the Euler coordinatization for SU(2). We show how to obtain the general element of the group in a form emphasizing the structure of the fibration of G 2 with fiber SO(4) and base H, the variety of quaternionic subalgebras of octonions. In particular this allows us to obtain a simple expression for the Haar measure on G 2 . Moreover, as a by-product it yields a concrete realization and an Einstein metric for H

New form of the Euler-Bernoulli rod equation applied to robotic systems

Directory of Open Access Journals (Sweden)

Filipović Mirjana

2008-01-01

Full Text Available This paper presents a theoretical background and an example of extending the Euler-Bernoulli equation from several aspects. Euler-Bernoulli equation (based on the known laws of dynamics should be supplemented with all the forces that are participating in the formation of the bending moment of the considered mode. The stiffness matrix is a full matrix. Damping is an omnipresent elasticity characteristic of real systems, so that it is naturally included in the Euler-Bernoulli equation. It is shown that Daniel Bernoulli's particular integral is just one component of the total elastic deformation of the tip of any mode to which we have to add a component of the elastic deformation of a stationary regime in accordance with the complexity requirements of motion of an elastic robot system. The elastic line equation mode of link of a complex elastic robot system is defined based on the so-called 'Euler-Bernoulli Approach' (EBA. It is shown that the equation of equilibrium of all forces present at mode tip point ('Lumped-mass approach' (LMA follows directly from the elastic line equation for specified boundary conditions. This, in turn, proves the essential relationship between LMA and EBA approaches. In the defined mathematical model of a robotic system with multiple DOF (degree of freedom in the presence of the second mode, the phenomenon of elasticity of both links and joints are considered simultaneously with the presence of the environment dynamics - all based on the previously presented theoretical premises. Simulation results are presented. .

Measure-valued solutions to the complete Euler system revisited

Science.gov (United States)

Březina, Jan; Feireisl, Eduard

2018-06-01

We consider the complete Euler system describing the time evolution of a general inviscid compressible fluid. We introduce a new concept of measure-valued solution based on the total energy balance and entropy inequality for the physical entropy without any renormalization. This class of so-called dissipative measure-valued solutions is large enough to include the vanishing dissipation limits of the Navier-Stokes-Fourier system. Our main result states that any sequence of weak solutions to the Navier-Stokes-Fourier system with vanishing viscosity and heat conductivity coefficients generates a dissipative measure-valued solution of the Euler system under some physically grounded constitutive relations. Finally, we discuss the same asymptotic limit for the bi-velocity fluid model introduced by H.Brenner.

Euler-Poincare reduction for discrete field theories

International Nuclear Information System (INIS)

Vankerschaver, Joris

2007-01-01

In this note, we develop a theory of Euler-Poincare reduction for discrete Lagrangian field theories. We introduce the concept of Euler-Poincare equations for discrete field theories, as well as a natural extension of the Moser-Veselov scheme, and show that both are equivalent. The resulting discrete field equations are interpreted in terms of discrete differential geometry. An application to the theory of discrete harmonic mappings is also briefly discussed

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Stability properties of the Euler-Korteweg system with nonmonotone pressures

KAUST Repository

Giesselmann, Jan

2016-12-21

We establish a relative energy framework for the Euler-Korteweg system with non-convex energy. This allows us to prove weak-strong uniqueness and to show convergence to a Cahn-Hilliard system in the large friction limit. We also use relative energy to show that solutions of Euler-Korteweg with convex energy converge to solutions of the Euler system in the vanishing capillarity limit, as long as the latter admits sufficiently regular strong solutions.

Using Euler buckling springs for vibration isolation

CERN Document Server

Winterflood, J; Blair, D G

2002-01-01

Difficulties in obtaining ideal vertical vibration isolation with mechanical springs are identified as being due to the mass of the elastic element which is in turn due to its energy storage requirement. A new technique to minimize this energy is presented - being an Euler column undergoing elastic buckling. The design of a high performance vertical vibration isolation stage based on this technique is presented together with its measured performance.

Using Euler buckling springs for vibration isolation

International Nuclear Information System (INIS)

Winterflood, J; Barber, T; Blair, D G

2002-01-01

Difficulties in obtaining ideal vertical vibration isolation with mechanical springs are identified as being due to the mass of the elastic element which is in turn due to its energy storage requirement. A new technique to minimize this energy is presented - being an Euler column undergoing elastic buckling. The design of a high performance vertical vibration isolation stage based on this technique is presented together with its measured performance

Dynamic behaviour of non-uniform Bernoulli-Euler beams subjected ...

African Journals Online (AJOL)

This paper investigates the dynamics behaviour of non-uniform Bernoulli-Euler beams subjected to concentrated loads ravelling at variable velocities. The solution technique is based on the Generalized Galerkin Method and the use of the generating function of the Bessel function type. The results show that, for all the ...

Drawing Euler Diagrams with Circles: The Theory of Piercings.

Science.gov (United States)

Stapleton, Gem; Leishi Zhang; Howse, John; Rodgers, Peter

2011-07-01

Euler diagrams are effective tools for visualizing set intersections. They have a large number of application areas ranging from statistical data analysis to software engineering. However, the automated generation of Euler diagrams has never been easy: given an abstract description of a required Euler diagram, it is computationally expensive to generate the diagram. Moreover, the generated diagrams represent sets by polygons, sometimes with quite irregular shapes that make the diagrams less comprehensible. In this paper, we address these two issues by developing the theory of piercings, where we define single piercing curves and double piercing curves. We prove that if a diagram can be built inductively by successively adding piercing curves under certain constraints, then it can be drawn with circles, which are more esthetically pleasing than arbitrary polygons. The theory of piercings is developed at the abstract level. In addition, we present a Java implementation that, given an inductively pierced abstract description, generates an Euler diagram consisting only of circles within polynomial time.

Euler numbers of four-dimensional rotating black holes with the Euclidean signature

International Nuclear Information System (INIS)

Ma Zhengze

2003-01-01

For a black hole's spacetime manifold in the Euclidean signature, its metric is positive definite and therefore a Riemannian manifold. It can be regarded as a gravitational instanton and a topological characteristic which is the Euler number to which it is associated. In this paper we derive a formula for the Euler numbers of four-dimensional rotating black holes by the integral of the Euler density on the spacetime manifolds of black holes. Using this formula, we obtain that the Euler numbers of Kerr and Kerr-Newman black holes are 2. We also obtain that the Euler number of the Kerr-Sen metric in the heterotic string theory with one boost angle nonzero is 2, which is in accordance with its topology

Euler polynomials and identities for non-commutative operators

Science.gov (United States)

De Angelis, Valerio; Vignat, Christophe

2015-12-01

Three kinds of identities involving non-commutating operators and Euler and Bernoulli polynomials are studied. The first identity, as given by Bender and Bettencourt [Phys. Rev. D 54(12), 7710-7723 (1996)], expresses the nested commutator of the Hamiltonian and momentum operators as the commutator of the momentum and the shifted Euler polynomial of the Hamiltonian. The second one, by Pain [J. Phys. A: Math. Theor. 46, 035304 (2013)], links the commutators and anti-commutators of the monomials of the position and momentum operators. The third appears in a work by Figuieira de Morisson and Fring [J. Phys. A: Math. Gen. 39, 9269 (2006)] in the context of non-Hermitian Hamiltonian systems. In each case, we provide several proofs and extensions of these identities that highlight the role of Euler and Bernoulli polynomials.

Symmetries of the Euler compressible flow equations for general equation of state

Energy Technology Data Exchange (ETDEWEB)

Boyd, Zachary M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ramsey, Scott D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Baty, Roy S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-10-15

The Euler compressible flow equations exhibit different Lie symmetries depending on the equation of state (EOS) of the medium in which the flow occurs. This means that, in general, different types of similarity solution will be available in different flow media. We present a comprehensive classification of all EOS’s to which the Euler equations apply, based on the Lie symmetries admitted by the corresponding flow equations, restricting to the case of 1-D planar, cylindrical, or spherical geometry. The results are conveniently summarized in tables. This analysis also clarifies past work by Axford and Ovsiannikov on symmetry classification.

Additivity for parametrized topological Euler characteristic and Reidemeister torsion

OpenAIRE

Badzioch, Bernard; Dorabiala, Wojciech

2005-01-01

Dwyer, Weiss, and Williams have recently defined the notions of parametrized topological Euler characteristic and parametrized topological Reidemeister torsion which are invariants of bundles of compact topological manifolds. We show that these invariants satisfy additivity formulas paralleling the additive properties of the classical Euler characteristic and Reidemeister torsion of finite CW-complexes.

Multi-dimensional Fuzzy Euler Approximation

Directory of Open Access Journals (Sweden)

Yangyang Hao

2017-05-01

Full Text Available Multi-dimensional Fuzzy differential equations driven by multi-dimen-sional Liu process, have been intensively applied in many fields. However, we can not obtain the analytic solution of every multi-dimensional fuzzy differential equation. Then, it is necessary for us to discuss the numerical results in most situations. This paper focuses on the numerical method of multi-dimensional fuzzy differential equations. The multi-dimensional fuzzy Taylor expansion is given, based on this expansion, a numerical method which is designed for giving the solution of multi-dimensional fuzzy differential equation via multi-dimensional Euler method will be presented, and its local convergence also will be discussed.

Euler-Lagrange Equations of Networks with Higher-Order Elements

Directory of Open Access Journals (Sweden)

Z. Biolek

2017-06-01

Full Text Available The paper suggests a generalization of the classic Euler-Lagrange equation for circuits compounded of arbitrary elements from Chua’s periodic table. Newly defined potential functions for general (α, β elements are used for the construction of generalized Lagrangians and generalized dissipative functions. Also procedures of drawing the Euler-Lagrange equations are demonstrated.

Stabilization analysis of Euler-Bernoulli beam equation with locally distributed disturbance

Directory of Open Access Journals (Sweden)

Pengcheng HAN

2017-12-01

Full Text Available In order to enrich the system stability theory of the control theories, taking Euler-Bernoulli beam equation as the research subject, the stability of Euler-Bernoulli beam equation with locally distributed disturbance is studied. A feedback controller based on output is designed to reduce the effects of the disturbances. The well-posedness of the nonlinear closed-loop system is investigated by the theory of maximal monotone operator, namely the existence and uniqueness of solutions for the closed-loop system. An appropriate state space is established, an appropriate inner product is defined, and a non-linear operator satisfying this state space is defined. Then, the system is transformed into the form of evolution equation. Based on this, the existence and uniqueness of solutions for the closed-loop system are proved. The asymptotic stability of the system is studied by constructing an appropriate Lyapunov function, which proves the asymptotic stability of the closed-loop system. The result shows that designing proper anti-interference controller is the foundation of investigating the system stability, and the research of the stability of Euler-bernoulli beam equation with locally distributed disturbance can prove the asymptotic stability of the system. This method can be extended to study the other equations such as wave equation, Timoshenko beam equation, Schrodinger equation, etc.

Euler Polynomials and Identities for Non-Commutative Operators

OpenAIRE

De Angelis, V.; Vignat, C.

2015-01-01

Three kinds of identities involving non-commutating operators and Euler and Bernoulli polynomials are studied. The first identity, as given by Bender and Bettencourt, expresses the nested commutator of the Hamiltonian and momentum operators as the commutator of the momentum and the shifted Euler polynomial of the Hamiltonian. The second one, due to J.-C. Pain, links the commutators and anti-commutators of the monomials of the position and momentum operators. The third appears in a work by Fig...

Euler's pioneering equation the most beautiful theorem in mathematics

CERN Document Server

Wilson, Robin

2018-01-01

In 1988 The Mathematical Intelligencer, a quarterly mathematics journal, carried out a poll to find the most beautiful theorem in mathematics. Twenty-four theorems were listed and readers were invited to award each a 'score for beauty'. While there were many worthy competitors, the winner was 'Euler's equation'. In 2004 Physics World carried out a similar poll of 'greatest equations', and found that among physicists Euler's mathematical result came second only to Maxwell's equations. The Stanford mathematician Keith Devlin reflected the feelings of many in describing it as "like a Shakespearian sonnet that captures the very essence of love, or a painting which brings out the beauty of the human form that is far more than just skin deep, Euler's equation reaches down into the very depths of existence."

Nilakantha, Euler and 1t

Indian Academy of Sciences (India)

It is not hard to show that the series converges, for by com- bining pairs of terms it can be ..... not escape Euler's attention-but then few things did!) We consider the function ... the proof. In particular there is no such thing as an unrig- orous proof.

Euler's fluid equations: Optimal control vs optimization

International Nuclear Information System (INIS)

Holm, Darryl D.

2009-01-01

An optimization method used in image-processing (metamorphosis) is found to imply Euler's equations for incompressible flow of an inviscid fluid, without requiring that the Lagrangian particle labels exactly follow the flow lines of the Eulerian velocity vector field. Thus, an optimal control problem and an optimization problem for incompressible ideal fluid flow both yield the same Euler fluid equations, although their Lagrangian parcel dynamics are different. This is a result of the gauge freedom in the definition of the fluid pressure for an incompressible flow, in combination with the symmetry of fluid dynamics under relabeling of their Lagrangian coordinates. Similar ideas are also illustrated for SO(N) rigid body motion.

Investigation of source location determination from Magsat magnetic anomalies: The Euler method approach

Science.gov (United States)

Ravat, Dhananjay

1996-01-01

The applicability of the Euler method of source location determination was investigated on several model situations pertinent to satellite-data scale situations as well as Magsat data of Europe. Our investigations enabled us to understand the end-member cases for which the Euler method will work with the present satellite magnetic data and also the cases for which the assumptions implicit in the Euler method will not be met by the present satellite magnetic data. These results have been presented in one invited lecture at the Indo-US workshop on Geomagnetism in Studies of the Earth's Interior in August 1994 in Pune, India, and at one presentation at the 21st General Assembly of the IUGG in July 1995 in Boulder, CO. A new method, called Anomaly Attenuation Rate (AAR) Method (based on the Euler method), was developed during this study. This method is scale-independent and is appropriate to locate centroids of semi-compact three dimensional sources of gravity and magnetic anomalies. The method was presented during 1996 Spring AGU meeting and a manuscript describing this method is being prepared for its submission to a high-ranking journal. The grant has resulted in 3 papers and presentations at national and international meetings and one manuscript of a paper (to be submitted shortly to a reputable journal).

On the Euler Function of the Catalan Numbers

Science.gov (United States)

2012-02-26

ON THE EULER FUNCTION OF THE CATALAN NUMBERS FLORIAN LUCA AND PANTELIMON STĂNICĂ Abstract. We study the solutions of the equation φ(Cm)/φ(Cn) = r...where r is a fixed rational number , Ck is the kth Catalan number and φ is the Euler function. We note that the number r = 4 is special for this...observation concerning φ(Cn+1)/φ(Cn) For a positive integer n, let (1) Cn = 1 n+ 1 ( 2n n ) be the n-th Catalan number . For a positive integer m we put φ(m) for

Beyond Euler's Method: Implicit Finite Differences in an Introductory ODE Course

Science.gov (United States)

Kull, Trent C.

2011-01-01

A typical introductory course in ordinary differential equations (ODEs) exposes students to exact solution methods. However, many differential equations must be approximated with numerical methods. Textbooks commonly include explicit methods such as Euler's and Improved Euler's. Implicit methods are typically introduced in more advanced courses…

On the estimation variance for the specific Euler-Poincaré characteristic of random networks.

Science.gov (United States)

Tscheschel, A; Stoyan, D

2003-07-01

The specific Euler number is an important topological characteristic in many applications. It is considered here for the case of random networks, which may appear in microscopy either as primary objects of investigation or as secondary objects describing in an approximate way other structures such as, for example, porous media. For random networks there is a simple and natural estimator of the specific Euler number. For its estimation variance, a simple Poisson approximation is given. It is based on the general exact formula for the estimation variance. In two examples of quite different nature and topology application of the formulas is demonstrated.

Bernoulli and Euler Numbers

Directory of Open Access Journals (Sweden)

Dae San Kim

2012-01-01

Full Text Available We derive some interesting identities and arithmetic properties of Bernoulli and Euler polynomials from the orthogonality of Hermite polynomials. Let Pn={p(x∈ℚ[x]∣deg p(x≤n} be the (n+1-dimensional vector space over ℚ. Then we show that {H0(x,H1(x,…,Hn(x} is a good basis for the space Pn for our purpose of arithmetical and combinatorial applications.

Extrapolating an Euler class

NARCIS (Netherlands)

Van der Kallen, Wilberd|info:eu-repo/dai/nl/117156108

2015-01-01

Let R be a noetherian ring of dimension d and let n be an integer so that n≤d≤2n-3. Let (a1,..., an+1) be a unimodular row so that the ideal J=(a1,..., an) has height n. Jean Fasel has associated to this row an element [(J, ωJ)] in the Euler

Euler-Poincare Reduction of a Rigid Body Motion

DEFF Research Database (Denmark)

Wisniewski, Rafal; Kulczycki, P.

2005-01-01

|If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system afected by an external force of a control action....... Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincare reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modeling, estimation and control of mechanical systems......-known Euler-Poincare reduction to a rigid body motion with forcing....

Exact, rotational, infinite energy, blowup solutions to the 3-dimensional Euler equations

International Nuclear Information System (INIS)

Yuen, Manwai

2011-01-01

In this Letter, we construct a new class of blowup or global solutions with elementary functions to the 3-dimensional compressible or incompressible Euler and Navier-Stokes equations. And the corresponding blowup or global solutions for the incompressible Euler and Naiver-Stokes equations are also given. Our constructed solutions are similar to the famous Arnold-Beltrami-Childress (ABC) flow. The obtained solutions with infinite energy can exhibit the interesting behaviors locally. Furthermore, due to divu → =0 for the solutions, the solutions also work for the 3-dimensional incompressible Euler and Navier-Stokes equations. -- Highlights: → We construct a new class of solutions to the 3D compressible or incompressible Euler and Navier-Stokes equations. → The constructed solutions are similar to the famous Arnold-Beltrami-Childress flow. → The solutions with infinite energy can exhibit the interesting behaviors locally.

Fractional Euler Limits and Their Applications

OpenAIRE

MacNamara, Shev; Henry, Bruce I; McLean, William

2016-01-01

Generalisations of the classical Euler formula to the setting of fractional calculus are discussed. Compound interest and fractional compound interest serve as motivation. Connections to fractional master equations are highlighted. An application to the Schlogl reactions with Mittag-Leffler waiting times is described.

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Closure relations for the multi-species Euler system. Construction and study of relaxation schemes for the multi-species and multi-components Euler systems; Relations de fermeture pour le systeme des equations d'Euler multi-especes. Construction et etude de schemas de relaxation en multi-especes et en multi-constituants

Energy Technology Data Exchange (ETDEWEB)

Dellacherie, St. [CEA Saclay, Dir. de l' Energie Nucleaire DEN/SFNME/LMPE, Lab. de Modelisation Physique et de l' Enrichissement, 91 - Gif sur Yvette (France); Rency, N. [Paris-11 Univ., CNRS UMR 8628, 91 - Orsay (France)

2001-07-01

After having recalled the formal convergence of the semi-classical multi-species Boltzmann equations toward the multi-species Euler system (i.e. mixture of gases having the same velocity), we generalize to this system the closure relations proposed by B. Despres and by F. Lagoutiere for the multi-components Euler system (i.e. mixture of non miscible fluids having the same velocity). Then, we extend the energy relaxation schemes proposed by F. Coquel and by B. Perthame for the numerical resolution of the mono-species Euler system to the multi-species isothermal Euler system and to the multi-components isobar-isothermal Euler system. This allows to obtain a class of entropic schemes under a CFL criteria. In the multi-components case, this class of entropic schemes is perhaps a way for the treatment of interface problems and, then, for the treatment of the numerical mixture area by using a Lagrange + projection scheme. Nevertheless, we have to find a good projection stage in the multi-components case. At last, in the last chapter, we discuss, through the study of a dynamical system, about a system proposed by R. Abgrall and by R. Saurel for the numerical resolution of the multi-components Euler system.

Evaluating of air flow movements and thermal comfort in a model room with Euler equation: Two dimensional study

Energy Technology Data Exchange (ETDEWEB)

Chafi, Fatima Zohra; Halle, Stephane [Mechanical engineering department, Ecole de technologie superieure, Quebec university, 1100 rue Notre-Dame Ouest, Montreal, Quebec H3C 1K3 (Canada)

2011-02-15

This paper presents the results of a study that consists of estimating the temperature distribution and air flow movement in a model room with a numerical model based on the Euler equations. Numerical results obtained for two scenarios of ventilation and heating are compared with the predictions of a Navier-Stokes model, as well as with experimental results. A comparison of the local thermal comfort indices PMV and PPD obtained experimentally and numerically is also presented. Results show that the Euler model is capable of properly estimating the temperature distribution, the air movement and the comfort indices in the room. Furthermore, the use of Euler equations allows a reduction of computational time in the order of 30% compared to the Navier-Stokes modeling. (author)

Intra- and interobserver reliability of glenoid fracture classifications by Ideberg, Euler and AO.

Science.gov (United States)

Gilbert, F; Eden, L; Meffert, R; Konietschke, F; Lotz, J; Bauer, L; Staab, W

2018-03-27

Representing 3%-5% of shoulder girdle injuries scapula fractures are rare. Furthermore, approximately 1% of scapula fractures are intraarticularfractures of the glenoid fossa. Because of uncertain fracture morphology and limited experience, the treatment of glenoid fossa fractures is difficult. The glenoid fracture classification by Ideberg (1984) and Euler (1996) is still commonly used in literature. In 2013 a new glenoid fracture classification was introduced by the AO. The purpose of this study was to examine the new AO classification in clinical practice in comparison with the classifications by Ideberg and Euler. In total CT images of 84 patients with glenoid fossa fractures from 2005 to 2018 were included. Parasagittal, paracoronary and axial reconstructions were examined according to the classifications of Ideberg, Euler and the AO by 3 investigators (orthopedic surgeon, radiologist, student of medicine) at three individual time settings. Inter- and intraobserver reliability of the three classification systems were ascertained by computing Inter- and Intraclass (ICCs) correlation coefficients using Spearman's rank correlation coefficient, 95%-confidence intervals as well as F-tests for correlation coefficients. Inter- and intraobserver reliability for the AO classification showed a perspicuous coherence (R = 0.74 and R = 0.79). Low to moderate intraobserver reliability for Ideberg (R = 0.46) and Euler classification (R = 0.41) was found. Furthermore, data show a low Interobserver reliability for both Ideberg and Euler classification (R reliability using AO is significantly higher than those using Ideberg and Euler (p reliable grading of glenoid fossa fractures with high inter- and intraobserver reliability in 84 patients using CT images. It should possibly be applied in order to enable a valid, reliable and consistent academic description of glenoid fossa fractures. The established classifications by Euler and Ideberg are not capable of

The matrix Euler-Fermat theorem

International Nuclear Information System (INIS)

Arnol'd, Vladimir I

2004-01-01

We prove many congruences for binomial and multinomial coefficients as well as for the coefficients of the Girard-Newton formula in the theory of symmetric functions. These congruences also imply congruences (modulo powers of primes) for the traces of various powers of matrices with integer elements. We thus have an extension of the matrix Fermat theorem similar to Euler's extension of the numerical little Fermat theorem

Euler-Poincare Reduction of Externall Forced Rigid Body Motion

DEFF Research Database (Denmark)

Wisniewski, Rafal; Kulczycki, P.

2004-01-01

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system affected by an external force of a control action....... Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincaré reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modelling, estimation and control of mechanical systems......-known Euler-Poincaré reduction to a rigid body motion with forcing....

Euler-Poincaré Reduction of a Rigid Body Motion

DEFF Research Database (Denmark)

Wisniewski, Rafal; Kulczycki, P.

2004-01-01

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system affected by an external force of a control action....... Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincaré reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modelling, estimation and control of mechanical systems......-known Euler-Poincaré reduction to a rigid body motion with forcing....

Exploitation of ISAR Imagery in Euler Parameter Space

National Research Council Canada - National Science Library

Baird, Christopher; Kersey, W. T; Giles, R; Nixon, W. E

2005-01-01

.... The Euler parameters have potential value in target classification but have historically met with limited success due to ambiguities that arise in decomposition as well as the parameters' sensitivity...

Difference Discrete Variational Principle,EULER-Lagrange Cohomology and Symplectic, Multisymplectic Structures

OpenAIRE

Guo, H. Y.; Li, Y. Q.; Wu, K.; Wang, S. K.

2001-01-01

We study the difference discrete variational principle in the framework of multi-parameter differential approach by regarding the forward difference as an entire geometric object in view of noncomutative differential geometry. By virtue of this variational principle, we get the difference discrete Euler-Lagrange equations and canonical ones for the difference discrete versions of the classical mechanics and classical field theory. We also explore the difference discrete versions for the Euler...

A Ritz approach for the static analysis of planar pantographic structures modeled with nonlinear Euler-Bernoulli beams

Science.gov (United States)

Andreaus, Ugo; Spagnuolo, Mario; Lekszycki, Tomasz; Eugster, Simon R.

2018-04-01

We present a finite element discrete model for pantographic lattices, based on a continuous Euler-Bernoulli beam for modeling the fibers composing the pantographic sheet. This model takes into account large displacements, rotations and deformations; the Euler-Bernoulli beam is described by using nonlinear interpolation functions, a Green-Lagrange strain for elongation and a curvature depending on elongation. On the basis of the introduced discrete model of a pantographic lattice, we perform some numerical simulations. We then compare the obtained results to an experimental BIAS extension test on a pantograph printed with polyamide PA2200. The pantographic structures involved in the numerical as well as in the experimental investigations are not proper fabrics: They are composed by just a few fibers for theoretically allowing the use of the Euler-Bernoulli beam theory in the description of the fibers. We compare the experiments to numerical simulations in which we allow the fibers to elastically slide one with respect to the other in correspondence of the interconnecting pivot. We present as result a very good agreement between the numerical simulation, based on the introduced model, and the experimental measures.

Free vibration of Euler and Timoshenko nanobeams using boundary characteristic orthogonal polynomials

Science.gov (United States)

Behera, Laxmi; Chakraverty, S.

2014-03-01

Vibration analysis of nonlocal nanobeams based on Euler-Bernoulli and Timoshenko beam theories is considered. Nonlocal nanobeams are important in the bending, buckling and vibration analyses of beam-like elements in microelectromechanical or nanoelectromechanical devices. Expressions for free vibration of Euler-Bernoulli and Timoshenko nanobeams are established within the framework of Eringen's nonlocal elasticity theory. The problem has been solved previously using finite element method, Chebyshev polynomials in Rayleigh-Ritz method and using other numerical methods. In this study, numerical results for free vibration of nanobeams have been presented using simple polynomials and orthonormal polynomials in the Rayleigh-Ritz method. The advantage of the method is that one can easily handle the specified boundary conditions at the edges. To validate the present analysis, a comparison study is carried out with the results of the existing literature. The proposed method is also validated by convergence studies. Frequency parameters are found for different scaling effect parameters and boundary conditions. The study highlights that small scale effects considerably influence the free vibration of nanobeams. Nonlocal frequency parameters of nanobeams are smaller when compared to the corresponding local ones. Deflection shapes of nonlocal clamped Euler-Bernoulli nanobeams are also incorporated for different scaling effect parameters, which are affected by the small scale effect. Obtained numerical solutions provide a better representation of the vibration behavior of short and stubby micro/nanobeams where the effects of small scale, transverse shear deformation and rotary inertia are significant.

Neural Network Based Load Frequency Control for Restructuring ...

African Journals Online (AJOL)

Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

eulerAPE: drawing area-proportional 3-Venn diagrams using ellipses.

Science.gov (United States)

Micallef, Luana; Rodgers, Peter

2014-01-01

Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.

eulerAPE: drawing area-proportional 3-Venn diagrams using ellipses.

Directory of Open Access Journals (Sweden)

Luana Micallef

Full Text Available Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.

Euler's fluid equations: Optimal control vs optimization

Energy Technology Data Exchange (ETDEWEB)

Holm, Darryl D., E-mail: d.holm@ic.ac.u [Department of Mathematics, Imperial College London, SW7 2AZ (United Kingdom)

2009-11-23

An optimization method used in image-processing (metamorphosis) is found to imply Euler's equations for incompressible flow of an inviscid fluid, without requiring that the Lagrangian particle labels exactly follow the flow lines of the Eulerian velocity vector field. Thus, an optimal control problem and an optimization problem for incompressible ideal fluid flow both yield the same Euler fluid equations, although their Lagrangian parcel dynamics are different. This is a result of the gauge freedom in the definition of the fluid pressure for an incompressible flow, in combination with the symmetry of fluid dynamics under relabeling of their Lagrangian coordinates. Similar ideas are also illustrated for SO(N) rigid body motion.

Discretization vs. Rounding Error in Euler's Method

Science.gov (United States)

Borges, Carlos F.

2011-01-01

Euler's method for solving initial value problems is an excellent vehicle for observing the relationship between discretization error and rounding error in numerical computation. Reductions in stepsize, in order to decrease discretization error, necessarily increase the number of steps and so introduce additional rounding error. The problem is…

Euler Polynomials, Fourier Series and Zeta Numbers

DEFF Research Database (Denmark)

Scheufens, Ernst E

2012-01-01

Fourier series for Euler polynomials is used to obtain information about values of the Riemann zeta function for integer arguments greater than one. If the argument is even we recover the well-known exact values, if the argument is odd we find integral representations and rapidly convergent series....

Euler-Poincaré Reduction of Externally Forced Rigid Body Motion

DEFF Research Database (Denmark)

Wisniewski, Rafal; Kulczycki, P.

2004-01-01

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system affected by an external force of a control action....... Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincaré reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modelling, estimation and control of mechanical systems......-known Euler-Poincaré reduction to a rigid body motion with forcing....

Cavitation Modeling in Euler and Navier-Stokes Codes

Science.gov (United States)

Deshpande, Manish; Feng, Jinzhang; Merkle, Charles L.

1993-01-01

Many previous researchers have modeled sheet cavitation by means of a constant pressure solution in the cavity region coupled with a velocity potential formulation for the outer flow. The present paper discusses the issues involved in extending these cavitation models to Euler or Navier-Stokes codes. The approach taken is to start from a velocity potential model to ensure our results are compatible with those of previous researchers and available experimental data, and then to implement this model in both Euler and Navier-Stokes codes. The model is then augmented in the Navier-Stokes code by the inclusion of the energy equation which allows the effect of subcooling in the vicinity of the cavity interface to be modeled to take into account the experimentally observed reduction in cavity pressures that occurs in cryogenic fluids such as liquid hydrogen. Although our goal is to assess the practicality of implementing these cavitation models in existing three-dimensional, turbomachinery codes, the emphasis in the present paper will center on two-dimensional computations, most specifically isolated airfoils and cascades. Comparisons between velocity potential, Euler and Navier-Stokes implementations indicate they all produce consistent predictions. Comparisons with experimental results also indicate that the predictions are qualitatively correct and give a reasonable first estimate of sheet cavitation effects in both cryogenic and non-cryogenic fluids. The impact on CPU time and the code modifications required suggests that these models are appropriate for incorporation in current generation turbomachinery codes.

Investigation of vortex breakdown on a delta wing using Euler and Navier-Stokes equations

Science.gov (United States)

Agrawal, S.; Barnett, R. M.; Robinson, B. A.

1991-01-01

A numerical investigation of leading edge vortex breakdown in a delta wing at high angles of attack is presented. The analysis was restricted to low speed flows on a flat plate wing with sharp leading edges. Both Euler and Navier-Stokes equations were used and the results were compared with experimental data. Predictions of vortex breakdown progression with angle of attack with both Euler and Navier-Stokes equations are shown to be consistent with the experimental data. However, the Navier-Stokes predictions show significant improvements in breakdown location at angles of attack where the vortex breakdown approaches the wing apex. The predicted trajectories of the primary vortex are in very good agreement with the test data, the laminar solutions providing the overall best comparison. The Euler shows a small displacement of the primary vortex, relative to experiment, due to the lack of secondary vortices. The turbulent Navier-Stokes, in general, fall between the Euler and laminar solutions.

Weyl-Euler-Lagrange Equations of Motion on Flat Manifold

Directory of Open Access Journals (Sweden)

Zeki Kasap

2015-01-01

Full Text Available This paper deals with Weyl-Euler-Lagrange equations of motion on flat manifold. It is well known that a Riemannian manifold is said to be flat if its curvature is everywhere zero. Furthermore, a flat manifold is one Euclidean space in terms of distances. Weyl introduced a metric with a conformal transformation for unified theory in 1918. Classical mechanics is one of the major subfields of mechanics. Also, one way of solving problems in classical mechanics occurs with the help of the Euler-Lagrange equations. In this study, partial differential equations have been obtained for movement of objects in space and solutions of these equations have been generated by using the symbolic Algebra software. Additionally, the improvements, obtained in this study, will be presented.

Self-adjusting entropy-stable scheme for compressible Euler equations

Institute of Scientific and Technical Information of China (English)

程晓晗; 聂玉峰; 封建湖; LuoXiao-Yu; 蔡力

2015-01-01

In this work, a self-adjusting entropy-stable scheme is proposed for solving compressible Euler equations. The entropy-stable scheme is constructed by combining the entropy conservative flux with a suitable diffusion operator. The entropy has to be preserved in smooth solutions and be dissipated at shocks. To achieve this, a switch function, based on entropy variables, is employed to make the numerical diffusion term added around discontinuities automatically. The resulting scheme is still entropy-stable. A number of numerical experiments illustrating the robustness and accuracy of the scheme are presented. From these numerical results, we observe a remarkable gain in accuracy.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Entropy viscosity method applied to Euler equations

International Nuclear Information System (INIS)

Delchini, M. O.; Ragusa, J. C.; Berry, R. A.

2013-01-01

The entropy viscosity method [4] has been successfully applied to hyperbolic systems of equations such as Burgers equation and Euler equations. The method consists in adding dissipative terms to the governing equations, where a viscosity coefficient modulates the amount of dissipation. The entropy viscosity method has been applied to the 1-D Euler equations with variable area using a continuous finite element discretization in the MOOSE framework and our results show that it has the ability to efficiently smooth out oscillations and accurately resolve shocks. Two equations of state are considered: Ideal Gas and Stiffened Gas Equations Of State. Results are provided for a second-order time implicit schemes (BDF2). Some typical Riemann problems are run with the entropy viscosity method to demonstrate some of its features. Then, a 1-D convergent-divergent nozzle is considered with open boundary conditions. The correct steady-state is reached for the liquid and gas phases with a time implicit scheme. The entropy viscosity method correctly behaves in every problem run. For each test problem, results are shown for both equations of state considered here. (authors)

Natural frequencies of Euler-Bernoulli beam with open cracks on elastic foundations

International Nuclear Information System (INIS)

Shin, Young Jae; Yun, Jong Hak; Seong, Kyeong Youn; Kim, Jae Ho; Kang, Sung Hwang

2006-01-01

A study of the natural vibrations of beam resting on elastic foundation with finite number of transverse open cracks is presented. Frequency equations are derived for beams with different end restraints. Euler-Bernoulli beam on Winkler foundation and Euler-Bernoulli beam on Paster nak foundation are investigated. The cracks are modeled by massless substitute spring. The effects of the crack location, size and its number and the foundation constants, on the natural frequencies of the beam, are investigated

A novel numerical flux for the 3D Euler equations with general equation of state

KAUST Repository

Toro, Eleuterio F.; Castro, Cristó bal E.; Bok Jik, Lee

2015-01-01

Euler equations for ideal gases and its extension presented in this paper is threefold: (i) we solve the three-dimensional Euler equations on general meshes; (ii) we use a general equation of state; and (iii) we achieve high order of accuracy in both

Optical-Correlator Neural Network Based On Neocognitron

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Genetic learning in rule-based and neural systems

Science.gov (United States)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Euler Strut: A Mechanical Analogy for Dynamics in the Vicinity of a Critical Point

Science.gov (United States)

Bobnar, Jaka; Susman, Katarina; Parsegian, V. Adrian; Rand, Peter R.; Cepic, Mojca; Podgornik, Rudolf

2011-01-01

An anchored elastic filament (Euler strut) under an external point load applied to its free end is a simple model for a second-order phase transition. In the static case, a load greater than the critical load causes a Euler buckling instability, leading to a change in the filament's shape. The analysis of filament dynamics with an external point…

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

Science.gov (United States)

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Convergence and stability of the exponential Euler method for semi-linear stochastic delay differential equations.

Science.gov (United States)

Zhang, Ling

2017-01-01

The main purpose of this paper is to investigate the strong convergence and exponential stability in mean square of the exponential Euler method to semi-linear stochastic delay differential equations (SLSDDEs). It is proved that the exponential Euler approximation solution converges to the analytic solution with the strong order [Formula: see text] to SLSDDEs. On the one hand, the classical stability theorem to SLSDDEs is given by the Lyapunov functions. However, in this paper we study the exponential stability in mean square of the exact solution to SLSDDEs by using the definition of logarithmic norm. On the other hand, the implicit Euler scheme to SLSDDEs is known to be exponentially stable in mean square for any step size. However, in this article we propose an explicit method to show that the exponential Euler method to SLSDDEs is proved to share the same stability for any step size by the property of logarithmic norm.

Convergence and stability of the exponential Euler method for semi-linear stochastic delay differential equations

Directory of Open Access Journals (Sweden)

Ling Zhang

2017-10-01

Full Text Available Abstract The main purpose of this paper is to investigate the strong convergence and exponential stability in mean square of the exponential Euler method to semi-linear stochastic delay differential equations (SLSDDEs. It is proved that the exponential Euler approximation solution converges to the analytic solution with the strong order 1 2 $\\frac{1}{2}$ to SLSDDEs. On the one hand, the classical stability theorem to SLSDDEs is given by the Lyapunov functions. However, in this paper we study the exponential stability in mean square of the exact solution to SLSDDEs by using the definition of logarithmic norm. On the other hand, the implicit Euler scheme to SLSDDEs is known to be exponentially stable in mean square for any step size. However, in this article we propose an explicit method to show that the exponential Euler method to SLSDDEs is proved to share the same stability for any step size by the property of logarithmic norm.

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Directory of Open Access Journals (Sweden)

Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

Self-adjusting entropy-stable scheme for compressible Euler equations

International Nuclear Information System (INIS)

Cheng Xiao-Han; Nie Yu-Feng; Cai Li; Feng Jian-Hu; Luo Xiao-Yu

2015-01-01

In this work, a self-adjusting entropy-stable scheme is proposed for solving compressible Euler equations. The entropy-stable scheme is constructed by combining the entropy conservative flux with a suitable diffusion operator. The entropy has to be preserved in smooth solutions and be dissipated at shocks. To achieve this, a switch function, which is based on entropy variables, is employed to make the numerical diffusion term be automatically added around discontinuities. The resulting scheme is still entropy-stable. A number of numerical experiments illustrating the robustness and accuracy of the scheme are presented. From these numerical results, we observe a remarkable gain in accuracy. (paper)

A Neural Network-Based Interval Pattern Matcher

Directory of Open Access Journals (Sweden)

Jing Lu

2015-07-01

Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

Euler-Lagrange CFD modelling of unconfined gas mixing in anaerobic digestion.

Science.gov (United States)

Dapelo, Davide; Alberini, Federico; Bridgeman, John

2015-11-15

A novel Euler-Lagrangian (EL) computational fluid dynamics (CFD) finite volume-based model to simulate the gas mixing of sludge for anaerobic digestion is developed and described. Fluid motion is driven by momentum transfer from bubbles to liquid. Model validation is undertaken by assessing the flow field in a labscale model with particle image velocimetry (PIV). Conclusions are drawn about the upscaling and applicability of the model to full-scale problems, and recommendations are given for optimum application. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Science.gov (United States)

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

Directory of Open Access Journals (Sweden)

Boutros Paul C

2011-01-01

Full Text Available Abstract Background Visualization of orthogonal (disjoint or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets. Results The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines. Conclusions The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.

VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R.

Science.gov (United States)

Chen, Hanbo; Boutros, Paul C

2011-01-26

Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets. The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines. The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.

Canonical form of Euler-Lagrange equations and gauge symmetries

Energy Technology Data Exchange (ETDEWEB)

Geyer, B [Naturwissenschaftlich-Theoretisches Zentrum und Institut fuer Theoretische Physik, Universitaet Leipzig, Leipzig (Germany); Gitman, D M [Institute of Physics, University of Sao Paulo, Sao Paulo (Brazil); Tyutin, I V [Lebedev Physics Institute, Moscow (Russian Federation)

2003-06-13

The structure of the Euler-Lagrange equations for a general Lagrangian theory (e.g. singular, with higher derivatives) is studied. For these equations we present a reduction procedure to the so-called canonical form. In the canonical form the equations are solved with respect to highest-order derivatives of nongauge coordinates, whereas gauge coordinates and their derivatives enter the right-hand sides of the equations as arbitrary functions of time. The reduction procedure reveals constraints in the Lagrangian formulation of singular systems and, in that respect, is similar to the Dirac procedure in the Hamiltonian formulation. Moreover, the reduction procedure allows one to reveal the gauge identities between the Euler-Lagrange equations. Thus, a constructive way of finding all the gauge generators within the Lagrangian formulation is presented. At the same time, it is proved that for local theories all the gauge generators are local in time operators.

On the Local Type I Conditions for the 3D Euler Equations

Science.gov (United States)

Chae, Dongho; Wolf, Jörg

2018-05-01

We prove local non blow-up theorems for the 3D incompressible Euler equations under local Type I conditions. More specifically, for a classical solution {v\\in L^∞ (-1,0; L^2 ( B(x_0,r)))\\cap L^∞_{loc} (-1,0; W^{1, ∞} (B(x_0, r)))} of the 3D Euler equations, where {B(x_0,r)} is the ball with radius r and the center at x 0, if the limiting values of certain scale invariant quantities for a solution v(·, t) as {t\\to 0} are small enough, then { \

Euler y la Conjetura de Fermat sobre Números Triangulares

Directory of Open Access Journals (Sweden)

José Manuel Sánchez Muñoz

2011-04-01

Full Text Available Este artículo describe la historia de como Euler demostró la existencia de infinitos números triangulares bicuadráticos, desde su correspondencia con su amigo Christian Goldbach hasta la publicación de sus resultados en la Academia de San Petesburgo.

Multipliers for the Absolute Euler Summability of Fourier Series

Indian Academy of Sciences (India)

In this paper, the author has investigated necessary and sufficient conditions for the absolute Euler summability of the Fourier series with multipliers. These conditions are weaker than those obtained earlier by some workers. It is further shown that the multipliers are best possible in certain sense.

A Convergence Result for the Euler-Maruyama Method for a Simple Stochastic Differential Equation with Discontinuous Drift

DEFF Research Database (Denmark)

Simonsen, Maria; Schiøler, Henrik; Leth, John-Josef

2014-01-01

The Euler-Maruyama method is applied to a simple stochastic differential equation (SDE) with discontinuous drift. Convergence aspects are investigated in the case, where the Euler-Maruyama method is simulated in dyadic points. A strong rate of convergence is presented for the numerical simulations...

N=2 topological gauge theory, the Euler characteristic of moduli spaces, and the Casson invariant

International Nuclear Information System (INIS)

Blau, M.; Thompson, G.

1991-11-01

Gauge theory with a topological N=2 symmetry is discussed. This theory captures the de Rahm complex and Riemannian geometry of some underlying moduli space M and the partition function equals the Euler number χ (M) of M. Moduli spaces of instantons and of flat connections in 2 and 3 dimensions are explicitly dealt with. To motivate the constructions the relation between the Mathai-Quillen formalism and supersymmetric quantum mechanics are explained and a new kind of supersymmetric quantum mechanics is introduced, based on the Gauss-Codazzi equations. The gauge theory actions are interpreted from the Atiyah-Jeffrey point of view and related to super-symmetric quantum mechanics on spaces of connections. As a consequence of these considerations the Euler number χ (M) of the moduli space of flat connections as a generalization to arbitrary three-manifolds of the Casson invariant. The possibility of constructing a topological version of the Penner matrix model is also commented. (author). 63 refs

Contact discontinuities in multi-dimensional isentropic Euler equations

Czech Academy of Sciences Publication Activity Database

Březina, J.; Chiodaroli, E.; Kreml, Ondřej

2018-01-01

Roč. 2018 (2018), č. článku 94. ISSN 1072-6691 R&D Projects: GA ČR(CZ) GJ17-01694Y Institutional support: RVO:67985840 Keywords : isentropic Euler equations * non-uniqueness * Riemann problem Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.954, year: 2016 https://ejde.math.txstate.edu/Volumes/2018/94/abstr.html

A general multiblock Euler code for propulsion integration. Volume 1: Theory document

Science.gov (United States)

Chen, H. C.; Su, T. Y.; Kao, T. J.

1991-01-01

A general multiblock Euler solver was developed for the analysis of flow fields over geometrically complex configurations either in free air or in a wind tunnel. In this approach, the external space around a complex configuration was divided into a number of topologically simple blocks, so that surface-fitted grids and an efficient flow solution algorithm could be easily applied in each block. The computational grid in each block is generated using a combination of algebraic and elliptic methods. A grid generation/flow solver interface program was developed to facilitate the establishment of block-to-block relations and the boundary conditions for each block. The flow solver utilizes a finite volume formulation and an explicit time stepping scheme to solve the Euler equations. A multiblock version of the multigrid method was developed to accelerate the convergence of the calculations. The generality of the method was demonstrated through the analysis of two complex configurations at various flow conditions. Results were compared to available test data. Two accompanying volumes, user manuals for the preparation of multi-block grids (vol. 2) and for the Euler flow solver (vol. 3), provide information on input data format and program execution.

Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

High resolution solutions of the Euler equations for vortex flows

International Nuclear Information System (INIS)

Murman, E.M.; Powell, K.G.; Rizzi, A.; Tel Aviv Univ., Israel)

1985-01-01

Solutions of the Euler equations are presented for M = 1.5 flow past a 70-degree-swept delta wing. At an angle of attack of 10 degrees, strong leading-edge vortices are produced. Two computational approaches are taken, based upon fully three-dimensional and conical flow theory. Both methods utilize a finite-volume discretization solved by a pseudounsteady multistage scheme. Results from the two approaches are in good agreement. Computations have been done on a 16-million-word CYBER 205 using 196 x 56 x 96 and 128 x 128 cells for the two methods. A sizable data base is generated, and some of the practical aspects of manipulating it are mentioned. The results reveal many interesting physical features of the compressible vortical flow field and also suggest new areas needing research. 16 references

Photosensitive-polyimide based method for fabricating various neural electrode architectures

Directory of Open Access Journals (Sweden)

Yasuhiro X Kato

2012-06-01

Full Text Available An extensive photosensitive polyimide (PSPI-based method for designing and fabricating various neural electrode architectures was developed. The method aims to broaden the design flexibility and expand the fabrication capability for neural electrodes to improve the quality of recorded signals and integrate other functions. After characterizing PSPI’s properties for micromachining processes, we successfully designed and fabricated various neural electrodes even on a non-flat substrate using only one PSPI as an insulation material and without the time-consuming dry etching processes. The fabricated neural electrodes were an electrocorticogram electrode, a mesh intracortical electrode with a unique lattice-like mesh structure to fixate neural tissue, and a guide cannula electrode with recording microelectrodes placed on the curved surface of a guide cannula as a microdialysis probe. In vivo neural recordings using anesthetized rats demonstrated that these electrodes can be used to record neural activities repeatedly without any breakage and mechanical failures, which potentially promises stable recordings for long periods of time. These successes make us believe that this PSPI-based fabrication is a powerful method, permitting flexible design and easy optimization of electrode architectures for a variety of electrophysiological experimental research with improved neural recording performance.

The symplectic structure of Euler-Lagrange superequations and Batalin-Vilkoviski formalism

CERN Document Server

Monterde, J

2003-01-01

We study the graded Euler-Lagrange equations from the viewpoint of graded Poincare-Cartan forms. An application to a certain class of solutions of the Batalin-Vilkoviski master equation is also given.

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

Generalized force in classical field theory. [Euler-Lagrange equations

Energy Technology Data Exchange (ETDEWEB)

Krause, J [Universidad Central de Venezuela, Caracas

1976-02-01

The source strengths of the Euler-Lagrange equations, for a system of interacting fields, are heuristically interpreted as generalized forces. The canonical form of the energy-momentum tensor thus consistently appears, without recourse to space-time symmetry arguments. A concept of 'conservative' generalized force in classical field theory is also briefly discussed.

Newton's Laws, Euler's Laws and the Speed of Light

Science.gov (United States)

Whitaker, Stephen

2009-01-01

Chemical engineering students begin their studies of mechanics in a department of physics where they are introduced to the mechanics of Newton. The approach presented by physicists differs in both perspective and substance from that encountered in chemical engineering courses where Euler's laws provide the foundation for studies of fluid and solid…

A Short Proof of Euler's Inequality R ≥ 2r Theorem. Let ∆ ABC be an ...

Indian Academy of Sciences (India)

IAS Admin

A Short Proof of Euler's Inequality R ≥ 2r. Theorem. Let ∆ ABC be an arbitrary triangle with circumradius R and inradius r. Then R ≥ 2r with equality holding if and only if ∆ABC is equilateral. This was first published by Euler in 1765. Since then several proofs have followed, some geometric and some algebraic. We will use ...

Parallel computation of Euler and Navier-Stokes flows

International Nuclear Information System (INIS)

Swisshelm, J.M.; Johnson, G.M.; Kumar, S.P.

1986-01-01

A multigrid technique useful for accelerating the convergence of Euler and Navier-Stokes flow computations has been restructured to improve its performance on both SIMD and MIMD computers. The new algorithm allows both the construction of longer coarse-grid vectors and the multitasking of entire grids. Computational results are presented for the CDC Cyber 205, Cray X-MP, and Denelcor HEP I. 15 references

Stability properties of the Euler-Korteweg system with nonmonotone pressures

KAUST Repository

Giesselmann, Jan; Tzavaras, Athanasios

2016-01-01

We establish a relative energy framework for the Euler-Korteweg system with non-convex energy. This allows us to prove weak-strong uniqueness and to show convergence to a Cahn-Hilliard system in the large friction limit. We also use relative energy

Large Scale Simulations of the Euler Equations on GPU Clusters

KAUST Repository

Liebmann, Manfred; Douglas, Craig C.; Haase, Gundolf; Horvá th, Zoltá n

2010-01-01

The paper investigates the scalability of a parallel Euler solver, using the Vijayasundaram method, on a GPU cluster with 32 Nvidia Geforce GTX 295 boards. The aim of this research is to enable large scale fluid dynamics simulations with up to one

Classical mechanics on the GL(n, R) group and Euler-Calogero-Sutherland model

International Nuclear Information System (INIS)

Khvedelidze, A.M.; Mladenov, D.M.

2002-01-01

Relations between free motion on the GL + (n, R) group manifold and the dynamics of an n-particle system with spin degrees of freedom on a line interacting with a pairwise 1/sinh 2 x 'potential' (Euler-Calogero-Sutherland model) are discussed within a Hamiltonian reduction. Two kinds of reductions of the degrees of freedom are considered: that which is due to continuous invariance and that which is due to discrete symmetry. It is shown that, upon projecting onto the corresponding invariant manifolds, the resulting Hamiltonian system represents the Euler-Calogero-Sutherland model in both cases

Euler-Vector Clustering of GPS Velocities Defines Microplate Geometry in Southwest Japan

Science.gov (United States)

Savage, J. C.

2018-02-01

I have used Euler-vector clustering to assign 469 GEONET stations in southwest Japan to k clusters (k = 2, 3,..., 9) so that, for any k, the velocities of stations within each cluster are most consistent with rigid-block motion on a sphere. That is, I attempt to explain the raw (i.e., uncorrected for strain accumulation), 1996-2006 velocities of those 469 Global Positioning System stations by rigid motion of k clusters on the surface of a spherical Earth. Because block geometry is maintained as strain accumulates, Euler-vector clustering may better approximate the block geometry than the values of the associated Euler vectors. The microplate solution for each k is constructed by merging contiguous clusters that have closely similar Euler vectors. The best solution consists of three microplates arranged along the Nankaido Trough-Ryukyu Trench between the Amurian and Philippine Sea Plates. One of these microplates, the South Kyushu Microplate (an extension of the Ryukyu forearc into the southeast corner of Kyushu), had previously been identified from paleomagnetic rotations. Relative to ITRF2000 the three microplates rotate at different rates about neighboring poles located close to the northwest corner of Shikoku. The microplate model is identical to that proposed in the block model of Wallace et al. (2009, https://doi.org/10.1130/G2522A.1) except in southernmost Kyushu. On Shikoku and Honshu, but not Kyushu, the microplate model is consistent with that proposed in the block models of Nishimura and Hashimoto (2006, https://doi.org/10.1016/j.tecto.2006.04.017) and Loveless and Meade (2010, https://doi.org/10.1029/2008JB006248) without the low-slip-rate boundaries proposed in the latter.

An Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Jesper; Larsson, Stig; Sandberg, Mattias; Szepessy, Anders; Tempone, Raul

2015-01-01

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns symplectic Euler solutions of the Hamiltonian system connected with the optimal control problem. The error representation has a leading-order term consisting of an error density that is computable from symplectic Euler solutions. Under an assumption of the pathwise convergence of the approximate dual function as the maximum time step goes to zero, we prove that the remainder is of higher order than the leading-error density part in the error representation. With the error representation, it is possible to perform adaptive time stepping. We apply an adaptive algorithm originally developed for ordinary differential equations. The performance is illustrated by numerical tests.

Different seeds to solve the equations of stochastic point kinetics using the Euler-Maruyama method; Diferentes semillas para solucionar las ecuaciones de la cinetica puntual estocastica empleando el metodo de Euler-Maruyama

Energy Technology Data Exchange (ETDEWEB)

Suescun D, D.; Oviedo T, M., E-mail: daniel.suescun@usco.edu.co [Universidad Surcolombiana, Av. Pastrana Borrero - Carrera 1, Neiva, Huila (Colombia)

2017-09-15

In this paper, a numerical study of stochastic differential equations that describe the kinetics in a nuclear reactor is presented. These equations, known as the stochastic equations of punctual kinetics they model temporal variations in neutron population density and concentrations of deferred neutron precursors. Because these equations are probabilistic in nature (since random oscillations in the neutrons and population of precursors were considered to be approximately normally distributed, and these equations also possess strong coupling and stiffness properties) the proposed method for the numerical simulations is the Euler-Maruyama scheme that provides very good approximations for calculating the neutron population and concentrations of deferred neutron precursors. The method proposed for this work was computationally tested for different seeds, initial conditions, experimental data and forms of reactivity for a group of precursors and then for six groups of deferred neutron precursors at each time step with 5000 Brownian movements per seed. In a paper reported in the literature, the Euler-Maruyama method was proposed, but there are many doubts about the reported values, in addition to not reporting the seed used, so in this work is expected to rectify the reported values. After taking the average of the different seeds used to generate the pseudo-random numbers the results provided by the Euler-Maruyama scheme will be compared in mean and standard deviation with other methods reported in the literature and results of the deterministic model of the equations of the punctual kinetics. This comparison confirms in particular that the Euler-Maruyama scheme is an efficient method to solve the equations of stochastic point kinetics but different from the values found and reported by another author. The Euler-Maruyama method is simple and easy to implement, provides acceptable results for neutron population density and concentration of deferred neutron precursors and

Regularity and energy conservation for the compressible Euler equations

Czech Academy of Sciences Publication Activity Database

Feireisl, Eduard; Gwiazda, P.; Swierczewska-Gwiazda, A.; Wiedemann, E.

2017-01-01

Roč. 223, č. 3 (2017), s. 1375-1395 ISSN 0003-9527 EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : compressible Euler equations Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 2.392, year: 2016 http://link.springer.com/article/10.1007%2Fs00205-016-1060-5

Weak solutions for Euler systems with non-local interactions

Czech Academy of Sciences Publication Activity Database

Carrillo, J. A.; Feireisl, Eduard; Gwiazda, P.; Swierczewska-Gwiazda, A.

2017-01-01

Roč. 95, č. 3 (2017), s. 705-724 ISSN 0024-6107 EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : Euler system * dissipative solutions * Newtonian interaction Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.895, year: 2016 http://onlinelibrary.wiley.com/doi/10.1112/jlms.12027/abstract

A non-linear multigrid method for the steady Euler equations

NARCIS (Netherlands)

Hemker, P.W.; Koren, B.; Dervieux, A.; Leer, van B.; Periaux, J.; Rizzi, A.

1989-01-01

Higher-order accurate Euler-flow solutions are presented for some airfoil test cases. Second-order accurate solutions are computed by an Iterative Defect Correction process. For two test cases even higher accuracy is obtained by the additional use of a ~xtrapolation technique. Finite volume

Euler-Lagrange modeling of the hydrodynamics of dense multiphase flows

NARCIS (Netherlands)

Padding, J.T.; Deen, N.G.; Peters, E. A. J. F.; Kuipers, J. A. M.

2015-01-01

The large-scale hydrodynamic behavior of relatively dense dispersed multiphase flows, such as encountered in fluidized beds, bubbly flows, and liquid sprays, can be predicted efficiently by use of Euler-Lagrange models. In these models, grid-averaged equations for the continuous-phase flow field are

Neural network based multiscale image restoration approach

Science.gov (United States)

de Castro, Ana Paula A.; da Silva, José D. S.

2007-02-01

This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

Leonhardi Euleri Opera omnia: Editing the works and correspondence of Leonhard Euler

Directory of Open Access Journals (Sweden)

Andreas KLEINERT

2015-12-01

Full Text Available The paper gives an overview on the history and present state of the edition of the complete works of Leonhard Euler (1707–1783. After several failed initiatives in the 19th century, the project began in 1907 with the edition of Euler’s printed works. The works were divided into three series: I. Mathematics (29 volumes; II. Mechanics and Astronomy (31 volumes; and III. Physics and Miscellaneous (12 volumes. After several ups and downs due to two World Wars and economic problems, the publication of the printed works with a total of 72 volumes is nearly finished. Only two volumes on perturbation theory in astronomy are still missing. The publication of series IV (manuscripts and correspondence started in 1967 as a joint project of the Swiss and the Soviet academies of sciences. The manuscript edition was postponed, and the project focussed on Euler’s correspondence which contains approximately 3000 letters, 1000 of them written by Euler. The correspondents include famous mathematicians of the 18th century like d’Alembert, Clairaut and the Bernoullis, but also many less-known people with whom Euler corresponded on a great variety of subjects. A major problem is to find and to finance appropriate editors who are able to read French, Latin, and the old German handwriting, and who are acquainted with history, culture and science of the 18th century. During the last 50 years, the editors gathered copies or scans of most of the preserved Euler’s letters. The original letters addressed to Euler were made available to the editorial group in Switzerland by the Russian Academy of Sciences before World War I, and before their restitution in 1947 the editors made fairly good photographs that are now an important part of the material basis of the edition. Each volume of the letter series (VIA contains Euler’s correspondence with one or more of his contemporaries, presented in a chronological order. Up to the present day, four volumes of the

A novel numerical flux for the 3D Euler equations with general equation of state

KAUST Repository

Toro, Eleuterio F.

2015-09-30

Here we extend the flux vector splitting approach recently proposed in (E F Toro and M E Vázquez-Cendón. Flux splitting schemes for the Euler equations. Computers and Fluids. Vol. 70, Pages 1-12, 2012). The scheme was originally presented for the 1D Euler equations for ideal gases and its extension presented in this paper is threefold: (i) we solve the three-dimensional Euler equations on general meshes; (ii) we use a general equation of state; and (iii) we achieve high order of accuracy in both space and time through application of the semi-discrete ADER methodology on general meshes. The resulting methods are systematically assessed for accuracy, robustness and efficiency on a carefully selected suite of test problems. Formal high accuracy is assessed through convergence rates studies for schemes of up to 4th order of accuracy in both space and time on unstructured meshes.

Face recognition based on improved BP neural network

Directory of Open Access Journals (Sweden)

Yue Gaili

2017-01-01

Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

Performance prediction and flow-field analysis of rotors in hover using a coupled Euler/boundary layer method; Previsions des performances et de l`ecoulement pour des rotors en vol stationnaire par une methode couplee Euler/couche limite

Energy Technology Data Exchange (ETDEWEB)

Beaumier, P. [ONERA, 92 - Chatillon (France); Castellin, C.; Arnaud, G. [Eurocopter France, 13 - Marignane (France)

1998-12-31

The performance prediction of helicopter in hover is of key importance for manufacturers because hover is a design configuration for the definition of a rotor-craft. A lot of efforts have been made for more than 10 years all over the world in order to develop and validate numerical methods based on CFD. An Euler method (WAVES) developed by ONERA and coupled with a boundary layer code (MI3DI) is presented, validated and applied to compute the total performance of rotors with different tip shapes. A new boundary condition for the Euler code has been tested and enables better calculation by eliminating `numerical` recirculation. The code has demonstrated its ability to rank two rotors with different planforms in good agreement with experiment. Under industrial requirements new grid strategies have been studied and should allow to reduce CPU time consumption. It is shown that WAVES/MI3DI can be efficiently used in the aerodynamic design process of a new rotor. (authors) 7 refs.

A recurrent neural network based on projection operator for extended general variational inequalities.

Science.gov (United States)

Liu, Qingshan; Cao, Jinde

2010-06-01

Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.

Implicit flux-split Euler schemes for unsteady aerodynamic analysis involving unstructured dynamic meshes

Science.gov (United States)

Batina, John T.

1990-01-01

Improved algorithm for the solution of the time-dependent Euler equations are presented for unsteady aerodynamic analysis involving unstructured dynamic meshes. The improvements were developed recently to the spatial and temporal discretizations used by unstructured grid flow solvers. The spatial discretization involves a flux-split approach which is naturally dissipative and captures shock waves sharply with at most one grid point within the shock structure. The temporal discretization involves an implicit time-integration scheme using a Gauss-Seidel relaxation procedure which is computationally efficient for either steady or unsteady flow problems. For example, very large time steps may be used for rapid convergence to steady state, and the step size for unsteady cases may be selected for temporal accuracy rather than for numerical stability. Steady and unsteady flow results are presented for the NACA 0012 airfoil to demonstrate applications of the new Euler solvers. The unsteady results were obtained for the airfoil pitching harmonically about the quarter chord. The resulting instantaneous pressure distributions and lift and moment coefficients during a cycle of motion compare well with experimental data. A description of the Euler solvers is presented along with results and comparisons which assess the capability.

Automatic interpretation of magnetic data using Euler deconvolution with nonlinear background

Digital Repository Service at National Institute of Oceanography (India)

Dewangan, P.; Ramprasad, T.; Ramana, M.V.; Desa, M.; Shailaja, B.

are close to each other. A possible solution to these problems is prposed by simultaneously estimating the source location, depth and structural index assuming nonlinear background. The Euler equation is solved in a nonlinear fashion using the optimization...

Microfluidic systems for stem cell-based neural tissue engineering.

Science.gov (United States)

Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

2016-07-05

Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

An addendum to the Heisenberg-Euler effective action beyond one loop

Energy Technology Data Exchange (ETDEWEB)

Gies, Holger; Karbstein, Felix [Helmholtz-Institut Jena,Fröbelstieg 3, 07743 Jena (Germany); Theoretisch-Physikalisches Institut, Abbe Center of Photonics,Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, 07743 Jena (Germany)

2017-03-21

We study the effective interactions of external electromagnetic fields induced by fluctuations of virtual particles in the vacuum of quantum electrodynamics. Our main focus is on these interactions at two-loop order. We discuss in detail the emergence of the renowned Heisenberg-Euler effective action from the underlying microscopic theory of quantum electrodynamics, emphasizing its distinction from a standard one-particle irreducible effective action. In our explicit calculations we limit ourselves to constant and slowly varying external fields, allowing us to adopt a locally constant field approximation. One of our main findings is that at two-loop order there is a finite one-particle reducible contribution to the Heisenberg-Euler effective action in constant fields, which was previously assumed to vanish. In addition to their conceptual significance, our results are relevant for high-precision probes of quantum vacuum nonlinearity in strong electromagnetic fields.

Neural bases of congenital amusia in tonal language speakers.

Science.gov (United States)

Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

2017-03-01

Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

An L∞/L1-Constrained Quadratic Optimization Problem with Applications to Neural Networks

International Nuclear Information System (INIS)

Leizarowitz, Arie; Rubinstein, Jacob

2003-01-01

Pattern formation in associative neural networks is related to a quadratic optimization problem. Biological considerations imply that the functional is constrained in the L ∞ norm and in the L 1 norm. We consider such optimization problems. We derive the Euler-Lagrange equations, and construct basic properties of the maximizers. We study in some detail the case where the kernel of the quadratic functional is finite-dimensional. In this case the optimization problem can be fully characterized by the geometry of a certain convex and compact finite-dimensional set

Implementation of neural network based non-linear predictive

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1998-01-01

The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

Flow fields in the supersonic through-flow fan. Comparison of the solutions of the linear potential theory and the numerical solution of the Euler equations; Choonsoku tsukaryu fan nai no nagareba. Senkei potential rironkai to Euler hoteishiki no suchikai no hikaku

Energy Technology Data Exchange (ETDEWEB)

Yamasaki, N; Nanba, M; Tashiro, K [Kyushu University, Fukuoka (Japan). Faculty of Engineering

1996-03-27

Comparison study between solutions of a linear potential theory and numerical solution of Euler equations was made for flow in a supersonic through-flow fan. In numerical fluid dynamic technique, Euler equations are solved by finite difference method under the assumption of air and perfect gas fluid, and neglected viscosity and thermal conductivity of fluid. As a result, in a linear potential theory, expansion wave was regarded as equipotential discontinuous surface, while in Euler numerical solution, it was regarded as finite pressure gradient where a wave front fans out toward downstream. The latter reflection point of shock wave on a wing existed upstream as compared with the former reflection point. The shock wave angle was dominated by Euler equations, and different from the Mach line of a linear potential theory in both angle and discontinuous quantities in front and behind. Both calculated solutions well agreed with each other until the first reflection point of the Mach line, however, thereafter the difference between them increased toward downstream. 5 refs., 5 figs., 1 tab.

Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

Science.gov (United States)

Dai, Chenyun; Zheng, Yang; Hu, Xiaogang

2018-01-01

Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

Chaotic dynamics of flexible Euler-Bernoulli beams

Energy Technology Data Exchange (ETDEWEB)

Awrejcewicz, J., E-mail: awrejcew@p.lodz.pl [Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, 1/15 Stefanowski St., 90-924 Lodz, Poland and Department of Vehicles, Warsaw University of Technology, 84 Narbutta St., 02-524 Warsaw (Poland); Krysko, A. V., E-mail: anton.krysko@gmail.com [Department of Applied Mathematics and Systems Analysis, Saratov State Technical University, Politehnicheskaya 77, 410054 Saratov (Russian Federation); Kutepov, I. E., E-mail: iekutepov@gmail.com; Zagniboroda, N. A., E-mail: tssrat@mail.ru; Dobriyan, V., E-mail: Dobriy88@yandex.ru; Krysko, V. A., E-mail: tak@san.ru [Department of Mathematics and Modeling, Saratov State Technical University, Politehnicheskaya 77, 410054 Saratov (Russian Federation)

2013-12-15

Mathematical modeling and analysis of spatio-temporal chaotic dynamics of flexible simple and curved Euler-Bernoulli beams are carried out. The Kármán-type geometric non-linearity is considered. Algorithms reducing partial differential equations which govern the dynamics of studied objects and associated boundary value problems are reduced to the Cauchy problem through both Finite Difference Method with the approximation of O(c{sup 2}) and Finite Element Method. The obtained Cauchy problem is solved via the fourth and sixth-order Runge-Kutta methods. Validity and reliability of the results are rigorously discussed. Analysis of the chaotic dynamics of flexible Euler-Bernoulli beams for a series of boundary conditions is carried out with the help of the qualitative theory of differential equations. We analyze time histories, phase and modal portraits, autocorrelation functions, the Poincaré and pseudo-Poincaré maps, signs of the first four Lyapunov exponents, as well as the compression factor of the phase volume of an attractor. A novel scenario of transition from periodicity to chaos is obtained, and a transition from chaos to hyper-chaos is illustrated. In particular, we study and explain the phenomenon of transition from symmetric to asymmetric vibrations. Vibration-type charts are given regarding two control parameters: amplitude q{sub 0} and frequency ω{sub p} of the uniformly distributed periodic excitation. Furthermore, we detected and illustrated how the so called temporal-space chaos is developed following the transition from regular to chaotic system dynamics.

Measure-valued solutions to the complete Euler system revisited

Czech Academy of Sciences Publication Activity Database

Březina, J.; Feireisl, Eduard

2018-01-01

Roč. 69, č. 3 (2018), č. článku 57. ISSN 0044-2275 EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : Euler system * measure-valued solution * vanishing dissipation limit Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 1.687, year: 2016 https://link.springer.com/article/10.1007/s00033-018-0951-8

Uniqueness of rarefaction waves in multidimensional compressible Euler system

Czech Academy of Sciences Publication Activity Database

Feireisl, Eduard; Kreml, Ondřej

2015-01-01

Roč. 12, č. 3 (2015), s. 489-499 ISSN 0219-8916 R&D Projects: GA ČR GA13-00522S EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : compressible Euler system * uniqueness * rarefaction wave * Riemann problem Subject RIV: BA - General Mathematics Impact factor: 0.556, year: 2015 http://www.worldscientific.com/doi/abs/10.1142/S0219891615500149

Larger bases and mixed analog/digital neural nets

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

A counterexample of the Euler condition: the Appell–Hamel dynamical system on a horizontally moving plate

International Nuclear Information System (INIS)

Shan-Shan, Xu; Shu-Min, Li; Jamal, Berakdar

2009-01-01

As a counterexample of the Euler condition for nonholonomic constraint problems [H. C. Shen, Acta Phys. Sin. 54, 2468 (2005)], we investigate the Apell–Hamel dynamical system on a horizontally moving plate. The inconsistency of the results with Newton mechanics suggests that the Euler condition is not a universal model for nonlinear nonholonomic systems. This is attributed to the fact that the virtual displacements so obtained are not normal to the constraint forces. (general)

Discovering Euler Circuits and Paths through a Culturally Relevant Lesson

Science.gov (United States)

Robichaux, Rebecca R.; Rodrigue, Paulette R.

2006-01-01

This article describes a middle school discrete mathematics lesson that uses the context of catching crawfish to provide students with a hands-on experience related to Euler circuits and paths. The lesson promotes mathematical communication through the use of cooperative learning as well as connections between mathematics and the real world…

Maximal dissipation and well-posedness for the compressible Euler system

Czech Academy of Sciences Publication Activity Database

Feireisl, Eduard

2014-01-01

Roč. 16, č. 3 (2014), s. 447-461 ISSN 1422-6928 EU Projects: European Commission(XE) 320078 - MATHEF Keywords : maximal dissipation * compressible Euler system * weak solution Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2014 http://link.springer.com/article/10.1007/s00021-014-0163-8

Monte Carlo Euler approximations of HJM term structure financial models

KAUST Repository

Björk, Tomas

2012-11-22

We present Monte Carlo-Euler methods for a weak approximation problem related to the Heath-Jarrow-Morton (HJM) term structure model, based on Itô stochastic differential equations in infinite dimensional spaces, and prove strong and weak error convergence estimates. The weak error estimates are based on stochastic flows and discrete dual backward problems, and they can be used to identify different error contributions arising from time and maturity discretization as well as the classical statistical error due to finite sampling. Explicit formulas for efficient computation of sharp error approximation are included. Due to the structure of the HJM models considered here, the computational effort devoted to the error estimates is low compared to the work to compute Monte Carlo solutions to the HJM model. Numerical examples with known exact solution are included in order to show the behavior of the estimates. © 2012 Springer Science+Business Media Dordrecht.

Monte Carlo Euler approximations of HJM term structure financial models

KAUST Repository

Bjö rk, Tomas; Szepessy, Anders; Tempone, Raul; Zouraris, Georgios E.

2012-01-01

We present Monte Carlo-Euler methods for a weak approximation problem related to the Heath-Jarrow-Morton (HJM) term structure model, based on Itô stochastic differential equations in infinite dimensional spaces, and prove strong and weak error convergence estimates. The weak error estimates are based on stochastic flows and discrete dual backward problems, and they can be used to identify different error contributions arising from time and maturity discretization as well as the classical statistical error due to finite sampling. Explicit formulas for efficient computation of sharp error approximation are included. Due to the structure of the HJM models considered here, the computational effort devoted to the error estimates is low compared to the work to compute Monte Carlo solutions to the HJM model. Numerical examples with known exact solution are included in order to show the behavior of the estimates. © 2012 Springer Science+Business Media Dordrecht.

Comparison Between Polynomial, Euler Beta-Function and Expo-Rational B-Spline Bases

Science.gov (United States)

Kristoffersen, Arnt R.; Dechevsky, Lubomir T.; Laksa˚, Arne; Bang, Børre

2011-12-01

Euler Beta-function B-splines (BFBS) are the practically most important instance of generalized expo-rational B-splines (GERBS) which are not true expo-rational B-splines (ERBS). BFBS do not enjoy the full range of the superproperties of ERBS but, while ERBS are special functions computable by a very rapidly converging yet approximate numerical quadrature algorithms, BFBS are explicitly computable piecewise polynomial (for integer multiplicities), similar to classical Schoenberg B-splines. In the present communication we define, compute and visualize for the first time all possible BFBS of degree up to 3 which provide Hermite interpolation in three consecutive knots of multiplicity up to 3, i.e., the function is being interpolated together with its derivatives of order up to 2. We compare the BFBS obtained for different degrees and multiplicities among themselves and versus the classical Schoenberg polynomial B-splines and the true ERBS for the considered knots. The results of the graphical comparison are discussed from analytical point of view. For the numerical computation and visualization of the new B-splines we have used Maple 12.

Swimming holonomy principles, exemplified with a Euler fluid in two dimensions

International Nuclear Information System (INIS)

Hannay, J H

2012-01-01

The idealized problem of swimming—the self-propulsion phenomenon whereby a cyclic change of shape of a ‘swimmer’ produces a net movement—is well studied for the case of a very viscous incompressible liquid. The opposite limit of zero viscosity, the ideal or ‘Euler’ fluid, has also received some attention. There remain to be articulated and explored some points of principle, set here in the context of the Euler fluid in two dimensions, though partly common to both limits and to both two and three dimensions. (i) Perhaps surprisingly, both limits are purely geometric effects, ‘holonomies’, not dependent on any timings or rates, but only on the sequence of shapes adopted by the swimmer. (ii) A principle fully determining swimming in a Euler fluid is simply stated: the fluid moves at every moment so as to minimize the sum of its and the swimmer's kinetic energy. (iii) Euler swimming would be solvable explicitly were it not for the standard impasse of potential theory: to find the boundary normal derivative of a function obeying Laplace's equation given its value around the boundary (or vice versa). As usual more analytical progress is possible in two dimensions (by complexifying) than three, but full tractability still requires the extreme of slight, rapid swimming strokes, and a simple example is given. In both limits, for a non-symmetrical swimming stroke, a rotation or orientation holonomy accompanies the translational one—the swimmer has turned somewhat as well as translated. The whole holonomy is non-Abelian (the order of the shape sequence matters), but (iv) for two dimensions the rotation part is Abelian. A benefit (albeit cosmetic) is that the one-stroke displacement and turning can be written down as a complex line integral. (v) Another benefit is that while Stokes's theorem (in shape space) is normally sacrificed in non-Abelian holonomies, a partial recovery of the theorem is possible in two-dimensional swimming. To illustrate this last

Stability of periodic steady-state solutions to a non-isentropic Euler-Poisson system

Science.gov (United States)

Liu, Cunming; Peng, Yue-Jun

2017-06-01

We study the stability of periodic smooth solutions near non-constant steady-states for a non-isentropic Euler-Poisson system without temperature damping term. The system arises in the theory of semiconductors for which the doping profile is a given smooth function. In this stability problem, there are no special restrictions on the size of the doping profile, but only on the size of the perturbation. We prove that small perturbations of periodic steady-states are exponentially stable for large time. For this purpose, we introduce new variables and choose a non-diagonal symmetrizer of the full Euler equations to recover dissipation estimates. This also allows to make the proof of the stability result very simple and concise.

On the motion of incompressible inhomogeneous Euler-Korteweg fluids

Czech Academy of Sciences Publication Activity Database

Bulíček, M.; Feireisl, Eduard; Málek, J.; Shvydkoy, R.

2010-01-01

Roč. 3, č. 3 (2010), s. 497-515 ISSN 1937-1632 R&D Projects: GA MŠk LC06052; GA ČR GA201/09/0917 Institutional research plan: CEZ:AV0Z10190503 Keywords : Korteweg fluid * inhomogeneous Euler fluid * Korteweg stress * local-in-time well-posedness * smooth solution Subject RIV: BA - General Mathematics http://www.aimsciences.org/journals/displayArticles.jsp?paperID=5226

Wind power prediction based on genetic neural network

Science.gov (United States)

Zhang, Suhan

2017-04-01

The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

Memristor-based neural networks

International Nuclear Information System (INIS)

Thomas, Andy

2013-01-01

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Nonlinear control strategy based on using a shape-tunable neural controller

Energy Technology Data Exchange (ETDEWEB)

Chen, C.; Peng, S. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of chemical Engineering; Chang, W. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of Automatic Control

1997-08-01

In this paper, a nonlinear control strategy based on using a shape-tunable neural network is developed for adaptive control of nonlinear processes. Based on the steepest descent method, a learning algorithm that enables the neural controller to possess the ability of automatic controller output range adjustment is derived. The novel feature of automatic output range adjustment provides the neural controller more flexibility and capability, and therefore the scaling procedure, which is usually unavoidable for the conventional fixed-shape neural controllers, becomes unnecessary. The advantages and effectiveness of the proposed nonlinear control strategy are demonstrated through the challenge problem of controlling an open-loop unstable nonlinear continuous stirred tank reactor (CSTR). 14 refs., 11 figs.

VennDIS: a JavaFX-based Venn and Euler diagram software to generate publication quality figures.

Science.gov (United States)

Ignatchenko, Vladimir; Ignatchenko, Alexandr; Sinha, Ankit; Boutros, Paul C; Kislinger, Thomas

2015-04-01

Venn diagrams are graphical representations of the relationships among multiple sets of objects and are often used to illustrate similarities and differences among genomic and proteomic datasets. All currently existing tools for producing Venn diagrams evince one of two traits; they require expertise in specific statistical software packages (such as R), or lack the flexibility required to produce publication-quality figures. We describe a simple tool that addresses both shortcomings, Venn Diagram Interactive Software (VennDIS), a JavaFX-based solution for producing highly customizable, publication-quality Venn, and Euler diagrams of up to five sets. The strengths of VennDIS are its simple graphical user interface and its large array of customization options, including the ability to modify attributes such as font, style and position of the labels, background color, size of the circle/ellipse, and outline color. It is platform independent and provides real-time visualization of figure modifications. The created figures can be saved as XML files for future modification or exported as high-resolution images for direct use in publications. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Different seeds to solve the equations of stochastic point kinetics using the Euler-Maruyama method

International Nuclear Information System (INIS)

Suescun D, D.; Oviedo T, M.

2017-09-01

In this paper, a numerical study of stochastic differential equations that describe the kinetics in a nuclear reactor is presented. These equations, known as the stochastic equations of punctual kinetics they model temporal variations in neutron population density and concentrations of deferred neutron precursors. Because these equations are probabilistic in nature (since random oscillations in the neutrons and population of precursors were considered to be approximately normally distributed, and these equations also possess strong coupling and stiffness properties) the proposed method for the numerical simulations is the Euler-Maruyama scheme that provides very good approximations for calculating the neutron population and concentrations of deferred neutron precursors. The method proposed for this work was computationally tested for different seeds, initial conditions, experimental data and forms of reactivity for a group of precursors and then for six groups of deferred neutron precursors at each time step with 5000 Brownian movements per seed. In a paper reported in the literature, the Euler-Maruyama method was proposed, but there are many doubts about the reported values, in addition to not reporting the seed used, so in this work is expected to rectify the reported values. After taking the average of the different seeds used to generate the pseudo-random numbers the results provided by the Euler-Maruyama scheme will be compared in mean and standard deviation with other methods reported in the literature and results of the deterministic model of the equations of the punctual kinetics. This comparison confirms in particular that the Euler-Maruyama scheme is an efficient method to solve the equations of stochastic point kinetics but different from the values found and reported by another author. The Euler-Maruyama method is simple and easy to implement, provides acceptable results for neutron population density and concentration of deferred neutron precursors and

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

Science.gov (United States)

Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

Accuracy of an unstructured-grid upwind-Euler algorithm for the ONERA M6 wing

Science.gov (United States)

Batina, John T.

1991-01-01

Improved algorithms for the solution of the three-dimensional, time-dependent Euler equations are presented for aerodynamic analysis involving unstructured dynamic meshes. The improvements have been developed recently to the spatial and temporal discretizations used by unstructured-grid flow solvers. The spatial discretization involves a flux-split approach that is naturally dissipative and captures shock waves sharply with at most one grid point within the shock structure. The temporal discretization involves either an explicit time-integration scheme using a multistage Runge-Kutta procedure or an implicit time-integration scheme using a Gauss-Seidel relaxation procedure, which is computationally efficient for either steady or unsteady flow problems. With the implicit Gauss-Seidel procedure, very large time steps may be used for rapid convergence to steady state, and the step size for unsteady cases may be selected for temporal accuracy rather than for numerical stability. Steady flow results are presented for both the NACA 0012 airfoil and the Office National d'Etudes et de Recherches Aerospatiales M6 wing to demonstrate applications of the new Euler solvers. The paper presents a description of the Euler solvers along with results and comparisons that assess the capability.

Derivation of the Euler equations in Thomas-Fermi theories of a hot nuclear system

International Nuclear Information System (INIS)

Wang, C.

1992-01-01

The variational extreme condition with respect to statistical distribution of nucleons in momentum space is applied to derive the Euler equation of the nuclear density profile. The resultant Euler equation of the nuclear density profile is proven to be identical with that obtained in the usual Thomas-Fermi theories of a hot nuclear system where the variation is made with respect to the nuclear density profile. A Fermi-Dirac-type distribution appears as a result of variation in the present approach, while it is used as a given expression in obtaining the variation of the nuclear density profile in the usual Thomas-Fermi theories

Analysis of A Uniform Bernoulli – Euler Beam on Winkler Foundation ...

African Journals Online (AJOL)

ADOWIE PERE

2018-03-09

Mar 9, 2018 ... method to analyze Winkler foundation subjected to a harmonic moving load on a uniform Bernoulli – Euler Beam. MATLAB software was used to implement the Newmark time integration method to ... A lot of engineering structures under moving loads .... Because numerical procedure produce stability issue,.

Pinning synchronization of memristor-based neural networks with time-varying delays.

Science.gov (United States)

Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

2017-09-01

In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effectiveness of firefly algorithm based neural network in time series ...

African Journals Online (AJOL)

Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

Neural Network-Based Resistance Spot Welding Control and Quality Prediction

Energy Technology Data Exchange (ETDEWEB)

Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

1999-07-10

This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

A spectral element-FCT method for the compressible Euler equations

International Nuclear Information System (INIS)

Giannakouros, J.; Karniadakis, G.E.

1994-01-01

A new algorithm based on spectral element discretizations and flux-corrected transport concepts is developed for the solution of the Euler equations of inviscid compressible fluid flow. A conservative formulation is proposed based on one- and two-dimensional cell-averaging and reconstruction procedures, which employ a staggered mesh of Gauss-Chebyshev and Gauss-Lobatto-Chebyshev collocation points. Particular emphasis is placed on the construction of robust boundary and interfacial conditions in one- and two-dimensions. It is demonstrated through shock-tube problems and two-dimensional simulations that the proposed algorithm leads to stable, non-oscillatory solutions of high accuracy. Of particular importance is the fact that dispersion errors are minimal, as show through experiments. From the operational point of view, casting the method in a spectral element formulation provides flexibility in the discretization, since a variable number of macro-elements or collocation points per element can be employed to accomodate both accuracy and geometric requirements

An A Posteriori Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Peer Jesper

2015-01-07

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns Symplectic Euler solutions of the Hamiltonian system connected with the optimal control problem. The error representation has a leading order term consisting of an error density that is computable from Symplectic Euler solutions. Under an assumption of the pathwise convergence of the approximate dual function as the maximum time step goes to zero, we prove that the remainder is of higher order than the leading error density part in the error representation. With the error representation, it is possible to perform adaptive time stepping. We apply an adaptive algorithm originally developed for ordinary differential equations.

MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

Science.gov (United States)

Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

Prediction of a Densely Loaded Particle-Laden Jet using a Euler-Lagrange Dense Spray Model

Science.gov (United States)

Pakseresht, Pedram; Apte, Sourabh V.

2017-11-01

Modeling of a dense spray regime using an Euler-Lagrange discrete-element approach is challenging because of local high volume loading. A subgrid cluster of droplets can lead to locally high void fractions for the disperse phase. Under these conditions, spatio-temporal changes in the carrier phase volume fractions, which are commonly neglected in spray simulations in an Euler-Lagrange two-way coupling model, could become important. Accounting for the carrier phase volume fraction variations, leads to zero-Mach number, variable density governing equations. Using pressure-based solvers, this gives rise to a source term in the pressure Poisson equation and a non-divergence free velocity field. To test the validity and predictive capability of such an approach, a round jet laden with solid particles is investigated using Direct Numerical Simulation and compared with available experimental data for different loadings. Various volume fractions spanning from dilute to dense regimes are investigated with and without taking into account the volume displacement effects. The predictions of the two approaches are compared and analyzed to investigate the effectiveness of the dense spray model. Financial support was provided by National Aeronautics and Space Administration (NASA).

Neural Cell Chip Based Electrochemical Detection of Nanotoxicity.

Science.gov (United States)

Kafi, Md Abdul; Cho, Hyeon-Yeol; Choi, Jeong Woo

2015-07-02

Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD) or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C), C(RGD)₄ ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot) or three dimensional (rod or pillar) like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD), graphene oxide (GO) and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

Neural Cell Chip Based Electrochemical Detection of Nanotoxicity

Directory of Open Access Journals (Sweden)

Md. Abdul Kafi

2015-07-01

Full Text Available Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C, C(RGD4 ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot or three dimensional (rod or pillar like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD, graphene oxide (GO and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

Unfolding code for neutron spectrometry based on neural nets technology

International Nuclear Information System (INIS)

Ortiz R, J. M.; Vega C, H. R.

2012-10-01

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the R obust Design of Artificial Neural Networks Methodology . The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

DWI-Based Neural Fingerprinting Technology: A Preliminary Study on Stroke Analysis

Directory of Open Access Journals (Sweden)

Chenfei Ye

2014-01-01

Full Text Available Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI and diffusion tensor imaging (DTI. Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

Simultaneous surface and depth neural activity recording with graphene transistor-based dual-modality probes.

Science.gov (United States)

Du, Mingde; Xu, Xianchen; Yang, Long; Guo, Yichuan; Guan, Shouliang; Shi, Jidong; Wang, Jinfen; Fang, Ying

2018-05-15

Subdural surface and penetrating depth probes are widely applied to record neural activities from the cortical surface and intracortical locations of the brain, respectively. Simultaneous surface and depth neural activity recording is essential to understand the linkage between the two modalities. Here, we develop flexible dual-modality neural probes based on graphene transistors. The neural probes exhibit stable electrical performance even under 90° bending because of the excellent mechanical properties of graphene, and thus allow multi-site recording from the subdural surface of rat cortex. In addition, finite element analysis was carried out to investigate the mechanical interactions between probe and cortex tissue during intracortical implantation. Based on the simulation results, a sharp tip angle of π/6 was chosen to facilitate tissue penetration of the neural probes. Accordingly, the graphene transistor-based dual-modality neural probes have been successfully applied for simultaneous surface and depth recording of epileptiform activity of rat brain in vivo. Our results show that graphene transistor-based dual-modality neural probes can serve as a facile and versatile tool to study tempo-spatial patterns of neural activities. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural network based electron identification in the ZEUS calorimeter

International Nuclear Information System (INIS)

Abramowicz, H.; Caldwell, A.; Sinkus, R.

1995-01-01

We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

Implementation of neural network based non-linear predictive control

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1999-01-01

This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

Measures of Coupling between Neural Populations Based on Granger Causality Principle.

Science.gov (United States)

Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

2016-01-01

This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

Chinese Sentence Classification Based on Convolutional Neural Network

Science.gov (United States)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Smooth values of the iterates of the Euler's Phi function

OpenAIRE

Lamzouri, Youness

2005-01-01

Let $\\phi(n)$ be the Euler-phi function, define $\\phi_0(n) = n$ and $\\phi_{k+1}(n)=\\phi(\\phi_{k}(n))$ for all $k\\geq 0$. We will determine an asymptotic formula for the set of integers $n$ less than $x$ for which $\\phi_k(n)$ is $y$-smooth, conditionally on a weak form of the Elliott-Halberstam conjecture.

Integration with respect to the Euler characteristic and its applications

Energy Technology Data Exchange (ETDEWEB)

Gusein-Zade, Sabir M [M. V. Lomonosov Moscow State University, Moscow (Russian Federation)

2010-09-16

The notion of integration with respect to the Euler characteristic and its generalizations are discussed: integration over the infinite-dimensional spaces of arcs and functions, motivic integration. The author describes applications of these notions to the computation of monodromy zeta functions, Poincare series of multi-index filtrations, generating series of classes of certain moduli spaces, and so on. Bibliography: 70 titles.

Further Generalization of Golden Mean in Relation to Euler Divine Equation

OpenAIRE

Rakocevic, Miloje M.

2006-01-01

In the paper a new generalization of the Golden mean, as a further generalization in relation to Stakhov (1989) and to Spinadel (1999), is presented. Also it is first observed that the Euler divine equation represents a possible generalization of Golden mean. In this second version the Section 6 is added.

The importance of Leonhard Euler's discoveries in the field of shipbuilding for the scientific evolution of academician A. N. Krylov

Science.gov (United States)

Sharkov, N. A.; Sharkova, O. A.

2018-05-01

The paper identifies the importance of the Leonhard Euler's discoveries in the field of shipbuilding for the scientific evolution of academician A. N. Krylov and for the modern knowledge in survivability and safety of ships. The works by Leonard Euler "Marine Science" and "The Moon Motion New Theory" are discussed.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

Viscous Regularization of the Euler Equations and Entropy Principles

KAUST Repository

Guermond, Jean-Luc

2014-03-11

This paper investigates a general class of viscous regularizations of the compressible Euler equations. A unique regularization is identified that is compatible with all the generalized entropies, à la [Harten et al., SIAM J. Numer. Anal., 35 (1998), pp. 2117-2127], and satisfies the minimum entropy principle. A connection with a recently proposed phenomenological model by [H. Brenner, Phys. A, 370 (2006), pp. 190-224] is made. © 2014 Society for Industrial and Applied Mathematics.

A neutron spectrum unfolding computer code based on artificial neural networks

International Nuclear Information System (INIS)

Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

2014-01-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

Standard cell-based implementation of a digital optoelectronic neural-network hardware.

Science.gov (United States)

Maier, K D; Beckstein, C; Blickhan, R; Erhard, W

2001-03-10

A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

An A Posteriori Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Peer Jesper; Larsson, Stig; Sandberg, Mattias; Szepessy, Anders; Tempone, Raul

2015-01-01

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns Symplectic Euler solutions of the Hamiltonian system

Enhanced Neural Cell Adhesion and Neurite Outgrowth on Graphene-Based Biomimetic Substrates

Directory of Open Access Journals (Sweden)

Suck Won Hong

2014-01-01

Full Text Available Neural cell adhesion and neurite outgrowth were examined on graphene-based biomimetic substrates. The biocompatibility of carbon nanomaterials such as graphene and carbon nanotubes (CNTs, that is, single-walled and multiwalled CNTs, against pheochromocytoma-derived PC-12 neural cells was also evaluated by quantifying metabolic activity (with WST-8 assay, intracellular oxidative stress (with ROS assay, and membrane integrity (with LDH assay. Graphene films were grown by using chemical vapor deposition and were then coated onto glass coverslips by using the scooping method. Graphene sheets were patterned on SiO2/Si substrates by using photolithography and were then covered with serum for a neural cell culture. Both types of CNTs induced significant dose-dependent decreases in the viability of PC-12 cells, whereas graphene exerted adverse effects on the neural cells just at over 62.5 ppm. This result implies that graphene and CNTs, even though they were the same carbon-based nanomaterials, show differential influences on neural cells. Furthermore, graphene-coated or graphene-patterned substrates were shown to substantially enhance the adhesion and neurite outgrowth of PC-12 cells. These results suggest that graphene-based substrates as biomimetic cues have good biocompatibility as well as a unique surface property that can enhance the neural cells, which would open up enormous opportunities in neural regeneration and nanomedicine.

Automated implementation of rule-based expert systems with neural networks for time-critical applications

Science.gov (United States)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Design of Artificial Neural Network-Based pH Estimator

Directory of Open Access Journals (Sweden)

Shebel A. Alsabbah

2010-10-01

Full Text Available Taking into consideration the cost, size and drawbacks might be found with real hardware instrument for measuring pH values such that the complications of the wiring, installing, calibrating and troubleshooting the system, would make a person look for a cheaper, accurate, and alternative choice to perform the measuring operation, Where’s hereby, a feedforward artificial neural network-based pH estimator has to be proposed. The proposed estimator has been designed with multi- layer perceptrons. One input which is a measured base stream and two outputs represent pH values at strong base and strong/weak acids for a titration process. The created data base has been obtained with consideration of temperature variation. The final numerical results ensure the effectiveness and robustness of the design neural network-based pH estimator.

Iris double recognition based on modified evolutionary neural network

Science.gov (United States)

Liu, Shuai; Liu, Yuan-Ning; Zhu, Xiao-Dong; Huo, Guang; Liu, Wen-Tao; Feng, Jia-Kai

2017-11-01

Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.

Fluid-structure coupling in Lagrange-Lagrange and Euler-Lagrange descriptions

International Nuclear Information System (INIS)

Jones, A.V.

1981-01-01

Fluid-structure interaction problems are very common in the reactor safety field, examples being containment loading in LMFBR systems and the downcomer problem in LWRs. This article reviews the principal finite difference methodes employed for their solution. After a survey of the chief representations of the equations of motion of the fluid and structure and of their coupling, the Lagrange-Lagrange and Euler-Lagrange representations are examined in detail. The practical necessity of treating the structure in Lagrangian coordinates and the respective merits of the Lagrangian and Eulerian representations for the fluid are explained, both for coupling between continua and for coupling between a fluid and a thin shell. Detailed analyses of the stability and numerical dissipation of the Lagrange-Lagrange and Euler-Lagrange coupling techniques in a very simple one-dimensional problem are provided to supply indicators as to stability and dissipation in more complex multidimensional situations and to bring out the theoretical complexity of seemingly simple coupling algorithms. The article then presents some practical examples of coupled problems in which calculations can be compared with experiment, and concludes with a section on future trends in the field of fluid-structure coupling

Unfolding code for neutron spectrometry based on neural nets technology

Energy Technology Data Exchange (ETDEWEB)

Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

2012-10-15

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

Neural Representation. A Survey-Based Analysis of the Notion

Directory of Open Access Journals (Sweden)

Oscar Vilarroya

2017-08-01

Full Text Available The word representation (as in “neural representation”, and many of its related terms, such as to represent, representational and the like, play a central explanatory role in neuroscience literature. For instance, in “place cell” literature, place cells are extensively associated with their role in “the representation of space.” In spite of its extended use, we still lack a clear, universal and widely accepted view on what it means for a nervous system to represent something, on what makes a neural activity a representation, and on what is re-presented. The lack of a theoretical foundation and definition of the notion has not hindered actual research. My aim here is to identify how active scientists use the notion of neural representation, and eventually to list a set of criteria, based on actual use, that can help in distinguishing between genuine or non-genuine neural-representation candidates. In order to attain this objective, I present first the results of a survey of authors within two domains, place-cell and multivariate pattern analysis (MVPA research. Based on the authors’ replies, and on a review of neuroscientific research, I outline a set of common properties that an account of neural representation seems to require. I then apply these properties to assess the use of the notion in two domains of the survey, place-cell and MVPA studies. I conclude by exploring a shift in the notion of representation suggested by recent literature.

The most precise computations using Euler's method in standard floating-point arithmetic applied to modelling of biological systems.

Science.gov (United States)

Kalinina, Elizabeth A

2013-08-01

The explicit Euler's method is known to be very easy and effective in implementation for many applications. This article extends results previously obtained for the systems of linear differential equations with constant coefficients to arbitrary systems of ordinary differential equations. Optimal (providing minimum total error) step size is calculated at each step of Euler's method. Several examples of solving stiff systems are included. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Generalizations of Steffensen’s inequality via some Euler-type identities

Directory of Open Access Journals (Sweden)

Pečarić Josip

2016-08-01

Full Text Available Using Euler-type identities some new generalizations of Steffensen’s inequality for n–convex functions are obtained. Moreover, the Ostrowski-type inequalities related to obtained generalizations are given. Furthermore, using inequalities for the Čebyšev functional in terms of the first derivative some new bounds for the remainder in identities related to generalizations of Steffensen’s inequality are proven.

A Markovian event-based framework for stochastic spiking neural networks.

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Adaptive Synchronization of Memristor-based Chaotic Neural Systems

Directory of Open Access Journals (Sweden)

Xiaofang Hu

2014-11-01

Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

Measures of coupling between neural populations based on Granger causality principle

Directory of Open Access Journals (Sweden)

Maciej Kaminski

2016-10-01

Full Text Available This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a weak node. Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

Analysis of preconditioning and multigrid for Euler flows with low-subsonic regions

NARCIS (Netherlands)

Koren, B.; Leer, van B.

1995-01-01

For subsonic flows and upwind-discretized, linearized 1-D Euler equations, the smoothing behavior of multigrid-accelerated point Gauss-Seidel relaxation is analyzed. Error decay by convection across domain boundaries is also discussed. A fix to poor convergence rates at low Mach numbers is sought in

Numeral eddy current sensor modelling based on genetic neural network

International Nuclear Information System (INIS)

Yu Along

2008-01-01

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

The neural correlates of gist-based true and false recognition

Science.gov (United States)

Gutchess, Angela H.; Schacter, Daniel L.

2012-01-01

When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

A novel word spotting method based on recurrent neural networks.

Science.gov (United States)

Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

2012-02-01

Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

On the Approximation of Generalized Lipschitz Function by Euler Means of Conjugate Series of Fourier Series

Science.gov (United States)

Kushwaha, Jitendra Kumar

2013-01-01

Approximation theory is a very important field which has various applications in pure and applied mathematics. The present study deals with a new theorem on the approximation of functions of Lipschitz class by using Euler's mean of conjugate series of Fourier series. In this paper, the degree of approximation by using Euler's means of conjugate of functions belonging to Lip (ξ(t), p) class has been obtained. Lipα and Lip (α, p) classes are the particular cases of Lip (ξ(t), p) class. The main result of this paper generalizes some well-known results in this direction. PMID:24379744

Reward-based training of recurrent neural networks for cognitive and value-based tasks.

Science.gov (United States)

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-13

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

Knowledge base and neural network approach for protein secondary structure prediction.

Science.gov (United States)

Patel, Maulika S; Mazumdar, Himanshu S

2014-11-21

Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

A further note on the force discrepancy for wing theory in Euler flow

Indian Academy of Sciences (India)

The Euler equations use the assumption that the fluid does not impart any resistance ... viscosity, the kinetic energy associated with these flow fields is now bounded, ..... Combining all the results together from Appendices B, C and D we get.

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

International Nuclear Information System (INIS)

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

2008-01-01

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

Three Dimensional Steady Subsonic Euler Flows in Bounded Nozzles

OpenAIRE

Chen, Chao; Xie, Chunjing

2013-01-01

In this paper, we study the existence and uniqueness of three dimensional steady Euler flows in rectangular nozzles when prescribing normal component of momentum at both the entrance and exit. If, in addition, the normal component of the voriticity and the variation of Bernoulli's function at the exit are both zero, then there exists a unique subsonic potential flow when the magnitude of the normal component of the momentum is less than a critical number. As the magnitude of the normal compon...

A-free rigidity and applications to the compressible Euler system

Czech Academy of Sciences Publication Activity Database

Chiodaroli, E.; Feireisl, Eduard; Kreml, Ondřej; Wiedemann, E.

2017-01-01

Roč. 196, č. 4 (2017), s. 1557-1572 ISSN 0373-3114 R&D Projects: GA ČR GA13-00522S EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : A-free condition * compressible Euler equations * measure-valued solutions Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.864, year: 2016 https://link.springer.com/article/10.1007%2Fs10231-016-0629-9

The Camassa-Holm equation as an incompressible Euler equation: A geometric point of view

Science.gov (United States)

Gallouët, Thomas; Vialard, François-Xavier

2018-04-01

The group of diffeomorphisms of a compact manifold endowed with the L2 metric acting on the space of probability densities gives a unifying framework for the incompressible Euler equation and the theory of optimal mass transport. Recently, several authors have extended optimal transport to the space of positive Radon measures where the Wasserstein-Fisher-Rao distance is a natural extension of the classical L2-Wasserstein distance. In this paper, we show a similar relation between this unbalanced optimal transport problem and the Hdiv right-invariant metric on the group of diffeomorphisms, which corresponds to the Camassa-Holm (CH) equation in one dimension. Geometrically, we present an isometric embedding of the group of diffeomorphisms endowed with this right-invariant metric in the automorphisms group of the fiber bundle of half densities endowed with an L2 type of cone metric. This leads to a new formulation of the (generalized) CH equation as a geodesic equation on an isotropy subgroup of this automorphisms group; On S1, solutions to the standard CH thus give radially 1-homogeneous solutions of the incompressible Euler equation on R2 which preserves a radial density that has a singularity at 0. An other application consists in proving that smooth solutions of the Euler-Arnold equation for the Hdiv right-invariant metric are length minimizing geodesics for sufficiently short times.

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Science.gov (United States)

Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Parallel implementations of 2D explicit Euler solvers

International Nuclear Information System (INIS)

Giraud, L.; Manzini, G.

1996-01-01

In this work we present a subdomain partitioning strategy applied to an explicit high-resolution Euler solver. We describe the design of a portable parallel multi-domain code suitable for parallel environments. We present several implementations on a representative range of MlMD computers that include shared memory multiprocessors, distributed virtual shared memory computers, as well as networks of workstations. Computational results are given to illustrate the efficiency, the scalability, and the limitations of the different approaches. We discuss also the effect of the communication protocol on the optimal domain partitioning strategy for the distributed memory computers

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

Science.gov (United States)

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Perturbational blowup solutions to the compressible Euler equations with damping.

Science.gov (United States)

Cheung, Ka Luen

2016-01-01

The N-dimensional isentropic compressible Euler system with a damping term is one of the most fundamental equations in fluid dynamics. Since it does not have a general solution in a closed form for arbitrary well-posed initial value problems. Constructing exact solutions to the system is a useful way to obtain important information on the properties of its solutions. In this article, we construct two families of exact solutions for the one-dimensional isentropic compressible Euler equations with damping by the perturbational method. The two families of exact solutions found include the cases [Formula: see text] and [Formula: see text], where [Formula: see text] is the adiabatic constant. With analysis of the key ordinary differential equation, we show that the classes of solutions include both blowup type and global existence type when the parameters are suitably chosen. Moreover, in the blowup cases, we show that the singularities are of essential type in the sense that they cannot be smoothed by redefining values at the odd points. The two families of exact solutions obtained in this paper can be useful to study of related numerical methods and algorithms such as the finite difference method, the finite element method and the finite volume method that are applied by scientists to simulate the fluids for applications.

General solutions of second-order linear difference equations of Euler type

Directory of Open Access Journals (Sweden)

Akane Hongyo

2017-01-01

Full Text Available The purpose of this paper is to give general solutions of linear difference equations which are related to the Euler-Cauchy differential equation \\(y^{\\prime\\prime}+(\\lambda/t^2y=0\\ or more general linear differential equations. We also show that the asymptotic behavior of solutions of the linear difference equations are similar to solutions of the linear differential equations.

Radioactivity nuclide identification based on BP and LM algorithm neural network

International Nuclear Information System (INIS)

Wang Jihong; Sun Jian; Wang Lianghou

2012-01-01

The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

Bending of Euler-Bernoulli nanobeams based on the strain-driven and stress-driven nonlocal integral models: a numerical approach

Science.gov (United States)

Oskouie, M. Faraji; Ansari, R.; Rouhi, H.

2018-04-01

Eringen's nonlocal elasticity theory is extensively employed for the analysis of nanostructures because it is able to capture nanoscale effects. Previous studies have revealed that using the differential form of the strain-driven version of this theory leads to paradoxical results in some cases, such as bending analysis of cantilevers, and recourse must be made to the integral version. In this article, a novel numerical approach is developed for the bending analysis of Euler-Bernoulli nanobeams in the context of strain- and stress-driven integral nonlocal models. This numerical approach is proposed for the direct solution to bypass the difficulties related to converting the integral governing equation into a differential equation. First, the governing equation is derived based on both strain-driven and stress-driven nonlocal models by means of the minimum total potential energy. Also, in each case, the governing equation is obtained in both strong and weak forms. To solve numerically the derived equations, matrix differential and integral operators are constructed based upon the finite difference technique and trapezoidal integration rule. It is shown that the proposed numerical approach can be efficiently applied to the strain-driven nonlocal model with the aim of resolving the mentioned paradoxes. Also, it is able to solve the problem based on the strain-driven model without inconsistencies of the application of this model that are reported in the literature.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Science.gov (United States)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic

The image recognition based on neural network and Bayesian decision

Science.gov (United States)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

Science.gov (United States)

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Uniform approximations of Bernoulli and Euler polynomials in terms of hyperbolic functions

NARCIS (Netherlands)

J.L. López; N.M. Temme (Nico)

1998-01-01

textabstractBernoulli and Euler polynomials are considered for large values of the order. Convergent expansions are obtained for $B_n(nz+1/2)$ and $E_n(nz+1/2)$ in powers of $n^{-1$, with coefficients being rational functions of $z$ and hyperbolic functions of argument $1/2z$. These expansions are

Well/ill posedness for the Euler-Korteweg-Poisson system and related problems

Czech Academy of Sciences Publication Activity Database

Donatelli, D.; Feireisl, Eduard; Marcati, P.

2015-01-01

Roč. 40, č. 7 (2015), s. 1314-1335 ISSN 0360-5302 EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : convex integration * Euler-Korteweg system * quantum hydrodynamics Subject RIV: BA - General Mathematics Impact factor: 1.444, year: 2015 http://www.tandfonline.com/doi/abs/10.1080/03605302.2014.972517

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

Science.gov (United States)

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Error Concealment using Neural Networks for Block-Based Image Coding

Directory of Open Access Journals (Sweden)

M. Mokos

2006-06-01

Full Text Available In this paper, a novel adaptive error concealment (EC algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison.

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

Science.gov (United States)

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Data systems and computer science: Neural networks base R/T program overview

Science.gov (United States)

Gulati, Sandeep

1991-01-01

The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

SU-8-based microneedles for in vitro neural applications

International Nuclear Information System (INIS)

Altuna, Ane; Tijero, María; Berganzo, Javier; Salido, Rafa; Fernández, Luis J; Gabriel, Gemma; Guimerá, Anton; Villa, Rosa; Menéndez de la Prida, Liset

2010-01-01

This paper presents novel design, fabrication, packaging and the first in vitro neural activity recordings of SU-8-based microneedles. The polymer SU-8 was chosen because it provides excellent features for the fabrication of flexible and thin probes. A microprobe was designed in order to allow a clean insertion and to minimize the damage caused to neural tissue during in vitro applications. In addition, a tetrode is patterned at the tip of the needle to obtain fine-scale measurements of small neuronal populations within a radius of 100 µm. Impedance characterization of the electrodes has been carried out to demonstrate their viability for neural recording. Finally, probes are inserted into 400 µm thick hippocampal slices, and simultaneous action potentials with peak-to-peak amplitudes of 200–250 µV are detected.

UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR

Directory of Open Access Journals (Sweden)

S. S. Andropov

2016-09-01

Full Text Available A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.

Non-uniqueness of admissible weak solutions to the Riemann problem for isentropic Euler equations

Science.gov (United States)

Chiodaroli, Elisabetta; Kreml, Ondřej

2018-04-01

We study the Riemann problem for multidimensional compressible isentropic Euler equations. Using the framework developed in Chiodaroli et al (2015 Commun. Pure Appl. Math. 68 1157–90), and based on the techniques of De Lellis and Székelyhidi (2010 Arch. Ration. Mech. Anal. 195 225–60), we extend the results of Chiodaroli and Kreml (2014 Arch. Ration. Mech. Anal. 214 1019–49) and prove that it is possible to characterize a set of Riemann data, giving rise to a self-similar solution consisting of one admissible shock and one rarefaction wave, for which the problem also admits infinitely many admissible weak solutions.

Battery-Free Love-Wave-Based Neural Probe and Its Wireless Characterizations

Science.gov (United States)

Jung, In Ki; Fu, Chen; Lee, Keekeun

2013-06-01

A wireless Love-wave-based neural probe that utilizes a one-port reflective delay line was developed for both reading and stimulating neurons in the brain. Poly(methyl methacrylate) (PMMA) as a waveguide layer and gold (Au) electrodes were structured on the top of a 41° YX LiNbO3 piezoelectric substrate, following the parameters extracted from coupling-of-mode (COM) modeling. For a one-port reflective delay line, single-phase unidirectional transducers (SPUDTs) and three shorted grating reflectors were employed, which made possible the implementation of a wireless and battery-free neural probe. The fabricated Love-wave-based neural probes were wirelessly measured using two antennas with a 440 MHz central frequency and a network analyzer. Sharp reflection peaks with a high signal-to-noise ratio were observed from the reflection peaks. The probe was immersed in 0.9% saline solution while applying input DC voltages. Good linearity, high sensitivity, and reproducibility were observed depending on DC applied voltage, in the range from 0 to 500 mV. The sensitivity obtained from the DC firings (artificial neural firings) was ˜0.04 µs/VDC, indicating that this prototype probe is very promising for the wireless reading and stimulation of neural firings in in vivo animal testing.

Fully anisotropic goal-oriented mesh adaptation for 3D steady Euler equations

Science.gov (United States)

Loseille, A.; Dervieux, A.; Alauzet, F.

2010-04-01

This paper studies the coupling between anisotropic mesh adaptation and goal-oriented error estimate. The former is very well suited to the control of the interpolation error. It is generally interpreted as a local geometric error estimate. On the contrary, the latter is preferred when studying approximation errors for PDEs. It generally involves non local error contributions. Consequently, a full and strong coupling between both is hard to achieve due to this apparent incompatibility. This paper shows how to achieve this coupling in three steps. First, a new a priori error estimate is proved in a formal framework adapted to goal-oriented mesh adaptation for output functionals. This estimate is based on a careful analysis of the contributions of the implicit error and of the interpolation error. Second, the error estimate is applied to the set of steady compressible Euler equations which are solved by a stabilized Galerkin finite element discretization. A goal-oriented error estimation is derived. It involves the interpolation error of the Euler fluxes weighted by the gradient of the adjoint state associated with the observed functional. Third, rewritten in the continuous mesh framework, the previous estimate is minimized on the set of continuous meshes thanks to a calculus of variations. The optimal continuous mesh is then derived analytically. Thus, it can be used as a metric tensor field to drive the mesh adaptation. From a numerical point of view, this method is completely automatic, intrinsically anisotropic, and does not depend on any a priori choice of variables to perform the adaptation. 3D examples of steady flows around supersonic and transsonic jets are presented to validate the current approach and to demonstrate its efficiency.

Euler: programa didáctico de elementos finitos

Directory of Open Access Journals (Sweden)

Dorian Luis Linero Segrera

2000-07-01

Full Text Available Este artículo muestra las características del programa Euler como herramienta para el aprendizaje del método de los elementos finitos, con énfasis en el análisis estructural. Euler puede resolver entre otros los siguientes problemas: análisis matricial estático de armaduras y pórticos planos, análisis de estabilidad, evaluación de frecuencias y modos de vibración en pórticos planos, deformaciones en vigas y en elementos sometidos a fuerza axial y otros problemas controlados por la ecuación diferencial de campo unidimensional indicada en este artículo. Además, se pueden solucionar problemas de torsión en secciones no circulares, flujo potencial, transferencia de calor y otros problemas controlados por la ecuación diferencial de campo bidimensional mostrada en este documento. También es posible resolver problemas de elasticidad bidimensional en condición plana de esfuerzos y en condición plana de deformaciones. Al operar el programa, el usuario debe escribir una de las instrucciones necesarias para obtener las cantidades de interés. Las instrucciones disponibles se clasifican así: edición de matrices, operaciones matriciales básicas, solución de sistemas de ecuaciones simultáneas, ensamblaje de matrices y vectores, numeración de grados de libertad, valores y vectores propios. Existen también instrucciones para la creación de matrices elementales como: funciones de forma, matriz gradiente, matriz de rigidez, vector de términos independientes, contribución interelemental, matriz de transformación y matriz de constantes elásticas.

Observable algebras for the rational and trigonometric Euler-Calogero-Moser Models

International Nuclear Information System (INIS)

Avan, J.; Billey, E.

1995-01-01

We construct polynomial Poisson algebras of observables for the classical Euler-Calogero-Moser (ECM) models. Their structure connects them to flavour-indexed non-linear W ∞ algebras, albeit with qualitative differences. The conserved Hamiltonians and symmetry algebras derived in a previous work are subsets of these algebra. We define their linear, N →∞ limits, realizing W ∞ type algebras coupled to current algebras. ((orig.))

Stochastic Optimal Prediction with Application to Averaged Euler Equations

Energy Technology Data Exchange (ETDEWEB)

Bell, John [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chorin, Alexandre J. [Univ. of California, Berkeley, CA (United States); Crutchfield, William [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2017-04-24

Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of complex problems through the use of an invariant measure as a prior measure in the Bayesian sense. In first-order OP, unresolved information is approximated by its conditional expectation with respect to the invariant measure. In higher-order OP, unresolved information is approximated by a stochastic estimator, leading to a system of random or stochastic differential equations. We explain the ideas through a simple example, and then apply them to the solution of Averaged Euler equations in two space dimensions.

The Euler anomaly and scale factors in Liouville/Toda CFTs

Energy Technology Data Exchange (ETDEWEB)

Balasubramanian, Aswin [Theory Group, Department of Physics, University of Texas at Austin,2515 Speedway Stop C1608, Austin, TX 78712-1197 (United States)

2014-04-22

The role played by the Euler anomaly in the dictionary relating sphere partition functions of four dimensional theories of class S and two dimensional non rational CFTs is clarified. On the two dimensional side, this involves a careful treatment of scale factors in Liouville/Toda correlators. Using ideas from tinkertoy constructions for Gaiotto duality, a framework is proposed for evaluating these scale factors. The representation theory of Weyl groups plays a critical role in this framework.

Conical Euler solution for a highly-swept delta wing undergoing wing-rock motion

Science.gov (United States)

Lee, Elizabeth M.; Batina, John T.

1990-01-01

Modifications to an unsteady conical Euler code for the free-to-roll analysis of highly-swept delta wings are described. The modifications involve the addition of the rolling rigid-body equation of motion for its simultaneous time-integration with the governing flow equations. The flow solver utilized in the Euler code includes a multistage Runge-Kutta time-stepping scheme which uses a finite-volume spatial discretization on an unstructured mesh made up of triangles. Steady and unsteady results are presented for a 75 deg swept delta wing at a freestream Mach number of 1.2 and an angle of attack of 30 deg. The unsteady results consist of forced harmonic and free-to-roll calculations. The free-to-roll case exhibits a wing rock response produced by unsteady aerodynamics consistent with the aerodynamics of the forced harmonic results. Similarities are shown with a wing-rock time history from a low-speed wind tunnel test.

Particular solutions of generalized Euler-Poisson-Darboux equation

Directory of Open Access Journals (Sweden)

Rakhila B. Seilkhanova

2015-01-01

Full Text Available In this article we consider the generalized Euler-Poisson-Darboux equation $$ {u}_{tt}+\\frac{2\\gamma }{t}{{u}_{t}}={u}_{xx}+{u}_{yy} +\\frac{2\\alpha }{x}{{u}_{x}}+\\frac{2\\beta }{y}{{u}_y},\\quad x>0,\\;y>0,\\;t>0. $$ We construct particular solutions in an explicit form expressed by the Lauricella hypergeometric function of three variables. Properties of each constructed solutions have been investigated in sections of surfaces of the characteristic cone. Precisely, we prove that found solutions have singularity $1/r$ at $r\\to 0$, where ${{r}^2}={{( x-{{x}_0}}^2}+{{( y-{{y}_0}}^2}-{{( t-{{t}_0}}^2}$.

Learning in neural networks based on a generalized fluctuation theorem

Science.gov (United States)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

Nuclear reactors project optimization based on neural network and genetic algorithm

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A.; Schirru, Roberto; Martinez, Aquilino S.

1997-01-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Directory of Open Access Journals (Sweden)

Ahcene Farah

2002-06-01

Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

Vanishing Viscosity Approach to the Compressible Euler Equations for Transonic Nozzle and Spherically Symmetric Flows

Science.gov (United States)

Chen, Gui-Qiang G.; Schrecker, Matthew R. I.

2018-04-01

We are concerned with globally defined entropy solutions to the Euler equations for compressible fluid flows in transonic nozzles with general cross-sectional areas. Such nozzles include the de Laval nozzles and other more general nozzles whose cross-sectional area functions are allowed at the nozzle ends to be either zero (closed ends) or infinity (unbounded ends). To achieve this, in this paper, we develop a vanishing viscosity method to construct globally defined approximate solutions and then establish essential uniform estimates in weighted L p norms for the whole range of physical adiabatic exponents γ\\in (1, ∞) , so that the viscosity approximate solutions satisfy the general L p compensated compactness framework. The viscosity method is designed to incorporate artificial viscosity terms with the natural Dirichlet boundary conditions to ensure the uniform estimates. Then such estimates lead to both the convergence of the approximate solutions and the existence theory of globally defined finite-energy entropy solutions to the Euler equations for transonic flows that may have different end-states in the class of nozzles with general cross-sectional areas for all γ\\in (1, ∞) . The approach and techniques developed here apply to other problems with similar difficulties. In particular, we successfully apply them to construct globally defined spherically symmetric entropy solutions to the Euler equations for all γ\\in (1, ∞).

Neural Network Based Real-time Correction of Transducer Dynamic Errors

Science.gov (United States)

Roj, J.

2013-12-01

In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.

Planning music-based amelioration and training in infancy and childhood based on neural evidence.

Science.gov (United States)

Huotilainen, Minna; Tervaniemi, Mari

2018-05-04

Music-based amelioration and training of the developing auditory system has a long tradition, and recent neuroscientific evidence supports using music in this manner. Here, we present the available evidence showing that various music-related activities result in positive changes in brain structure and function, becoming helpful for auditory cognitive processes in everyday life situations for individuals with typical neural development and especially for individuals with hearing, learning, attention, or other deficits that may compromise auditory processing. We also compare different types of music-based training and show how their effects have been investigated with neural methods. Finally, we take a critical position on the multitude of error sources found in amelioration and training studies and on publication bias in the field. We discuss some future improvements of these issues in the field of music-based training and their potential results at the neural and behavioral levels in infants and children for the advancement of the field and for a more complete understanding of the possibilities and significance of the training. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

Science.gov (United States)

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Task-dependent neural bases of perceiving emotionally expressive targets

Directory of Open Access Journals (Sweden)

Jamil eZaki

2012-08-01

Full Text Available Social cognition is fundamentally interpersonal: individuals’ behavior and dispositions critically affect their interaction partners’ information processing. However, cognitive neuroscience studies, partially because of methodological constraints, have remained largely perceiver-centric: focusing on the abilities, motivations, and goals of social perceivers while largely ignoring interpersonal effects. Here, we address this knowledge gap by examining the neural bases of perceiving emotionally expressive and inexpressive social targets. Sixteen perceivers were scanned using fMRI while they watched targets discussing emotional autobiographical events. Perceivers continuously rated each target’s emotional state or eye-gaze direction. The effects of targets’ emotional expressivity on perceiver’s brain activity depended on task set: when perceivers explicitly attended to targets’ emotions, expressivity predicted activity in neural structures—including medial prefrontal and posterior cingulate cortex—associated with drawing inferences about mental states. When perceivers instead attended to targets’ eye-gaze, target expressivity predicted activity in regions—including somatosensory cortex, fusiform gyrus, and motor cortex—associated with monitoring sensorimotor states and biological motion. These findings suggest that expressive targets affect information processing in manner that depends on perceivers’ goals. More broadly, these data provide an early step towards understanding the neural bases of interpersonal social cognition.

Neural Network Based Sensory Fusion for Landmark Detection

Science.gov (United States)

Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

1997-01-01

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Study of vortex breakdown of F-106B by Euler code

Science.gov (United States)

Pao, Jenn Louh

1990-01-01

The 'Three-dimensional Euler Aerodynamic Method' (TEAM) is presently applied to the F-106B at subsonic speed, in order to examine the relationship between off- and on-surface flow features at angles-of-attack sufficiently great for the occurrence of vortex breakdown. Although TEAM's flow separation is triggered by numerical dissipation, the general trend of vortex-breakdown effect on computed lift characteristics is similar to extant wind tunnel results.

NEWTON'S SECOND LAW OF MOTION, F=MA; EULER'S OR NEWTON'S?

OpenAIRE

Ajay Sharma

2017-01-01

Objective: F =ma is taught as Newton’s second law of motion all over the world. But it was given by Euler in 1775, forty-eight years after the death of Newton. It is debated here with scientific logic. Methods/Statistical analysis: The discussion partially deals with history of science so various aspects are quoted from original references. Newton did not give any equation in the Principia for second, third laws motion and law of gravitation. Conceptually, in Newton’s time, neither accele...

Image object recognition based on the Zernike moment and neural networks

Science.gov (United States)

Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

1998-03-01

This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

Analysis of neural networks through base functions

NARCIS (Netherlands)

van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Science.gov (United States)

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Stability of the isentropic Riemann solutions of the full multidimensional Euler system

Czech Academy of Sciences Publication Activity Database

Feireisl, Eduard; Kreml, Ondřej; Vasseur, A.

2015-01-01

Roč. 47, č. 3 (2015), s. 2416-2425 ISSN 0036-1410 R&D Projects: GA ČR GA13-00522S EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : Euler system * isentropic solutions * Riemann problem * rarefaction wave Subject RIV: BA - General Mathematics Impact factor: 1.486, year: 2015 http://epubs.siam.org/doi/abs/10.1137/140999827

Nuclear power history calculation for subcritical systems using Euler-MacLaurin formula

International Nuclear Information System (INIS)

Henrice Junior, Edson; Goncalves, Alessandro da Cruz

2013-01-01

This paper presents an efficient method for calculating the reactivity using inverse point kinetic equation for subcritical systems by applying the Euler-MacLaurin summation formula to calculate the nuclear power history. In accordance with the accuracy of the numerical results, this method does not require a large number of points for calculation, providing accurate results with low computational cost. (author)

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

Science.gov (United States)

Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

2017-12-01

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Science.gov (United States)

Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

2017-10-01

To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Numerical solution of Euler's equation by perturbed functionals

Science.gov (United States)

Dey, S. K.

1985-01-01

A perturbed functional iteration has been developed to solve nonlinear systems. It adds at each iteration level, unique perturbation parameters to nonlinear Gauss-Seidel iterates which enhances its convergence properties. As convergence is approached these parameters are damped out. Local linearization along the diagonal has been used to compute these parameters. The method requires no computation of Jacobian or factorization of matrices. Analysis of convergence depends on properties of certain contraction-type mappings, known as D-mappings. In this article, application of this method to solve an implicit finite difference approximation of Euler's equation is studied. Some representative results for the well known shock tube problem and compressible flows in a nozzle are given.

Detecting danger labels with RAM-based neural networks

DEFF Research Database (Denmark)

Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

1996-01-01

An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Result...

Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

International Nuclear Information System (INIS)

Peng Yafu; Hsu, C.-F.

2009-01-01

This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

Directory of Open Access Journals (Sweden)

William Taube Navaraj

2017-09-01

Full Text Available This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs based hardware-implementable neural network (HNN approach for tactile data processing in electronic skin (e-skin. The viability of Si nanowires (NWs as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals.

A Research on a Certain Family of Numbers and Polynomials Related to Stirling Numbers, Central Factorial Numbers, and Euler Numbers

Directory of Open Access Journals (Sweden)

J. Y. Kang

2013-01-01

Full Text Available Recently, many mathematicians have studied different kinds of the Euler, Bernoulli, and Genocchi numbers and polynomials. In this paper, we give another definition of polynomials Ũn(x. We observe an interesting phenomenon of “scattering” of the zeros of the polynomials Ũn(x in complex plane. We find out some identities and properties related to polynomials Ũn(x. Finally, we also derive interesting relations between polynomials Ũn(x, Stirling numbers, central factorial numbers, and Euler numbers.

PID position regulation in one-degree-of-freedom Euler-Lagrange systems actuated by a PMSM

Science.gov (United States)

Verastegui-Galván, J.; Hernández-Guzmán, V. M.; Orrante-Sakanassi, J.

2018-02-01

This paper is concerned with position regulation in one-degree-of-freedom Euler-Lagrange Systems. We consider that the mechanical subsystem is actuated by a permanent magnet synchronous motor (PMSM). Our proposal consists of a Proportional-Integral-Derivative (PID) controller for the mechanical subsystem and a slight variation of field oriented control for the PMSM. We take into account the motor electric dynamics during the stability analysis. We present, for the first time, a global asymptotic stability proof for such a control scheme without requiring the mechanical subsystem to naturally possess viscous friction. Finally, as a corollary of our main result we prove global asymptotic stability for output feedback PID regulation of one-degree-of-freedom Euler-Lagrange systems when generated torque is considered as the system input, i.e. when the electric dynamics of PMSM's is not taken into account.

A new representation of rotational flow fields satisfying Euler's equation of an ideal compressible fluid

International Nuclear Information System (INIS)

Kambe, Tsutomu

2013-01-01

A new representation of the solution to Euler's equation of motion is presented by using a system of expressions for compressible rotational flows of an ideal fluid. This is regarded as a generalization of Bernoulli's theorem to compressible rotational flows. The present expressions are derived from the variational principle. The action functional for the principle consists of the main terms of the total kinetic, potential and internal energies, together with three additional terms yielding the equations of continuity, entropy and a third term that provides the rotational component of velocity field. The last term has the form of scalar product satisfying gauge symmetry with respect to both translation and rotation. This is a generalization of the Clebsch transformation from a physical point of view. It is verified that the system of new expressions, in fact, satisfies Euler's equation of motion. (paper)

The harmonics detection method based on neural network applied ...

African Journals Online (AJOL)

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Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...

A rigorous justification of the Euler and Navier-Stokes equations with geometric effects

Czech Academy of Sciences Publication Activity Database

Bella, P.; Feireisl, Eduard; Lewicka, M.; Novotný, A.

2016-01-01

Roč. 48, č. 6 (2016), s. 3907-3930 ISSN 0036-1410 EU Projects: European Commission(XE) 320078 - MATHEF Institutional support: RVO:67985840 Keywords : isentropic Navier-Stokes system * isentropic Euler system * inviscid limit Subject RIV: BA - General Mathematics Impact factor: 1.648, year: 2016 http://epubs.siam.org/doi/10.1137/15M1048963

Some results on the well-posedness of Euler-Voigt and Navier-Stokes-Voigt models

OpenAIRE

Berselli, Luigi C.; Bisconti, Luca

2010-01-01

We consider the Euler-Voigt equations and the Navier-Stokes-Voigt equations, which are obtained by an inviscid alpha-regularization from the corresponding equations. The main result we show is the structural stability of the system in term of the variations of both viscosity of regularization parameters.

The development of a PZT-based microdrive for neural signal recording

International Nuclear Information System (INIS)

Park, Sangkyu; Yoon, Euisung; Park, Sukho; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh

2008-01-01

A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice. (technical note)

De la representación de sistemas Euler - Lagrange a la Hamiltoniana generalizada

Directory of Open Access Journals (Sweden)

L. H. Rodríguez - Alfaro

2015-01-01

Full Text Available La representación Hamiltoniana generalizada de sistemas brinda una estructura que puede ser utilizada con ventaja en muchas áreas, entre las cuales se puede mencionar el diseño de observadores y el diagnóstico de fallas basado en modelos. Muchos de los trabajos en estos te mas tienen como punto de partida al sistema en forma Hamiltoniana generalizada y, en general, se omite la explicación de cómo llegar a esta representación, por ejemplo, a partir de un modelo no lineal basado en las ecuaciones de Euler - Lagrange. En este tra bajo se presenta un análisis detallado de cómo es que se obtiene la representación Hamiltoniana generalizada de un sistema a partir de las n ecuaciones diferenciales de segundo orden obtenidas con el formalismo Euler - Lagrange. Con la finalidad de mostrar e n lo particular, después del caso general, cómo se obtiene la representación Hamiltoniana generalizada, se presentan algunos casos de estudio.

Real-time camera-based face detection using a modified LAMSTAR neural network system

Science.gov (United States)

Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

2003-03-01

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

Manufactured solutions for the three-dimensional Euler equations with relevance to Inertial Confinement Fusion

International Nuclear Information System (INIS)

Waltz, J.; Canfield, T.R.; Morgan, N.R.; Risinger, L.D.; Wohlbier, J.G.

2014-01-01

We present a set of manufactured solutions for the three-dimensional (3D) Euler equations. The purpose of these solutions is to allow for code verification against true 3D flows with physical relevance, as opposed to 3D simulations of lower-dimensional problems or manufactured solutions that lack physical relevance. Of particular interest are solutions with relevance to Inertial Confinement Fusion (ICF) capsules. While ICF capsules are designed for spherical symmetry, they are hypothesized to become highly 3D at late time due to phenomena such as Rayleigh–Taylor instability, drive asymmetry, and vortex decay. ICF capsules also involve highly nonlinear coupling between the fluid dynamics and other physics, such as radiation transport and thermonuclear fusion. The manufactured solutions we present are specifically designed to test the terms and couplings in the Euler equations that are relevant to these phenomena. Example numerical results generated with a 3D Finite Element hydrodynamics code are presented, including mesh convergence studies

VennDiagramWeb: a web application for the generation of highly customizable Venn and Euler diagrams.

Science.gov (United States)

Lam, Felix; Lalansingh, Christopher M; Babaran, Holly E; Wang, Zhiyuan; Prokopec, Stephenie D; Fox, Natalie S; Boutros, Paul C

2016-10-03

Visualization of data generated by high-throughput, high-dimensionality experiments is rapidly becoming a rate-limiting step in computational biology. There is an ongoing need to quickly develop high-quality visualizations that can be easily customized or incorporated into automated pipelines. This often requires an interface for manual plot modification, rapid cycles of tweaking visualization parameters, and the generation of graphics code. To facilitate this process for the generation of highly-customizable, high-resolution Venn and Euler diagrams, we introduce VennDiagramWeb: a web application for the widely used VennDiagram R package. VennDiagramWeb is hosted at http://venndiagram.res.oicr.on.ca/ . VennDiagramWeb allows real-time modification of Venn and Euler diagrams, with parameter setting through a web interface and immediate visualization of results. It allows customization of essentially all aspects of figures, but also supports integration into computational pipelines via download of R code. Users can upload data and download figures in a range of formats, and there is exhaustive support documentation. VennDiagramWeb allows the easy creation of Venn and Euler diagrams for computational biologists, and indeed many other fields. Its ability to support real-time graphics changes that are linked to downloadable code that can be integrated into automated pipelines will greatly facilitate the improved visualization of complex datasets. For application support please contact Paul.Boutros@oicr.on.ca.

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Directory of Open Access Journals (Sweden)

Antonino Laudani

2015-01-01

Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

Container-code recognition system based on computer vision and deep neural networks

Science.gov (United States)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Neural-net based real-time economic dispatch for thermal power plants

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

1996-12-01

This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

Science.gov (United States)

Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

2017-09-01

This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator

Science.gov (United States)

Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung

2005-12-01

The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

A Gain-Scheduling PI Control Based on Neural Networks

Directory of Open Access Journals (Sweden)

Stefania Tronci

2017-01-01

Full Text Available This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR, considering both single-input single-output (SISO and multi-input multi-output (MIMO control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

Directory of Open Access Journals (Sweden)

Carlos López-Franco

2015-01-01

Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

Well-posed Euler model of shock-induced two-phase flow in bubbly liquid

Science.gov (United States)

Tukhvatullina, R. R.; Frolov, S. M.

2018-03-01

A well-posed mathematical model of non-isothermal two-phase two-velocity flow of bubbly liquid is proposed. The model is based on the two-phase Euler equations with the introduction of an additional pressure at the gas bubble surface, which ensures the well-posedness of the Cauchy problem for a system of governing equations with homogeneous initial conditions, and the Rayleigh-Plesset equation for radial pulsations of gas bubbles. The applicability conditions of the model are formulated. The model is validated by comparing one-dimensional calculations of shock wave propagation in liquids with gas bubbles with a gas volume fraction of 0.005-0.3 with experimental data. The model is shown to provide satisfactory results for the shock propagation velocity, pressure profiles, and the shock-induced motion of the bubbly liquid column.

The Euler equation with habits and measurement errors: Estimates on Russian micro data

Directory of Open Access Journals (Sweden)

Khvostova Irina

2016-01-01

Full Text Available This paper presents estimates of the consumption Euler equation for Russia. The estimation is based on micro-level panel data and accounts for the heterogeneity of agents’ preferences and measurement errors. The presence of multiplicative habits is checked using the Lagrange multiplier (LM test in a generalized method of moments (GMM framework. We obtain estimates of the elasticity of intertemporal substitution and of the subjective discount factor, which are consistent with the theoretical model and can be used for the calibration and the Bayesian estimation of dynamic stochastic general equilibrium (DSGE models for the Russian economy. We also show that the effects of habit formation are not significant. The hypotheses of multiplicative habits (external, internal, and both external and internal are not supported by the data.

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Directory of Open Access Journals (Sweden)

Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

Directory of Open Access Journals (Sweden)

Kun Zhang

2015-01-01

Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

Results from a MA16-based neural trigger in an experiment looking for beauty

International Nuclear Information System (INIS)

Baldanza, C.; Beichter, J.; Bisi, F.; Bruels, N.; Bruschini, C.; Cotta-Ramusino, A.; D'Antone, I.; Malferrari, L.; Mazzanti, P.; Musico, P.; Novelli, P.; Odorici, F.; Odorico, R.; Passaseo, M.; Zuffa, M.

1996-01-01

Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel's analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.)

Results from a MA16-based neural trigger in an experiment looking for beauty

Energy Technology Data Exchange (ETDEWEB)

Baldanza, C. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Beichter, J. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bisi, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Bruels, N. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bruschini, C. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Cotta-Ramusino, A. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); D`Antone, I. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Malferrari, L. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Mazzanti, P. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Musico, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Novelli, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Odorici, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Odorico, R. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Passaseo, M. [CERN, 1211 Geneva 23 (Switzerland); Zuffa, M. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy)

1996-07-11

Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel`s analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 {mu}s for a Fisher discriminant (1-layer net) and of 6 {mu}s for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.).

Entropy-based viscous regularization for the multi-dimensional Euler equations in low-Mach and transonic flows

Energy Technology Data Exchange (ETDEWEB)

Marc O Delchini; Jean E. Ragusa; Ray A. Berry

2015-07-01

We present a new version of the entropy viscosity method, a viscous regularization technique for hyperbolic conservation laws, that is well-suited for low-Mach flows. By means of a low-Mach asymptotic study, new expressions for the entropy viscosity coefficients are derived. These definitions are valid for a wide range of Mach numbers, from subsonic flows (with very low Mach numbers) to supersonic flows, and no longer depend on an analytical expression for the entropy function. In addition, the entropy viscosity method is extended to Euler equations with variable area for nozzle flow problems. The effectiveness of the method is demonstrated using various 1-D and 2-D benchmark tests: flow in a converging–diverging nozzle; Leblanc shock tube; slow moving shock; strong shock for liquid phase; low-Mach flows around a cylinder and over a circular hump; and supersonic flow in a compression corner. Convergence studies are performed for smooth solutions and solutions with shocks present.

Breakout Prediction Based on BP Neural Network in Continuous Casting Process

Directory of Open Access Journals (Sweden)

Zhang Ben-guo

2016-01-01

Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.

ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

OpenAIRE

Visin, Francesco; Ciccone, Marco; Romero, Adriana; Kastner, Kyle; Cho, Kyunghyun; Bengio, Yoshua; Matteucci, Matteo; Courville, Aaron

2015-01-01

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally ...

Neural network-based retrieval from software reuse repositories

Science.gov (United States)

Eichmann, David A.; Srinivas, Kankanahalli

1992-01-01

A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

Science.gov (United States)

Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

Directory of Open Access Journals (Sweden)

Shao Jie

2014-01-01

Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

International Nuclear Information System (INIS)

Zhou Gang; Ge Shengqi; Yang Li

2010-01-01

A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

Directory of Open Access Journals (Sweden)

S Safinaz

2017-08-01

Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

High Speed PAM -8 Optical Interconnects with Digital Equalization based on Neural Network

DEFF Research Database (Denmark)

Gaiarin, Simone; Pang, Xiaodan; Ozolins, Oskars

2016-01-01

We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission.......We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission....

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

Science.gov (United States)

de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

2018-07-01

The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this

On the stability of solutions, compacted to eleven dimensions, with the Euler invariants

International Nuclear Information System (INIS)

Fabris, J.C.

1991-01-01

The Supergravity Lagrangian at eleven dimensions has been modified by the inclusion of Euler invariants. Compact solutions have been obtained where the space-time is the Minkowski one, preserving, the internal space as a seven-sphere. The stability study of this configuration allows the restriction of the acceptable values for the coupling constants present in this model. (A.C.A.S.)

A Brief Historical Introduction to Euler's Formula for Polyhedra, Topology, Graph Theory and Networks

Science.gov (United States)

Debnath, Lokenath

2010-01-01

This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Konigsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real…

A patch-based convolutional neural network for remote sensing image classification.

Science.gov (United States)

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cyclone track forecasting based on satellite images using artificial neural networks

OpenAIRE

Kovordanyi, Rita; Roy, Chandan

2009-01-01

Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on sate...

PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

Directory of Open Access Journals (Sweden)

MARABA, V. A.

2011-11-01

Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

Characterizing the velocity of a wandering black hole and properties of the surrounding medium using convolutional neural networks

Science.gov (United States)

González, J. A.; Guzmán, F. S.

2018-03-01

We present a method for estimating the velocity of a wandering black hole and the equation of state for the gas around it based on a catalog of numerical simulations. The method uses machine-learning methods based on convolutional neural networks applied to the classification of images resulting from numerical simulations. Specifically we focus on the supersonic velocity regime and choose the direction of the black hole to be parallel to its spin. We build a catalog of 900 simulations by numerically solving Euler's equations onto the fixed space-time background of a black hole, for two parameters: the adiabatic index Γ with values in the range [1.1, 5 /3 ], and the asymptotic relative velocity of the black hole with respect to the surroundings v∞, with values within [0.2 ,0.8 ]c . For each simulation we produce a 2D image of the gas density once the process of accretion has approached a stationary regime. The results obtained show that the implemented convolutional neural networks are able to correctly classify the adiabatic index 87.78% of the time within an uncertainty of ±0.0284 , while the prediction of the velocity is correct 96.67% of the time within an uncertainty of ±0.03 c . We expect that this combination of a massive number of numerical simulations and machine-learning methods will help us analyze more complicated scenarios related to future high-resolution observations of black holes, like those from the Event Horizon Telescope.

Neural bases of ingroup altruistic motivation in soccer fans.

Science.gov (United States)

Bortolini, Tiago; Bado, Patrícia; Hoefle, Sebastian; Engel, Annerose; Zahn, Roland; de Oliveira Souza, Ricardo; Dreher, Jean-Claude; Moll, Jorge

2017-11-23

Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.

Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

Science.gov (United States)

Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

2018-01-01

In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

OpenAIRE

Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

2017-01-01

In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

Adaptive online state-of-charge determination based on neuro-controller and neural network

Energy Technology Data Exchange (ETDEWEB)

Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

2010-05-15

This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

Science.gov (United States)

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Three-Dimensional-Bioprinted Dopamine-Based Matrix for Promoting Neural Regeneration.

Science.gov (United States)

Zhou, Xuan; Cui, Haitao; Nowicki, Margaret; Miao, Shida; Lee, Se-Jun; Masood, Fahed; Harris, Brent T; Zhang, Lijie Grace

2018-03-14

Central nerve repair and regeneration remain challenging problems worldwide, largely because of the extremely weak inherent regenerative capacity and accompanying fibrosis of native nerves. Inadequate solutions to the unmet needs for clinical therapeutics encourage the development of novel strategies to promote nerve regeneration. Recently, 3D bioprinting techniques, as one of a set of valuable tissue engineering technologies, have shown great promise toward fabricating complex and customizable artificial tissue scaffolds. Gelatin methacrylate (GelMA) possesses excellent biocompatible and biodegradable properties because it contains many arginine-glycine-aspartic acids (RGD) and matrix metalloproteinase sequences. Dopamine (DA), as an essential neurotransmitter, has proven effective in regulating neuronal development and enhancing neurite outgrowth. In this study, GelMA-DA neural scaffolds with hierarchical structures were 3D-fabricated using our custom-designed stereolithography-based printer. DA was functionalized on GelMA to synthesize a biocompatible printable ink (GelMA-DA) for improving neural differentiation. Additionally, neural stem cells (NSCs) were employed as the primary cell source for these scaffolds because of their ability to terminally differentiate into a variety of cell types including neurons, astrocytes, and oligodendrocytes. The resultant GelMA-DA scaffolds exhibited a highly porous and interconnected 3D environment, which is favorable for supporting NSC growth. Confocal microscopy analysis of neural differentiation demonstrated that a distinct neural network was formed on the GelMA-DA scaffolds. In particular, the most significant improvements were the enhanced neuron gene expression of TUJ1 and MAP2. Overall, our results demonstrated that 3D-printed customizable GelMA-DA scaffolds have a positive role in promoting neural differentiation, which is promising for advancing nerve repair and regeneration in the future.

Low-diffusion rotated upwind schemes, multigrid and defect correction for steady, multi-dimensional Euler flows

NARCIS (Netherlands)

Koren, B.; Hackbusch, W.; Trottenberg, U.

1991-01-01

Two simple, multi-dimensional upwind discretizations for the steady Euler equations are derived, with the emphasis Iying on bath a good accuracy and a good solvability. The multi-dimensional upwinding consists of applying a one-dimensional Riemann solver with a locally rotated left and right state,

A discontinuous Galerkin finite element discretization of the Euler equations for compressible and incompressible fluids

NARCIS (Netherlands)

Pesch, L.; van der Vegt, Jacobus J.W.

2008-01-01

Using the generalized variable formulation of the Euler equations of fluid dynamics, we develop a numerical method that is capable of simulating the flow of fluids with widely differing thermodynamic behavior: ideal and real gases can be treated with the same method as an incompressible fluid. The

Flexible poly(methyl methacrylate)-based neural probe: An affordable implementation

Science.gov (United States)

Gasemi, Pejman; Veladi, Hadi; Shahabi, Parviz; Khalilzadeh, Emad

2018-03-01

This research presents a novel technique used to fabricate a deep brain stimulation probe based on a commercial poly(methyl methacrylate) (PMMA) polymer. This technique is developed to overcome the high cost of available probes crucial for chronic stimulation and recording in neural disorders such as Parkinson’s disease and epilepsy. The probe is made of PMMA and its mechanical properties have been customized by controlling the reaction conditions. The polymer is adjusted to be stiff enough to be easily inserted and, on the other hand, soft enough to perform required movements. As cost is one of the issues in the use of neural probes, a simple process is proposed for the production of PMMA neural probes without using expensive equipment and operations, and without compromising performance and quality. An in vivo animal test was conducted to observe the recording capability of a PMMA probe.

Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

Directory of Open Access Journals (Sweden)

Alma Y. Alanis

2013-01-01

Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

Artificial Neural Network Based State Estimators Integrated into Kalmtool

DEFF Research Database (Denmark)

Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

2012-01-01

In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

Directory of Open Access Journals (Sweden)

Diego José Luis Botia Valderrama

2016-01-01

Full Text Available The measurement and evaluation of the QoE (Quality of Experience have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks and the RNNs (Random Neural Networks is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score and the PSNR (Peak Signal Noise Ratio, SSIM (Structural Similarity Index Metric, VQM (Video Quality Metric, and QIBF (Quality Index Based Frame. The proposed model allows establishing the QoS (Quality of Service based in the strategy Diffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error, and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Science.gov (United States)

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Parametric Jominy profiles predictor based on neural networks

Directory of Open Access Journals (Sweden)

Valentini, R.

2005-12-01

Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.

El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.

Large Scale Simulations of the Euler Equations on GPU Clusters

KAUST Repository

Liebmann, Manfred

2010-08-01

The paper investigates the scalability of a parallel Euler solver, using the Vijayasundaram method, on a GPU cluster with 32 Nvidia Geforce GTX 295 boards. The aim of this research is to enable large scale fluid dynamics simulations with up to one billion elements. We investigate communication protocols for the GPU cluster to compensate for the slow Gigabit Ethernet network between the GPU compute nodes and to maintain overall efficiency. A diesel engine intake-port and a nozzle, meshed in different resolutions, give good real world examples for the scalability tests on the GPU cluster. © 2010 IEEE.

On the Use of Linearized Euler Equations in the Prediction of Jet Noise

Science.gov (United States)

Mankbadi, Reda R.; Hixon, R.; Shih, S.-H.; Povinelli, L. A.

1995-01-01

Linearized Euler equations are used to simulate supersonic jet noise generation and propagation. Special attention is given to boundary treatment. The resulting solution is stable and nearly free from boundary reflections without the need for artificial dissipation, filtering, or a sponge layer. The computed solution is in good agreement with theory and observation and is much less CPU-intensive as compared to large-eddy simulations.

A case study to estimate costs using Neural Networks and regression based models

Directory of Open Access Journals (Sweden)

Nadia Bhuiyan

2012-07-01

Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

A neutron spectrum unfolding computer code based on artificial neural networks

Science.gov (United States)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2014-02-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

A plausible neural circuit for decision making and its formation based on reinforcement learning.

Science.gov (United States)

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control

Synchronization stability of memristor-based complex-valued neural networks with time delays.

Science.gov (United States)

Liu, Dan; Zhu, Song; Ye, Er

2017-12-01

This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lower Bounds for Possible Singular Solutions for the Navier-Stokes and Euler Equations Revisited

Science.gov (United States)

Cortissoz, Jean C.; Montero, Julio A.

2018-03-01

In this paper we give optimal lower bounds for the blow-up rate of the \\dot{H}s( T^3) -norm, 1/2Navier-Stokes equations, and we also present an elementary proof for a lower bound on blow-up rate of the Sobolev norms of possible singular solutions to the Euler equations when s>5/2.

An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

Directory of Open Access Journals (Sweden)

Yao Junyang

2014-06-01

Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

International Nuclear Information System (INIS)

Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

2009-01-01

In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

Science.gov (United States)

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

Science.gov (United States)

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Development of an Output-based Adaptive Method for Multi-Dimensional Euler and Navier-Stokes Simulations

Science.gov (United States)

Darmofal, David L.

2003-01-01

The use of computational simulations in the prediction of complex aerodynamic flows is becoming increasingly prevalent in the design process within the aerospace industry. Continuing advancements in both computing technology and algorithmic development are ultimately leading to attempts at simulating ever-larger, more complex problems. However, by increasing the reliance on computational simulations in the design cycle, we must also increase the accuracy of these simulations in order to maintain or improve the reliability arid safety of the resulting aircraft. At the same time, large-scale computational simulations must be made more affordable so that their potential benefits can be fully realized within the design cycle. Thus, a continuing need exists for increasing the accuracy and efficiency of computational algorithms such that computational fluid dynamics can become a viable tool in the design of more reliable, safer aircraft. The objective of this research was the development of an error estimation and grid adaptive strategy for reducing simulation errors in integral outputs (functionals) such as lift or drag from from multi-dimensional Euler and Navier-Stokes simulations. In this final report, we summarize our work during this grant.

Plasmid-based generation of induced neural stem cells from adult human fibroblasts

Directory of Open Access Journals (Sweden)

Philipp Capetian

2016-10-01

Full Text Available Direct reprogramming from somatic to neural cell types has become an alternative to induced pluripotent stem cells. Most protocols employ viral expression systems, posing the risk of random genomic integration. Recent developments led to plasmid-based protocols, lowering this risk. However, these protocols either relied on continuous presence of a variety of small molecules or were only able to reprogram murine cells. We therefore established a reprogramming protocol based on vectors containing the Epstein-Barr virus (EBV-derived oriP/EBNA1 as well as the defined expression factors Oct3/4, Sox2, Klf4, L-myc, Lin28, and a small hairpin directed against p53. We employed a defined neural medium in combination with the neurotrophins bFGF, EGF and FGF4 for cultivation without the addition of small molecules. After reprogramming, cells demonstrated a temporary increase in the expression of endogenous Oct3/4. We obtained induced neural stem cells (iNSC 30 days after transfection. In contrast to previous results, plasmid vectors as well as a residual expression of reprogramming factors remained detectable in all cell lines. Cells showed a robust differentiation into neuronal (72% and glial cells (9% astrocytes, 6% oligodendrocytes. Despite the temporary increase of pluripotency-associated Oct3/4 expression during reprogramming, we did not detect pluripotent stem cells or non-neural cells in culture (except occasional residual fibroblasts. Neurons showed electrical activity and functional glutamatergic synapses. Our results demonstrate that reprogramming adult human fibroblasts to iNSC by plasmid vectors and basic neural medium without small molecules is possible and feasible. However, a full set of pluripotency-associated transcription factors may indeed result in the acquisition of a transient (at least partial pluripotent intermediate during reprogramming. In contrast to previous reports, the EBV-based plasmid system remained present and active inside

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

Directory of Open Access Journals (Sweden)

Yang Liu

2015-01-01

Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

Directory of Open Access Journals (Sweden)

Farahnaz SADOUGHI

2014-03-01

Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

Invariant moments based convolutional neural networks for image analysis

Directory of Open Access Journals (Sweden)

Vijayalakshmi G.V. Mahesh

2017-01-01

Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

A neutron spectrum unfolding code based on generalized regression artificial neural networks

International Nuclear Information System (INIS)

Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O.; Vega C, H. R.

2015-10-01

The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

A neutron spectrum unfolding code based on generalized regression artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico); Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas, Zac. (Mexico)

2015-10-15

The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a {sup 6}LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

Neural network based system for script identification in Indian ...

Indian Academy of Sciences (India)

2016-08-26

Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

Solution of Euler unsteady equations using a second order numerical scheme

International Nuclear Information System (INIS)

Devos, J.P.

1992-08-01

In thermal power plants, the steam circuits experience incidents due to the noise and vibration induced by trans-sonic flow. In these configurations, the compressible fluid can be considered the perfect ideal. Euler equations therefore constitute a good model. However, processing of the discontinuities induced by the shockwaves are a particular problem. We give a bibliographical synthesis of the work done on this subject. The research by Roe and Harten leads to TVD (Total Variation Decreasing) type schemes. These second order schemes generate no oscillation and converge towards physically acceptable weak solutions. (author). 12 refs

Interpretation of high resolution airborne magnetic data (HRAMD of Ilesha and its environs, Southwest Nigeria, using Euler deconvolution method

Directory of Open Access Journals (Sweden)

Olurin Oluwaseun Tolutope

2017-12-01

Full Text Available Interpretation of high resolution aeromagnetic data of Ilesha and its environs within the basement complex of the geological setting of Southwestern Nigeria was carried out in the study. The study area is delimited by geographic latitudes 7°30′–8°00′N and longitudes 4°30′–5°00′E. This investigation was carried out using Euler deconvolution on filtered digitised total magnetic data (Sheet Number 243 to delineate geological structures within the area under consideration. The digitised airborne magnetic data acquired in 2009 were obtained from the archives of the Nigeria Geological Survey Agency (NGSA. The airborne magnetic data were filtered, processed and enhanced; the resultant data were subjected to qualitative and quantitative magnetic interpretation, geometry and depth weighting analyses across the study area using Euler deconvolution filter control file in Oasis Montag software. Total magnetic intensity distribution in the field ranged from –77.7 to 139.7 nT. Total magnetic field intensities reveal high-magnitude magnetic intensity values (high-amplitude anomaly and magnetic low intensities (low-amplitude magnetic anomaly in the area under consideration. The study area is characterised with high intensity correlated with lithological variation in the basement. The sharp contrast is enhanced due to the sharp contrast in magnetic intensity between the magnetic susceptibilities of the crystalline and sedimentary rocks. The reduced-to-equator (RTE map is characterised by high frequencies, short wavelengths, small size, weak intensity, sharp low amplitude and nearly irregular shaped anomalies, which may due to near-surface sources, such as shallow geologic units and cultural features. Euler deconvolution solution indicates a generally undulating basement, with a depth ranging from −500 to 1000 m. The Euler deconvolution results show that the basement relief is generally gentle and flat, lying within the basement terrain.

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Directory of Open Access Journals (Sweden)

Fangzhao Li

2018-01-01

Full Text Available Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

A hybrid model based on neural networks for biomedical relation extraction.

Science.gov (United States)

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

Science.gov (United States)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

Science.gov (United States)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

An efficient coupled polynomial interpolation scheme to eliminate material-locking in the Euler-Bernoulli piezoelectric beam finite element

Directory of Open Access Journals (Sweden)

Litesh N. Sulbhewar

Full Text Available The convergence characteristic of the conventional two-noded Euler-Bernoulli piezoelectric beam finite element depends on the configuration of the beam cross-section. The element shows slower convergence for the asymmetric material distribution in the beam cross-section due to 'material-locking' caused by extension-bending coupling. Hence, the use of conventional Euler-Bernoulli beam finite element to analyze piezoelectric beams which are generally made of the host layer with asymmetrically surface bonded piezoelectric layers/patches, leads to increased computational effort to yield converged results. Here, an efficient coupled polynomial interpolation scheme is proposed to improve the convergence of the Euler-Bernoulli piezoelectric beam finite elements, by eliminating ill-effects of material-locking. The equilibrium equations, derived using a variational formulation, are used to establish relationships between field variables. These relations are used to find a coupled quadratic polynomial for axial displacement, having contributions from an assumed cubic polynomial for transverse displacement and assumed linear polynomials for layerwise electric potentials. A set of coupled shape functions derived using these polynomials efficiently handles extension-bending and electromechanical couplings at the field interpolation level itself in a variationally consistent manner, without increasing the number of nodal degrees of freedom. The comparison of results obtained from numerical simulation of test problems shows that the convergence characteristic of the proposed element is insensitive to the material configuration of the beam cross-section.

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

Science.gov (United States)

Zhang, Jinao; Zhong, Yongmin; Gu, Chengfan

2018-05-30

Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

2017-01-01

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

RBF neural network based H∞ H∞ H∞ synchronization for ...

Indian Academy of Sciences (India)

Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

Science.gov (United States)

Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

2017-10-17

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

Neural Network based Minimization of BER in Multi-User Detection in SDMA

OpenAIRE

VENKATA REDDY METTU; KRISHAN KUMAR,; SRIKANTH PULLABHATLA

2011-01-01

In this paper we investigate the use of neural network based minimization of BER in MUD. Neural networks can be used for linear design, Adaptive prediction, Amplitude detection, Character Recognition and many other applications. Adaptive prediction is used in detecting the errors caused in AWGN channel. These errors are rectified by using Widrow-Hoff algorithm by updating their weights andAdaptive prediction methods. Both Widrow-Hoff and Adaptive prediction have been used for rectifying the e...

Nuclear reactors project optimization based on neural network and genetic algorithm; Otimizacao em projetos de reatores nucleares baseada em rede neural e algoritmo genetico

Energy Technology Data Exchange (ETDEWEB)

Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil); Schirru, Roberto; Martinez, Aquilino S. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia

1997-12-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

Science.gov (United States)

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

2015-03-01

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Directory of Open Access Journals (Sweden)

Yuzheng Yang

2014-01-01

Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Science.gov (United States)

Wang, Fen; Chen, Yuanlong; Liu, Meichun

2018-02-01

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

Science.gov (United States)

Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-02-05

A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

Science.gov (United States)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Well-balanced schemes for the Euler equations with gravitation: Conservative formulation using global fluxes

Science.gov (United States)

Chertock, Alina; Cui, Shumo; Kurganov, Alexander; Özcan, Şeyma Nur; Tadmor, Eitan

2018-04-01

We develop a second-order well-balanced central-upwind scheme for the compressible Euler equations with gravitational source term. Here, we advocate a new paradigm based on a purely conservative reformulation of the equations using global fluxes. The proposed scheme is capable of exactly preserving steady-state solutions expressed in terms of a nonlocal equilibrium variable. A crucial step in the construction of the second-order scheme is a well-balanced piecewise linear reconstruction of equilibrium variables combined with a well-balanced central-upwind evolution in time, which is adapted to reduce the amount of numerical viscosity when the flow is at (near) steady-state regime. We show the performance of our newly developed central-upwind scheme and demonstrate importance of perfect balance between the fluxes and gravitational forces in a series of one- and two-dimensional examples.

A developmental perspective on the neural bases of human empathy.

Science.gov (United States)

Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

2017-08-01

While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

Acciones equivalentes y solución en desplazamientos interpolada en la viga de Bernouilli-Euler

OpenAIRE

Romero, J. L.; Ortega, M. A.

1998-01-01

Se propone un método para el cálculo de la viga de Bernoulli-Euler que permite optimizar los resultados obtenidos mediante los elementos finitos hermíticos tradicionales. La principal ventaja es que puede aproximar con gran bondad los desplazamientos y esfuerzos en el interior de los elementos, incluso para elementos de gran tamaño.

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Directory of Open Access Journals (Sweden)

Shu-zhi Gao

2014-01-01

Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

2014-01-01

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

Nonlinear earthquake analysis of reinforced concrete frames with fiber and Bernoulli-Euler beam-column element.

Science.gov (United States)

Karaton, Muhammet

2014-01-01

A beam-column element based on the Euler-Bernoulli beam theory is researched for nonlinear dynamic analysis of reinforced concrete (RC) structural element. Stiffness matrix of this element is obtained by using rigidity method. A solution technique that included nonlinear dynamic substructure procedure is developed for dynamic analyses of RC frames. A predicted-corrected form of the Bossak-α method is applied for dynamic integration scheme. A comparison of experimental data of a RC column element with numerical results, obtained from proposed solution technique, is studied for verification the numerical solutions. Furthermore, nonlinear cyclic analysis results of a portal reinforced concrete frame are achieved for comparing the proposed solution technique with Fibre element, based on flexibility method. However, seismic damage analyses of an 8-story RC frame structure with soft-story are investigated for cases of lumped/distributed mass and load. Damage region, propagation, and intensities according to both approaches are researched.

Thermoelastic steam turbine rotor control based on neural network

Science.gov (United States)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

2006-10-15

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Science.gov (United States)

Wang, L.; Zhang, Y. Y.; Ding, L.

2006-10-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

International Nuclear Information System (INIS)

Wang, L; Zhang, Y Y; Ding, L

2006-01-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

Implicit flux-split schemes for the Euler equations

Science.gov (United States)

Thomas, J. L.; Walters, R. W.; Van Leer, B.

1985-01-01

Recent progress in the development of implicit algorithms for the Euler equations using the flux-vector splitting method is described. Comparisons of the relative efficiency of relaxation and spatially-split approximately factored methods on a vector processor for two-dimensional flows are made. For transonic flows, the higher convergence rate per iteration of the Gauss-Seidel relaxation algorithms, which are only partially vectorizable, is amply compensated for by the faster computational rate per iteration of the approximately factored algorithm. For supersonic flows, the fully-upwind line-relaxation method is more efficient since the numerical domain of dependence is more closely matched to the physical domain of dependence. A hybrid three-dimensional algorithm using relaxation in one coordinate direction and approximate factorization in the cross-flow plane is developed and applied to a forebody shape at supersonic speeds and a swept, tapered wing at transonic speeds.

Fibrous dysplasia of the cranial vault: quantitative analysis based on neural networks

International Nuclear Information System (INIS)

Arana, E.; Marti-Bonmati, L.; Paredes, R.; Molla, E.

1998-01-01

To assess the utility of statistical analysis and neural networks in the quantitative analysis of fibrous dysplasia of the cranial vault. Ten patients with fibrous dysplasia (six women and four men with a mean age of 23.60±17.85 years) were selected from a series of 167 patients with lesions of the cranial vault evaluated by plain radiography and computed tomography (CT). Nineteen variables were taken from their medical records and radiological study. Their characterization was based on statistical analysis and neural network, and was validated by means of the leave-one-out method. The performance of the neural network was estimated by means of receiver operating characteristics (ROC) curves, using as a parameter the area under the curve A z . Bivariate analysis identified age, duration of symptoms, lytic and sclerotic patterns, sclerotic margin, ovoid shape, soft-tissue mas and periosteal reaction as significant variables. The area under the neural network curve was 0.9601±0.0435. The network selected the matrix and soft-tissue mass a variables that were indispensable for diagnosis. The neural network presents a high performance in the characterization of fibrous dysplasia of the cranial vault, disclosing occult interactions among the variables. (Author) 24 refs

Stereo-vision-based cooperative-vehicle positioning using OCC and neural networks

Science.gov (United States)

Ifthekhar, Md. Shareef; Saha, Nirzhar; Jang, Yeong Min

2015-10-01

Vehicle positioning has been subjected to extensive research regarding driving safety measures and assistance as well as autonomous navigation. The most common positioning technique used in automotive positioning is the global positioning system (GPS). However, GPS is not reliably accurate because of signal blockage caused by high-rise buildings. In addition, GPS is error prone when a vehicle is inside a tunnel. Moreover, GPS and other radio-frequency-based approaches cannot provide orientation information or the position of neighboring vehicles. In this study, we propose a cooperative-vehicle positioning (CVP) technique by using the newly developed optical camera communications (OCC). The OCC technique utilizes image sensors and cameras to receive and decode light-modulated information from light-emitting diodes (LEDs). A vehicle equipped with an OCC transceiver can receive positioning and other information such as speed, lane change, driver's condition, etc., through optical wireless links of neighboring vehicles. Thus, the target vehicle position that is too far away to establish an OCC link can be determined by a computer-vision-based technique combined with the cooperation of neighboring vehicles. In addition, we have devised a back-propagation (BP) neural-network learning method for positioning and range estimation for CVP. The proposed neural-network-based technique can estimate target vehicle position from only two image points of target vehicles using stereo vision. For this, we use rear LEDs on target vehicles as image points. We show from simulation results that our neural-network-based method achieves better accuracy than that of the computer-vision method.

Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning

Directory of Open Access Journals (Sweden)

Lejla Banjanovic-Mehmedovic

2011-01-01

Full Text Available This paper presents implementation of optimal search strategy (OSS in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.

Neural Network Classifier Based on Growing Hyperspheres

Czech Academy of Sciences Publication Activity Database

Jiřina Jr., Marcel; Jiřina, Marcel

2000-01-01

Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

Evaluation of the cranial base in amnion rupture sequence involving the anterior neural tube: implications regarding recurrence risk.

Science.gov (United States)

Jones, Kenneth Lyons; Robinson, Luther K; Benirschke, Kurt

2006-09-01

Amniotic bands can cause disruption of the cranial end of the developing fetus, leading in some cases to a neural tube closure defect. Although recurrence for unaffected parents of an affected child with a defect in which the neural tube closed normally but was subsequently disrupted by amniotic bands is negligible; for a primary defect in closure of the neural tube to which amnion has subsequently adhered, recurrence risk is 1.7%. In that primary defects of neural tube closure are characterized by typical abnormalities of the base of the skull, evaluation of the cranial base in such fetuses provides an approach for making a distinction between these 2 mechanisms. This distinction has implications regarding recurrence risk. The skull base of 2 fetuses with amnion rupture sequence involving the cranial end of the neural tube were compared to that of 1 fetus with anencephaly as well as that of a structurally normal fetus. The skulls were cleaned, fixed in 10% formalin, recleaned, and then exposed to 10% KOH solution. After washing and recleaning, the skulls were exposed to hydrogen peroxide for bleaching and photography. Despite involvement of the anterior neural tube in both fetuses with amnion rupture sequence, in Case 3 the cranial base was normal while in Case 4 the cranial base was similar to that seen in anencephaly. This technique provides a method for determining the developmental pathogenesis of anterior neural tube defects in cases of amnion rupture sequence. As such, it provides information that can be used to counsel parents of affected children with respect to recurrence risk.

Ads' click-through rates predicting based on gated recurrent unit neural networks

Science.gov (United States)

Chen, Qiaohong; Guo, Zixuan; Dong, Wen; Jin, Lingzi

2018-05-01

In order to improve the effect of online advertising and to increase the revenue of advertising, the gated recurrent unit neural networks(GRU) model is used as the ads' click through rates(CTR) predicting. Combined with the characteristics of gated unit structure and the unique of time sequence in data, using BPTT algorithm to train the model. Furthermore, by optimizing the step length algorithm of the gated unit recurrent neural networks, making the model reach optimal point better and faster in less iterative rounds. The experiment results show that the model based on the gated recurrent unit neural networks and its optimization of step length algorithm has the better effect on the ads' CTR predicting, which helps advertisers, media and audience achieve a win-win and mutually beneficial situation in Three-Side Game.

Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

Directory of Open Access Journals (Sweden)

Yu Bo

2017-01-01

Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

Positivity-preserving CE/SE schemes for solving the compressible Euler and Navier–Stokes equations on hybrid unstructured meshes

KAUST Repository

Shen, Hua

2018-05-28

We construct positivity-preserving space–time conservation element and solution element (CE/SE) schemes for solving the compressible Euler and Navier–Stokes equations on hybrid unstructured meshes consisting of triangular and rectangular elements. The schemes use an a posteriori limiter to prevent negative densities and pressures based on the premise of preserving optimal accuracy. The limiter enforces a constraint for spatial derivatives and does not change the conservative property of CE/SE schemes. Several numerical examples suggest that the proposed schemes preserve accuracy for smooth flows and strictly preserve positivity of densities and pressures for the problems involving near vacuum and very strong discontinuities.

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Science.gov (United States)

Saive, Anne-Lise; Royet, Jean-Pierre; Plailly, Jane

2014-01-01

Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory. PMID:25071494

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Directory of Open Access Journals (Sweden)

Anne-Lise eSaive

2014-07-01

Full Text Available Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory.

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Directory of Open Access Journals (Sweden)

Jianjun Ni

2014-01-01

Full Text Available Robot simultaneous localization and mapping (SLAM problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

Science.gov (United States)

Li, Hongfei; Jiang, Haijun; Hu, Cheng

2016-03-01

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

A systematic review of the neural bases of psychotherapy for anxiety and related disorders.

Science.gov (United States)

Brooks, Samantha J; Stein, Dan J

2015-09-01

Brain imaging studies over two decades have delineated the neural circuitry of anxiety and related disorders, particularly regions involved in fear processing and in obsessive-compulsive symptoms. The neural circuitry of fear processing involves the amygdala, anterior cingulate, and insular cortex, while cortico-striatal-thalamic circuitry plays a key role in obsessive-compulsive disorder. More recently, neuroimaging studies have examined how psychotherapy for anxiety and related disorders impacts on these neural circuits. Here we conduct a systematic review of the findings of such work, which yielded 19 functional magnetic resonance imaging studies examining the neural bases of cognitive-behavioral therapy (CBT) in 509 patients with anxiety and related disorders. We conclude that, although each of these related disorders is mediated by somewhat different neural circuitry, CBT may act in a similar way to increase prefrontal control of subcortical structures. These findings are consistent with an emphasis in cognitive-affective neuroscience on the potential therapeutic value of enhancing emotional regulation in various psychiatric conditions.

Iterative methods for compressible Navier-Stokes and Euler equations

Energy Technology Data Exchange (ETDEWEB)

Tang, W.P.; Forsyth, P.A.

1996-12-31

This workshop will focus on methods for solution of compressible Navier-Stokes and Euler equations. In particular, attention will be focused on the interaction between the methods used to solve the non-linear algebraic equations (e.g. full Newton or first order Jacobian) and the resulting large sparse systems. Various types of block and incomplete LU factorization will be discussed, as well as stability issues, and the use of Newton-Krylov methods. These techniques will be demonstrated on a variety of model transonic and supersonic airfoil problems. Applications to industrial CFD problems will also be presented. Experience with the use of C++ for solution of large scale problems will also be discussed. The format for this workshop will be four fifteen minute talks, followed by a roundtable discussion.

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

Recursive Neural Networks Based on PSO for Image Parsing

Directory of Open Access Journals (Sweden)

Guo-Rong Cai

2013-01-01

Full Text Available This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO and Recursive Neural Networks (RNNs. State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

Flexible deep brain neural probes based on a parylene tube structure

Science.gov (United States)

Zhao, Zhiguo; Kim, Eric; Luo, Hao; Zhang, Jinsheng; Xu, Yong

2018-01-01

Most microfabricated neural probes have limited shank length, which prevents them from reaching many deep brain structures. This paper reports deep brain neural probes with ultra-long penetrating shanks based on a simple but novel parylene tube structure. The mechanical strength of the parylene tube shank is temporarily enhanced during implantation by inserting a metal wire. The metal wire can be removed after implantation, making the implanted probe very flexible and thus minimizing the stress caused by micromotions of brain tissues. Optogenetic stimulation and chemical delivery capabilities can be potentially integrated by taking advantage of the tube structure. Single-shank prototypes with a shank length of 18.2 mm have been developed. The microfabrication process comprises of deep reactive ion etching (DRIE) of silicon, parylene conformal coating/refilling, and XeF2 isotropic silicon etching. In addition to bench-top insertion characterization, the functionality of developed probes has been preliminarily demonstrated by implanting into the amygdala of a rat and recording neural signals.

Computer interpretation of thallium SPECT studies based on neural network analysis

Science.gov (United States)

Wang, David C.; Karvelis, K. C.

1991-06-01

A class of artificial intelligence (Al) programs known as neural networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from "expert system" Al programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The "bullseye" images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of a trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging.

Computer interpretation of thallium SPECT studies based on neural network analysis

International Nuclear Information System (INIS)

Wang, D.C.; Karvelis, K.C.

1991-01-01

This paper reports that a class of artificial intelligence (AI) programs known as neural-networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from expert system AI programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The bullseye images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of the trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging

Robust synchronization of delayed neural networks based on adaptive control and parameters identification

International Nuclear Information System (INIS)

Zhou Jin; Chen Tianping; Xiang Lan

2006-01-01

This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

Evolution of an artificial neural network based autonomous land vehicle controller.

Science.gov (United States)

Baluja, S

1996-01-01

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

Optical implementation of a feature-based neural network with application to automatic target recognition

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1993-01-01

An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

DEFF Research Database (Denmark)

Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

2016-01-01

Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior...

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

OpenAIRE

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and di...

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

Science.gov (United States)

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Bifurcation and chaotic behavior in the Euler method for a Kaplan-Yorke prototype delay model

International Nuclear Information System (INIS)

Peng Mingshu

2004-01-01

A discrete model with a simple cubic nonlinearity term is treated in the study the rich dynamics of a prototype delayed dynamical system under Euler discretization. The effect of breaking the symmetry of the system is to create a wide complex operating conditions which would not otherwise be seen. These include multiple steady states, complex periodic oscillations, chaos by period doubling bifurcations

Neural network-based control of an intelligent solar Stirling pump

International Nuclear Information System (INIS)

Tavakolpour-Saleh, A.R.; Jokar, H.

2016-01-01

In this paper, an ANN (artificial neural network) control system is applied to a novel solar-powered active LTD (low temperature differential) Stirling pump. First, a mathematical description of the proposed Stirling pump is presented. Then, optimum operating frequencies of the converter corresponding to different operating conditions (i.e. different sink and source temperatures and water heads) are investigated using the proposed mathematical framework. It is found that the proposed complex mathematical scheme has a very slow convergence and thus, is not appropriate for real-time implementation of the model-based controller. Consequently, a NN (neural network) model with a lower complexity is proposed to learn the simulation data obtained from the mathematical model. The designed neural network controller is thus applied to a digital processor to effectively tune the converter frequency so that a maximum output power is acquired. Finally, the performance of the proposed mechatronic system is evaluated experimentally. The experimental results clearly demonstrate the feasibility of pumping water at low temperature difference under variable operating conditions using the proposed intelligent Stirling converter. - Highlights: • A novel intelligent solar-powered active LTD Stirling pump was introduced. • A neural network controller was used to tune the converter speed. • The intelligent converter was able to adapt itself to different operating conditions. • It was possible to excite the water column with its resonance mode. • Experimental results showed the effectiveness of the proposed converter.

Chaos Control and Synchronization of Cellular Neural Network with Delays Based on OPNCL Control

International Nuclear Information System (INIS)

Qian, Tang; Xing-Yuan, Wang

2010-01-01

The problem of chaos control and complete synchronization of cellular neural network with delays is studied. Based on the open plus nonlinear closed loop (OPNCL) method, the control scheme and synchronization scheme are designed. Both the schemes can achieve the chaos control and complete synchronization of chaotic neural network respectively, and their validity is further verified by numerical simulation experiments. (general)

Neural Network Based Intrusion Detection System for Critical Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Ondrej Linda; Milos Manic

2009-07-01

Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

A new fractional nonlocal model and its application in free vibration of Timoshenko and Euler-Bernoulli beams

Science.gov (United States)

Rahimi, Zaher; Sumelka, Wojciech; Yang, Xiao-Jun

2017-11-01

The application of fractional calculus in fractional models (FMs) makes them more flexible than integer models inasmuch they can conclude all of integer and non-integer operators. In other words FMs let us use more potential of mathematics to modeling physical phenomena due to the use of both integer and fractional operators to present a better modeling of problems, which makes them more flexible and powerful. In the present work, a new fractional nonlocal model has been proposed, which has a simple form and can be used in different problems due to the simple form of numerical solutions. Then the model has been used to govern equations of the motion of the Timoshenko beam theory (TBT) and Euler-Bernoulli beam theory (EBT). Next, free vibration of the Timoshenko and Euler-Bernoulli simply-supported (S-S) beam has been investigated. The Galerkin weighted residual method has been used to solve the non-linear governing equations.

Nonlinear Correction to the Euler Buckling Formula for Compressed Cylinders with Guided-Guided End Conditions

KAUST Repository

De Pascalis, Riccardo

2010-07-22

Euler\\'s celebrated buckling formula gives the critical load N for the buckling of a slender cylindrical column with radius B and length L as N/(π3B2)=(E/4)(B/L)2 where E is Young\\'s modulus. Its derivation relies on the assumptions that linear elasticity applies to this problem, and that the slenderness (B/L) is an infinitesimal quantity. Here we ask the following question: What is the first non-linear correction in the right hand-side of this equation when terms up to (B/L)4 are kept? To answer this question, we specialize the exact solution of incremental non-linear elasticity for the homogeneous compression of a thick compressible cylinder with lubricated ends to the theory of third-order elasticity. In particular, we highlight the way second- and third-order constants-including Poisson\\'s ratio-all appear in the coefficient of (B/L)4. © 2010 Springer Science+Business Media B.V.

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

Science.gov (United States)

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A convolutional neural network-based screening tool for X-ray serial crystallography.

Science.gov (United States)

Ke, Tsung Wei; Brewster, Aaron S; Yu, Stella X; Ushizima, Daniela; Yang, Chao; Sauter, Nicholas K

2018-05-01

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization. open access.

Image Fusion Based on the Self-Organizing Feature Map Neural Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Zhaoli; SUN Shenghe

2001-01-01

This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

Polymer SU-8 Based Microprobes for Neural Recording and Drug Delivery

Science.gov (United States)

Altuna, Ane; Fernandez, Luis; Berganzo, Javier

2015-06-01

This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a) minimize injury in neural tissue, b) obtain high-quality electrical signals and c) deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

POLYMER SU-8 BASED MICROPROBES FOR NEURAL RECORDING AND DRUG DELIVERY

Directory of Open Access Journals (Sweden)

Ane eAltuna

2015-06-01

Full Text Available This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a minimize injury in neural tissue, b obtain high-quality electrical signals and c deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

One-way hash function based on hyper-chaotic cellular neural network

International Nuclear Information System (INIS)

Yang Qunting; Gao Tiegang

2008-01-01

The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge–Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability. (general)

A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

OpenAIRE

Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

2016-01-01

A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

Advanced mechanics from Euler's determinism to Arnold's chaos

CERN Document Server

Rajeev, S G

2013-01-01

Classical Mechanics is the oldest and best understood part of physics. This does not mean that it is cast in marble yet, a museum piece to be admired from a distance. Instead, mechanics continues to be an active area of research by physicists and mathematicians. Every few years, we need to re-evaluate the purpose of learning mechanics and look at old material in the light of modern developments. Once you have learned basic mechanics (Newton's laws, the solution of the Kepler problem) and quantum mechanics (the Schrodinger equation, hydrogen atom) it is time to go back and relearn classical mechanics in greater depth. It is the intent of this book to take you through the ancient (the original meaning of "classical") parts of the subject quickly: the ideas started by Euler and ending roughly with Poincare. We then take up the developments of twentieth century physics that have largely to do with chaos and discrete time evolution (the basis of numerical solutions).

Conformal Field Theory as Microscopic Dynamics of Incompressible Euler and Navier-Stokes Equations

International Nuclear Information System (INIS)

Fouxon, Itzhak; Oz, Yaron

2008-01-01

We consider the hydrodynamics of relativistic conformal field theories at finite temperature. We show that the limit of slow motions of the ideal hydrodynamics leads to the nonrelativistic incompressible Euler equation. For viscous hydrodynamics we show that the limit of slow motions leads to the nonrelativistic incompressible Navier-Stokes equation. We explain the physical reasons for the reduction and discuss the implications. We propose that conformal field theories provide a fundamental microscopic viewpoint of the equations and the dynamics governed by them

Conformal field theory as microscopic dynamics of incompressible Euler and Navier-Stokes equations.

Science.gov (United States)

Fouxon, Itzhak; Oz, Yaron

2008-12-31

We consider the hydrodynamics of relativistic conformal field theories at finite temperature. We show that the limit of slow motions of the ideal hydrodynamics leads to the nonrelativistic incompressible Euler equation. For viscous hydrodynamics we show that the limit of slow motions leads to the nonrelativistic incompressible Navier-Stokes equation. We explain the physical reasons for the reduction and discuss the implications. We propose that conformal field theories provide a fundamental microscopic viewpoint of the equations and the dynamics governed by them.

Higher order solution of the Euler equations on unstructured grids using quadratic reconstruction

Science.gov (United States)

Barth, Timothy J.; Frederickson, Paul O.

1990-01-01

High order accurate finite-volume schemes for solving the Euler equations of gasdynamics are developed. Central to the development of these methods are the construction of a k-exact reconstruction operator given cell-averaged quantities and the use of high order flux quadrature formulas. General polygonal control volumes (with curved boundary edges) are considered. The formulations presented make no explicit assumption as to complexity or convexity of control volumes. Numerical examples are presented for Ringleb flow to validate the methodology.

Template measurement for plutonium pit based on neural networks

International Nuclear Information System (INIS)

Zhang Changfan; Gong Jian; Liu Suping; Hu Guangchun; Xiang Yongchun

2012-01-01

Template measurement for plutonium pit extracts characteristic data from-ray spectrum and the neutron counts emitted by plutonium. The characteristic data of the suspicious object are compared with data of the declared plutonium pit to verify if they are of the same type. In this paper, neural networks are enhanced as the comparison algorithm for template measurement of plutonium pit. Two kinds of neural networks are created, i.e. the BP and LVQ neural networks. They are applied in different aspects for the template measurement and identification. BP neural network is used for classification for different types of plutonium pits, which is often used for management of nuclear materials. LVQ neural network is used for comparison of inspected objects to the declared one, which is usually applied in the field of nuclear disarmament and verification. (authors)

Artificial Neural Network Based Model of Photovoltaic Cell

Directory of Open Access Journals (Sweden)

Messaouda Azzouzi

2017-03-01

Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Directory of Open Access Journals (Sweden)

Mohammad S. Islam

2017-01-01

Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.

Optical supervised filtering technique based on Hopfield neural network

Science.gov (United States)

Bal, Abdullah

2004-11-01

Hopfield neural network is commonly preferred for optimization problems. In image segmentation, conventional Hopfield neural networks (HNN) are formulated as a cost-function-minimization problem to perform gray level thresholding on the image histogram or the pixels' gray levels arranged in a one-dimensional array [R. Sammouda, N. Niki, H. Nishitani, Pattern Rec. 30 (1997) 921-927; K.S. Cheng, J.S. Lin, C.W. Mao, IEEE Trans. Med. Imag. 15 (1996) 560-567; C. Chang, P. Chung, Image and Vision comp. 19 (2001) 669-678]. In this paper, a new high speed supervised filtering technique is proposed for image feature extraction and enhancement problems by modifying the conventional HNN. The essential improvement in this technique is to use 2D convolution operation instead of weight-matrix multiplication. Thereby, neural network based a new filtering technique has been obtained that is required just 3 × 3 sized filter mask matrix instead of large size weight coefficient matrix. Optical implementation of the proposed filtering technique is executed easily using the joint transform correlator. The requirement of non-negative data for optical implementation is provided by bias technique to convert the bipolar data to non-negative data. Simulation results of the proposed optical supervised filtering technique are reported for various feature extraction problems such as edge detection, corner detection, horizontal and vertical line extraction, and fingerprint enhancement.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Science.gov (United States)

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Calculation for Primary Combustion Characteristics of Boron-Based Fuel-Rich Propellant Based on BP Neural Network

OpenAIRE

Wan'e, Wu; Zuoming, Zhu

2012-01-01

A practical scheme for selecting characterization parameters of boron-based fuel-rich propellant formulation was put forward; a calculation model for primary combustion characteristics of boron-based fuel-rich propellant based on backpropagation neural network was established, validated, and then was used to predict primary combustion characteristics of boron-based fuel-rich propellant. The results show that the calculation error of burning rate is less than ± 7 . 3 %; in the formulation rang...

Euler and Navier endash Stokes limits of the Uehling endash Uhlenbeck quantum kinetic equations

International Nuclear Information System (INIS)

Arlotti, L.; Lachowicz, M.

1997-01-01

The Uehling endash Uhlenbeck evolution equations for gases of identical quantum particles either fermions or bosons, in the case in which the collision kernel does not depend on the distribution function, are considered. The existence of solutions and their asymptotic relations with solutions of the hydrodynamic equations both at the level of the Euler system and at the level of the Navier endash Stokes system are proved. copyright 1997 American Institute of Physics

neural network based model o work based model of an industrial oil

African Journals Online (AJOL)

eobe

technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

Science.gov (United States)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Upwind MacCormack Euler solver with non-equilibrium chemistry

Science.gov (United States)

Sherer, Scott E.; Scott, James N.

1993-01-01

A computer code, designated UMPIRE, is currently under development to solve the Euler equations in two dimensions with non-equilibrium chemistry. UMPIRE employs an explicit MacCormack algorithm with dissipation introduced via Roe's flux-difference split upwind method. The code also has the capability to employ a point-implicit methodology for flows where stiffness is introduced through the chemical source term. A technique consisting of diagonal sweeps across the computational domain from each corner is presented, which is used to reduce storage and execution requirements. Results depicting one dimensional shock tube flow for both calorically perfect gas and thermally perfect, dissociating nitrogen are presented to verify current capabilities of the program. Also, computational results from a chemical reactor vessel with no fluid dynamic effects are presented to check the chemistry capability and to verify the point implicit strategy.

On the nature and evolution of the neural bases of human language

Science.gov (United States)

Lieberman, Philip

2002-01-01

The traditional theory equating the brain bases of language with Broca's and Wernicke's neocortical areas is wrong. Neural circuits linking activity in anatomically segregated populations of neurons in subcortical structures and the neocortex throughout the human brain regulate complex behaviors such as walking, talking, and comprehending the meaning of sentences. When we hear or read a word, neural structures involved in the perception or real-world associations of the word are activated as well as posterior cortical regions adjacent to Wernicke's area. Many areas of the neocortex and subcortical structures support the cortical-striatal-cortical circuits that confer complex syntactic ability, speech production, and a large vocabulary. However, many of these structures also form part of the neural circuits regulating other aspects of behavior. For example, the basal ganglia, which regulate motor control, are also crucial elements in the circuits that confer human linguistic ability and abstract reasoning. The cerebellum, traditionally associated with motor control, is active in motor learning. The basal ganglia are also key elements in reward-based learning. Data from studies of Broca's aphasia, Parkinson's disease, hypoxia, focal brain damage, and a genetically transmitted brain anomaly (the putative "language gene," family KE), and from comparative studies of the brains and behavior of other species, demonstrate that the basal ganglia sequence the discrete elements that constitute a complete motor act, syntactic process, or thought process. Imaging studies of intact human subjects and electrophysiologic and tracer studies of the brains and behavior of other species confirm these findings. As Dobzansky put it, "Nothing in biology makes sense except in the light of evolution" (cited in Mayr, 1982). That applies with as much force to the human brain and the neural bases of language as it does to the human foot or jaw. The converse follows: the mark of evolution on

Stability analysis of the Euler discretization for SIR epidemic model

International Nuclear Information System (INIS)

Suryanto, Agus

2014-01-01

In this paper we consider a discrete SIR epidemic model obtained by the Euler method. For that discrete model, existence of disease free equilibrium and endemic equilibrium is established. Sufficient conditions on the local asymptotical stability of both disease free equilibrium and endemic equilibrium are also derived. It is found that the local asymptotical stability of the existing equilibrium is achieved only for a small time step size h. If h is further increased and passes the critical value, then both equilibriums will lose their stability. Our numerical simulations show that a complex dynamical behavior such as bifurcation or chaos phenomenon will appear for relatively large h. Both analytical and numerical results show that the discrete SIR model has a richer dynamical behavior than its continuous counterpart

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

Science.gov (United States)

Zelić, B; Bolf, N; Vasić-Racki, D

2006-06-01

Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Science.gov (United States)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Introduction to Artificial Neural Networks

DEFF Research Database (Denmark)

Larsen, Jan

1999-01-01

The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

International Nuclear Information System (INIS)

Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

2006-01-01

In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

A prediction method for the wax deposition rate based on a radial basis function neural network

Directory of Open Access Journals (Sweden)

Ying Xie

2017-06-01

Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

An adaptive Petrov-Galerkin formulation for solving the compressible Euler and Navier-Stokes

International Nuclear Information System (INIS)

Almeida, Regina Celia Cerqueira de

1993-01-01

A space-time finite element finite element formulation for the compressible Euler and Navier-Stokes equations is proposed. The present work develops a stable generalized CAU method which represents shocks and boundary-layers accurately. An h-adaptive remeshing refinement, which takes into account directional stretching and stretching ratio, is used leading to a very good way to indicate and refine the flow regions with singularities. Numerical experiment were conducted for some steady and unsteady problems and the performance of the proposed methods is discussed. (author)

A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

OpenAIRE

Christian de Peretti; Carole Siani; Mario Cerrato

2010-01-01

This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

Energy Technology Data Exchange (ETDEWEB)

Olyaee, Saeed; Hamedi, Samaneh, E-mail: s_olyaee@srttu.edu [Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran (Iran, Islamic Republic of)

2011-02-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

International Nuclear Information System (INIS)

Olyaee, Saeed; Hamedi, Samaneh

2011-01-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Online Particle Detection by Neural Networks Based on Topologic Calorimetry Information

CERN Document Server

Ciodaro, T; The ATLAS collaboration; Damazio, D; de Seixas, JM

2011-01-01

This paper presents the last results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction over the ATLAS calorimetry information (energy measurements). Later, the extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time in 59%. Also, the payload necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount

Artificial neural network based approach to transmission lines protection

International Nuclear Information System (INIS)

Joorabian, M.

1999-05-01

The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management

Directory of Open Access Journals (Sweden)

Somaye Yeylaghi

2017-06-01

Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.

Neural Substrates of Similarity and Rule-based Strategies in Judgment

Directory of Open Access Journals (Sweden)

Bettina eVon Helversen

2014-10-01

Full Text Available Making accurate judgments is a core human competence and a prerequisite for success in many areas of life. Plenty of evidence exists that people can employ different judgment strategies to solve identical judgment problems. In categorization, it has been demonstrated that similarity-based and rule-based strategies are associated with activity in different brain regions. Building on this research, the present work tests whether solving two identical judgment problems recruits different neural substrates depending on people's judgment strategies. Combining cognitive modeling of judgment strategies at the behavioral level with functional magnetic resonance imaging (fMRI, we compare brain activity when using two archetypal judgment strategies: a similarity-based exemplar strategy and a rule-based heuristic strategy. Using an exemplar-based strategy should recruit areas involved in long-term memory processes to a larger extent than a heuristic strategy. In contrast, using a heuristic strategy should recruit areas involved in the application of rules to a larger extent than an exemplar-based strategy. Largely consistent with our hypotheses, we found that using an exemplar-based strategy led to relatively higher BOLD activity in the anterior prefrontal and inferior parietal cortex, presumably related to retrieval and selective attention processes. In contrast, using a heuristic strategy led to relatively higher activity in areas in the dorsolateral prefrontal and the temporal-parietal cortex associated with cognitive control and information integration. Thus, even when people solve identical judgment problems, different neural substrates can be recruited depending on the judgment strategy involved.

Resting State EEG-based biometrics for individual identification using convolutional neural networks.

Science.gov (United States)

Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

2015-08-01

Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

Science.gov (United States)

Baertlein, Brian A.; Liao, Wen-Jiao

2000-08-01

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

Neural correlates of conventional and harm/welfare-based moral decision-making.

Science.gov (United States)

White, Stuart F; Zhao, Hui; Leong, Kelly Kimiko; Smetana, Judith G; Nucci, Larry P; Blair, R James R

2017-12-01

The degree to which social norms are processed by a unitary system or dissociable systems remains debated. Much research on children's social-cognitive judgments has supported the distinction between "moral" (harm/welfare-based) and "conventional" norms. However, the extent to which these norms are processed by dissociable neural systems remains unclear. To address this issue, 23 healthy participants were scanned with functional magnetic resonance imaging (fMRI) while they rated the wrongness of harm/welfare-based and conventional transgressions and neutral vignettes. Activation significantly greater than the neutral vignette baseline was observed in regions implicated in decision-making regions including rostral/ventral medial frontal, anterior insula and dorsomedial frontal cortices when evaluating both harm/welfare-based and social-conventional transgressions. Greater activation when rating harm/welfare-based relative to social-conventional transgressions was seen through much of ACC and bilateral inferior frontal gyrus. Greater activation was observed in superior temporal gyrus, bilateral middle temporal gyrus, left PCC, and temporal-parietal junction when rating social-conventional transgressions relative to harm/welfare-based transgressions. These data suggest that decisions regarding the wrongness of actions, irrespective of whether they involve care/harm-based or conventional transgressions, recruit regions generally implicated in affect-based decision-making. However, there is neural differentiation between harm/welfare-based and conventional transgressions. This may reflect the particular importance of processing the intent of transgressors of conventional norms and perhaps the greater emotional content or salience of harm/welfare-based transgressions.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

Science.gov (United States)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Neural correlates of attentional and mnemonic processing in event-based prospective memory.

Science.gov (United States)

Knight, Justin B; Ethridge, Lauren E; Marsh, Richard L; Clementz, Brett A

2010-01-01

Prospective memory (PM), or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT), followed by a LDT with an embedded PM component. Event-based cues were constituted by color and lexicality (red words). Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP) revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.

Entropy-stable summation-by-parts discretization of the Euler equations on general curved elements

Science.gov (United States)

Crean, Jared; Hicken, Jason E.; Del Rey Fernández, David C.; Zingg, David W.; Carpenter, Mark H.

2018-03-01

We present and analyze an entropy-stable semi-discretization of the Euler equations based on high-order summation-by-parts (SBP) operators. In particular, we consider general multidimensional SBP elements, building on and generalizing previous work with tensor-product discretizations. In the absence of dissipation, we prove that the semi-discrete scheme conserves entropy; significantly, this proof of nonlinear L2 stability does not rely on integral exactness. Furthermore, interior penalties can be incorporated into the discretization to ensure that the total (mathematical) entropy decreases monotonically, producing an entropy-stable scheme. SBP discretizations with curved elements remain accurate, conservative, and entropy stable provided the mapping Jacobian satisfies the discrete metric invariants; polynomial mappings at most one degree higher than the SBP operators automatically satisfy the metric invariants in two dimensions. In three-dimensions, we describe an elementwise optimization that leads to suitable Jacobians in the case of polynomial mappings. The properties of the semi-discrete scheme are verified and investigated using numerical experiments.

Real-time process optimization based on grey-box neural models

Directory of Open Access Journals (Sweden)

F. A. Cubillos

2007-09-01

Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

A stochastic Galerkin method for the Euler equations with Roe variable transformation

KAUST Repository

Pettersson, Per; Iaccarino, Gianluca; Nordströ m, Jan

2014-01-01

The Euler equations subject to uncertainty in the initial and boundary conditions are investigated via the stochastic Galerkin approach. We present a new fully intrusive method based on a variable transformation of the continuous equations. Roe variables are employed to get quadratic dependence in the flux function and a well-defined Roe average matrix that can be determined without matrix inversion.In previous formulations based on generalized polynomial chaos expansion of the physical variables, the need to introduce stochastic expansions of inverse quantities, or square roots of stochastic quantities of interest, adds to the number of possible different ways to approximate the original stochastic problem. We present a method where the square roots occur in the choice of variables, resulting in an unambiguous problem formulation.The Roe formulation saves computational cost compared to the formulation based on expansion of conservative variables. Moreover, the Roe formulation is more robust and can handle cases of supersonic flow, for which the conservative variable formulation fails to produce a bounded solution. For certain stochastic basis functions, the proposed method can be made more effective and well-conditioned. This leads to increased robustness for both choices of variables. We use a multi-wavelet basis that can be chosen to include a large number of resolution levels to handle more extreme cases (e.g. strong discontinuities) in a robust way. For smooth cases, the order of the polynomial representation can be increased for increased accuracy. © 2013 Elsevier Inc.

Study of functional viability of SU-8-based microneedles for neural applications

International Nuclear Information System (INIS)

Fernández, Luis J; Altuna, Ane; Tijero, Maria; Vilares, Roman; Berganzo, Javier; Blanco, F J; Gabriel, Gemma; Villa, Rosa; Rodríguez, Manuel J; Batlle, Montse

2009-01-01

This paper presents the design, fabrication, packaging and first test results of SU-8-based microneedles for neural applications. By the use of photolithography, sputtering and bonding techniques, polymer needles with integrated microchannels and electrodes have been successfully fabricated. The use of photolithography for the patterning of the fluidic channel integrated in the needle allows the design of multiple outlet ports at the needle tip, minimizing the possibility of being blocked by the tissue. Furthermore, the flexibility of the polymer reduces the risk of fracture and tissue damage once the needle is inserted, while it is still rigid enough to allow a perfect insertion into the neural tissue. Fluidic and electric characterization of the microneedles has shown their viability for drug delivery and monitoring in neural applications. First drug delivery tests in ex vivo tissue demonstrated the functional viability of the needle to deliver drugs to precise points. Furthermore, in vivo experiments have demonstrated lower associated damages during insertion than those by stereotaxic standard needles

Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States); Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

1995-12-01

This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

OpenAIRE

Lehký , David; Slowik , Ondřej; Novák , Drahomír

2014-01-01

Part 7: Genetic Algorithms; International audience; The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficie...

Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

Institute of Scientific and Technical Information of China (English)

SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

2004-01-01

Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

Micropolar curved rods. 2-D, high order, Timoshenko’s and Euler-Bernoulli models

Directory of Open Access Journals (Sweden)

Zozulya V.V.

2017-01-01

Full Text Available New models for micropolar plane curved rods have been developed. 2-D theory is developed from general 2-D equations of linear micropolar elasticity using a special curvilinear system of coordinates related to the middle line of the rod and special hypothesis based on assumptions that take into account the fact that the rod is thin.High order theory is based on the expansion of the equations of the theory of elasticity into Fourier series in terms of Legendre polynomials. First stress and strain tensors,vectors of displacements and rotation and body force shave been expanded into Fourier series in terms of Legendre polynomials with respect to a thickness coordinate.Thereby all equations of elasticity including Hooke’s law have been transformed to the corresponding equations for Fourier coefficients. Then in the same way as in the theory of elasticity, system of differential equations in term of displacements and boundary conditions for Fourier coefficients have been obtained. The Timoshenko’s and Euler-Bernoulli theories are based on the classical hypothesis and 2-D equations of linear micropolar elasticity in a special curvilinear system. The obtained equations can be used to calculate stress-strain and to model thin walled structures in macro, micro and nano scale when taking in to account micropolar couple stress and rotation effects.

A neurally inspired musical instrument classification system based upon the sound onset.

Science.gov (United States)

Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

Science.gov (United States)

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Directory of Open Access Journals (Sweden)

Jianyong Liu

2015-01-01

Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Direction-of-change forecasting using a volatility-based recurrent neural network

NARCIS (Netherlands)

Bekiros, S.D.; Georgoutsos, D.A.

2008-01-01

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub-period 8

A DSP-based neural network non-uniformity correction algorithm for IRFPA

Science.gov (United States)

Liu, Chong-liang; Jin, Wei-qi; Cao, Yang; Liu, Xiu

2009-07-01

An effective neural network non-uniformity correction (NUC) algorithm based on DSP is proposed in this paper. The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise(FPN).We introduced and analyzed the artificial neural network scene-based non-uniformity correction (SBNUC) algorithm. A design of DSP-based NUC development platform for IRFPA is described. The DSP hardware platform designed is of low power consumption, with 32-bit fixed point DSP TMS320DM643 as the kernel processor. The dependability and expansibility of the software have been improved by DSP/BIOS real-time operating system and Reference Framework 5. In order to realize real-time performance, the calibration parameters update is set at a lower task priority then video input and output in DSP/BIOS. In this way, calibration parameters updating will not affect video streams. The work flow of the system and the strategy of real-time realization are introduced. Experiments on real infrared imaging sequences demonstrate that this algorithm requires only a few frames to obtain high quality corrections. It is computationally efficient and suitable for all kinds of non-uniformity.

Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation

Energy Technology Data Exchange (ETDEWEB)

Alam, Azimil Gani; Rahman, Haolia; Kim, Jung-Kyung; Han, Hwataik [Kookmin University, Seoul (Korea, Republic of)

2017-05-15

Demand control ventilation is employed to save energy by adjusting airflow rate according to the ventilation load of a building. This paper investigates a method for occupancy estimation by using a dynamic neural network model based on carbon dioxide concentration in an occupied zone. The method can be applied to most commercial and residential buildings where human effluents to be ventilated. An indoor simulation program CONTAMW is used to generate indoor CO{sub 2} data corresponding to various occupancy schedules and airflow patterns to train neural network models. Coefficients of variation are obtained depending on the complexities of the physical parameters as well as the system parameters of neural networks, such as the numbers of hidden neurons and tapped delay lines. We intend to identify the uncertainties caused by the model parameters themselves, by excluding uncertainties in input data inherent in measurement. Our results show estimation accuracy is highly influenced by the frequency of occupancy variation but not significantly influenced by fluctuation in the airflow rate. Furthermore, we discuss the applicability and validity of the present method based on passive environmental conditions for estimating occupancy in a room from the viewpoint of demand control ventilation applications.

Neural substrates of social facilitation effects on incentive-based performance

Science.gov (United States)

Chib, Vikram S; Adachi, Ryo; O’Doherty, John P

2018-01-01

Abstract Throughout our lives we must perform tasks while being observed by others. Previous studies have shown that the presence of an audience can cause increases in an individual’s performance as compared to when they are not being observed—a phenomenon called ‘social facilitation’. However, the neural mechanisms underlying this effect, in the context of skilled-task performance for monetary incentives, are not well understood. We used functional magnetic resonance imaging to monitor brain activity while healthy human participants performed a skilled-task during conditions in which they were paid based on their performance and observed and not observed by an audience. We found that during social facilitation, social signals represented in the dorsomedial prefrontal cortex (dmPFC) enhanced reward value computations in ventromedial prefrontal cortex (vmPFC). We also found that functional connectivity between dmPFC and ventral striatum was enhanced when participants exhibited social facilitation effects, indicative of a means by which social signals serve to modulate brain regions involved in regulating behavioral motivation. These findings illustrate how neural processing of social judgments gives rise to the enhanced motivational state that results in social facilitation of incentive-based performance. PMID:29648653

The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders.

Science.gov (United States)

Galandra, Caterina; Basso, Gianpaolo; Cappa, Stefano; Canessa, Nicola

2018-03-01

Neuroeconomics is providing insights into the neural bases of decision-making in normal and pathological conditions. In the neuropsychiatric domain, this discipline investigates how abnormal functioning of neural systems associated with reward processing and cognitive control promotes different disorders, and whether such evidence may inform treatments. This endeavor is crucial when studying different types of addiction, which share a core promoting mechanism in the imbalance between impulsive subcortical neural signals associated with immediate pleasurable outcomes and inhibitory signals mediated by a prefrontal reflective system. The resulting impairment in behavioral control represents a hallmark of alcohol use disorders (AUDs), a chronic relapsing disorder characterized by excessive alcohol consumption despite devastating consequences. This review aims to summarize available magnetic resonance imaging (MRI) evidence on reward-related decision-making alterations in AUDs, and to envision possible future research directions. We review functional MRI (fMRI) studies using tasks involving monetary rewards, as well as MRI studies relating decision-making parameters to neurostructural gray- or white-matter metrics. The available data suggest that excessive alcohol exposure affects neural signaling within brain networks underlying adaptive behavioral learning via the implementation of prediction errors. Namely, weaker ventromedial prefrontal cortex activity and altered connectivity between ventral striatum and dorsolateral prefrontal cortex likely underpin a shift from goal-directed to habitual actions which, in turn, might underpin compulsive alcohol consumption and relapsing episodes despite adverse consequences. Overall, these data highlight abnormal fronto-striatal connectivity as a candidate neurobiological marker of impaired choice in AUDs. Further studies are needed, however, to unveil its implications in the multiple facets of decision-making.

The method in γ spectrum analysis with artificial neural network based on MATLAB

International Nuclear Information System (INIS)

Bai Lixin; Zhang Yiyun; Xu Jiayun; Wu Liping

2003-01-01

Analyzing γ spectrum with artificial neural network have the advantage of using the information of whole spectrum and having high analyzing precision. A convenient realization based on MATLAB was present in this

A neutron spectrum unfolding code based on generalized regression artificial neural networks

International Nuclear Information System (INIS)

Rosario Martinez-Blanco, Ma. del

2016-01-01

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a "6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. - Highlights: • Main drawback of neutron spectrometry with BPNN is network topology optimization. • Compared to BPNN, it’s usually much faster to train a (GRNN). • GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest. • This computational code, automates the pre-processing, training

Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

Science.gov (United States)

Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

2018-05-01

Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

Cauchy problem with general discontinuous initial data along a smooth curve for 2-d Euler system

Science.gov (United States)

Chen, Shuxing; Li, Dening

2014-09-01

We study the Cauchy problems for the isentropic 2-d Euler system with discontinuous initial data along a smooth curve. All three singularities are present in the solution: shock wave, rarefaction wave and contact discontinuity. We show that the usual restrictive high order compatibility conditions for the initial data are automatically satisfied. The local existence of piecewise smooth solution containing all three waves is established.

Linear programming based on neural networks for radiotherapy treatment planning

International Nuclear Information System (INIS)

Xingen Wu; Limin Luo

2000-01-01

In this paper, we propose a neural network model for linear programming that is designed to optimize radiotherapy treatment planning (RTP). This kind of neural network can be easily implemented by using a kind of 'neural' electronic system in order to obtain an optimization solution in real time. We first give an introduction to the RTP problem and construct a non-constraint objective function for the neural network model. We adopt a gradient algorithm to minimize the objective function and design the structure of the neural network for RTP. Compared to traditional linear programming methods, this neural network model can reduce the time needed for convergence, the size of problems (i.e., the number of variables to be searched) and the number of extra slack and surplus variables needed. We obtained a set of optimized beam weights that result in a better dose distribution as compared to that obtained using the simplex algorithm under the same initial condition. The example presented in this paper shows that this model is feasible in three-dimensional RTP. (author)

An algebraic method to develop well-posed PML models Absorbing layers, perfectly matched layers, linearized Euler equations

International Nuclear Information System (INIS)

Rahmouni, Adib N.

2004-01-01

In 1994, Berenger [Journal of Computational Physics 114 (1994) 185] proposed a new layer method: perfectly matched layer, PML, for electromagnetism. This new method is based on the truncation of the computational domain by a layer which absorbs waves regardless of their frequency and angle of incidence. Unfortunately, the technique proposed by Berenger (loc. cit.) leads to a system which has lost the most important properties of the original one: strong hyperbolicity and symmetry. We present in this paper an algebraic technique leading to well-known PML model [IEEE Transactions on Antennas and Propagation 44 (1996) 1630] for the linearized Euler equations, strongly well-posed, preserving the advantages of the initial method, and retaining symmetry. The technique proposed in this paper can be extended to various hyperbolic problems

ID card number detection algorithm based on convolutional neural network

Science.gov (United States)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

Sequence memory based on coherent spin-interaction neural networks.

Science.gov (United States)

Xia, Min; Wong, W K; Wang, Zhijie

2014-12-01

Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

Science.gov (United States)

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

Directory of Open Access Journals (Sweden)

Yongcheng Li

Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

VoIP attacks detection engine based on neural network

Science.gov (United States)

Safarik, Jakub; Slachta, Jiri

2015-05-01

The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

Science.gov (United States)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

Directory of Open Access Journals (Sweden)

Aichun Zhu

2018-03-01

Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

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

International Nuclear Information System (INIS)

Potters, Max; Vaillant, Timothee; Bouchet, Freddy

2013-01-01

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

Neural correlates of attentional and mnemonic processing in event-based prospective memory

Directory of Open Access Journals (Sweden)

Justin B Knight

2010-02-01

Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.

A fast button surface defects detection method based on convolutional neural network