WorldWideScience

Sample records for neural nets amanda

  1. Neural Net Safety Monitor Design

    Science.gov (United States)

    Larson, Richard R.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

  2. Results from AMANDA

    CERN Document Server

    Wiebusch, C; Bai, X; Barwick, S W; Becka, T; Becker, K H; Bertrand, D; Bernadini, E; Binon, Freddy G; Biron, A; Boser, S; Botner, O; Bouchta, A; Bouhali, O; Burgess, T; Carius, S; Castermans, T; Chen, A; Chirkin, D; Conrad, J; Cooley, J; Cowen, D F; Davour, A; De Clercq, C; De Young, T R; Desiati, P; Dewulf, J P; Doksus, P; Ekstrom, P; Feser, T; Gaisser, T K; Gaug, M; Gerhardt, L; Goldschmidt, A; Hallgren, A; Halzen, F; Hanson, K; Hardtke, R; Hauschildt, T; Hellwig, M; Herquet, P; Hill, G C; Hulth, P O; Hundertmark, S; Jacobsen, J; Karle, A; Koci, B; Köpke, L; Kowalski, M; Kühn, K; Lamoureux, J I; Leich, H; Leuthold, M J; Lindahl, P; Liubarsky, I; Madsen, J; Marciniewski, P; Matis, H S; McParland, C P; Minaeva, Y; Minocinovic, P; Mock, P C; Morse, R; Nahnhauer, R; Neunhoffer, T; Niessen, P; Nygren, D R; Ögelman, H B; Olbrechts, P; Pérez de los Heros, C; Pohl, A C; Price, P B; Przybylski, G T; Rawlins, K; Resconi, E; Rhode, W; Ribordy, M; Richter, S; Rodríguez-Martino, J; Ross, D; Sander, H G; Schmidt, T; Schneider, D; Schwarz, R; Silvestri, A; Solarz, M; Spiczak, G M; Spiering, C; Steele, D; Steffen, P; Stokstad, R G; Sudhoff, P; Sulanke, K H; Taboada, I; Thollander, L; Tilav, S; Walck, C; Weinheimer, C; Wiebusch, C; Wiedemann, C; Wischnewski, R; Wissing, H; Woschnagg, K; Yodh, G B; Young, S

    2002-01-01

    The Antarctic Muon and Neutrino Detector Array (AMANDA) is a high- energy neutrino telescope operating at the geographic South Pole. It is a lattice of photomultiplier tubes buried deep in the polar ice. The primary goal of this detector is to discover astrophysical sources of high energy neutrinos. We describe the detector methods of operation and present results from the AMANDA-B10 prototype. We demonstrate the improved sensitivity of the current AMANDA-II detector. We conclude with an outlook to the envisioned sensitivity of the future IceCube detector. (37 refs).

  3. Modulated error diffusion CGHs for neural nets

    Science.gov (United States)

    Vermeulen, Pieter J. E.; Casasent, David P.

    1990-05-01

    New modulated error diffusion CGHs (computer generated holograms) for optical computing are considered. Specific attention is given to their use in optical matrix-vector, associative processor, neural net and optical interconnection architectures. We consider lensless CGH systems (many CGHs use an external Fourier transform (FT) lens), the Fresnel sampling requirements, the effects of finite CGH apertures (sample and hold inputs), dot size correction (for laser recorders), and new applications for this novel encoding method (that devotes attention to quantization noise effects).

  4. Recent results from AMANDA II

    International Nuclear Information System (INIS)

    Hanson, K.; Ahrens, J.; Bai, X.; Barwick, S.W.; Becka, T.; Becker, K.-H.; Bernardini, E.; Bertrand, D.; Binon, F.; Biron, A.; Boeser, S.; Botner, O.; Bouhali, O.; Burgess, T.; Carius, S.; Castermans, T.; Chen, A.; Chirkin, D.; Conrad, J.; Cooley, J.; Cowen, D.F.; Davour, A.; De Clercq, C.; De Young, T.; Desiati, P.; Dewulf, J.-P.; Doksus, P.; Ekstroem, P.; Feser, T.; Gaisser, T.K.; Gaug, M.; Gerhardt, L.; Goldschmidt, A.; Hallgren, A.; Halzen, F.; Hanson, K.; Hardtke, R.; Hauschildt, T.; Hellwig, M.; Herque, P.; Hill, G.C.; Hulth, P.O.; Hundertmark, S.; Jacobsen, J.; Karle, A.; Koci, B.; Koepke, L.; Kuehn, K.; Kowalski, M.; Lamoureux, J.I.; Leich, H.; Leuthold, M.; Lindahl, P.; Liubarsky, I.; Madsen, J.; Marciniewski, P.; Matis, H.S.; McParland, C.P.; Minaeva, Y.; Miocinovic, P.; Morse, R.; Nahnhauer, R.; Neunhoeffer, T.; Niessen, P.; Nygren, D.R.; Ogelman, H.; Olbrechts, Ph.; Perez de los Heros, C.; Pohl, A.C.; Price, P.B.; Przybylski, G.T.; Rawlins, K.; Resconi, E.; Rhode, W.; Ribordy, M.; Richter, S.; Rodriguez Martino, J.; Ross, D.; Sander, H.-G.; Schmidt, T.; Schneider, D.; Schwarz, R.; Silvestri, A.; Solarz, M.; Spiczak, G.M.; Spiering, C.; Steele, D.; Steffen, P.; Stokstad, R.G.; Sudhoff, P.; Sulanke, K.-H.; Taboada, I.; Thollander, L.; Tilav, S.; Walck, C.; Weinheimer, C.; Wiebusch, C.H.; Wiedemann, C.; Wischnewski, R.; Wissing, H.; Woschnagg, K.; Yodh, G.; Young, S.

    2003-01-01

    We present new data taken with the AMANDA-II neutrino telescope array. The AMANDA-II upgrade was completed at the beginning of 2000. It significantly extends the sensitivity of the 10-string AMANDA-B10 detector to high- and ultrahigh-energy neutrino fluxes into regions of interest for probing current astrophysical models which remain unexplored by other experiments

  5. Neural net prediction of tokamak plasma disruptions

    International Nuclear Information System (INIS)

    Hernandez, J.V.; Lin, Z.; Horton, W.; McCool, S.C.

    1994-10-01

    The computation based on neural net algorithms in predicting minor and major disruptions in TEXT tokamak discharges has been performed. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal, measured by a single Mirnov coil. The time step used (= 0.04ms) corresponds to the experimental data sampling rate. Two kinds of approaches are adopted for the task, the contiguous future prediction and the multi-timescale prediction. Results are shown for comparison. Both networks are trained through the back-propagation algorithm with inertial terms. The degree of this success indicates that the magnetic fluctuations associated with tokamak disruptions may be characterized by a relatively low-dimensional dynamical system

  6. Amanda Cross and Androgyny

    Directory of Open Access Journals (Sweden)

    Kania Richard R. E.

    2016-12-01

    Full Text Available Amanda Cross is the pen name Carolyn Heilbrun used for her mystery fiction. In two of her novels she employed the theme of androgyny. She also wrote the non-fiction, 1973 Toward a Recognition of Androgyny in which she promoted androgyny as aspect of her approach to feminism, an intellectual denial of any significant differences between the sexes. While that thread of American feminism has lost favor in current feminist ideologies, matters of gender identity are rising in prominence in American social and political thought, reviving the debate on male and female roles and identities in the United States.

  7. Real-time applications of neural nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs

  8. Accelerator diagnosis and control by Neural Nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs

  9. Accelerator diagnosis and control by Neural Nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphore for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems and therefore provide an ideal adaptive control system. Thus, where A1 may be good for maintaining a golden orbit, NN should be good for obtaining it via a quantitative approach to look and adjust methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs

  10. Real-time applications of neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  11. Real-time applications of neural nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-01-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas e.g. improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. In this paper, such issues are considered, examples given and possibilities discussed

  12. A Simple Quantum Neural Net with a Periodic Activation Function

    OpenAIRE

    Daskin, Ammar

    2018-01-01

    In this paper, we propose a simple neural net that requires only $O(nlog_2k)$ number of qubits and $O(nk)$ quantum gates: Here, $n$ is the number of input parameters, and $k$ is the number of weights applied to these parameters in the proposed neural net. We describe the network in terms of a quantum circuit, and then draw its equivalent classical neural net which involves $O(k^n)$ nodes in the hidden layer. Then, we show that the network uses a periodic activation function of cosine values o...

  13. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

    This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

  14. Musical Audio Synthesis Using Autoencoding Neural Nets

    OpenAIRE

    Sarroff, Andy; Casey, Michael A.

    2014-01-01

    With an optimal network topology and tuning of hyperpa-\\ud rameters, artificial neural networks (ANNs) may be trained\\ud to learn a mapping from low level audio features to one\\ud or more higher-level representations. Such artificial neu-\\ud ral networks are commonly used in classification and re-\\ud gression settings to perform arbitrary tasks. In this work\\ud we suggest repurposing autoencoding neural networks as\\ud musical audio synthesizers. We offer an interactive musi-\\ud cal audio synt...

  15. Neural nets for massively parallel optimization

    Science.gov (United States)

    Dixon, Laurence C. W.; Mills, David

    1992-07-01

    To apply massively parallel processing systems to the solution of large scale optimization problems it is desirable to be able to evaluate any function f(z), z (epsilon) Rn in a parallel manner. The theorem of Cybenko, Hecht Nielsen, Hornik, Stinchcombe and White, and Funahasi shows that this can be achieved by a neural network with one hidden layer. In this paper we address the problem of the number of nodes required in the layer to achieve a given accuracy in the function and gradient values at all points within a given n dimensional interval. The type of activation function needed to obtain nonsingular Hessian matrices is described and a strategy for obtaining accurate minimal networks presented.

  16. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  17. Computation and control with neural nets

    International Nuclear Information System (INIS)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-01-01

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future 'microprocessors' are predicted and requested on this basis. 19 refs., 18 figs

  18. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  19. The AMANDA Neutrino Detector - Status report

    International Nuclear Information System (INIS)

    Wischnewski, R.; Andres, E.; Bai, X.; Barouch, G.; Barwick, S.; Bay, R.; Becker, K.; Bergstroem, L.; Bertrand, D.; Besson, D.; Biron, A.; Booth, J.; Botner, O.; Bouchta, A.; Carius, S.; Carlson, M.; Chinowsky, W.; Chirkin, D.; Conrad, J.; Cowen, D.F.; Costa, C.; Dalberg, E.; Desiati, P.; Dewulf, J.; Deyoung, T.; Doksus, P.; Edsjoe, J.; Ekstroem, P.; Feser, T.; Frichter, G.; Gaisser, T.; Goldschmidt, A.; Goobar, A.; Hallgren, A.; Halzen, F.; Hardtke, R.; Hellwig, M.; Hill, G.; Hulth, P.; Hundertmark, S.; Jacobsen, J.; Karle, A.; Kim, J.; Koepke, L.; Kowalski, M.; Kravchenko, I.; Lamoureux, J.; Leich, H.; Leuthold, M.; Lindahl, P.; Liss, T.; Loaiza, P.; Lowder, D.; Ludvig, J.; Marciniewski, P.; Matis, H.; Miller, T.; Miocinovic, P.; Mock, P.; Morse, R.; Neunhoeffer, T.; Newcomer, M.; Niessen, P.; Nygren, D.; Perez de los Heros, C.; Porrata, R.; Price, P.; Przybylski, G.; Rawlins, K.; Rhode, W.; Richter, S.; Rodriguez, J.; Romenesko, P.; Ross, D.; Rubinstein, H.; Sander, H.; Schaefer, U.; Schmidt, T.; Schneider, E.; Schwarz, R.; Schwendicke, U.; Silvestri, A.; Smoot, G.; Solarz, M.; Spiczak, G.; Spiering, C.; Starinski, N.; Steffen, P.; Stokstad, R.; Streicher, O.; Taboada, I.; Thollander, L.; Thon, T.; Tilav, S.; Vander Donckt, M.; Walck, C.; Wiebusch, C.; Woschnagg, K.; Wu, W.; Yodh, G.; Young, S.

    2000-01-01

    The first stage of the AMANDA High Energy Neutrino Detector at the South Pole, the 302 PMT array AMANDA-B10, is taking data since 1997. We describe results on atmospheric neutrinos, limits on indirect WIMP detection, seasonal muon flux variation, relativistic monopole flux limits, a search for gravitational collapse neutrinos, and a depth scan of the optical ice properties. The next stage 19-string detector AMANDA-II with ∼650 PMTs will be completed in spring 2000

  20. 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)

  1. 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)

  2. Face recognition: Eigenface, elastic matching, and neural nets

    International Nuclear Information System (INIS)

    Zhang, J.; Lades, M.

    1997-01-01

    This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice

  3. Neural net generated seismic facies map and attribute facies map

    International Nuclear Information System (INIS)

    Addy, S.K.; Neri, P.

    1998-01-01

    The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

  4. Selected recent results from AMANDA

    CERN Document Server

    Andrés, E; Bai, X; Barouch, G; Barwick, S W; Bay, R C; Becker, K H; Bergström, L; Bertrand, D; Bierenbaum, D; Biron, A; Booth, J; Botner, O; Bouchta, A; Boyce, M M; Carius, S; Chen, A; Chirkin, D; Conrad, J; Cooley, J; Costa, C G S; Cowen, D F; Dailing, J; Dalberg, E; De Young, T R; Desiati, P; Dewulf, J P; Doksus, P; Edsjö, J; Ekstrom, P; Erlandsson, B; Feser, T; Gaug, M; Goldschmidt, A; Goobar, A; Gray, L; Haase, H; Hallgren, A; Halzen, F; Hanson, K; Hardtke, R; He, Y D; Hellwig, M; Heukenkamp, H; Hill, G C; Hulth, P O; Hundertmark, S; Jacobsen, J; Kandhadai, V; Karle, A; Kim, J; Koci, B; Köpke, L; Kowalski, M; Leich, H; Leuthold, M; Lindahl, P; Liubarsky, I; Loaiza, P; Lowder, D M; Ludvig, J; Madsen, J; Marciniewski, P; Matis, H S; Mihályi, A; Mikolajski, T; Miller, T C; Minaeva, Y; Miocinovic, P; Mock, P C; Morse, R; Neunhoffer, T; Newcomer, F M; Niessen, P; Nygren, D R; Ogelman, H; Heros, C P D L; Porrata, R; Price, P B; Rawlins, K; Reed, C; Rhode, W; Richards, A; Richter, S; Martino, J R; Romenesko, P; Ross, D; Rubinstein, H; Sander, H G; Scheider, T; Schmidt, T; Schneider, D; Schneider, E; Schwarzl, R; Silvestri, A; Solarz, M; Spiczak, G M; Spiering, C; Starinsky, N; Steele, D; Steffen, P; Stokstad, R G; Streicher, O; Sun, A; Taboada, I; Thollander, L; Thon, T; Tilav, S; Usechak, N; Donckt, M V; Walck, C; Weinheimer, C; Wiebusch, C; Wischnewski, R; Wissing, H; Woschnagg, K; Wu, W; Yodh, G; Young, S

    2001-01-01

    We present a selection of results based on data taken in 1997 with the 302-PMT Antarctic Muon and Neutrino Detector Array-B10 ("AMANDA-B10") array. Atmospheric neutrinos created in the northern hemisphere are observed indirectly through their charged current interactions which produce relativistic, Cherenkov-light-emitting upgoing muons in the South Pole ice cap. The reconstructed angular distribution of these events is in good agreement with expectation and demonstrates the viability of this ice-based device as a neutrino telescope. Studies of nearly vertical upgoing muons limit the available parameter space for WIMP dark matter under the assumption that WIMPS are trapped in the earth's gravitational potential well and annihilate with one another near the earth's center.

  5. Results from the AMANDA detector

    CERN Document Server

    Ackermann, M; Albrecht, H; Bai, X; Bartelt, M; Barwick, S W; Bay, R; Becka, T; Becker, J K; Becker, K H; Bernardini, E; Bertrand, D; Boersma, D J; Boser, S; Botner, O; Bouchta, A; Bouhali, O; Braun, J; Burgess, C; Burgess, T; Castermans, T; Chirkin, D; Collin, B; Conrad, J; Cooley, J; Cowen, D F; Davour, A; De Clercq, C; Pérez de los Heros, C; De Young, T R; Desiati, P; Ekstrom, P; Feser, T; Gaisser, T K; Ganugapati, R; Geenen, H; Gerhardt, L; Goldschmidt, A; Gross, A; Hallgren, A; Halzen, F; Hanson, K; Hardtke, R; Harenberg, T; Hauschildt, T; Helbing, K; Hellwig, M; Herquet, P; Hill, G C; Hodges, J; Hubert, D; Hughey, B; Hulth, P O; Hultqvist, K; Hundertmark, S; Jacobsen, J; Kampert, K H; Karle, A; Kelley, J; Kestel, M; Köpke, L; Kowalski, M; Krasberg, M; Kühn, K; Leich, H; Leuthold, M; Liubarsky, I; Madsen, J; Mandli, K; Marciniewski, P; Martino, J R; Matis, H S; McParland, C P; Messarius, T; Minaeva, Y; Miocinovic, P; Morse, R; Munich, K; Nahnhauer, R; Nam, J W; Neunhoffer, T; Niessen, P; Nygren, D R; Ogelman, H; Olbrechts, P; Pohl, A C; Porrata, R; Price, P B; Przybylski, G T; Rawlins, K; Resconi, E; Rhode, W; Ribordy, M; Richter, S; Sander, H G; Schinarakis, K; Schlenstedt, S; Schneider, D; Schwarz, R; Silvestri, A; Solarz, M; Spiczak, G M; Spiering, C; Stamatikos, M; Steele, D; Steffen, P; Stokstad, R G; Sulanke, K H; Taboada, I; Thollander, L; Tilav, S; Wagner, W; Walck, C; Walter, M; Wang, Y R; Wiebusch, C; Wischnewski, R; Wissing, H; Woschnagg, K; Yodh, G

    2004-01-01

    The Antarctic Muon And Neutrino Detector Array (AMANDA) is a high- energy neutrino telescope based at the geographic south pole. It is a lattice of photomultiplier tubes buried deep in the polar ice, which is used as interaction and detection medium. The primary goal of this detector is the observation of astronomical sources of high-energy neutrinos. This paper shows the latest results of the search for a diffuse flux of extraterrestrial nu /sub mu /s with energies between 10/sup 11/ eV and 10/sup 18/ eV, nu /sub mu /s emitted from point sources and nu /sub mu /s from dark matter annihilation in the Earth and the Sun.

  6. Neural-net disruption predictor in JT-60U

    International Nuclear Information System (INIS)

    Yoshino, R.

    2003-01-01

    The prediction of major disruptions caused by the density limit, the plasma current ramp-down with high internal inductance l i , the low density locked mode and the β-limit has been investigated in JT-60U. The concept of 'stability level', newly proposed in this paper to predict the occurrence of a major disruption, is calculated from nine input parameters every 2 ms by the neural network and the start of a major disruption is predicted when the stability level decreases to a certain level, the 'alarm level'. The neural network is trained in two steps. It is first trained with 12 disruptive and six non-disruptive shots (total of 8011 data points). Second, the target output data for 12 disruptive shots are modified and the network is trained again with additional data points generated by the operator. The 'neural-net disruption predictor' obtained has been tested for 300 disruptive shots (128 945 data points) and 1008 non-disruptive shots (982 800 data points) selected from nine years of operation (1991-1999) of JT-60U. Major disruptions except for those caused by the -limit have been predicted with a prediction success rate of 97-98% at 10 ms prior to the disruption and higher than 90% at 30 ms prior to the disruption while the false alarm rate is 2.1% for non-disruptive shots. This prediction performance has been confirmed for 120 disruptive shots (56 163 data points), caused by the density limit, as well as 1032 non-disruptive shots (1004 611 data points) in the last four years of operation (1999-2002) of JT-60U. A careful selection of the input parameters supplied to the network and the newly developed two-step training of the network have reduced the false alarm rate resulting in a considerable improvement of the prediction success rate. (author)

  7. Neural nets for job-shop scheduling, will they do the job?

    NARCIS (Netherlands)

    Rooda, J.E.; Willems, T.M.; Goodwin, G.C.; Evans, R.J.

    1993-01-01

    A neural net structure has been developed which is capable of solving deterministic jobshop scheduling problems, part of the large class of np-complete problems. The problem was translated in an integer linear-programming format which facilitated translation in an adequate neural net structure. Use

  8. Enhancing the top-quark signal at Fermilab Tevatron using neural nets

    International Nuclear Information System (INIS)

    Ametller, L.; Garrido, L.; Talavera, P.

    1994-01-01

    We show, in agreement with previous studies, that neural nets can be useful for top-quark analysis at the Fermilab Tevatron. The main features of t bar t and background events in a mixed sample are projected on a single output, which controls the efficiency, purity, and statistical significance of the t bar t signal. We consider a feed-forward multilayer neural net for the CDF reported top-quark mass, using six kinematical variables as inputs. Our main results are based on the exhaustive comparison of the neural net performances with those obtainable from the standard experimental analysis, by imposing different sets of linear cuts over the same variables, showing how the neural net approach improves the standard analysis results

  9. Amanda Jasmiin, habras sidepidaja / Enn Lillemets

    Index Scriptorium Estoniae

    Lillemets, Enn

    2002-01-01

    Rootsis elava, 30. märtsil 100-aastaseks saanud Amanda Jasmiini elust, õpingutest, skulptuuriloomingust, arvustustest. Lõpetas 1937. a. Pallase skulptorina, on töötanud joonistusõpetaja, moekunstniku ja illustraatorina, kavandanud teatrikostüüme, 1997. aastast kunstiühingu Pallas auliige

  10. Kodust kehakinnitust Amanda Puhvetist / Vivika Veski

    Index Scriptorium Estoniae

    Veski, Vivika

    2007-01-01

    Psühhiaater Tiina Keldrimale kuuluvast Amanda Puhvetist Üdruma külas Läänemaal. Endisele kauplusehoonele ehitati juurde torn, mille I korrusel on ehtekunstnik Maarja Jõevee Käsitöökuur. Ruumid sisustati vana mööbli ja vanavaraga. Sisearhitekt Martti Ruus. 6 värv. vaadet

  11. DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging

    International Nuclear Information System (INIS)

    Barhen, J.; Protopopescu, V.; Reister, D.

    1999-01-01

    A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools

  12. A bat's ear view of neural nets in physics

    International Nuclear Information System (INIS)

    Denby, B.

    1997-01-01

    The use of neural networks in high energy physics has become a field of its own which now has been in existence for ten years. This paper attempts to draw some conclusions on the utility of neural networks for physics applications, and also to make some projections for the future of this line of research. (orig.)

  13. Do neural nets learn statistical laws behind natural language?

    Directory of Open Access Journals (Sweden)

    Shuntaro Takahashi

    Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

  14. 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.

  15. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  16. Goal-seeking neural net for recall and recognition

    Science.gov (United States)

    Omidvar, Omid M.

    1990-07-01

    Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

  17. 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.

  18. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  19. Calibration and survey of AMANDA with the SPASE detectors

    CERN Document Server

    Ahrens, J; Barwick, S W; Bay, R C; Becka, T; Becker, K H; Bernardini, E; Bertrand, E; Binon, F; Biron, A; Boser, S; Botner, O; Bouchta, A; Bouhali, O; Burgess, T; Carius, S; Castermans, T; Chirkin, D; Conrad, J; Cooley, J; Cowen, D F; Davour, A; De Clercq, C; De Young, T R; Desiati, P; Dewulf, J P; Dickinson, E; Doksus, P; Ekstrom, P; Engel, R; Evenson, P; Feser, T; Gaisser, T K; Ganugapati, R; Gaug, M; Geenen, H; Gerhardt, L; Goldschmidt, A; Hallgren, A; Halzen, F; Hanson, K; Hardtke, R; Hauschildt, T; Hellwig, M; Herquet, P; Hill, G C; Hinton, J A; Hughey, B; Hulth, P O; Hultqvist, K; Hundertmark, S; Jacobsen, J; Karle, A; Kim, J; Köpke, L; Kowalski, M; Kühn, K; Lamoureux, J I; Leich, H; Leuthold, M; Lindahl, P; Liubarsky, I; Lloyd Evans, J; Madsen, J; Mandli, K; Marciniewski, P; Martello, D; Matis, H S; McParland, C P; Messarius, T; Miller, T C; Minaeva, Y; Miocinovic, P; Mock, P C; Morse, R; Neunhoffer, T; Niessen, P; Nygren, D R; Ogelman, H; Olbrechts, P; Perez de los Heros, C; Pohl, A C; Porrata, R; Price, P B; Przybylski, G T; Rawlins, K; Resconi, E; Rhode, W; Ribordy, M; Richter, S; Rochester, K; Rodríguez-Martino, J; Romenesko, P; Ross, D; Sander, H G; Schinarakis, K; Schlenstedt, S; Schmidt, T; Schneider, D; Schwarz, R; Silvestri, A; Solarz, M; Spiczak, G M; Spiering, C; Stamatikos, M; Stanev, T; Steele, D; Steffen, P; Stokstad, R G; Sulanke, K H; Taboada, I; Tilav, S; Wagner, W; Walck, C; Wang, Y R; Watson, A A; Weinheimer, C; Wiebusch, C; Wiedemann, C; Wischnewski, R; Wissing, H; Woschnagg, K; Wu, W; Yodh, G; Young, S; 10.1016/j.nima.2003.12.007

    2004-01-01

    We report on the analysis of air showers observed in coincidence by the Antarctic Muon and Neutrino detector array (AMANDA-B10) and the South Pole Air Shower Experiment (SPASE-1 and SPASE-2). We discuss the use of coincident events for calibration and survey of the deep AMANDA detector as well as the response of AMANDA to muon bundles. This analysis uses data taken during 1997 when both SPASE-1 and SPASE-2 were in operation to provide a stereo view of AMANDA.

  20. Application of artificial neural nets to Shashlik calorimetry

    International Nuclear Information System (INIS)

    Bonesini, M.; Paganoni, M.; Terranova, F.

    1997-01-01

    Artificial neural networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given. (orig.)

  1. Neural net classification of x-ray pistachio nut data

    Science.gov (United States)

    Casasent, David P.; Sipe, Michael A.; Schatzki, Thomas F.; Keagy, Pamela M.; Le, Lan Chau

    1996-12-01

    Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.

  2. ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.

    2017-12-08

    With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

  3. A new neural net approach to robot 3D perception and visuo-motor coordination

    Science.gov (United States)

    Lee, Sukhan

    1992-01-01

    A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

  4. Tips From Former Smokers – Amanda

    Centers for Disease Control (CDC) Podcasts

    2014-07-07

    Amanda talks about the weeks her baby spent in a hospital incubator. Amanda smoked during her pregnancy, and her baby was born two months early.  Created: 7/7/2014 by Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion.   Date Released: 7/7/2014.

  5. Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

    Science.gov (United States)

    Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

    2018-04-01

    The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

  6. Development of a neural net paradigm that predicts simulator sickness

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  7. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

    Directory of Open Access Journals (Sweden)

    Min Peng

    2016-10-01

    Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

  8. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    Science.gov (United States)

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

    Science.gov (United States)

    Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

    2016-11-01

    tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

  10. Exemplar-based optical neural net classifier for color pattern recognition

    Science.gov (United States)

    Yu, Francis T. S.; Uang, Chii-Maw; Yang, Xiangyang

    1992-10-01

    We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

  11. [A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

    Science.gov (United States)

    Shi, Wei-Jie; Yao, Yong; Zhang, Tie-Qiang; Meng, Xian-Jiang

    2008-05-01

    A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.

  12. Wet gas metering with the v-cone and neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Toral, Haluk; Cai, Shiqian; Peters, Robert

    2005-07-01

    The paper presents analysis of extensive measurements taken at NEL, K-Lab and CEESI wet gas test loops. Differential and absolute pressure signals were sampled at high frequency across V-Cone meters. Turbulence characteristics of the flow captured in the sampled signals were characterized by pattern recognition techniques and related to the fractions and flow rates of individual phases. The sensitivity of over-reading to first and higher order features of the high frequency signals were investigated qualitatively. The sensitivities were quantified by means of the saliency test based on back propagating neural nets. A self contained wet gas meter based on neural net characterization of first and higher order features of the pressure, differential pressure and capacitance signals was proposed. Alternatively, a wet gas meter based on a neural net model of just pressure sensor inputs (based on currently available data) and liquid Froude number was shown to offer an accuracy of under 5% if the Froude number could be estimated with 25% accuracy. (author) (tk)

  13. Deep neural nets as a method for quantitative structure-activity relationships.

    Science.gov (United States)

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

  14. LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

    Directory of Open Access Journals (Sweden)

    Ziyang He

    2018-04-01

    Full Text Available By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

  15. LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

    Science.gov (United States)

    He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

    2018-04-17

    By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

  16. Practitioner Profile: An Interview With Amanda Mills

    Directory of Open Access Journals (Sweden)

    Amanda Mills

    2012-01-01

    Full Text Available For more than 25 years, Amanda Mills has been working with arts organizations across Canada and with artists of all kinds to assist them in achieving financial sanity. She has taught business management at the University of Victoria and has prepared thousands of tax returns for writers, visual artists, choreographers, actors, filmmakers, broadcasters, and creative entrepreneurs. Ten years ago, bringing together her work on trauma, with twenty years of business management, Mills founded Loose Change Financial Therapy – the place where money and feelings meet. Mills has presented Loose Change workshops for social workers, teachers, psychotherapists, artists, anti-poverty activists, sex trade workers, women’s groups, and the general public.  She has been a guest on major Canadian radio and television broadcasts and profiled in many major Canadian newspapers and periodicals. Mills is also a crisis counselor and co-wrote a bestselling book on recovering from trauma. A tax professional and business manager, she is certified as a financial counselor under the Bankruptcy and Insolvency Act. Mills is also currently completing a certificate in mediation.

  17. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Directory of Open Access Journals (Sweden)

    Seong-Gon Kim

    2011-06-01

    Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

  18. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  19. Artificial neural net system for interactive tissue classification with MR imaging and image segmentation

    International Nuclear Information System (INIS)

    Clarke, L.P.; Silbiger, M.; Naylor, C.; Brown, K.

    1990-01-01

    This paper reports on the development of interactive methods for MR tissue classification that permit mathematically rigorous methods for three-dimensional image segmentation and automatic organ/tumor contouring, as required for surgical and RTP planning. The authors investigate a number of image-intensity based tissue- classification methods that make no implicit assumptions on the MR parameters and hence are not limited by image data set. Similarly, we have trained artificial neural net (ANN) systems for both supervised and unsupervised tissue classification

  20. Three-dimensional neural net for learning visuomotor coordination of a robot arm.

    Science.gov (United States)

    Martinetz, T M; Ritter, H J; Schulten, K J

    1990-01-01

    An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a three-dimensional lattice consisting of the units of the neural net.

  1. Assessment of the expected construction company’s net profit using neural network and multiple regression models

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

    This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

  2. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACHINE INDEPENDENT VERSION)

    Science.gov (United States)

    Baffes, P. T.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  3. NETS - A NEURAL NETWORK DEVELOPMENT TOOL, VERSION 3.0 (MACINTOSH VERSION)

    Science.gov (United States)

    Phillips, T. A.

    1994-01-01

    NETS, A Tool for the Development and Evaluation of Neural Networks, provides a simulation of Neural Network algorithms plus an environment for developing such algorithms. Neural Networks are a class of systems modeled after the human brain. Artificial Neural Networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to brain neurons. Problems which involve pattern matching readily fit the class of problems which NETS is designed to solve. NETS uses the back propagation learning method for all of the networks which it creates. The nodes of a network are usually grouped together into clumps called layers. Generally, a network will have an input layer through which the various environment stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to some features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. NETS allows the user to customize the patterns of connections between layers of a network. NETS also provides features for saving the weight values of a network during the learning process, which allows for more precise control over the learning process. NETS is an interpreter. Its method of execution is the familiar "read-evaluate-print" loop found in interpreted languages such as BASIC and LISP. The user is presented with a prompt which is the simulator's way of asking for input. After a command is issued, NETS will attempt to evaluate the command, which may produce more prompts requesting specific information or an error if the command is not understood. The typical process involved when using NETS consists of translating the problem into a format which uses input/output pairs, designing a network configuration for the problem, and finally training the network with input/output pairs until an acceptable error is reached. NETS

  4. Practitioner Profile: An Interview with Amanda Clayman, LMSW, CFSW

    Directory of Open Access Journals (Sweden)

    Amanda Clayman

    2014-03-01

    Full Text Available Amanda Clayman, is a Licensed Master of Social Work and a Certified Financial Social Worker who helps individuals, couples, and families bring money into balance. Since 2006, Amanda has led the Financial Wellness Program at The Actors Fund, a national non-profit human services agency that supports professionals in performing arts and entertainment. She maintains a private financial wellness counseling practice in New York City and is a public speaker on life and money topics. Amanda's work has been featured in media outlets, such as the New York Times, the Wall Street Journal, SELF magazine, REAL SIMPLE magazine, Women's Health, Parenting, and Fit Pregnancy. She lives in Brooklyn with her husband and daughters.

  5. ConvNetQuake: Convolutional Neural Network for Earthquake Detection and Location

    Science.gov (United States)

    Denolle, M.; Perol, T.; Gharbi, M.

    2017-12-01

    Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. In this work, we leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for probabilistic earthquake detection and location from single stations. We apply our technique to study two years of induced seismicity in Oklahoma (USA). We detect 20 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm detection performances are at least one order of magnitude faster than other established methods.

  6. Door and cabinet recognition using convolutional neural nets and real-time method fusion for handle detection and grasping

    DEFF Research Database (Denmark)

    Maurin, Adrian Llopart; Ravn, Ole; Andersen, Nils Axel

    2017-01-01

    In this paper we present a new method that robustly identifies doors, cabinets and their respective handles, with special emphasis on extracting useful features from handles to be then manipulated. The novelty of this system relies on the combination of a Convolutional Neural Net (CNN), as a form...

  7. Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

    DEFF Research Database (Denmark)

    Johansen, Morten Bo; Gonzalez-Izarzugaza, Jose Maria; Brunak, Søren

    2013-01-01

    We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features...

  8. MosquitoNet: investigating the use of UAV and artificial neural networks for integrated mosquito management

    Science.gov (United States)

    Case, E.; Ren, Y.; Shragai, T.; Erickson, D.

    2017-12-01

    Integrated mosquito control is expensive and resource intensive, and changing climatic factors are predicted to expand the habitable range of disease-carrying mosquitoes into new regions in the United States. Of particular concern in the northeastern United States are aedes albopictus, an aggressive, invasive species of mosquito that can transmit both native and exotic disease. Ae. albopictus prefer to live near human populations and breed in artificial containers with as little as two millimeters of standing water, exponentially increasing the difficulty of source control in suburban and urban areas. However, low-cost unmanned aerial vehicles (UAVs) can be used to photograph large regions at centimeter-resolution, and can image containers of interest in suburban neighborhoods. While proofs-of-concepts have been shown using UAVs to identify naturally occurring bodies of water, they have not been used to identify mosquito habitat in more populated areas. One of the primary challenges is that post-processing high-resolution aerial imagery is still time intensive, often labelled by hand or with programs built for satellite imagery. Artificial neural networks have been highly successful at image recognition tasks; in the past five years, convolutional neural networks (CNN) have surpassed or aided trained humans in identification of skin cancer, agricultural crops, and poverty levels from satellite imagery. MosquitoNet, a dual classifier built from the Single Shot Multibox Detector and VGG16 architectures, was trained on UAV­­­­­ aerial imagery taken during a larval study in Westchester County in southern New York State in July and August 2017. MosquitoNet was designed to assess the habitat risk of suburban properties by automating the identification and counting of containers like tires, toys, garbage bins, flower pots, etc. The SSD-based architecture marked small containers and other habitat indicators while the VGG16-based architecture classified the type of

  9. Schema generation in recurrent neural nets for intercepting a moving target.

    Science.gov (United States)

    Fleischer, Andreas G

    2010-06-01

    The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

  10. 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.

  11. A Restricted Boltzman Neural Net to Infer Carbon Uptake from OCO-2 Satellite Data

    Science.gov (United States)

    Halem, M.; Dorband, J. E.; Radov, A.; Barr-Dallas, M.; Gentine, P.

    2015-12-01

    For several decades, scientists have been using satellite observations to infer climate budgets of terrestrial carbon uptake employing inverse methods in conjunction with ecosystem models and coupled global climate models. This is an extremely important Big Data calculation today since the net annual photosynthetic carbon uptake changes annually over land and removes on average ~20% of the emissions from human contributions to atmospheric loading of CO2 from fossil fuels. Unfortunately, such calculations have large uncertainties validated with in-situ networks of measuring stations across the globe. One difficulty in using satellite data for these budget calculations is that the models need to assimilate surface fluxes of CO2 as well as soil moisture, vegatation cover and the eddy covariance of latent and sensible heat to calculate the carbon fixed in the soil while satellite spectral observations only provide near surface concentrations of CO2. In July 2014, NASA successfully launched OCO-2 which provides 3km surface measurements of CO2 over land and oceans. We have collected nearly one year of Level 2 XCO2 data from the OCO-2 satellite for 3 sites of ~200 km2 at equatorial, temperate and high latitudes. Each selected site was part of the Fluxnet or ARM system with tower stations for measuring and collecting CO2 fluxes on an hourly basis, in addition to eddy transports of the other parameters. We are also planning to acquire the 4km NDVI products from MODIS and registering the data to the 3km XCO2 footprints for the three sites. We have implemented a restricted Boltzman machine on the quantum annealing D-Wave computer, a novel deep learning neural net, to be used for training with station data to infer CO2 fluxes from collocated XCO2, MODIS vegetative land cover and MERRA reanalysis surface exchange products. We will present performance assessments of the D-Wave Boltzman machine for generating XCO2 fluxes from the OCO-2 satellite observations for the 3 sites by

  12. EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.

    Science.gov (United States)

    Amidi, Afshine; Amidi, Shervine; Vlachakis, Dimitrios; Megalooikonomou, Vasileios; Paragios, Nikos; Zacharaki, Evangelia I

    2018-01-01

    During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

  13. AMANDA Observations Constrain the Ultrahigh Energy Neutrino Flux

    Energy Technology Data Exchange (ETDEWEB)

    Halzen, Francis; /Wisconsin U., Madison; Hooper, Dan; /Fermilab

    2006-05-01

    A number of experimental techniques are currently being deployed in an effort to make the first detection of ultra-high energy cosmic neutrinos. To accomplish this goal, techniques using radio and acoustic detectors are being developed, which are optimally designed for studying neutrinos with energies in the PeV-EeV range and above. Data from the AMANDA experiment, in contrast, has been used to place limits on the cosmic neutrino flux at less extreme energies (up to {approx}10 PeV). In this letter, we show that by adopting a different analysis strategy, optimized for much higher energy neutrinos, the same AMANDA data can be used to place a limit competitive with radio techniques at EeV energies. We also discuss the sensitivity of the IceCube experiment, in various stages of deployment, to ultra-high energy neutrinos.

  14. Results from the AMANDA high-energy neutrino detector

    International Nuclear Information System (INIS)

    Biron, A.

    2001-01-01

    This paper briefly summarizes the search for astronomical sources of high-energy neutrinos using the AMANDA-B10 detector. The complete data set from 1997 was analyzed. For E μ > 10 TeV, the detector exceeds 10,000 m 2 in effective area between declinations of 25 and 90 degrees. Neutrinos generated in the atmosphere by cosmic ray interactions were used to verify the overall sensitivity of the coincident events between the SPASE air shower array and the AMANDA detector. Preliminary flux limits from point source candidates are presented. For declinations larger than +45 degrees, our results compare favourably to existing limits for sources in the Southern sky. We also present the current status of the searches for high-energy neutrino emission from diffusely distributed sources, GRBs, and WIMPs from the center of the Earth

  15. Muon track reconstruction and data selection techniques in AMANDA

    International Nuclear Information System (INIS)

    Ahrens, J.; Bai, X.; Bay, R.; Barwick, S.W.; Becka, T.; Becker, J.K.; Becker, K.-H.; Bernardini, E.; Bertrand, D.; Biron, A.; Boersma, D.J.; Boeser, S.; Botner, O.; Bouchta, A.; Bouhali, O.; Burgess, T.; Carius, S.; Castermans, T.; Chirkin, D.; Collin, B.; Conrad, J.; Cooley, J.; Cowen, D.F.; Davour, A.; De Clercq, C.; DeYoung, T.; Desiati, P.; Dewulf, J.-P.; Ekstroem, P.; Feser, T.; Gaug, M.; Gaisser, T.K.; Ganugapati, R.; Geenen, H.; Gerhardt, L.; Gross, A.; Goldschmidt, A.; Hallgren, A.; Halzen, F.; Hanson, K.; Hardtke, R.; Harenberg, T.; Hauschildt, T.; Helbing, K.; Hellwig, M.; Herquet, P.; Hill, G.C.; Hubert, D.; Hughey, B.; Hulth, P.O.; Hultqvist, K.; Hundertmark, S.; Jacobsen, J.; Karle, A.; Kestel, M.; Koepke, L.; Kowalski, M.; Kuehn, K.; Lamoureux, J.I.; Leich, H.; Leuthold, M.; Lindahl, P.; Liubarsky, I.; Madsen, J.; Marciniewski, P.; Matis, H.S.; McParland, C.P.; Messarius, T.; Minaeva, Y.; Miocinovic, P.; Mock, P.C.; Morse, R.; Muenich, K.S.; Nam, J.; Nahnhauer, R.; Neunhoeffer, T.; Niessen, P.; Nygren, D.R.; Oegelman, H.; Olbrechts, Ph.; Perez de los Heros, C.; Pohl, A.C.; Porrata, R.; Price, P.B.; Przybylski, G.T.; Rawlins, K.; Resconi, E.; Rhode, W.; Ribordy, M.; Richter, S.; Rodriguez Martino, J.; Ross, D.; Sander, H.-G.; Schinarakis, K.; Schlenstedt, S.; Schmidt, T.; Schneider, D.; Schwarz, R.; Silvestri, A.; Solarz, M.; Spiczak, G.M.; Spiering, C.; Stamatikos, M.; Steele, D.; Steffen, P.; Stokstad, R.G.; Sulanke, K.-H.; Streicher, O.; Taboada, I.; Thollander, L.; Tilav, S.; Wagner, W.; Walck, C.; Wang, Y.-R.; Wiebusch, C.H.; Wiedemann, C.; Wischnewski, R.; Wissing, H.; Woschnagg, K.; Yodh, G.

    2004-01-01

    The Antarctic Muon And Neutrino Detector Array (AMANDA) is a high-energy neutrino telescope operating at the geographic South Pole. It is a lattice of photo-multiplier tubes buried deep in the polar ice between 1500 and 2000 m. The primary goal of this detector is to discover astrophysical sources of high-energy neutrinos. A high-energy muon neutrino coming through the earth from the Northern Hemisphere can be identified by the secondary muon moving upward through the detector. The muon tracks are reconstructed with a maximum likelihood method. It models the arrival times and amplitudes of Cherenkov photons registered by the photo-multipliers. This paper describes the different methods of reconstruction, which have been successfully implemented within AMANDA. Strategies for optimizing the reconstruction performance and rejecting background are presented. For a typical analysis procedure the direction of tracks are reconstructed with about 2 deg. accuracy

  16. Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots

    Directory of Open Access Journals (Sweden)

    Nicholas Livingston

    2016-12-01

    Full Text Available While modularity is thought to be central for the evolution of complexity and evolvability, it remains unclear how systems boot-strap themselves into modularity from random or fully integrated starting conditions. Clune et al. (2013 suggested that a positive correlation between sparsity and modularity is the prime cause of this transition. We sought to test the generality of this modularity-sparsity hypothesis by testing it for the first time in physically embodied robots. A population of ten Tadros — autonomous, surface-swimming robots propelled by a flapping tail — was used. Individuals varied only in the structure of their neural net control, a 2 x 6 x 2 network with recurrence in the hidden layer. Each of the 60 possible connections was coded in the genome, and could achieve one of three states: -1, 0, 1. Inputs were two light-dependent resistors and outputs were two motor control variables to the flapping tail, one for the frequency of the flapping and the other for the turning offset. Each Tadro was tested separately in a circular tank lit by a single overhead light source. Fitness was the amount of light gathered by a vertically oriented sensor that was disconnected from the controller net. Reproduction was asexual, with the top performer cloned and then all individuals entered into a roulette wheel selection process, with genomes mutated to create the offspring. The starting population of networks was randomly generated. Over ten generations, the population’s mean fitness increased two-fold. This evolution occurred in spite of an unintentional integer overflow problem in recurrent nodes in the hidden layer that caused outputs to oscillate. Our investigation of the oscillatory behavior showed that the mutual information of inputs and outputs was sufficient for the reactive behaviors observed. While we had predicted that both modularity and sparsity would follow the same trend as fitness, neither did so. Instead, selection gradients

  17. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

    Science.gov (United States)

    Fabijańska, Anna

    2018-04-18

    Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network. Particularly, the network is trained to discriminate pixels located at the borders between cells. The edge probability map outputted by the network is next binarized and skeletonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared edges. The corresponding mean absolute percentage error of cell number is at the level of 4.5% which confirms the high accuracy of the proposed approach. The resulting cell edges are well aligned to the ground truths and require a limited number of manual corrections. This also results in accurate values of the cell morphometric parameters. The corresponding errors range from 5.2% for endothelial cell density, through 6.2% for cell hexagonality to 11.93% for the coefficient of variation of the cell size. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

    International Nuclear Information System (INIS)

    Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

    2014-01-01

    We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.

  19. Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.

    Directory of Open Access Journals (Sweden)

    Morten Bo Johansen

    Full Text Available We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.

  20. Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

    Science.gov (United States)

    Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

    2017-11-01

    Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic

  1. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

    Science.gov (United States)

    2017-01-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

  2. Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

    Directory of Open Access Journals (Sweden)

    Todor Petkov

    2013-12-01

    Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.

  3. Competition and Cooperation in Neural Nets : U.S.-Japan Joint Seminar

    CERN Document Server

    Arbib, Michael

    1982-01-01

    The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers ...

  4. Pattern recognition neural-net by spatial mapping of biology visual field

    Science.gov (United States)

    Lin, Xin; Mori, Masahiko

    2000-05-01

    The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

  5. Healing in forgiveness: A discussion with Amanda Lindhout and Katherine Porterfield, PhD

    Directory of Open Access Journals (Sweden)

    Katherine A. Porterfield

    2014-09-01

    Full Text Available In 2008, Amanda Lindhout was kidnapped by a group of extremists while traveling as a freelance journalist in Somalia. She and a colleague were held captive for more than 15 months, released only after their families paid a ransom. In this interview, Amanda discusses her experiences in captivity and her ongoing recovery from this experience with Katherine Porterfield, Ph.D. a clinical psychologist at the Bellevue/NYU Program for Survivors of Torture. Specifically, Amanda describes the childhood experiences that shaped her thirst for travel and knowledge, the conditions of her kidnapping, and her experiences after she was released from captivity. Amanda outlines the techniques that she employed to survive in the early aftermath of her capture, and how these coping strategies changed as her captivity lengthened. She reflects on her transition home, her recovery process, and her experiences with mental health professionals. Amanda's insights provide an example of resilience in the face of severe, extended trauma to researchers, clinicians, and survivors alike. The article ends with an discussion of the ways that Amanda's coping strategies and recovery process are consistent with existing resilience literature. Amanda's experiences as a hostage, her astonishing struggle for physical and mental survival, and her life after being freed are documented in her book, co-authored with Sara Corbett, A House in the Sky.

  6. Manlighet, mod och moral: Amanda Kerfstedts Carl Bergs skoltid

    Directory of Open Access Journals (Sweden)

    Andersson, Maria

    2011-03-01

    Full Text Available Amanda Kerfstedt’s ”Carl Bergs skoltid” describes a boy’s journey to manhood in late 19th century Sweden. This article pertains that the text contrasts an ideal of masculinity based on physical strength with an ideal emphasizing morality. Although a true man, as described in the story, is expected to be strong as well as righteous, the moral component is the most important. The text shows that courage and force, which are not checked by a sense of ethics, may turn into brutality and abuse of power. Different types of masculinity are compared in ”Carl Bergs skoltid” but masculinity is also defined in relation to ideas about femininity. The association with women is utilized to stress a lack of courage in men as well as to redefine a masculine ideal that has focused too much on physical prowess instead of strength of character.

  7. Neural nets with varying topology for high energy particle recognition. Theory and applications

    International Nuclear Information System (INIS)

    Perrone, A.L.; Basti, G.; Messi, R.; Paoluzi, L.; Picozza, P.

    1995-01-01

    In this paper we propose a strategy to solve the problem of parallel compuation based on a dynamic definition of the net topology showing its effectiveness for problems of particle track recognition in high-energy physics. In this way, we can maintain the linear architecture like in the geometric perceptron, but with a partial and dynamic connectivity so to overcome the intrinsic limiations of the geometric perceptron. Namely, the computation is truly parallel because of the partial connectivity but the net topology is always the optimal one because of its dynamic redefinition on the single input pattern. For these properties, we call this new architecture dynamic perceptron

  8. Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

    Science.gov (United States)

    Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

    2013-04-01

    Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

  9. A biologically inspired neural net for trajectory formation and obstacle avoidance.

    Science.gov (United States)

    Glasius, R; Komoda, A; Gielen, S C

    1996-06-01

    In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

  10. Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

    Science.gov (United States)

    Johnson, J L

    1994-09-10

    The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

  11. Review of Amanda E. Herbert, Female Alliances: Gender, Identity, and Friendship in Early Modern Britain

    Directory of Open Access Journals (Sweden)

    Angela Rehbein

    2015-03-01

    Full Text Available Review of Amanda E. Herbert, Female Alliances: Gender, Identity, and Friendship in Early Modern Britain. New Haven: Yale UP, 2014. xi, 256 pages: illustrations; 24 cm. ISBN 978-0-300-17740-4.

  12. Neural-net predictor for beta limit disruptions in JT-60U

    International Nuclear Information System (INIS)

    Yoshino, R.

    2005-01-01

    Prediction of major disruptions occurring at the β -limit for tokamak plasmas with a normal magnetic shear in JT-60U was conducted using neural networks. Since no clear precursors are generally observed a few tens of milliseconds before the β -limit disruption, a sub-neural network is trained to output the value of the β N limit every 2 ms. The target β N limit is artificially set by the operator in the first step to train a network with non-disruptive shots as well as disruptive shots, and then in the second step the target limit is modified using the β N limit output from the trained network. The adjusted target greatly improves the consistency between the input data and the output. This training, the 'self-teaching method', has greatly reduced the false alarm rate triggered for non-disruptive shots. To improve the prediction performance further, the difference between the output β N limit and the measured β N , and 11 parameters, are inputted to the main neural network to calculate the 'stability level'. The occurrence of a major disruption is predicted when the stability level decreases to the 'alarm level'. Major disruptions at the β -limit have been predicted by the main network with a prediction success rate of 80% at 10 ms prior to the disruption while the false alarm rate is lower than 4% for non-disruptive shots. This 80% value is much higher than that obtained for a network trained with a fixed target β N limit set to be the maximum β N observed at the start of a major disruption, lower than 10%. A prediction success rate of 90% with a false alarm rate of 12% at 10 ms prior to the disruption has also been obtained. This 12% value is about half of that obtained for a network trained with a fixed target β N limit

  13. Fast neural-net based fake track rejection in the LHCb reconstruction

    CERN Document Server

    De Cian, Michel; Seyfert, Paul; Stahl, Sascha

    2017-01-01

    A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, retains more than 99 % of well reconstructed tracks while reducing the number of fake tracks by 60 %. It is fast enough to fit into the CPU time budget of the software trigger farm and thus reduces the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment in Run II of the LHC.

  14. Development of the neural net technique for particle physics. Study of the e+e- → Z0 → γH reaction

    International Nuclear Information System (INIS)

    Guicheney, C.

    1992-01-01

    This study is concerned with the application of pattern recognition methods through neural networks to High Energy physics. Two methods, Hopfield nets and multilayer nets, are analyzed and shown to have high potential for (resp.) clusterization and classification. Hopfield nets are used for the recognition of jets occurring during the fragmentation process of the e + e - reaction. Multilayer nets are used for the whole reaction analysis. Impediments are pointed out. Associated background noise is also examined. Multilayer nets may enhance the signal to noise ratio when looking for an upper limit for the production of a Higgs boson in the expected canal, and allow for the specific study of the γ b anti b

  15. LOGIC WITH EXCEPTION ON THE ALGEBRA OF FOURIER-DUAL OPERATIONS: NEURAL NET MECHANISM OF COGNITIVE DISSONANCE REDUCING

    Directory of Open Access Journals (Sweden)

    A. V. Pavlov

    2014-01-01

    Full Text Available A mechanism of cognitive dissonance reducing is demonstrated with approach for non-monotonic fuzzy-valued logics by Fourier-holography technique implementation developing. Cognitive dissonance occurs under perceiving of new information that contradicts to the existing subjective pattern of the outside world, represented by double Fourier-transform cascade with a hologram – neural layers interconnections matrix of inner information representation and logical conclusion. The hologram implements monotonic logic according to “General Modus Ponens” rule. New information is represented by a hologram of exclusion that implements interconnections of logical conclusion and exclusion for neural layers. The latter are linked by Fourier transform that determines duality of the algebra forming operations of conjunction and disjunction. Hologram of exclusion forms conclusion that is dual to the “General Modus Ponens” conclusion. It is shown, that trained for the main rule and exclusion system can be represented by two-layered neural network with separate interconnection matrixes for direct and inverse iterations. The network energy function is involved determining the cyclic dynamics character; dissipative factor causing convergence type of the dynamics is analyzed. Both “General Modus Ponens” and exclusion holograms recording conditions on the dynamics and convergence of the system are demonstrated. The system converges to a stable status, in which logical conclusion doesn’t depend on the inner information. Such kind of dynamics, leading to tolerance forming, is typical for ordinary kind of thinking, aimed at inner pattern of outside world stability. For scientific kind of thinking, aimed at adequacy of the inner pattern of the world, a mechanism is needed to stop the net relaxation; the mechanism has to be external relative to the model of logic. Computer simulation results for the learning conditions adequate to real holograms recording are

  16. Built-in self-repair of VLSI memories employing neural nets

    Science.gov (United States)

    Mazumder, Pinaki

    1998-10-01

    The decades of the Eighties and the Nineties have witnessed the spectacular growth of VLSI technology, when the chip size has increased from a few hundred devices to a staggering multi-millon transistors. This trend is expected to continue as the CMOS feature size progresses towards the nanometric dimension of 100 nm and less. SIA roadmap projects that, where as the DRAM chips will integrate over 20 billion devices in the next millennium, the future microprocessors may incorporate over 100 million transistors on a single chip. As the VLSI chip size increase, the limited accessibility of circuit components poses great difficulty for external diagnosis and replacement in the presence of faulty components. For this reason, extensive work has been done in built-in self-test techniques, but little research is known concerning built-in self-repair. Moreover, the extra hardware introduced by conventional fault-tolerance techniques is also likely to become faulty, therefore causing the circuit to be useless. This research demonstrates the feasibility of implementing electronic neural networks as intelligent hardware for memory array repair. Most importantly, we show that the neural network control possesses a robust and degradable computing capability under various fault conditions. Overall, a yield analysis performed on 64K DRAM's shows that the yield can be improved from as low as 20 percent to near 99 percent due to the self-repair design, with overhead no more than 7 percent.

  17. Comparisons of a Quantum Annealing and Classical Computer Neural Net Approach for Inferring Global Annual CO2 Fluxes over Land

    Science.gov (United States)

    Halem, M.; Radov, A.; Singh, D.

    2017-12-01

    Investigations of mid to high latitude atmospheric CO2 show growing amplitudes in seasonal variations over the past several decades. Recent high-resolution satellite measurements of CO2 concentration are now available for three years from the Orbiting Carbon Observatory-2. The Atmospheric Radiation Measurement (ARM) program of DOE has been making long-term CO2-flux measurements (in addition to CO2 concentration and an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour frequencies. Recent papers have shown CO2 fluxes inferred by assimilating CO2 observations into ecosystem models are largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, new approaches for calculating CO2-flux for assimilation into land surface models are necessary for improving the prediction of annual carbon uptake. In this study, we calculate and compare the predicted CO2 fluxes results employing a Feed Forward Backward Propagation Neural Network model on two architectures, (i) an IBM Minsky Computer node and (ii) a hybrid version of the ARC D-Wave quantum annealing computer. We compare the neural net results of predictions of CO2 flux from ARM station data for three different DOE ecosystem sites; an arid plains near Oklahoma City, a northern arctic site at Barrows AL, and a tropical rainforest site in the Amazon. Training times and predictive results for the calculating annual CO2 flux for the two architectures for each of the three sites are presented. Comparative results of predictions as measured by RMSE and MAE are discussed. Plots and correlations of observed vs predicted CO2 flux are also presented for all three sites. We show the estimated training times for quantum and classical calculations when extended to calculating global annual Carbon Uptake over land. We also

  18. Real-time classification of signals from three-component seismic sensors using neural nets

    Science.gov (United States)

    Bowman, B. C.; Dowla, F.

    1992-05-01

    Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

  19. Invariant visual object and face recognition: neural and computational bases, and a model, VisNet

    Directory of Open Access Journals (Sweden)

    Edmund T eRolls

    2012-06-01

    Full Text Available Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

  20. Recognition of malignant processes with neural nets from ESR spectra of serum albumin

    Energy Technology Data Exchange (ETDEWEB)

    Seidel, P. [Inst. of Medical Physics and Biophysics, Univ. Leipzig (Germany); Gurachevsky, A.; Muravsky, V.; Schnurr, K.; Seibt, G. [Medinnovation GmbH, Wildau (Germany); Matthes, G. [Inst. of Transfusion Medicine, Univ. Hospital Leipzig (Germany)

    2005-07-01

    Cancer diseases are the focus of intense research due to their frequent occurrence. It is known from the literature that serum proteins are changed in the case of malignant processes. Changes of albumin conformation, transport efficiency, and binding characteristics can be determined by electron spin resonance spectroscopy (ESR). The present study analysed the binding/dissociation function of albumin with an ESR method using 16-doxyl stearate spin probe as reporter molecule and ethanol as modifier of hydrophobic interactions. Native and frozen plasma of healthy donors (608 samples), patients with malignant diseases (423 samples), and patients with benign conditions (221 samples) were analysed. The global specificity was 91% and the sensitivity 96%. In look-back samples of 27 donors, a malignant process could be detected up to 30 months before clinical diagnosis. To recognise different entities of malignant diseases from the ESR spectra, Artificial neural networks were implemented. For 48 female donors with breast cancer, the recognition specificity was 85%. Other carcinoma entities (22 colon, 18 prostate, 12 stomach) were recognised with specificities between 75% and 84%. Should these specificity values be reproduced in larger studies, the described method could be used as a new specific tumour marker for the early detection of malignant processes. Since transmission of cancer via blood transfusion cannot be excluded as yet, the described ESR method could also be used as a quality test for plasma products. (orig.)

  1. Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks

    Directory of Open Access Journals (Sweden)

    Jian Jin

    2015-01-01

    Full Text Available When pure linear neural network (PLNN is used to predict tropical cyclone tracks (TCTs in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1 and (0, 1; with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1 and (100, 1. We present the following results: (1 data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2 mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.

  2. HAWC Analysis of the Crab Nebula Using Neural-Net Energy Reconstruction

    Science.gov (United States)

    Marinelli, Samuel; HAWC Collaboration

    2017-01-01

    The HAWC (High-Altitude Water-Cherenkov) experiment is a TeV γ-ray observatory located 4100 m above sea level on the Sierra Negra mountain in Puebla, Mexico. The detector consists of 300 water-filled tanks, each instrumented with 4 photomuliplier tubes that utilize the water-Cherenkov technique to detect atmospheric air showers produced by cosmic γ rays. Construction of HAWC was completed in March, 2015. The experiment's wide field of view (2 sr) and high duty cycle (> 95 %) make it a powerful survey instrument sensitive to pulsar wind nebulae, supernova remnants, active galactic nuclei, and other γ-ray sources. The mechanisms of particle acceleration at these sources can be studied by analyzing their energy spectra. To this end, we have developed an event-by-event energy-reconstruction algorithm employing an artificial neural network to estimate energies of primary γ rays. The Crab Nebula, the brightest source of TeV photons, makes an excellent calibration source for this technique. We will present preliminary results from an analysis of the Crab energy spectrum using this new energy-reconstruction method. This work was supported by the National Science Foundation.

  3. Recognition of malignant processes with neural nets from ESR spectra of serum albumin

    International Nuclear Information System (INIS)

    Seidel, P.; Gurachevsky, A.; Muravsky, V.; Schnurr, K.; Seibt, G.; Matthes, G.

    2005-01-01

    Cancer diseases are the focus of intense research due to their frequent occurrence. It is known from the literature that serum proteins are changed in the case of malignant processes. Changes of albumin conformation, transport efficiency, and binding characteristics can be determined by electron spin resonance spectroscopy (ESR). The present study analysed the binding/dissociation function of albumin with an ESR method using 16-doxyl stearate spin probe as reporter molecule and ethanol as modifier of hydrophobic interactions. Native and frozen plasma of healthy donors (608 samples), patients with malignant diseases (423 samples), and patients with benign conditions (221 samples) were analysed. The global specificity was 91% and the sensitivity 96%. In look-back samples of 27 donors, a malignant process could be detected up to 30 months before clinical diagnosis. To recognise different entities of malignant diseases from the ESR spectra, Artificial neural networks were implemented. For 48 female donors with breast cancer, the recognition specificity was 85%. Other carcinoma entities (22 colon, 18 prostate, 12 stomach) were recognised with specificities between 75% and 84%. Should these specificity values be reproduced in larger studies, the described method could be used as a new specific tumour marker for the early detection of malignant processes. Since transmission of cancer via blood transfusion cannot be excluded as yet, the described ESR method could also be used as a quality test for plasma products. (orig.)

  4. Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

    Science.gov (United States)

    Rolls, Edmund T

    2012-01-01

    Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

  5. Generation of daily solar irradiation by means of artificial neural net works

    Energy Technology Data Exchange (ETDEWEB)

    Siqueira, Adalberto N.; Tiba, Chigueru; Fraidenraich, Naum [Departamento de Energia Nuclear, da Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, 1000 - CDU, CEP 50.740-540 Recife, Pernambuco (Brazil)

    2010-11-15

    The present study proposes the utilization of Artificial Neural Networks (ANN) as an alternative for generating synthetic series of daily solar irradiation. The sequences were generated from the use of daily temporal series of a group of meteorological variables that were measured simultaneously. The data used were measured between the years of 1998 and 2006 in two temperate climate localities of Brazil, Ilha Solteira (Sao Paulo) and Pelotas (Rio Grande do Sul). The estimates were taken for the months of January, April, July and October, through two models which are distinguished regarding the use or nonuse of measured bright sunshine hours as an input variable. An evaluation of the performance of the 56 months of solar irradiation generated by way of ANN showed that by using the measured bright sunshine hours as an input variable (model 1), the RMSE obtained were less or equal to 23.2% being that of those, although 43 of those months presented RMSE less or equal to 12.3%. In the case of the model that did not use the measured bright sunshine hours but used a daylight length (model 2), RMSE were obtained that varied from 8.5% to 37.5%, although 38 of those months presented RMSE less or equal to 20.0%. A comparison of the monthly series for all of the years, achieved by means of the Kolmogorov-Smirnov test (to a confidence level of 99%), demonstrated that of the 16 series generated by ANN model only two, obtained by model 2 for the months of April and July in Pelotas, presented significant difference in relation to the distributions of the measured series and that all mean deviations obtained were inferior to 0.39 MJ/m{sup 2}. It was also verified that the two ANN models were able to reproduce the principal statistical characteristics of the frequency distributions of the measured series such as: mean, mode, asymmetry and Kurtosis. (author)

  6. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

    2017-12-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Net's convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign.Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users' input against pre-classified coral imagery to gauge their accuracy and utilizes in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  7. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

    2017-01-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  8. Search for prompt neutrinos with AMANDA-II

    Energy Technology Data Exchange (ETDEWEB)

    Gozzini, Sara Rebecca

    2008-09-11

    The investigation performed in this work aims to identify and disentangle the signal of prompt neutrinos from the inclusive atmospheric spectrum. We have analysed data recorded in the years 2000-2003 by the AMANDA-II detector at the geographical South Pole. After a tight event selection, our sample is composed of about 4 . 10{sup 3} atmospheric neutrinos. Prompt neutrinos are decay products of heavy quark hadrons, which are produced in the collision of a cosmic ray particle with a nucleon in the atmosphere. The technique used to recognise prompt neutrinos is based on a simulated information of their energy spectrum, which appears harder than that of the conventional component from light quarks. Models accounting for different hadron production and decay schemes have been included in a Monte Carlo simulation and convoluted with the detector response, in order to reproduce the different spectra. The background of conventional events has been described with the Bartol 2006 tables. The energy spectrum of our data has been reconstructed through a numerical unfolding algorithm. The reconstruction is based on a Monte Carlo simulation and uses as an input three parameters of the neutrino track which are correlated with the energy of the event. Numerical regularisation is introduced to achieve a result free of unphysical oscillations, typical unfortunate feature of unfolding. The reconstructed data spectrum has been compared with different predictions using the model rejection factor technique. The prompt neutrino models differ in the choice of the hadron interaction model, the set of parton distribution functions and the numerical parameterisation of the fragmentation functions describing the transition from quark to hadrons. Here we considered mainly three classes of models, known in the literature as the Recombination Quark Parton Model, the Quark Gluon String Model and the Perturbative QCD model. Upper limits have been set on the expected flux predictions, based on our

  9. Search for prompt neutrinos with AMANDA-II

    International Nuclear Information System (INIS)

    Gozzini, Sara Rebecca

    2008-01-01

    The investigation performed in this work aims to identify and disentangle the signal of prompt neutrinos from the inclusive atmospheric spectrum. We have analysed data recorded in the years 2000-2003 by the AMANDA-II detector at the geographical South Pole. After a tight event selection, our sample is composed of about 4 . 10 3 atmospheric neutrinos. Prompt neutrinos are decay products of heavy quark hadrons, which are produced in the collision of a cosmic ray particle with a nucleon in the atmosphere. The technique used to recognise prompt neutrinos is based on a simulated information of their energy spectrum, which appears harder than that of the conventional component from light quarks. Models accounting for different hadron production and decay schemes have been included in a Monte Carlo simulation and convoluted with the detector response, in order to reproduce the different spectra. The background of conventional events has been described with the Bartol 2006 tables. The energy spectrum of our data has been reconstructed through a numerical unfolding algorithm. The reconstruction is based on a Monte Carlo simulation and uses as an input three parameters of the neutrino track which are correlated with the energy of the event. Numerical regularisation is introduced to achieve a result free of unphysical oscillations, typical unfortunate feature of unfolding. The reconstructed data spectrum has been compared with different predictions using the model rejection factor technique. The prompt neutrino models differ in the choice of the hadron interaction model, the set of parton distribution functions and the numerical parameterisation of the fragmentation functions describing the transition from quark to hadrons. Here we considered mainly three classes of models, known in the literature as the Recombination Quark Parton Model, the Quark Gluon String Model and the Perturbative QCD model. Upper limits have been set on the expected flux predictions, based on our

  10. NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    Li, A. S. X.; Chirayath, V.; Segal-Rosenhaimer, M.; Das, K.

    2017-12-01

    In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8% error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Python's extensive libraries for machine learning, as well as extending integration to GPU

  11. Design and realization of the AMANDA software trigger for the TWA data readout system; Entwurf und Realisierung des AMANDA-Softwaretriggers fuer das TWR-Datenauslese-System

    Energy Technology Data Exchange (ETDEWEB)

    Messarius, Timo

    2006-07-15

    The thesis begins with a short introduction in the theory of astroparticle physics. Especially the processes, which lead to signal respectively background events in neutrino detectors, are discussed. Thereafter follows the descripotion of the basing detection principle and the detectors AMANDA and IceCube. The following chapter considers the two data-acquisition systems of the AMANDA detector and explains the motivation for the construction of a new data-acquisition system. The newly designed trigger system is then extensively treated. First a completely on software basing system is discussed, and then the implemented version is considered more detailedly. A procedure to detect and to mark events from atmospheric muons directly on trigger level is presented.

  12. Geochemical record of high emperor penguin populations during the Little Ice Age at Amanda Bay, Antarctica

    International Nuclear Information System (INIS)

    Huang, Tao; Yang, Lianjiao; Chu, Zhuding; Sun, Liguang; Yin, Xijie

    2016-01-01

    Emperor penguins (Aptenodytes forsteri) are sensitive to the Antarctic climate change because they breed on the fast sea ice. Studies of paleohistory for the emperor penguin are rare, due to the lack of archives on land. In this study, we obtained an emperor penguin ornithogenic sediment profile (PI) and performed geochronological, geochemical and stable isotope analyses on the sediments and feather remains. Two radiocarbon dates of penguin feathers in PI indicate that emperor penguins colonized Amanda Bay as early as CE 1540. By using the bio-elements (P, Se, Hg, Zn and Cd) in sediments and stable isotope values (δ"1"5N and δ"1"3C) in feathers, we inferred relative population size and dietary change of emperor penguins during the period of CE 1540–2008, respectively. An increase in population size with depleted N isotope ratios for emperor penguins on N island at Amanda Bay during the Little Ice Age (CE 1540–1866) was observed, suggesting that cold climate affected the penguin's breeding habitat, prey availability and thus their population and dietary composition. - Highlights: • Emperor penguin colonized at Amanda Bay, East Antarctic as early as AD 1540. • Populations of emperor penguin at Amanda Bay increase during the little ice age. • Depleted N isotope ratios of Emperor penguins during the LIA were observed.

  13. Geochemical record of high emperor penguin populations during the Little Ice Age at Amanda Bay, Antarctica

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Tao, E-mail: huangt@ahu.edu.cn [School of Resources and Environmental Engineering, Anhui University, Hefei 230601 (China); School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026 (China); Yang, Lianjiao; Chu, Zhuding [School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026 (China); Sun, Liguang, E-mail: slg@ustc.edu.cn [School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026 (China); Yin, Xijie [Third Institute of Oceanography, State Oceanic Administration, Xiamen 361005 (China)

    2016-09-15

    Emperor penguins (Aptenodytes forsteri) are sensitive to the Antarctic climate change because they breed on the fast sea ice. Studies of paleohistory for the emperor penguin are rare, due to the lack of archives on land. In this study, we obtained an emperor penguin ornithogenic sediment profile (PI) and performed geochronological, geochemical and stable isotope analyses on the sediments and feather remains. Two radiocarbon dates of penguin feathers in PI indicate that emperor penguins colonized Amanda Bay as early as CE 1540. By using the bio-elements (P, Se, Hg, Zn and Cd) in sediments and stable isotope values (δ{sup 15}N and δ{sup 13}C) in feathers, we inferred relative population size and dietary change of emperor penguins during the period of CE 1540–2008, respectively. An increase in population size with depleted N isotope ratios for emperor penguins on N island at Amanda Bay during the Little Ice Age (CE 1540–1866) was observed, suggesting that cold climate affected the penguin's breeding habitat, prey availability and thus their population and dietary composition. - Highlights: • Emperor penguin colonized at Amanda Bay, East Antarctic as early as AD 1540. • Populations of emperor penguin at Amanda Bay increase during the little ice age. • Depleted N isotope ratios of Emperor penguins during the LIA were observed.

  14. Estimação do volume de árvores utilizando redes neurais artificiais Estimate of tree volume using artificial neural nets

    Directory of Open Access Journals (Sweden)

    Eric Bastos Gorgens

    2009-12-01

    Full Text Available Rede neural artificial consiste em um conjunto de unidades que contêm funções matemáticas, unidas por pesos. As redes são capazes de aprender, mediante modificação dos pesos sinápticos, e generalizar o aprendizado para outros arquivos desconhecidos. O projeto de redes neurais é composto por três etapas: pré-processamento, processamento e, por fim, pós-processamento dos dados. Um dos problemas clássicos que podem ser abordados por redes é a aproximação de funções. Nesse grupo, pode-se incluir a estimação do volume de árvores. Foram utilizados quatro arquiteturas diferentes, cinco pré-processamentos e duas funções de ativação. As redes que se apresentaram estatisticamente iguais aos dados observados também foram analisadas quanto ao resíduo e à distribuição dos volumes e comparadas com a estimação de volume pelo modelo de Schumacher e Hall. As redes neurais formadas por neurônios, cuja função de ativação era exponencial, apresentaram estimativas estatisticamente iguais aos dados observados. As redes treinadas com os dados normalizados pelo método da interpolação linear e equalizados tiveram melhor desempenho na estimação.The artificial neural network consists of a set of units containing mathematical functions connected by weights. Such nets are capable of learning by means of synaptic weight modification, generalizing learning for other unknown archives. The neural network project comprises three stages: pre-processing, processing and post-processing of data. One of the classical problems approached by networks is function approximation. Tree volume estimate can be included in this group. Four different architectures, five pre-processings and two activation functions were used. The nets which were statistically similar to the observed data were also analyzed in relation to residue and volume and compared to the volume estimate provided by the Schumacher and Hall equation. The neural nets formed by

  15. Neural nets for the plausibility check of measured values in the integrated measurement and information system for the surveillance of environmental radioactivity (IMIS)

    International Nuclear Information System (INIS)

    Haase, G.

    2003-01-01

    Neural nets to the plausibility check of measured values in the ''integrated measurement and information system for the surveillance of environmental radioactivity, IMIS'' is a research project supported by the Federal Minister for the Environment, Nature Conservation and Nuclear Safety. A goal of this project was the automatic recognition of implausible measured values in the data base ORACLE, which measured values from surveillance of environmental radioactivity of most diverse environmental media contained. The conversion of this project [ 1 ] was realized by institut of logic, complexity and deduction systems of the university Karlsruhe under the direction of Professor Dr. Menzel, Dr. Martin Riedmueller and Martin Lauer. (orig.)

  16. Multispectral confocal microscopy images and artificial neural nets to monitor the photosensitizer uptake and degradation in Candida albicans cells

    Science.gov (United States)

    Romano, Renan A.; Pratavieira, Sebastião.; da Silva, Ana P.; Kurachi, Cristina; Guimarães, Francisco E. G.

    2017-07-01

    This study clearly demonstrates that multispectral confocal microscopy images analyzed by artificial neural networks provides a powerful tool to real-time monitoring photosensitizer uptake, as well as photochemical transformations occurred.

  17. NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

    DEFF Research Database (Denmark)

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-01-01

    is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino......β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method...... NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which...

  18. Method for detecting neutrinos from internal shocks in GRB fireballs with AMANDA

    CERN Document Server

    Stamatikos, M

    2004-01-01

    Neutrino-based astronomy provides a new window on the most energetic processes in the universe. The discovery of high-energy (E >or= 10 /sup 14/ eV) muonic neutrinos (v/sub mu /) from gamma-ray bursts (GRBs) would confirm hadronic acceleration in the relativistic GRB- wind, validate the phenomenology of the canonical fireball model and possibly reveal an acceleration mechanism for the highest energy cosmic rays (CRs). The Antarctic Muon and Neutrino Detector Array (AMANDA) is the world's largest operational neutrino telescope with a PeV muon effective area (averaged over zenith angle) ~ 50,000 m/sup 2 /. AMANDA uses the natural ice at the geographic South Pole as a Cherenkov medium and has been successfully calibrated on the signal of atmospheric neutrinos (v/sub atm/). Contrary to previous diffuse searches, we describe an analysis based upon confronting AMANDA observations of individual GRBs, adequately modeled by fireball phenomenology, with the predictions of the canonical fireball model. The expected neut...

  19. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

    Directory of Open Access Journals (Sweden)

    Bent Petersen

    Full Text Available UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

  20. NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

    Science.gov (United States)

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-01-01

    β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Qtotal = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. Conclusion The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences. PMID:21152409

  1. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

    Science.gov (United States)

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-11-30

    β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

  2. NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

    Science.gov (United States)

    Chirayath, Ved

    2018-01-01

    We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with

  3. The electromagnetic shower library for the Stockholm AMANDA Monte Carlo program

    International Nuclear Information System (INIS)

    Sun Qin.

    1996-05-01

    The Shower Library has been applied in the simulation for the AMANDA experiment which measures up-going muons induced by high energy neutrinos at the South Pole. With a detector measuring Cherenkov radiation in mind, only the information of the Cherenkov photons from the showers were stored in the library in the version of the program described here. The library contains 1000 electromagnetic showers with energies from 0.1 GeV to 100 GeV in steps according to a flat logarithmic distribution. 11 refs, 18 figs

  4. Search for Ultra High-Energy Neutrinos with AMANDA-II

    International Nuclear Information System (INIS)

    IceCube Collaboration; Klein, Spencer; Ackermann, M.

    2007-01-01

    A search for diffuse neutrinos with energies in excess of 10 5 GeV is conducted with AMANDA-II data recorded between 2000 and 2002. Above 10 7 GeV, the Earth is essentially opaque to neutrinos. This fact, combined with the limited overburden of the AMANDA-II detector (roughly 1.5 km), concentrates these ultra high-energy neutrinos at the horizon. The primary background for this analysis is bundles of downgoing, high-energy muons from the interaction of cosmic rays in the atmosphere. No statistically significant excess above the expected background is seen in the data, and an upper limit is set on the diffuse all-flavor neutrino flux of E 2 Φ 90%CL -7 GeV cm -2 s -1 sr -1 valid over the energy range of 2 x 10 5 GeV to 10 9 GeV. A number of models which predict neutrino fluxes from active galactic nuclei are excluded at the 90% confidence level

  5. Time calibration of AMANDA three variations of a theme of T$_{0}$

    CERN Document Server

    Hanson, K

    2002-01-01

    The AMANDA-II neutrino telescope currently operating at the South Pole is an array of 677 optical modules (OMs) deployed in the ice at depths between 1200 m and 2300 m beneath the surface. Calibration of the timing offsets of each OM is effected primarily by means of in- ice light pulses transmitted via optical fibers from a surface YAG laser. Discriminator walk, which is significant due to the transmission of electrical signals over 2 km distances, is also calibrated using the YAG laser. Another way to calibrate the timing offsets is to use downgoing cosmic ray muons. This method has the advantages of fuller coverage and year-round availability, i.e., it can be done anytime the detector is taking data. Finally, preliminary results of a technique used to calibrate, with nanosecond precision, the local clocks in "digital optical modules" (DOMs), which digitize and timestamp PMT signals in situ, are presented using DOMs in operation in AMANDA-II. The DOM is part of the baseline design for the planned IceCube de...

  6. The Search for Muon Neutrinos from Northern HemisphereGamma-Ray Bursts with AMANDA

    Energy Technology Data Exchange (ETDEWEB)

    IceCube Collaboration; Klein, Spencer; Achterberg, A.

    2007-05-08

    We present the results of the analysis of neutrino observations by the Antarctic Muon and Neutrino Detector Array (AMANDA) correlated with photon observations of more than 400 gamma-ray bursts (GRBs) in the Northern Hemisphere from 1997 to 2003. During this time period, AMANDA's effective collection area for muon neutrinos was larger than that of any other existing detector. Based on our observations of zero neutrinos during and immediately prior to the GRBs in the dataset, we set the most stringent upper limit on muon neutrino emission correlated with gamma-ray bursts. Assuming a Waxman-Bahcall spectrum and incorporating all systematic uncertainties, our flux upper limit has a normalization at 1 PeV of E{sup 2}{Phi}{sub {nu}} {le} 6.0 x 10{sup -9} GeV cm{sup -2}s{sup -1}sr{sup -1}, with 90% of the events expected within the energy range of {approx}10 TeV to {approx}3 PeV. The impact of this limit on several theoretical models of GRBs is discussed, as well as the future potential for detection of GRBs by next generation neutrino telescopes. Finally, we briefly describe several modifications to this analysis in order to apply it to other types of transient point sources.

  7. Method for the traveling salesman problem by controlling two parameters of the Hopfield neural network; Parameter seigyogata hop field net ni yoru junkai salesman mondai no kaiho

    Energy Technology Data Exchange (ETDEWEB)

    Setsu, N.; Murakami, K.; Ohori, T.; Watanabe, K. [Hokkaido Institute of Technology, Sapporo (Japan)

    1996-01-20

    For solving the traveling salesman problem (TSP) by using a continuous value outputting neural net (NN), an investigation was given on the accuracy of solution and the possibility on traveling routes by using the penalty coefficient and temperature as the parameters for energy functions. The parameter range to obtain high-quality traveling routes was shown by a numerical experiment. The experimental result revealed that, when the penalty coefficient `r` is large, the traveling route possibility tends to become higher, but the route length increases, and when the `r` is small, the traveling route possibility becomes lower, but the route length decreases, also in the continuous value outputting NN as in the two-value outputting NN. Noticing this fact, and in order to improve the traveling route possibility as well as the solution quality, a method was proposed to expand the penalty control method which was proposed previously by the authors on the two-value outputting NN, into the continuous value outputting NN. In addition, a proposal was also made on a method to derive an optimal temperature efficiently by using the golden section method. It was found that the relative error has been reduced by 48% on the average as compared with that in the conventional method in which the temperature is fixed. 6 refs., 5 figs.

  8. Limits on diffuse fluxes of high energy extraterrestrial neutrinos with the AMANDA-B10 detector

    International Nuclear Information System (INIS)

    Ahrens, J.; Bai, X.; Barwick, S.W.; Bay, R.C.; Becka, T.; Becker, K.-H.; Bernardini, E.; Bertrand, D.; Binon, F.; Boeser, S.; Botner, O.; Bouchta, A.; Bouhali, O.; Burgess, T.; Carius, S.; Castermans, T.; Chirkin, D.; Conrad, J.; Cooley, J.; Cowen, D.F.; Davour, A.; De Clercq, C.; DeYoung, T.; Desiati, P.; Doksus, P.; Ekstrom, P.; Feser, T.; Gaisser, T.K.; Ganugapati, R.; Gaug, M.; Geenen, H.; Gerhardt, L.; Goldschmidt, A.; Hallgren, A.; Halzen, F.; Hanson, K.; Hardtke, R.; Hauschildt, T.; Hellwig, M.; Herquet, P.; Hill, G.C.; Hulth, P.O.; Hughey, B.; Hultqvist, K.; Hundertmark, S.; Jacobsen, J.; Karle, A.; Kuehn, K.; Kim, J.; Kopke, L.; Kowalski, M.; Lamoureux, J.I.; Leich, H.; Leuthold, M.; Lindahl, P.; Liubarsky, I.; Madsen, J.; Mandli, K.; Marciniewski, P.; Matis, H.S.; McParland, C.P.; Messarius, T.; Miller, T.C.; Minaeva, Y.; Miocinovic, P.; Mock, P.C.; Morse, R.; Neunhoffer, T.; Niessen, P.; Nygren, D.R.; Ogelman, H.; Olbrechts, P.; Perez de los Heros, C.; Pohl, A.C.; Porrata, R.; Price, P.B.; Przybylski, G.T.; Rawlins, K.; Resconi, E.; Rhode, W.; Ribordy, M.; Richter, S.; Rodriguez Martino, J.; Romenesko, P.; Ross, D.; Sander, H.-G.; Schlenstedt, S.; Schinarakis, K.; Schmidt, T.; Schneider, D.; Schwarz, R.; Silvestri, A.; Solarz, M.; Stamatikos, M.; Spiczak, G.M.; Spiering, C.; Steele, D.; Steffen, P.; Stokstad, R.G.; Sulanke, K.-H.; Taboada, I.; Tilav, S.; Wagner, W.; Walck, C.; Wang, Y.-R.; Wiebusch, C.H.; Wiedemann, C.; Wischnewski, R.; Wissing, H.; Woschnagg, K.; Wu, W.; Yodh, G.; Young, S.

    2003-01-01

    Data from the AMANDA-B10 detector taken during the austral winter of 1997 have been searched for a diffuse flux of high energy extraterrestrial muon-neutrinos, as predicted from, e.g., the sum of all active galaxies in the universe. This search yielded no excess events above those expected from the background atmospheric neutrinos, leading to upper limits on the extraterrestrial neutrino flux. For an assumed E -2 spectrum, a 90 percent classical confidence level upper limit has been placed at a level E 2 Phi(E) = 8.4 x 10 -7 GeV cm -2 s -1 1 sr -1 (for a predominant neutrino energy range 6-1000 TeV) which is the most restrictive bound placed by any neutrino detector. When specific predicted spectral forms are considered, it is found that some are excluded

  9. A Monte Carlo study of atmospheric muon-neutrinos in Amanda

    Energy Technology Data Exchange (ETDEWEB)

    Dalberg, E.

    1998-01-01

    The response of AMANDA detector to atmospheric muon-neutrinos has been simulated. The neutrino flux, which has its origin from cosmic ray interactions with the atmosphere, induce muons in the vicinity of the detector. These muons will be relativistic and emit Cerenkov photons which can be detected by the optical modules buried in the deep South Pole glacier ice. The aim of the simulations is to predict the trigger rates in the existing detector, as well as in future extensions. The efficiency to detect muons with different angles and energies is also studied. Some of the simulated events have been analysed and it is discussed how the quality of this analysis can be judged. 35 refs, 30 figs.

  10. Search for relativistic magnetic monopoles with the AMANDA-II detector

    International Nuclear Information System (INIS)

    Wissing, Henrike

    2009-01-01

    Cherenkov emissions of magnetically charged particles passing through a transparent medium will exceed those of electrically charged particles by several orders of magnitude. The Antarctic Muon And Neutrino Detector Array (AMANDA), a neutrino telescope utilizing the glacial ice at the geographic South Pole as Cherenkov medium, is capable of efficiently detecting relativistic magnetic monopoles that may pass through its sensitive volume. This thesis presents the search for Cherenkov signatures from relativistic magnetic monopoles in data taken with AMANDA during the 2000. No such signal is observed in the data, and the analysis allows to place upper limits on the flux of relativistic magnetic monopoles. The limit obtained for monopoles reaching the detector from below the horizon, i.e., those monopoles that are capable of crossing the Earth, is the most stringent experimental constraint on the flux of magnetic monopoles to date: Dependent on the monopole speed, the flux limit (at 90% confidence level) varies between 3.8 x 10 -17 cm -2 s -1 sr -1 (for monopoles moving at the vacuum speed of light) and 8.8 x 10 -16 cm -2 s -1 sr -1 (for monopoles moving at a speed just above the Cherenkov threshold). The limit obtained for monopoles reaching the detector from above the horizon is less stringent by roughly an order of magnitude, owing to the much larger background from down-going atmospheric muons. This looser limit is valid for a larger class of magnetic monopoles, since the monopole's capability to pass through the Earth is not a requirement. (orig.)

  11. Check of the accuracy of the relativity theory with atmospheric muon neutrinos from the AMANDA data of the years 2000 to 2003; Ueberpruefung der Genauigkeit der Relativitaetstheorie mit atmosphaerischen Myonneutrinos aus den AMANDA-Daten der Jahre 2000 bis 2003

    Energy Technology Data Exchange (ETDEWEB)

    Ahrens, J.C.

    2006-11-08

    Atmospheric neutrinos allow one to test the principles of the Theory of Relativity in particular Lorentz invariance and the equivalence principle. Small deviations from these principles could lead, according to some theories, to detectable neutrino oscillations. Such oscillation effects are analysed in this thesis, using the data collected by the AMANDA detector. The neutrino telescope AMANDA is located at the South Pole and embedded in the Antarctic ice shield at a depth between 1500 m and 2000 m. AMANDA detects muon neutrinos via the Cherenkov light of neutrino induced muons allowing the reconstruction of the original neutrino direction. From the data of the years 2000 to 2003, which contain about seven billion recorded events and which mainly consist of the background of atmospheric muons, a sample of 3401 neutrino induced events has been selected. No indication for alternative oscillation effects has been found. For maximal mixing angles, a lower limit for parameters which violate Lorentz invariance or the equivalence principle could be set to {delta}{beta}(2 vertical stroke {phi} vertical stroke {delta}{gamma}){<=}5.15.10{sup -27}. (orig)

  12. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.

    Science.gov (United States)

    Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi

    2018-04-01

    Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity

  13. A novel neural-net-based nonlinear adaptive control and application to the cross-direction deviations control of a polymer film spread line

    International Nuclear Information System (INIS)

    Chen Zengqiang; Li Xiang; Liu Zhongxin; Yuan Zhuzhi

    2008-01-01

    A novel neural adaptive controller is presented to effectively control multivariable nonlinear systems. The proposed neural controller has been successfully applied to the cross-direction deviation control system of a polymer film spread line, whose good performance has been verified with real-time running results

  14. Petri Nets

    Indian Academy of Sciences (India)

    In a computer system, for example, typical discrete events ... This project brought out a series of influential reports on Petri net theory in the mid and late ... Technology became a leading centre for Petri net research and from then on, Petri nets ...

  15. Search for atmospheric muon-neutrinos and extraterrestric neutrino point sources in the 1997 AMANDA-B10 data

    International Nuclear Information System (INIS)

    Biron von Curland, A.

    2002-07-01

    The young field of high energy neutrino astronomy can be motivated by the search for the origin of the charged cosmic rays. Large astrophysical objects like AGNs or supernova remnants are candidates to accelerate hadrons which then can interact to eventually produce high energy neutrinos. Neutrino-induced muons can be detected via their emission of Cherenkov light in large neutrino telescopes like AMANDA. More than 10 9 atmospheric muon events and approximately 5000 atmospheric neutrino events were registered by AMANDA-B10 in 1997. Out of these, 223 atmospheric neutrino candidate events have been extracted. This data set contains approximately 15 background events. It allows to confirm the expected sensitivity of the detector towards neutrino events. A second set containing 369 (approximately 270 atmospheric neutrino events and 100 atmospheric muon events) was used to search for extraterrestrial neutrino point sources. Neither a binned search, nor a cluster search, nor a search for preselected sources gave indications for the existence of a strong neutrino point source. Based on this result, flux limits were derived. Assuming E ν -2 spectra, typical flux limits for selected sources of the order of Φ μ limit ∝ 10 -14 cm -2 s -1 for muons and Φ ν limit ∝ 10 -7 cm -2 s -1 for neutrinos have been obtained. (orig.)

  16. Mothers, daughters and midlife (self)-discoveries: gender and aging in the Amanda Cross' Kate Fansler series.

    Science.gov (United States)

    Domínguez-Rué, Emma

    2012-12-01

    In the same way that many aspects of gender cannot be understood aside from their relationship to race, class, culture, nationality and/or sexuality, the interactions between gender and aging constitute an interesting field for academic research, without which we cannot gain full insight into the complex and multi-faceted nature of gender studies. Although the American writer and Columbia professor Carolyn Gold Heilbrun (1926-2003) is more widely known for her best-selling mystery novels, published under the pseudonym of Amanda Cross, she also authored remarkable pieces of non-fiction in which she asserted her long-standing commitment to feminism, while she also challenged established notions on women and aging and advocated for a reassessment of those negative views. To my mind, the Kate Fansler novels became an instrument to reach a massive audience of female readers who might not have read her non-fiction, but who were perhaps finding it difficult to reach fulfillment as women under patriarchy, especially upon reaching middle age. Taking her essays in feminism and literary criticism as a basis and her later fiction as substantiation to my argument, this paper will try to reveal the ways in which Heilbrun's seemingly more superficial and much more commercial mystery novels as Amanda Cross were used a catalyst that informed her feminist principles while vindicating the need to rethink about issues concerning literary representations of mature women and cultural stereotypes about motherhood. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Net Neutrality

    DEFF Research Database (Denmark)

    Savin, Andrej

    2017-01-01

    Repealing “net neutrality” in the US will have no bearing on Internet freedom or security there or anywhere else.......Repealing “net neutrality” in the US will have no bearing on Internet freedom or security there or anywhere else....

  18. Analysis of Salinity Intrusion in the San Francisco Bay-Delta Using a GA-Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

    Science.gov (United States)

    Thompson, D. E.; Rajkumar, T.

    2002-12-01

    The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Suisan Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Suisan Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located along the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay / Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with DSM2 is that the numerical simulation takes roughly 16 hours to complete a prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple gauging stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Suisan Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the Bay-Delta is strongly tidally driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects

  19. Petri Nets

    Indian Academy of Sciences (India)

    GENERAL I ARTICLE ... In Part 1 of this two-part article, we have seen im- ..... mable logic controller and VLSI arrays, office automation systems, workflow management systems, ... complex discrete event and real-time systems; and Petri nets.

  20. Turkey's net energy consumption

    International Nuclear Information System (INIS)

    Soezen, Adnan; Arcaklioglu, Erol; Oezkaymak, Mehmet

    2005-01-01

    The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R 2 -value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R 2 values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies

  1. RESTful NET

    CERN Document Server

    Flanders, Jon

    2008-01-01

    RESTful .NET is the first book that teaches Windows developers to build RESTful web services using the latest Microsoft tools. Written by Windows Communication Foundation (WFC) expert Jon Flanders, this hands-on tutorial demonstrates how you can use WCF and other components of the .NET 3.5 Framework to build, deploy and use REST-based web services in a variety of application scenarios. RESTful architecture offers a simpler approach to building web services than SOAP, SOA, and the cumbersome WS- stack. And WCF has proven to be a flexible technology for building distributed systems not necessa

  2. Improved netting

    International Nuclear Information System (INIS)

    Bramley, A.; Clabburn, R.J.T.

    1976-01-01

    A method is described for producing netting composed of longitudinal and transverse threads of irradiation cross linked thermoplastic material, the threads being joined together at their crossings by moulded masses of cross linked thermoplastic material. The thread may be formed of polyethylene filaments, subjected to a radiation dose of 15 to 25 MR. The moulding can be conducted at 245 0 to 260 0 C or higher. The product is claimed to be an improved quality of netting, with bonds of increased strength between crossing threads. (U.K.)

  3. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  4. Petri Nets

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 9. Petri Nets - Applications. Y Narahari. General Article Volume 4 Issue 9 September 1999 pp 44-52 ... Author Affiliations. Y Narahari1. Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560 012, India.

  5. Net Gain

    International Development Research Centre (IDRC) Digital Library (Canada)

    Describing the effect of tax incentives for import, production, and sale of nets and insecticides; and ..... So far, China is the only country where a system for the routine treatment of ...... 1993), and the trials in Ecuador and Peru (Kroeger et al.

  6. Net Locality

    DEFF Research Database (Denmark)

    de Souza e Silva, Adriana Araujo; Gordon, Eric

    Provides an introduction to the new theory of Net Locality and the profound effect on individuals and societies when everything is located or locatable. Describes net locality as an emerging form of location awareness central to all aspects of digital media, from mobile phones, to Google Maps......, to location-based social networks and games, such as Foursquare and facebook. Warns of the threats these technologies, such as data surveillance, present to our sense of privacy, while also outlining the opportunities for pro-social developments. Provides a theory of the web in the context of the history...... of emerging technologies, from GeoCities to GPS, Wi-Fi, Wiki Me, and Google Android....

  7. Unlearning in feed-forward multi-nets

    NARCIS (Netherlands)

    Spaanenburg, L; Kurkova,; Steele, NC; Neruda, R; Karny, M

    2001-01-01

    Multi-nets promise an improved performance over monolithic neural networks by virtue of their distributed implementation. Modular neural networks are multi-nets based on an judicious assembly of functionally different parts. This can be viewed as again a monolithic network, but with more complex

  8. Check of the accuracy of the relativity theory with atmospheric muon neutrinos from the AMANDA data of the years 2000 to 2003

    International Nuclear Information System (INIS)

    Ahrens, J.C.

    2006-01-01

    Atmospheric neutrinos allow one to test the principles of the Theory of Relativity in particular Lorentz invariance and the equivalence principle. Small deviations from these principles could lead, according to some theories, to detectable neutrino oscillations. Such oscillation effects are analysed in this thesis, using the data collected by the AMANDA detector. The neutrino telescope AMANDA is located at the South Pole and embedded in the Antarctic ice shield at a depth between 1500 m and 2000 m. AMANDA detects muon neutrinos via the Cherenkov light of neutrino induced muons allowing the reconstruction of the original neutrino direction. From the data of the years 2000 to 2003, which contain about seven billion recorded events and which mainly consist of the background of atmospheric muons, a sample of 3401 neutrino induced events has been selected. No indication for alternative oscillation effects has been found. For maximal mixing angles, a lower limit for parameters which violate Lorentz invariance or the equivalence principle could be set to Δβ(2 vertical stroke φ vertical stroke Δγ)≤5.15.10 -27 . (orig)

  9. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  10. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  11. An adaptable Boolean net trainable to control a computing robot

    International Nuclear Information System (INIS)

    Lauria, F. E.; Prevete, R.; Milo, M.; Visco, S.

    1999-01-01

    We discuss a method to implement in a Boolean neural network a Hebbian rule so to obtain an adaptable universal control system. We start by presenting both the Boolean neural net and the Hebbian rule we have considered. Then we discuss, first, the problems arising when the latter is naively implemented in a Boolean neural net, second, the method consenting us to overcome them and the ensuing adaptable Boolean neural net paradigm. Next, we present the adaptable Boolean neural net as an intelligent control system, actually controlling a writing robot, and discuss how to train it in the execution of the elementary arithmetic operations on operands represented by numerals with an arbitrary number of digits

  12. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

  13. The gamma model : a new neural network for temporal processing

    NARCIS (Netherlands)

    Vries, de B.

    1992-01-01

    In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided.

  14. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1987-01-01

    The author describes a Petri net model, called coloured Petri nets (CP-nets), by means of which it is possible to describe large systems without having to cope with unnecessary details. The author introduces CP-nets and provide a first impression of their modeling power and the suitability...

  15. Learning Visual Basic NET

    CERN Document Server

    Liberty, Jesse

    2009-01-01

    Learning Visual Basic .NET is a complete introduction to VB.NET and object-oriented programming. By using hundreds of examples, this book demonstrates how to develop various kinds of applications--including those that work with databases--and web services. Learning Visual Basic .NET will help you build a solid foundation in .NET.

  16. Planning of nets

    International Nuclear Information System (INIS)

    Carberry, M

    1996-01-01

    The paper is about the planning of nets in areas of low density like it is the case of the rural areas. The author includes economic and technological aspects, planning of nets, demands and management among others

  17. Annotating Coloured Petri Nets

    DEFF Research Database (Denmark)

    Lindstrøm, Bo; Wells, Lisa Marie

    2002-01-01

    Coloured Petri nets (CP-nets) can be used for several fundamentally different purposes like functional analysis, performance analysis, and visualisation. To be able to use the corresponding tool extensions and libraries it is sometimes necessary to include extra auxiliary information in the CP......-net. An example of such auxiliary information is a counter which is associated with a token to be able to do performance analysis. Modifying colour sets and arc inscriptions in a CP-net to support a specific use may lead to creation of several slightly different CP-nets – only to support the different uses...... of the same basic CP-net. One solution to this problem is that the auxiliary information is not integrated into colour sets and arc inscriptions of a CP-net, but is kept separately. This makes it easy to disable this auxiliary information if a CP-net is to be used for another purpose. This paper proposes...

  18. Fuzzy Neuroidal Nets and Recurrent Fuzzy Computations

    Czech Academy of Sciences Publication Activity Database

    Wiedermann, Jiří

    2001-01-01

    Roč. 11, č. 6 (2001), s. 675-686 ISSN 1210-0552. [SOFSEM 2001 Workshop on Soft Computing. Piešťany, 29.11.2001-30.11.2001] R&D Projects: GA ČR GA201/00/1489; GA AV ČR KSK1019101 Institutional research plan: AV0Z1030915 Keywords : fuzzy computing * fuzzy neural nets * fuzzy Turing machines * non-uniform computational complexity Subject RIV: BA - General Mathematics

  19. Quantum net dynamics

    International Nuclear Information System (INIS)

    Finkelstein, D.

    1989-01-01

    The quantum net unifies the basic principles of quantum theory and relativity in a quantum spacetime having no ultraviolet infinities, supporting the Dirac equation, and having the usual vacuum as a quantum condensation. A correspondence principle connects nets to Schwinger sources and further unifies the vertical structure of the theory, so that the functions of the many hierarchic levels of quantum field theory (predicate algebra, set theory, topology,hor-ellipsis, quantum dynamics) are served by one in quantum net dynamics

  20. Programming NET Web Services

    CERN Document Server

    Ferrara, Alex

    2007-01-01

    Web services are poised to become a key technology for a wide range of Internet-enabled applications, spanning everything from straight B2B systems to mobile devices and proprietary in-house software. While there are several tools and platforms that can be used for building web services, developers are finding a powerful tool in Microsoft's .NET Framework and Visual Studio .NET. Designed from scratch to support the development of web services, the .NET Framework simplifies the process--programmers find that tasks that took an hour using the SOAP Toolkit take just minutes. Programming .NET

  1. Game Coloured Petri Nets

    DEFF Research Database (Denmark)

    Westergaard, Michael

    2006-01-01

    This paper introduces the notion of game coloured Petri nets. This allows the modeler to explicitly model what parts of the model comprise the modeled system and what parts are the environment of the modeled system. We give the formal definition of game coloured Petri nets, a means of reachability...... analysis of this net class, and an application of game coloured Petri nets to automatically generate easy-to-understand visualizations of the model by exploiting the knowledge that some parts of the model are not interesting from a visualization perspective (i.e. they are part of the environment...

  2. Tutorial on neural network applications in high energy physics: A 1992 perspective

    International Nuclear Information System (INIS)

    Denby, B.

    1992-04-01

    Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given

  3. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1991-01-01

    This paper describes how Coloured Petri Nets (CP-nets) have been developed — from being a promising theoretical model to being a full-fledged language for the design, specification, simulation, validation and implementation of large software systems (and other systems in which human beings and...

  4. Net zero water

    CSIR Research Space (South Africa)

    Lindeque, M

    2013-01-01

    Full Text Available the national grid. The unfortunate situation with water is that there is no replacement technology for water. Water can be supplied from many different sources. A net zero energy development will move closer to a net zero water development by reducing...

  5. Construction of monophase nets

    International Nuclear Information System (INIS)

    Suarez A, Jose Antonio

    1996-01-01

    The paper refers to the use of monophase loads in commercial residential urbanizations and in small industries, for this reason it is considered unnecessary the construction of three-phase nets. The author makes a historical recount of these nets in Bogota, his capacities, uses and energy savings

  6. Fusion through the NET

    International Nuclear Information System (INIS)

    Spears, B.

    1987-01-01

    The paper concerns the next generation of fusion machines which are intended to demonstrate the technical viability of fusion. In Europe, the device that will follow on from JET is known as NET - the Next European Torus. If the design programme for NET proceeds, Europe could start to build the machine in 1994. The present JET programme hopes to achieve breakeven in the early 1990's. NET hopes to reach ignition in the next century, and so lay the foundation for a demonstration reactor. A description is given of the technical specifications of the components of NET, including: the first wall, the divertors to protect the wall, the array of magnets that provide the fields containing the plasma, the superconducting magnets, and the shield of the machine. NET's research programme is briefly outlined, including the testing programme to optimise conditions in the machine to achieve ignition, and its safety work. (U.K.)

  7. Global reinforcement training of CrossNets

    Science.gov (United States)

    Ma, Xiaolong

    2007-10-01

    Hybrid "CMOL" integrated circuits, incorporating advanced CMOS devices for neural cell bodies, nanowires as axons and dendrites, and latching switches as synapses, may be used for the hardware implementation of extremely dense (107 cells and 1012 synapses per cm2) neuromorphic networks, operating up to 10 6 times faster than their biological prototypes. We are exploring several "Cross- Net" architectures that accommodate the limitations imposed by CMOL hardware and should allow effective training of the networks without a direct external access to individual synapses. Our studies have show that CrossNets based on simple (two-terminal) crosspoint devices can work well in at least two modes: as Hop-field networks for associative memory and multilayer perceptrons for classification tasks. For more intelligent tasks (such as robot motion control or complex games), which do not have "examples" for supervised learning, more advanced training methods such as the global reinforcement learning are necessary. For application of global reinforcement training algorithms to CrossNets, we have extended Williams's REINFORCE learning principle to a more general framework and derived several learning rules that are more suitable for CrossNet hardware implementation. The results of numerical experiments have shown that these new learning rules can work well for both classification tasks and reinforcement tasks such as the cartpole balancing control problem. Some limitations imposed by the CMOL hardware need to be carefully addressed for the the successful application of in situ reinforcement training to CrossNets.

  8. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  9. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  10. Net Zero Energy Buildings

    DEFF Research Database (Denmark)

    Marszal, Anna Joanna; Bourrelle, Julien S.; Gustavsen, Arild

    2010-01-01

    and identify possible renewable energy supply options which may be considered in calculations. Finally, the gap between the methodology proposed by each organisation and their respective national building code is assessed; providing an overview of the possible changes building codes will need to undergo......The international cooperation project IEA SHC Task 40 / ECBCS Annex 52 “Towards Net Zero Energy Solar Buildings”, attempts to develop a common understanding and to set up the basis for an international definition framework of Net Zero Energy Buildings (Net ZEBs). The understanding of such buildings...

  11. Getting to Net Zero

    Energy Technology Data Exchange (ETDEWEB)

    2016-09-01

    The technology necessary to build net zero energy buildings (NZEBs) is ready and available today, however, building to net zero energy performance levels can be challenging. Energy efficiency measures, onsite energy generation resources, load matching and grid interaction, climatic factors, and local policies vary from location to location and require unique methods of constructing NZEBs. It is recommended that Components start looking into how to construct and operate NZEBs now as there is a learning curve to net zero construction and FY 2020 is just around the corner.

  12. Pro NET Best Practices

    CERN Document Server

    Ritchie, Stephen D

    2011-01-01

    Pro .NET Best Practices is a practical reference to the best practices that you can apply to your .NET projects today. You will learn standards, techniques, and conventions that are sharply focused, realistic and helpful for achieving results, steering clear of unproven, idealistic, and impractical recommendations. Pro .NET Best Practices covers a broad range of practices and principles that development experts agree are the right ways to develop software, which includes continuous integration, automated testing, automated deployment, and code analysis. Whether the solution is from a free and

  13. PhysioNet

    Data.gov (United States)

    U.S. Department of Health & Human Services — The PhysioNet Resource is intended to stimulate current research and new investigations in the study of complex biomedical and physiologic signals. It offers free...

  14. NetSig

    DEFF Research Database (Denmark)

    Horn, Heiko; Lawrence, Michael S; Chouinard, Candace R

    2018-01-01

    Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (Net......Sig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that Net......Sig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified...

  15. Blanket testing in NET

    International Nuclear Information System (INIS)

    Chazalon, M.; Daenner, W.; Libin, B.

    1989-01-01

    The testing stages in NET for the performance assessment of the various breeding blanket concepts developed at the present time in Europe for DEMO (LiPb and ceramic blankets) and the requirements upon NET to perform these tests are reviewed. Typical locations available in NET for blanket testing are the central outboard segments and the horizontal ports of in-vessel sectors. These test positions will be connectable with external test loops. The number of test loops (helium, water, liquid metal) will be such that each major class of blankets can be tested in NET. The test positions, the boundary conditions and the external test loops are identified and the requirements for test blankets are summarized (author). 6

  16. Programming NET 35

    CERN Document Server

    Liberty, Jesse

    2009-01-01

    Bestselling author Jesse Liberty and industry expert Alex Horovitz uncover the common threads that unite the .NET 3.5 technologies, so you can benefit from the best practices and architectural patterns baked into the new Microsoft frameworks. The book offers a Grand Tour" of .NET 3.5 that describes how the principal technologies can be used together, with Ajax, to build modern n-tier and service-oriented applications. "

  17. NET SALARY ADJUSTMENT

    CERN Multimedia

    Finance Division

    2001-01-01

    On 15 June 2001 the Council approved the correction of the discrepancy identified in the net salary adjustment implemented on 1st January 2001 by retroactively increasing the scale of basic salaries to achieve the 2.8% average net salary adjustment approved in December 2000. We should like to inform you that the corresponding adjustment will be made to your July salary. Full details of the retroactive adjustments will consequently be shown on your pay slip.

  18. Reconstruction of neutron spectra through neural networks

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

    2003-01-01

    A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

  19. Los ojos de Amanda Berenguer y de Marosa di Giorgio para una exégesis de “El Jardín de las delicias” de El Bosco

    Directory of Open Access Journals (Sweden)

    Antonella Cancellier

    2015-07-01

    Full Text Available If in Hieronymus Bosch’s (1453-­‐‑1516 hybrids and metamorphosis Marosa di Giorgio (1932-­‐‑2004 absorbs the most symbolic and disturbing aspects of his “maravilloso negro”, in the same flamish painter Amanda Berenguer (1921-­‐‑2010 finds the inspiration for a subversive discourse. Writings and images enlighten and strengthen each other while the two extraordinary texts of uruguayan literature (Papeles salvajes and Los culos de El Bosco intertwine and converse with triptych of The Garden of Earthly Delights, thus participating in its own complex exegesis.

  20. Biological Petri Nets

    CERN Document Server

    Wingender, E

    2011-01-01

    It was suggested some years ago that Petri nets might be well suited to modeling metabolic networks, overcoming some of the limitations encountered by the use of systems employing ODEs (ordinary differential equations). Much work has been done since then which confirms this and demonstrates the usefulness of this concept for systems biology. Petri net technology is not only intuitively understood by scientists trained in the life sciences, it also has a robust mathematical foundation and provides the required degree of flexibility. As a result it appears to be a very promising approach to mode

  1. Reconfiguration of distribution nets

    International Nuclear Information System (INIS)

    Latorre Bayona, Gerardo; Angarita Marquez, Jorge Luis

    2000-01-01

    Starting of the location of the reconfiguration problem inside the context of the operation of distribution nets, of the quality indicators definition and of the presentation of the alternatives more used for reduction of technical losses, they are related diverse reconfiguration methodologies proposed in the technical literature, pointing out their three principals limitations; also are presents the results of lost obtained starting from simulation works carried out in distribution circuits of the ESSA ESP, which permitting to postulate the reconfiguration of nets like an excellent alternative to reduce technical losses

  2. NET system integration

    International Nuclear Information System (INIS)

    Farfaletti-Casali, F.; Mitchell, N.; Salpietro, E.; Buzzi, U.; Gritzmann, P.

    1985-01-01

    The NET system integration procedure is the process by which the requirements of the various Tokamak machine design areas are brought together to form a compatible machine layout. Each design area produces requirements which generally allow components to be built at minimum cost and operate with minimum technical risk, and the final machine assembly should be achieved with minimum departure from these optimum designs. This is carried out in NET by allowing flexibility in the maintenance and access methods to the machine internal components which must be regularly replaced by remote handling, in segmentation of these internal components and in the number of toroidal field coils

  3. Coloured Petri Nets

    CERN Document Server

    Jensen, Kurt

    2009-01-01

    Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. This book introduces the constructs of the CPN modelling language and presents the related analysis methods. It provides a comprehensive road map for the practical use of CPN.

  4. Safety nets or straitjackets?

    DEFF Research Database (Denmark)

    Ilsøe, Anna

    2012-01-01

    Does regulation of working hours at national and sector level impose straitjackets, or offer safety nets to employees seeking working time flexibility? This article compares legislation and collective agreements in the metal industries of Denmark, Germany and the USA. The industry has historically...

  5. Neuronal nets in robotics

    International Nuclear Information System (INIS)

    Jimenez Sanchez, Raul

    1999-01-01

    The paper gives a generic idea of the solutions that the neuronal nets contribute to the robotics. The advantages and the inconveniences are exposed that have regarding the conventional techniques. It also describe the more excellent applications as the pursuit of trajectories, the positioning based on images, the force control or of the mobile robots management, among others

  6. Net4Care platform

    DEFF Research Database (Denmark)

    2012-01-01

    , that in turn enables general practitioners and clinical staff to view observations. Use the menus above to explore the site's information resources. To get started, follow the short Hello, World! tutorial. The Net4Care project is funded by The Central Denmark Region and EU via Caretech Innovation....

  7. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt; Kristensen, Lars Michael

    Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. The development of such systems is particularly challenging because of inherent intricacies like possible nondeterminism an...

  8. Game Theory .net.

    Science.gov (United States)

    Shor, Mikhael

    2003-01-01

    States making game theory relevant and accessible to students is challenging. Describes the primary goal of GameTheory.net is to provide interactive teaching tools. Indicates the site strives to unite educators from economics, political and computer science, and ecology by providing a repository of lecture notes and tests for courses using…

  9. BacillusRegNet

    DEFF Research Database (Denmark)

    Misirli, Goksel; Hallinan, Jennifer; Röttger, Richard

    2014-01-01

    As high-throughput technologies become cheaper and easier to use, raw sequence data and corresponding annotations for many organisms are becoming available. However, sequence data alone is not sufficient to explain the biological behaviour of organisms, which arises largely from complex molecular...... the associated BacillusRegNet website (http://bacillus.ncl.ac.uk)....

  10. Boom Booom Net Radio

    DEFF Research Database (Denmark)

    Grimshaw, Mark Nicholas; Yong, Louisa; Dobie, Ian

    1999-01-01

    of an existing Internet radio station; Boom Booom Net Radio. Whilst necessity dictates some use of technology-related terminology, wherever possible we have endeavoured to keep such jargon to a minimum and to either explain it in the text or to provide further explanation in the appended glossary....

  11. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  12. SolNet

    DEFF Research Database (Denmark)

    Jordan, Ulrike; Vajen, Klaus; Bales, Chris

    2014-01-01

    -accompanying Master courses, placements of internships, and PhD scholarship projects. A new scholarship project, “SHINE”, was launched in autumn 2013 in the frame work of the Marie Curie program of the European Union (Initial Training Network, ITN). 13 PhD-scholarships on solar district heating, solar heat......SolNet, founded in 2006, is the first coordinated International PhD education program on Solar Thermal Engineering. The SolNet network is coordinated by the Institute of Thermal Engineering at Kassel University, Germany. The network offers PhD courses on solar heating and cooling, conference...... for industrial processes, as well as sorption stores and materials started in December 2013. Additionally, the project comprises a training program with five PhD courses and several workshops on solar thermal engineering that will be open also for other PhD students working in the field. The research projects...

  13. Markers of neural degeneration and regeneration in Down ...

    African Journals Online (AJOL)

    Iman Ehsan Abdel-Meguid

    2012-11-02

    Nov 2, 2012 ... The Egyptian Journal of Medical Human Genetics www.ejmhg.eg.net ... stem cells (TCSCs), including neural TCSCs and endothelial. TCSCs [10,11]. ..... directs human embryonic stem cell proliferation and differentia- tion into.

  14. Universal approximation in p-mean by neural networks

    NARCIS (Netherlands)

    Burton, R.M; Dehling, H.G

    A feedforward neural net with d input neurons and with a single hidden layer of n neurons is given by [GRAPHICS] where a(j), theta(j), w(ji) is an element of R. In this paper we study the approximation of arbitrary functions f: R-d --> R by a neural net in an L-p(mu) norm for some finite measure mu

  15. Net one, net two: the primary care network income statement.

    Science.gov (United States)

    Halley, M D; Little, A W

    1999-10-01

    Although hospital-owned primary care practices have been unprofitable for most hospitals, some hospitals are achieving competitive advantage and sustainable practice operations. A key to the success of some has been a net income reporting tool that separates practice operating expenses from the costs of creating and operating a network of practices to help healthcare organization managers, physicians, and staff to identify opportunities to improve the network's financial performance. This "Net One, Net Two" reporting allows operations leadership to be held accountable for Net One expenses and strategic leadership to be held accountable for Net Two expenses.

  16. Proof Nets for Lambek Calculus

    NARCIS (Netherlands)

    Roorda, Dirk

    1992-01-01

    The proof nets of linear logic are adapted to the non-commutative Lambek calculus. A different criterion for soundness of proof nets is given, which gives rise to new algorithms for proof search. The order sensitiveness of the Lambek calculus is reflected by the planarity condition on proof nets;

  17. Net metering: zero electricity bill

    International Nuclear Information System (INIS)

    Mangi, A.; Khan, Z.

    2011-01-01

    Worldwide move towards renewable energy sources, environmental concerns and decentralization of the power sector have made net metering an attractive option for power generation at small scale. This paper discusses the net metering, economical issues of renewable sources in Pakistan, technical aspects, installation suitability according to varying terrain, existing utility rules and formulation of legislation for net metering making it economically attractive. (author)

  18. The Net Advance of Physics

    Science.gov (United States)

    THE NET ADVANCE OF PHYSICS Review Articles and Tutorials in an Encyclopædic Format Established 1995 [Link to MIT] Computer support for The Net Advance of Physics is furnished by The Massachusetts Newest Additions SPECIAL FEATURES: Net Advance RETRO: Nineteenth Century Physics History of Science

  19. Horizontal ichthyoplankton tow-net system with unobstructed net opening

    Science.gov (United States)

    Nester, Robert T.

    1987-01-01

    The larval fish sampler described here consists of a modified bridle, frame, and net system with an obstruction-free net opening and is small enough for use on boats 10 m or less in length. The tow net features a square net frame attached to a 0.5-m-diameter cylinder-on-cone plankton net with a bridle designed to eliminate all obstructions forward of the net opening, significantly reducing currents and vibrations in the water directly preceding the net. This system was effective in collecting larvae representing more than 25 species of fish at sampling depths ranging from surface to 10 m and could easily be used at greater depths.

  20. Master Robotic Net

    Directory of Open Access Journals (Sweden)

    Vladimir Lipunov

    2010-01-01

    Full Text Available The main goal of the MASTER-Net project is to produce a unique fast sky survey with all sky observed over a single night down to a limiting magnitude of 19-20. Such a survey will make it possible to address a number of fundamental problems: search for dark energy via the discovery and photometry of supernovae (including SNIa, search for exoplanets, microlensing effects, discovery of minor bodies in the Solar System, and space-junk monitoring. All MASTER telescopes can be guided by alerts, and we plan to observe prompt optical emission from gamma-ray bursts synchronously in several filters and in several polarization planes.

  1. Limitations of shallow nets approximation.

    Science.gov (United States)

    Lin, Shao-Bo

    2017-10-01

    In this paper, we aim at analyzing the approximation abilities of shallow networks in reproducing kernel Hilbert spaces (RKHSs). We prove that there is a probability measure such that the achievable lower bound for approximating by shallow nets can be realized for all functions in balls of reproducing kernel Hilbert space with high probability, which is different with the classical minimax approximation error estimates. This result together with the existing approximation results for deep nets shows the limitations for shallow nets and provides a theoretical explanation on why deep nets perform better than shallow nets. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Shielding calculations for NET

    International Nuclear Information System (INIS)

    Verschuur, K.A.; Hogenbirk, A.

    1991-05-01

    In the European Fusion Technology Programme there is only a small activity on research and development for fusion neutronics. Never-the-less, looking further than blanket design now, as ECN is getting involved in design of radiation shields for the coils and biological shields, it becomes apparent that fusion neutronics as a whole still needs substantial development. Existing exact codes for calculation of complex geometries like MCNP and DORT/TORT are put over the limits of their numerical capabilities, whilst approximate codes for complex geometries like FURNACE and MERCURE4 are put over the limits of their modelling capabilities. The main objective of this study is just to find out how far we can get with existing codes in obtaining reliable values for the radiation levels inside and outside the cryostat/shield during operation and after shut-down. Starting with a 1D torus model for preliminary parametric studies, more dimensional approximation of the torus or parts of it including the main heterogeneities should follow. Regular contacts with the NET-Team are kept, to be aware of main changes in NET design that might affect our calculation models. Work on the contract started 1 July 1990. The technical description of the contract is given. (author). 14 refs.; 4 figs.; 1 tab

  3. Neural network error correction for solving coupled ordinary differential equations

    Science.gov (United States)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  4. Neural Network Approach to Locating Cryptography in Object Code

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2009-09-01

    Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

  5. Vector neural net identifying many strongly distorted and correlated patterns

    Science.gov (United States)

    Kryzhanovsky, Boris V.; Mikaelian, Andrei L.; Fonarev, Anatoly B.

    2005-01-01

    We suggest an effective and simple algorithm providing a polynomial storage capacity of a network of the form M ~ N2s+1, where N is the dimension of the stored binary patterns. In this problem the value of the free parameter s is restricted by the inequalities N >> slnN >= 1. The algorithm allows us to identify a large number of highly distorted similar patterns. The negative influence of correlations of the patterns is suppressed by choosing a sufficiently large value of the parameter s. We show the efficiency of the algorithm by the example of a perceptron identifier, but it also can be used to increase the storage capacity of full connected systems of associative memory.

  6. Intelligent control aspects of fuzzy logic and neural nets

    CERN Document Server

    Harris, C J; Brown, M

    1993-01-01

    With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent expe

  7. An Automation Framework for Neural Nets that Learn

    Science.gov (United States)

    Kilmer, W. L.; Arbib, M. A.

    1973-01-01

    A discussion of several types of formal neurons, many of whose functions are modifiable by their own input stimuli. The language of finite automata is used to mathematicize the problem of adaptation sufficiently to remove some ambiguities of Brindley's approach. (Author)

  8. Speech Recognition Using Neural Nets and Dynamic Time Warping

    Science.gov (United States)

    1988-12-01

    flost tmap [~J20J0[16J; mnt r, Ci: double diet ; double minimum =99M9.9; for (r =0; r < fysize ; ri-i-){ for (c =0; c f xeize ; c+ +){ diet 6 .0; for...i)[1] = (location[iflO] 0); Lmindist (I map, inp, close) double inp[16J; mrt close [2] float tmap [20(201[161 double diet ; double minimum 9.99e31

  9. Statistical interpretation of WEBNET seismograms by artificial neural nets

    Czech Academy of Sciences Publication Activity Database

    Plešinger, Axel; Růžek, Bohuslav; Boušková, Alena

    2000-01-01

    Roč. 44, č. 2 (2000), s. 251-271 ISSN 0039-3169 R&D Projects: GA AV ČR IAA312104; GA ČR GA205/99/0907 Institutional research plan: CEZ:AV0Z3012916 Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.761, year: 2000

  10. The equivalency between logic Petri workflow nets and workflow nets.

    Science.gov (United States)

    Wang, Jing; Yu, ShuXia; Du, YuYue

    2015-01-01

    Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in this paper is modeled to prove the equivalence between LPWNs and WF-nets, and advantages of LPWNs are presented.

  11. The Equivalency between Logic Petri Workflow Nets and Workflow Nets

    Science.gov (United States)

    Wang, Jing; Yu, ShuXia; Du, YuYue

    2015-01-01

    Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in this paper is modeled to prove the equivalence between LPWNs and WF-nets, and advantages of LPWNs are presented. PMID:25821845

  12. Art/Net/Work

    DEFF Research Database (Denmark)

    Andersen, Christian Ulrik; Lindstrøm, Hanne

    2006-01-01

    The seminar Art|Net|Work deals with two important changes in our culture. On one side, the network has become essential in the latest technological development. The Internet has entered a new phase, Web 2.0, including the occurrence of as ‘Wiki’s’, ‘Peer-2-Peer’ distribution, user controlled...... on the ‘network’ itself as a phenomenon and are often using technological networks as a mean of production and distribution. This changes the artistic practice and the distribution channels of art works – and the traditional notions of ‘work’, ‘origin’ and ‘rights’ are increasingly perceived as limiting...... the praxis of the artist. We see different kinds of interventions and activism (including ‘hacktivism’) using the network as a way of questioning the invisible rules that govern public and semi-public spaces. Who ‘owns’ them? What kind of social relationships do they generate? On what principle...

  13. Net4Care

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Hansen, Klaus Marius

    2012-01-01

    , health centers are getting larger and more distributed, and the number of healthcare professionals does not follow the trend in chronic diseases. All of this leads to a need for telemedical and mobile health applications. In a Danish context, these applications are often developed through local...... (innovative) initiatives with little regards for national and global (standardization) initiatives. A reason for this discrepancy is that the software architecture for national (and global) systems and standards are hard to understand, hard to develop systems based on, and hard to deploy. To counter this, we...... propose a software ecosystem approach for telemedicine applications, providing a framework, Net4Care, encapsulating national/global design decisions with respect to standardization while allowing for local innovation. This paper presents an analysis of existing systems, of requirements for a software...

  14. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  15. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  16. High-level Petri Nets

    DEFF Research Database (Denmark)

    various journals and collections. As a result, much of this knowledge is not readily available to people who may be interested in using high-level nets. Within the Petri net community this problem has been discussed many times, and as an outcome this book has been compiled. The book contains reprints...... of some of the most important papers on the application and theory of high-level Petri nets. In this way it makes the relevant literature more available. It is our hope that the book will be a useful source of information and that, e.g., it can be used in the organization of Petri net courses. To make......High-level Petri nets are now widely used in both theoretical analysis and practical modelling of concurrent systems. The main reason for the success of this class of net models is that they make it possible to obtain much more succinct and manageable descriptions than can be obtained by means...

  17. Towards semen quality assessment using neural networks

    DEFF Research Database (Denmark)

    Linneberg, Christian; Salamon, P.; Svarer, C.

    1994-01-01

    The paper presents the methodology and results from a neural net based classification of human sperm head morphology. The methodology uses a preprocessing scheme in which invariant Fourier descriptors are lumped into “energy” bands. The resulting networks are pruned using optimal brain damage. Pe...

  18. WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL ...

    African Journals Online (AJOL)

    This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.

  19. Net neutrality and audiovisual services

    OpenAIRE

    van Eijk, N.; Nikoltchev, S.

    2011-01-01

    Net neutrality is high on the European agenda. New regulations for the communication sector provide a legal framework for net neutrality and need to be implemented on both a European and a national level. The key element is not just about blocking or slowing down traffic across communication networks: the control over the distribution of audiovisual services constitutes a vital part of the problem. In this contribution, the phenomenon of net neutrality is described first. Next, the European a...

  20. NetView technical research

    Science.gov (United States)

    1993-01-01

    This is the Final Technical Report for the NetView Technical Research task. This report is prepared in accordance with Contract Data Requirements List (CDRL) item A002. NetView assistance was provided and details are presented under the following headings: NetView Management Systems (NMS) project tasks; WBAFB IBM 3090; WPAFB AMDAHL; WPAFB IBM 3084; Hill AFB; McClellan AFB AMDAHL; McClellan AFB IBM 3090; and Warner-Robins AFB.

  1. Initial CAD investigations for NET

    International Nuclear Information System (INIS)

    Katz, F.; Leinemann, K.; Ludwig, A.; Marek, U.; Olbrich, W.; Schlechtendahl, E.G.

    1985-11-01

    This report summarizes the work done under contract no. 164/84-7/FU-D-/NET between the Commission of the European Communities and KfK during the period from June 1, 1984, through May 31, 1985. The following topics are covered in this report: Initial modelling of NET version NET2A, CAD system extension for remote handling studies, analysis of the CAD information structure, work related to the transfer of CAD information between KfK and the NET team. (orig.) [de

  2. Understanding Net Zero Energy Buildings

    DEFF Research Database (Denmark)

    Salom, Jaume; Widén, Joakim; Candanedo, José

    2011-01-01

    Although several alternative definitions exist, a Net-Zero Energy Building (Net ZEB) can be succinctly described as a grid-connected building that generates as much energy as it uses over a year. The “net-zero” balance is attained by applying energy conservation and efficiency measures...... and by incorporating renewable energy systems. While based on annual balances, a complete description of a Net ZEB requires examining the system at smaller time-scales. This assessment should address: (a) the relationship between power generation and building loads and (b) the resulting interaction with the power grid...

  3. Rare, but challenging tumors: NET

    International Nuclear Information System (INIS)

    Ivanova, D.; Balev, B.

    2013-01-01

    Full text: Introduction: Gastroenteropancreatic Neuroendocrine Tumors (GEP - NET) are a heterogeneous group of tumors with different locations and many different clinical, histological, and imaging performance. In a part of them a secretion of various organic substances is present. The morbidity of GEP - NET in the EU is growing, and this leads to increase the attention to them. What you will learn: Imaging methods used for localization and staging of GEP - NET, characteristics of the study’s protocols; Classification of GEP - NET; Demonstration of typical and atypical imaging features of GEP - NET in patients registered at the NET Center at University Hospital ‘St. Marina’, Varna; Features of metastatic NET, The role of imaging in the evaluation of treatment response and follow-up of the patients. Discussion: The image semiotics analysis is based on 19 cases of GEP - NET registered NET Center at University Hospital ‘St. Marina’. The main imaging method is multidetector CT (MDCT), and magnetic resonance imaging (MRI ) has advantages in the evaluation of liver lesions and the local prevalence of anorectal tumors. In patients with advanced disease and liver lesions the assessment of skeletal involvement (MRI/ nuclear medical method) is mandatory. The majority of GEP - NET have not any specific imaging findings. Therefore it is extremely important proper planning and conducting of the study (MDCT and MR enterography; accurate assessment phase of scanning, positive and negative contrast). Conclusion: GEP - NET is a major diagnostic challenge due to the absence of typical imaging characteristics and often an overlap with those of the tumors of different origin can be observed. Therefore, a good knowledge of clinical and imaging changes occurring at different locations is needed. MDCT is the basis for the diagnosis, staging and follow-up of these neoplasms

  4. Linear Logic on Petri Nets

    DEFF Research Database (Denmark)

    Engberg, Uffe Henrik; Winskel, Glynn

    This article shows how individual Petri nets form models of Girard's intuitionistic linear logic. It explores questions of expressiveness and completeness of linear logic with respect to this interpretation. An aim is to use Petri nets to give an understanding of linear logic and give some apprai...

  5. Net neutrality and audiovisual services

    NARCIS (Netherlands)

    van Eijk, N.; Nikoltchev, S.

    2011-01-01

    Net neutrality is high on the European agenda. New regulations for the communication sector provide a legal framework for net neutrality and need to be implemented on both a European and a national level. The key element is not just about blocking or slowing down traffic across communication

  6. Properties of porous netted materials

    International Nuclear Information System (INIS)

    Daragan, V.D.; Drozdov, B.G.; Kotov, A.Yu.; Mel'nikov, G.N.; Pustogarov, A.V.

    1987-01-01

    Hydraulic and strength characteristics, efficient heat conduction and inner heat exchange coefficient are experimentally studied for porous netted materials on the base of the brass nets as dependent on porosity, cell size and method of net laying. Results of the studies are presented. It is shown that due to anisotropy of the material properties the hydraulic resistance in the direction parallel to the nets plane is 1.3-1.6 times higher than in the perpendicular one. Values of the effective heat conduction in the direction perpendicular to the nets plane at Π>0.45 agree with the data from literature, at Π<0.45 a deviation from the calculated values is marked in the direction of the heat conduction decrease

  7. NET remote workstation

    International Nuclear Information System (INIS)

    Leinemann, K.

    1990-10-01

    The goal of this NET study was to define the functionality of a remote handling workstation and its hardware and software architecture. The remote handling workstation has to fulfill two basic functions: (1) to provide the man-machine interface (MMI), that means the interface to the control system of the maintenance equipment and to the working environment (telepresence) and (2) to provide high level (task level) supporting functions (software tools) during the maintenance work and in the preparation phase. Concerning the man-machine interface, an important module of the remote handling workstation besides the standard components of man-machine interfacing is a module for graphical scene presentation supplementing viewing by TV. The technique of integrated viewing is well known from JET BOOM and TARM control using the GBsim and KISMET software. For integration of equipment dependent MMI functions the remote handling workstation provides a special software module interface. Task level support of the operator is based on (1) spatial (geometric/kinematic) models, (2) remote handling procedure models, and (3) functional models of the equipment. These models and the related simulation modules are used for planning, programming, execution monitoring, and training. The workstation provides an intelligent handbook guiding the operator through planned procedures illustrated by animated graphical sequences. For unplanned situations decision aids are available. A central point of the architectural design was to guarantee a high flexibility with respect to hardware and software. Therefore the remote handling workstation is designed as an open system based on widely accepted standards allowing the stepwise integration of the various modules starting with the basic MMI and the spatial simulation as standard components. (orig./HP) [de

  8. Conformal Nets II: Conformal Blocks

    Science.gov (United States)

    Bartels, Arthur; Douglas, Christopher L.; Henriques, André

    2017-08-01

    Conformal nets provide a mathematical formalism for conformal field theory. Associated to a conformal net with finite index, we give a construction of the `bundle of conformal blocks', a representation of the mapping class groupoid of closed topological surfaces into the category of finite-dimensional projective Hilbert spaces. We also construct infinite-dimensional spaces of conformal blocks for topological surfaces with smooth boundary. We prove that the conformal blocks satisfy a factorization formula for gluing surfaces along circles, and an analogous formula for gluing surfaces along intervals. We use this interval factorization property to give a new proof of the modularity of the category of representations of a conformal net.

  9. Pro asynchronous programming with .NET

    CERN Document Server

    Blewett, Richard; Ltd, Rock Solid Knowledge

    2014-01-01

    Pro Asynchronous Programming with .NET teaches the essential skill of asynchronous programming in .NET. It answers critical questions in .NET application development, such as: how do I keep my program responding at all times to keep my users happy how do I make the most of the available hardware how can I improve performanceIn the modern world, users expect more and more from their applications and devices, and multi-core hardware has the potential to provide it. But it takes carefully crafted code to turn that potential into responsive, scalable applications.With Pro Asynchronous Programming

  10. KM3NeT

    CERN Multimedia

    KM3NeT is a large scale next-generation neutrino telescope located in the deep waters of the Mediterranean Sea, optimized for the discovery of galactic neutrino sources emitting in the TeV energy region.

  11. Pickering nuclear fish diversion net

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, J.; Lew, A. [Ontario Power Generation, Toronto, Ontario (Canada)

    2013-07-01

    Pickering Fish Diversion Net - An Engineered Environmental Solution that has significantly reduced fish impingement at the Pickering Nuclear Facility. Note: As a recent urgent request/discussed by Mark Elliot, CNE-OPG and Jacques Plourde, CNS.

  12. PolicyNet Publication System

    Data.gov (United States)

    Social Security Administration — The PolicyNet Publication System project will merge the Oracle-based Policy Repository (POMS) and the SQL-Server CAMP system (MSOM) into a new system with an Oracle...

  13. Net Neutrality: Background and Issues

    National Research Council Canada - National Science Library

    Gilroy, Angele A

    2006-01-01

    .... The move to place restrictions on the owners of the networks that compose and provide access to the Internet, to ensure equal access and nondiscriminatory treatment, is referred to as "net neutrality...

  14. Analisis Determinan Net Ekspor Indonesia

    OpenAIRE

    Daulay, Rahmawaty

    2010-01-01

    This study is to analyzing empirically among Indonesia GDP, trade partnership GDP (Malaysia, Singapore, US and Thailand) and real exchange rate toward Indonesia Net Export. To find out which one from those three variables is significant in order to fluctuating (increasing or decreasing) Indonesia Net Export either in the short run or in the long run. Data collection is obtained using secondary data, namely Indonesia GDP, Malaysia GDP, Singapura GDP, US GDP, Thailand GDP and real exchange rate...

  15. NetBeans GUI Builder

    OpenAIRE

    Pusiankova, Tatsiana

    2009-01-01

    This work aims at making readers familiar with the powerful tool NetBeans IDE GUI Builder and helping them make their first steps to creation of their own graphical user interface in the Java programming language. The work includes theoretical description of NetBeans IDE GUI Builder, its most important characteristics and peculiarities and also a set of practical instructions that will help readers in creation of their first GUI. The readers will be introduced to the environment of this tool ...

  16. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  17. Multiflavor string-net models

    Science.gov (United States)

    Lin, Chien-Hung

    2017-05-01

    We generalize the string-net construction to multiple flavors of strings, each of which is labeled by the elements of an Abelian group Gi. The same flavor of strings can branch, while different flavors of strings can cross one another and thus they form intersecting string nets. We systematically construct the exactly soluble lattice Hamiltonians and the ground-state wave functions for the intersecting string-net condensed phases. We analyze the braiding statistics of the low-energy quasiparticle excitations and find that our model can realize all the topological phases as the string-net model with group G =∏iGi . In this respect, our construction provides various ways of building lattice models which realize topological order G , corresponding to different partitions of G and thus different flavors of string nets. In fact, our construction concretely demonstrates the Künneth formula by constructing various lattice models with the same topological order. As an example, we construct the G =Z2×Z2×Z2 string-net model which realizes a non-Abelian topological phase by properly intersecting three copies of toric codes.

  18. Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP

    Directory of Open Access Journals (Sweden)

    Stanimir Surchev

    2013-10-01

    Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.

  19. Quantitative phase microscopy using deep neural networks

    Science.gov (United States)

    Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

    2018-02-01

    Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

  20. Hindcasting of storm waves using neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, S.; Mandal, S.

    Department NN neural network net i weighted sum of the inputs of neuron i o k network output at kth output node P total number of training pattern s i output of neuron i t k target output at kth output node 1. Introduction Severe storms occur in Bay of Bengal...), forecasting of runoff (Crespo and Mora, 1993), concrete strength (Kasperkiewicz et al., 1995). The uses of neural network in the coastal the wave conditions will change from year to year, thus a proper statistical and climatological treatment requires several...

  1. Automatic slice identification in 3D medical images with a ConvNet regressor

    NARCIS (Netherlands)

    de Vos, Bob D.; Viergever, Max A.; de Jong, Pim A.; Išgum, Ivana

    2016-01-01

    Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were

  2. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  3. MATT: Multi Agents Testing Tool Based Nets within Nets

    Directory of Open Access Journals (Sweden)

    Sara Kerraoui

    2016-12-01

    As part of this effort, we propose a model based testing approach for multi agent systems based on such a model called Reference net, where a tool, which aims to providing a uniform and automated approach is developed. The feasibility and the advantage of the proposed approach are shown through a short case study.

  4. Control of 12-Cylinder Camless Engine with Neural Networks

    OpenAIRE

    Ashhab Moh’d Sami

    2017-01-01

    The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and appl...

  5. 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 ...

  6. Implementing NetScaler VPX

    CERN Document Server

    Sandbu, Marius

    2014-01-01

    An easy-to-follow guide with detailed step-by step-instructions on how to implement the different key components in NetScaler, with real-world examples and sample scenarios.If you are a Citrix or network administrator who needs to implement NetScaler in your virtual environment to gain an insight on its functionality, this book is ideal for you. A basic understanding of networking and familiarity with some of the different Citrix products such as XenApp or XenDesktop is a prerequisite.

  7. Net4Care PHMR Library

    DEFF Research Database (Denmark)

    2014-01-01

    The Net4Care PHMR library contains a) A GreenCDA approach for constructing a data object representing a PHMR document: SimpleClinicalDocument, and b) A Builder which can produce a XML document representing a valid Danish PHMR (following the MedCom profile) document from the SimpleClinicalDocument......The Net4Care PHMR library contains a) A GreenCDA approach for constructing a data object representing a PHMR document: SimpleClinicalDocument, and b) A Builder which can produce a XML document representing a valid Danish PHMR (following the MedCom profile) document from the Simple...

  8. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

  9. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  10. Coloured Petri Nets and the Invariant Method

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1981-01-01

    processes to be described by a common subnet, without losing the ability to distinguish between them. Our generalization, called coloured Petri nets, is heavily influenced by predicate transition-nets introduced by H.J. Genrich and K. Lautenbach. Moreover our paper shows how the invariant-method, introduced...... for Petri nets by K. Lautenbach, can be generalized to coloured Petri nets....

  11. Developing Scene Understanding Neural Software for Realistic Autonomous Outdoor Missions

    Science.gov (United States)

    2017-09-01

    computer using a single graphics processing unit (GPU). To the best of our knowledge, an implementation of the open-source Python -based AlexNet CNN on...1. Introduction Neurons in the brain enable us to understand scenes by assessing the spatial, temporal, and feature relations of objects in the...effort to use computer neural networks to augment human neural intelligence to improve our scene understanding (Krizhevsky et al. 2012; Zhou et al

  12. D.NET case study

    International Development Research Centre (IDRC) Digital Library (Canada)

    lremy

    The mission was defined to build, “A society where information and ... innovative ideas and projects around different themes (using ICT), and piloting them to test .... like D.Net with several projects that had moved beyond their pilot phase.

  13. Petri Nets in Cryptographic Protocols

    DEFF Research Database (Denmark)

    Crazzolara, Federico; Winskel, Glynn

    2001-01-01

    A process language for security protocols is presented together with a semantics in terms of sets of events. The denotation of process is a set of events, and as each event specifies a set of pre and postconditions, this denotation can be viewed as a Petri net. By means of an example we illustrate...

  14. Complexity metrics for workflow nets

    NARCIS (Netherlands)

    Lassen, K.B.; Aalst, van der W.M.P.

    2009-01-01

    Process modeling languages such as EPCs, BPMN, flow charts, UML activity diagrams, Petri nets, etc., are used to model business processes and to configure process-aware information systems. It is known that users have problems understanding these diagrams. In fact, even process engineers and system

  15. Reference Guide Microsoft.NET

    NARCIS (Netherlands)

    Zee M van der; Verspaij GJ; Rosbergen S; IMP; NMD

    2003-01-01

    Met behulp van het rapport kunnen ontwikkelaars, beheerders en betrokken managers bij ICT projecten meer inzicht krijgen in de .NET technologie en een goede keuze maken in de inzetbaarheid van deze technologie. Het rapport geeft de bevindingen en conclusies van een verkennende studie naar het

  16. Complexity Metrics for Workflow Nets

    DEFF Research Database (Denmark)

    Lassen, Kristian Bisgaard; van der Aalst, Wil M.P.

    2009-01-01

    Process modeling languages such as EPCs, BPMN, flow charts, UML activity diagrams, Petri nets, etc.\\ are used to model business processes and to configure process-aware information systems. It is known that users have problems understanding these diagrams. In fact, even process engineers and system...

  17. Communicating with the Net Generation

    Science.gov (United States)

    2011-03-11

    resource investment is necessary to sustain a high quality all-volunteer force. 9 Leadership Technique for the Net Generation Army Regulation 600...Generations at Work, Millenials at Work, http://www.generationsatwork. com /articles_millennials_at_work.php (accessed November 21, 2010). 31 Thomas

  18. Net Neutrality in the Netherlands

    NARCIS (Netherlands)

    van Eijk, N.

    2014-01-01

    The Netherlands is among the first countries that have put specific net neutrality standards in place. The decision to implement specific regulation was influenced by at least three factors. The first was the prevailing social and academic debate, partly due to developments in the United States. The

  19. Surgery for GEP-NETs

    DEFF Research Database (Denmark)

    Knigge, Ulrich; Hansen, Carsten Palnæs

    2012-01-01

    Surgery is the only treatment that may cure the patient with gastroentero-pancreatic (GEP) neuroendocrine tumours (NET) and neuroendocrine carcinomas (NEC) and should always be considered as first line treatment if R0/R1 resection can be achieved. The surgical and interventional procedures for GEP...

  20. Net4Care PHMR Tutorial

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak

    Goal To demonstrate how to use the Net4Care PHMR builder module to a) Create a SimpleClinicalDocument instance and populate it with relevant administrative and medical information to form a tele medical report of a set of measurements, b) Use the provided DanishPHMRBuilder to generate a correctly...

  1. .net

    Directory of Open Access Journals (Sweden)

    Le Comité de Rédaction d' EspacesTemps.net

    2002-06-01

    Full Text Available EspacesTemps lance aujourd'hui deux objets différents : un site internet et, sur ce site, Le Journal . Il s'agit donc de bien plus, et, au fond, de tout autre chose qu'un simple outil de communication destiné à informer nos lecteurs de nos parutions. Ce n'est pas non plus la « mise en ligne » de nos numéros-papier. L'internet nous donne au contraire l'occasion de réaliser, dans de meilleures conditions, ce que nous avons tenté de faire depuis quelques ...

  2. Caught in the Net: Perineuronal Nets and Addiction

    Directory of Open Access Journals (Sweden)

    Megan Slaker

    2016-01-01

    Full Text Available Exposure to drugs of abuse induces plasticity in the brain and creates persistent drug-related memories. These changes in plasticity and persistent drug memories are believed to produce aberrant motivation and reinforcement contributing to addiction. Most studies have explored the effect drugs of abuse have on pre- and postsynaptic cells and astrocytes; however, more recently, attention has shifted to explore the effect these drugs have on the extracellular matrix (ECM. Within the ECM are unique structures arranged in a net-like manner, surrounding a subset of neurons called perineuronal nets (PNNs. This review focuses on drug-induced changes in PNNs, the molecules that regulate PNNs, and the expression of PNNs within brain circuitry mediating motivation, reward, and reinforcement as it pertains to addiction.

  3. End-to-end unsupervised deformable image registration with a convolutional neural network

    NARCIS (Netherlands)

    de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

    2017-01-01

    In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial

  4. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  5. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  6. Army Net Zero Prove Out. Net Zero Energy Best Practices

    Science.gov (United States)

    2014-11-18

    recovery and cogeneration opportunities, offsetting the remaining demand with the production of renewable energy from onsite sources so that the Net...implementing energy recovery and cogeneration opportunities, and then offsetting the remaining demand with the production of renewable energy from on-site...they impact overall energy performance. The use of energy modeling in the design stage provides insights that can contribute to more effective design

  7. Net alkalinity and net acidity 2: Practical considerations

    Science.gov (United States)

    Kirby, C.S.; Cravotta, C.A.

    2005-01-01

    The pH, alkalinity, and acidity of mine drainage and associated waters can be misinterpreted because of the chemical instability of samples and possible misunderstandings of standard analytical method results. Synthetic and field samples of mine drainage having various initial pH values and concentrations of dissolved metals and alkalinity were titrated by several methods, and the results were compared to alkalinity and acidity calculated based on dissolved solutes. The pH, alkalinity, and acidity were compared between fresh, unoxidized and aged, oxidized samples. Data for Pennsylvania coal mine drainage indicates that the pH of fresh samples was predominantly acidic (pH 2.5-4) or near neutral (pH 6-7); ??? 25% of the samples had pH values between 5 and 6. Following oxidation, no samples had pH values between 5 and 6. The Standard Method Alkalinity titration is constrained to yield values >0. Most calculated and measured alkalinities for samples with positive alkalinities were in close agreement. However, for low-pH samples, the calculated alkalinity can be negative due to negative contributions by dissolved metals that may oxidize and hydrolyze. The Standard Method hot peroxide treatment titration for acidity determination (Hot Acidity) accurately indicates the potential for pH to decrease to acidic values after complete degassing of CO2 and oxidation of Fe and Mn, and it indicates either the excess alkalinity or that required for neutralization of the sample. The Hot Acidity directly measures net acidity (= -net alkalinity). Samples that had near-neutral pH after oxidation had negative Hot Acidity; samples that had pH mine drainage treatment can lead to systems with insufficient Alkalinity to neutralize metal and H+ acidity and is not recommended. The use of net alkalinity = -Hot Acidity titration is recommended for the planning of mine drainage treatment. The use of net alkalinity = (Alkalinitymeasured - Aciditycalculated) is recommended with some cautions

  8. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that conv...

  9. Net alkalinity and net acidity 1: Theoretical considerations

    International Nuclear Information System (INIS)

    Kirby, Carl S.; Cravotta, Charles A.

    2005-01-01

    Net acidity and net alkalinity are widely used, poorly defined, and commonly misunderstood parameters for the characterization of mine drainage. The authors explain theoretical expressions of 3 types of alkalinity (caustic, phenolphthalein, and total) and acidity (mineral, CO 2 , and total). Except for rarely-invoked negative alkalinity, theoretically defined total alkalinity is closely analogous to measured alkalinity and presents few practical interpretation problems. Theoretically defined 'CO 2 -acidity' is closely related to most standard titration methods with an endpoint pH of 8.3 used for determining acidity in mine drainage, but it is unfortunately named because CO 2 is intentionally driven off during titration of mine-drainage samples. Using the proton condition/mass-action approach and employing graphs to illustrate speciation with changes in pH, the authors explore the concept of principal components and how to assign acidity contributions to aqueous species commonly present in mine drainage. Acidity is defined in mine drainage based on aqueous speciation at the sample pH and on the capacity of these species to undergo hydrolysis to pH 8.3. Application of this definition shows that the computed acidity in mgL -1 as CaCO 3 (based on pH and analytical concentrations of dissolved Fe II , Fe III , Mn, and Al in mgL -1 ):acidity calculated =50{1000(10 -pH )+[2(Fe II )+3(Fe III )]/56+2(Mn) /55+3(Al)/27}underestimates contributions from HSO 4 - and H + , but overestimates the acidity due to Fe 3+ and Al 3+ . However, these errors tend to approximately cancel each other. It is demonstrated that 'net alkalinity' is a valid mathematical construction based on theoretical definitions of alkalinity and acidity. Further, it is shown that, for most mine-drainage solutions, a useful net alkalinity value can be derived from: (1) alkalinity and acidity values based on aqueous speciation (2) measured alkalinity minus calculated acidity, or (3) taking the negative of the

  10. Net alkalinity and net acidity 1: Theoretical considerations

    Science.gov (United States)

    Kirby, C.S.; Cravotta, C.A.

    2005-01-01

    Net acidity and net alkalinity are widely used, poorly defined, and commonly misunderstood parameters for the characterization of mine drainage. The authors explain theoretical expressions of 3 types of alkalinity (caustic, phenolphthalein, and total) and acidity (mineral, CO2, and total). Except for rarely-invoked negative alkalinity, theoretically defined total alkalinity is closely analogous to measured alkalinity and presents few practical interpretation problems. Theoretically defined "CO 2-acidity" is closely related to most standard titration methods with an endpoint pH of 8.3 used for determining acidity in mine drainage, but it is unfortunately named because CO2 is intentionally driven off during titration of mine-drainage samples. Using the proton condition/mass- action approach and employing graphs to illustrate speciation with changes in pH, the authors explore the concept of principal components and how to assign acidity contributions to aqueous species commonly present in mine drainage. Acidity is defined in mine drainage based on aqueous speciation at the sample pH and on the capacity of these species to undergo hydrolysis to pH 8.3. Application of this definition shows that the computed acidity in mg L -1 as CaCO3 (based on pH and analytical concentrations of dissolved FeII, FeIII, Mn, and Al in mg L -1):aciditycalculated=50{1000(10-pH)+[2(FeII)+3(FeIII)]/56+2(Mn)/ 55+3(Al)/27}underestimates contributions from HSO4- and H+, but overestimates the acidity due to Fe3+ and Al3+. However, these errors tend to approximately cancel each other. It is demonstrated that "net alkalinity" is a valid mathematical construction based on theoretical definitions of alkalinity and acidity. Further, it is shown that, for most mine-drainage solutions, a useful net alkalinity value can be derived from: (1) alkalinity and acidity values based on aqueous speciation, (2) measured alkalinity minus calculated acidity, or (3) taking the negative of the value obtained in a

  11. Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

    Science.gov (United States)

    Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

    2017-10-01

    Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

  12. Les dispositifs du Net art

    OpenAIRE

    Fourmentraux, Jean-Paul

    2010-01-01

    La pratique du Net art radicalise la question du potentiel communicationnel d’un média —Internet— qui constitue tout à la fois le support technique, l’outil créatif et le dispositif social de l’œuvre. Les technologies de l’information et de la communication (TIC) placent en effet l’œuvre d’art au cœur d’une négociation socialement distribuée entre l’artiste et le public. L’article est focalisé sur cette construction collective du Net art et sur ses mises en scènes. Il montre le travail artist...

  13. Complexity Metrics for Workflow Nets

    DEFF Research Database (Denmark)

    Lassen, Kristian Bisgaard; van der Aalst, Wil M.P.

    2009-01-01

    analysts have difficulties grasping the dynamics implied by a process model. Recent empirical studies show that people make numerous errors when modeling complex business processes, e.g., about 20 percent of the EPCs in the SAP reference model have design flaws resulting in potential deadlocks, livelocks......, etc. It seems obvious that the complexity of the model contributes to design errors and a lack of understanding. It is not easy to measure complexity, however. This paper presents three complexity metrics that have been implemented in the process analysis tool ProM. The metrics are defined...... for a subclass of Petri nets named Workflow nets, but the results can easily be applied to other languages. To demonstrate the applicability of these metrics, we have applied our approach and tool to 262 relatively complex Protos models made in the context of various student projects. This allows us to validate...

  14. dotNet som multimediaplattform

    OpenAIRE

    Johansson, Glenn

    2008-01-01

    As the speed and complexity of computers have increased so have software and the expectations of users. Software development follows a straightforward evolution where complicated tasks are made easier by better tools; this repeats itself as those tasks in turn are automated. Software mechanics that were seen as revolutionary a decade ago are seen as obvious requirements that no multimedia application can be without. dotNet is the next step in line and makes it easier and faster to build softw...

  15. NET model coil test possibilities

    International Nuclear Information System (INIS)

    Erb, J.; Gruenhagen, A.; Herz, W.; Jentzsch, K.; Komarek, P.; Lotz, E.; Malang, S.; Maurer, W.; Noether, G.; Ulbricht, A.; Vogt, A.; Zahn, G.; Horvath, I.; Kwasnitza, K.; Marinucci, C.; Pasztor, G.; Sborchia, C.; Weymuth, P.; Peters, A.; Roeterdink, A.

    1987-11-01

    A single full size coil for NET/INTOR represents an investment of the order of 40 MUC (Million Unit Costs). Before such an amount of money or even more for the 16 TF coils is invested as much risks as possible must be eliminated by a comprehensive development programme. In the course of such a programme a coil technology verification test should finally prove the feasibility of NET/INTOR TF coils. This study report is almost exclusively dealing with such a verification test by model coil testing. These coils will be built out of two Nb 3 Sn-conductors based on two concepts already under development and investigation. Two possible coil arrangements are discussed: A cluster facility, where two model coils out of the two Nb 3 TF-conductors are used, and the already tested LCT-coils producing a background field. A solenoid arrangement, where in addition to the two TF model coils another model coil out of a PF-conductor for the central PF-coils of NET/INTOR is used instead of LCT background coils. Technical advantages and disadvantages are worked out in order to compare and judge both facilities. Costs estimates and the time schedules broaden the base for a decision about the realisation of such a facility. (orig.) [de

  16. NET-2 Network Analysis Program

    International Nuclear Information System (INIS)

    Malmberg, A.F.

    1974-01-01

    The NET-2 Network Analysis Program is a general purpose digital computer program which solves the nonlinear time domain response and the linearized small signal frequency domain response of an arbitrary network of interconnected components. NET-2 is capable of handling a variety of components and has been applied to problems in several engineering fields, including electronic circuit design and analysis, missile flight simulation, control systems, heat flow, fluid flow, mechanical systems, structural dynamics, digital logic, communications network design, solid state device physics, fluidic systems, and nuclear vulnerability due to blast, thermal, gamma radiation, neutron damage, and EMP effects. Network components may be selected from a repertoire of built-in models or they may be constructed by the user through appropriate combinations of mathematical, empirical, and topological functions. Higher-level components may be defined by subnetworks composed of any combination of user-defined components and built-in models. The program provides a modeling capability to represent and intermix system components on many levels, e.g., from hole and electron spatial charge distributions in solid state devices through discrete and integrated electronic components to functional system blocks. NET-2 is capable of simultaneous computation in both the time and frequency domain, and has statistical and optimization capability. Network topology may be controlled as a function of the network solution. (U.S.)

  17. predicting water levels at kainji dam using artificial neural networks

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... Apart from insufficient number of power generation plants, existing ones are ... ture and/or functional aspects of biological neural net-. Nigerian Journal of ... Their model used the radiosonde-based 700-hPa wind direction and ...

  18. The Uniframe .Net Web Service Discovery Service

    National Research Council Canada - National Science Library

    Berbeco, Robert W

    2003-01-01

    Microsoft .NET allows the creation of distributed systems in a seamless manner Within NET small, discrete applications, referred to as Web services, are utilized to connect to each other or larger applications...

  19. Long Term RadNet Quality Data

    Data.gov (United States)

    U.S. Environmental Protection Agency — This RadNet Quality Data Asset includes all data since initiation and when ERAMS was expanded to become RadNet, name changed to reflect new mission. This includes...

  20. Special Section on Coloured Petri Nets

    DEFF Research Database (Denmark)

    1998-01-01

    Special section on coloured Petri nets, their basic concepts, analysis methods, tool support and industrial applications.......Special section on coloured Petri nets, their basic concepts, analysis methods, tool support and industrial applications....

  1. Reconstruction of neutron spectra through neural networks; Reconstruccion de espectros de neutrones mediante redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E. [Cuerpo Academico de Radiobiologia, Estudios Nucleares, Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico)] e-mail: rvega@cantera.reduaz.mx [and others

    2003-07-01

    A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

  2. NetBeans IDE 8 cookbook

    CERN Document Server

    Salter, David

    2014-01-01

    If you're a Java developer of any level using NetBeans and want to learn how to get the most out of NetBeans, then this book is for you. Learning how to utilize NetBeans will provide a firm foundation for your Java application development.

  3. History-dependent stochastic Petri nets

    NARCIS (Netherlands)

    Schonenberg, H.; Sidorova, N.; Aalst, van der W.M.P.; Hee, van K.M.; Pnueli, A.; Virbitskaite, I.; Voronkov, A.

    2010-01-01

    Stochastic Petri Nets are a useful and well-known tool for performance analysis. However, an implicit assumption in the different types of Stochastic Petri Nets is the Markov property. It is assumed that a choice in the Petri net only depends on the current state and not on earlier choices. For many

  4. Putting Petri nets to work in Industry

    NARCIS (Netherlands)

    Aalst, van der W.M.P.

    1994-01-01

    Petri nets exist for over 30 years. Especially in the last decade Petri nets have been put into practive extensively. Thanks to several useful extensions and the availability of computer tools, Petri nets have become a mature tool for modelling and analysing industrial systems. This paper describes

  5. Aplicació Microsoft .Net : Hotel Spa

    OpenAIRE

    Marquès Palmer, Jordi

    2010-01-01

    Desenvolupament d'una aplicació amb Microsoft .NET, WCF, WPF, Linq2SQL, d'un Hotel Spa. Desarrollo de una aplicación con Microsoft .NET, WCF, WPF, Linq2SQL, de un Hotel Spa. Application development using Microsoft .NET, WCF, WPF, Linq2SQL, for a Spa Hotel.

  6. Delta Semantics Defined By Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt; Kyng, Morten; Madsen, Ole Lehrmann

    and the possibility of using predicates to specify state changes. In this paper a formal semantics for Delta is defined and analysed using Petri nets. Petri nets was chosen because the ideas behind Petri nets and Delta concide on several points. A number of proposals for changes in Delta, which resulted from...

  7. The K-NET - A year after

    International Nuclear Information System (INIS)

    Kinoshita, S.; Ohtani, K.; Katayama, T.

    2001-01-01

    We started to release the K-NET strong-motion data from June 1996 and about one year passed. In this article, we report the development of K-NET and some applications using the K-NET information released on the Internet. (author)

  8. 47 CFR 65.500 - Net income.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Net income. 65.500 Section 65.500... OF RETURN PRESCRIPTION PROCEDURES AND METHODOLOGIES Interexchange Carriers § 65.500 Net income. The net income methodology specified in § 65.450 shall be utilized by all interexchange carriers that are...

  9. 47 CFR 65.450 - Net income.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Net income. 65.450 Section 65.450... OF RETURN PRESCRIPTION PROCEDURES AND METHODOLOGIES Exchange Carriers § 65.450 Net income. (a) Net income shall consist of all revenues derived from the provision of interstate telecommunications services...

  10. INMARSAT-C SafetyNET

    Science.gov (United States)

    Tsunamis 406 EPIRB's National Weather Service Marine Forecasts INMARSAT-C SafetyNET Marine Forecast Offices greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE PRODUCTS VIA INMARSAT-C SafetyNET Inmarsat-C SafetyNET is an internationally adopted, automated satellite system for promulgating

  11. Neural Network for Image-to-Image Control of Optical Tweezers

    Science.gov (United States)

    Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

    2004-01-01

    A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

  12. 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.)

  13. 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.).

  14. NET 40 Generics Beginner's Guide

    CERN Document Server

    Mukherjee, Sudipta

    2012-01-01

    This is a concise, practical guide that will help you learn Generics in .NET, with lots of real world and fun-to-build examples and clear explanations. It is packed with screenshots to aid your understanding of the process. This book is aimed at beginners in Generics. It assumes some working knowledge of C# , but it isn't mandatory. The following would get the most use out of the book: Newbie C# developers struggling with Generics. Experienced C++ and Java Programmers who are migrating to C# and looking for an alternative to other generic frameworks like STL and JCF would find this book handy.

  15. Towards a Standard for Modular Petri Nets

    DEFF Research Database (Denmark)

    Kindler, Ekkart; Petrucci, Laure

    2009-01-01

    concepts could or should be subject to import and export in high-level Petri nets. In this paper, we formalise a minimal version of modular high-level Petri nets, which is based on the concepts of modular PNML. This shows that modular PNML can be formalised once a specific version of Petri net is fixed....... Moreover, we present and discuss some more advanced features of modular Petri nets that could be included in the standard. This way, we provide a formal foundation and a basis for a discussion of features to be included in the upcoming standard of a module concept for Petri nets in general and for high-level...

  16. Net metering in British Columbia : white paper

    International Nuclear Information System (INIS)

    Berry, T.

    2003-01-01

    Net metering was described as being the reverse registration of an electricity customer's revenue meter when interconnected with a utility's grid. It is a provincial policy designed to encourage small-distributed renewable power generation such as micro-hydro, solar energy, fuel cells, and larger-scale wind energy. It was noted that interconnection standards for small generation is an important issue that must be addressed. The British Columbia Utilities Commission has asked BC Hydro to prepare a report on the merits of net metering in order to support consultations on a potential net metering tariff application by the utility. This report provides information on net metering with reference to experience in other jurisdictions with net metering, and the possible costs and benefits associated with net metering from both a utility and consumer perspective. Some of the barriers and policy considerations for successful implementation of net metering were also discussed. refs., tabs., figs

  17. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  18. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  19. Pro-Nets versus No-Nets: Differences in Urban Older Adults' Predilections for Internet Use

    Science.gov (United States)

    Cresci, M. Kay; Yarandi, Hossein N.; Morrell, Roger W.

    2010-01-01

    Enthusiasm for information technology (IT) is growing among older adults. Many older adults enjoy IT and the Internet (Pro-Nets), but others have no desire to use it (No-Nets). This study found that Pro-Nets and No-Nets were different on a number of variables that might predict IT use. No-Nets were older, had less education and income, were…

  20. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  1. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  2. Bound states in string nets

    Science.gov (United States)

    Schulz, Marc Daniel; Dusuel, Sébastien; Vidal, Julien

    2016-11-01

    We discuss the emergence of bound states in the low-energy spectrum of the string-net Hamiltonian in the presence of a string tension. In the ladder geometry, we show that a single bound state arises either for a finite tension or in the zero-tension limit depending on the theory considered. In the latter case, we perturbatively compute the binding energy as a function of the total quantum dimension. We also address this issue in the honeycomb lattice where the number of bound states in the topological phase depends on the total quantum dimension. Finally, the internal structure of these bound states is analyzed in the zero-tension limit.

  3. -Net Approach to Sensor -Coverage

    Directory of Open Access Journals (Sweden)

    Fusco Giordano

    2010-01-01

    Full Text Available Wireless sensors rely on battery power, and in many applications it is difficult or prohibitive to replace them. Hence, in order to prolongate the system's lifetime, some sensors can be kept inactive while others perform all the tasks. In this paper, we study the -coverage problem of activating the minimum number of sensors to ensure that every point in the area is covered by at least sensors. This ensures higher fault tolerance, robustness, and improves many operations, among which position detection and intrusion detection. The -coverage problem is trivially NP-complete, and hence we can only provide approximation algorithms. In this paper, we present an algorithm based on an extension of the classical -net technique. This method gives an -approximation, where is the number of sensors in an optimal solution. We do not make any particular assumption on the shape of the areas covered by each sensor, besides that they must be closed, connected, and without holes.

  4. The Net Reclassification Index (NRI)

    DEFF Research Database (Denmark)

    Pepe, Margaret S.; Fan, Jing; Feng, Ziding

    2015-01-01

    The Net Reclassification Index (NRI) is a very popular measure for evaluating the improvement in prediction performance gained by adding a marker to a set of baseline predictors. However, the statistical properties of this novel measure have not been explored in depth. We demonstrate the alarming...... result that the NRI statistic calculated on a large test dataset using risk models derived from a training set is likely to be positive even when the new marker has no predictive information. A related theoretical example is provided in which an incorrect risk function that includes an uninformative...... marker is proven to erroneously yield a positive NRI. Some insight into this phenomenon is provided. Since large values for the NRI statistic may simply be due to use of poorly fitting risk models, we suggest caution in using the NRI as the basis for marker evaluation. Other measures of prediction...

  5. NETS - Danish participation. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Alsen, S. (Grontmij - Carl Bro, Glostrup (Denmark)); Theel, C. (Baltic Sea Solutions, Holeby (Denmark))

    2008-12-15

    Within the NICe-funded project 'Nordic Environmental Technology Solutions (NETS)' a new type of networking at the Nordic level was organized in order to jointly exploit the rapidly growing market potential in the environmental technology sector. The project aimed at increased and professionalized commercialization of Nordic Cleantech in energy and water business segments through 1) closer cooperation and joint marketing activities, 2) a website, 3) cleantech product information via brochures and publications 4) and participating in relevant trade fairs and other industry events. Facilitating business-to-business activities was another core task for the NETS project partners from Norway, Sweden, Finland and Denmark with the aim to encourage total solutions for combined Cleantech system offers. The project has achieved to establish a Cleantech register of 600 Nordic Cleantech companies, a network of 86 member enterprises, produced several publications and brochures for direct technology promotion and a website for direct access to company profiles and contact data. The project partners have attended 14 relevant international Cleantech trade fairs and conferences and facilitated business-to-business contacts added by capacity building offers through two company workshops. The future challenge for the project partners and Nordic Cleantech will be to coordinate the numerous efforts within the Nordic countries in order to reach concerted action and binding of member companies for reliable services, an improved visibility and knowledge exchange. With Cleantech's growing market influence and public awareness, the need to develop total solutions is increasing likewise. Marketing efforts should be encouraged cross-sectional and cross-border among the various levels of involved actors from both the public and the private sector. (au)

  6. NETS - Danish participation. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Alsen, S [Grontmij - Carl Bro, Glostrup (Denmark); Theel, C [Baltic Sea Solutions, Holeby (Denmark)

    2008-12-15

    Within the NICe-funded project 'Nordic Environmental Technology Solutions (NETS)' a new type of networking at the Nordic level was organized in order to jointly exploit the rapidly growing market potential in the environmental technology sector. The project aimed at increased and professionalized commercialization of Nordic Cleantech in energy and water business segments through 1) closer cooperation and joint marketing activities, 2) a website, 3) cleantech product information via brochures and publications 4) and participating in relevant trade fairs and other industry events. Facilitating business-to-business activities was another core task for the NETS project partners from Norway, Sweden, Finland and Denmark with the aim to encourage total solutions for combined Cleantech system offers. The project has achieved to establish a Cleantech register of 600 Nordic Cleantech companies, a network of 86 member enterprises, produced several publications and brochures for direct technology promotion and a website for direct access to company profiles and contact data. The project partners have attended 14 relevant international Cleantech trade fairs and conferences and facilitated business-to-business contacts added by capacity building offers through two company workshops. The future challenge for the project partners and Nordic Cleantech will be to coordinate the numerous efforts within the Nordic countries in order to reach concerted action and binding of member companies for reliable services, an improved visibility and knowledge exchange. With Cleantech's growing market influence and public awareness, the need to develop total solutions is increasing likewise. Marketing efforts should be encouraged cross-sectional and cross-border among the various levels of involved actors from both the public and the private sector. (au)

  7. Higher-moment measurements of net-kaon, net-charge and net-proton multiplicity distributions at STAR

    International Nuclear Information System (INIS)

    Sarkar, Amal

    2014-01-01

    In this paper, we report the measurements of the various moments, such as mean, standard deviation (σ), skewness (S) and kurtosis (κ) of the net-kaon, net-charge and net-proton multiplicity distributions at mid-rapidity in Au + Au collisions from √(s NN )=7.7 to 200 GeV with the STAR experiment at RHIC. This work has been done with the aim to locate the critical point on the QCD phase diagram. These moments and their products are related to the thermodynamic susceptibilities of conserved quantities such as net baryon number, net charge, and net strangeness as well as to the correlation length of the system which diverges in an ideal infinite thermodynamic system at the critical point. For a finite system, existing for a finite time, a non-monotonic behavior of these variables would indicate the presence of the critical point. Furthermore, we also present the moment products Sσ, κσ 2 of net-kaon, net-charge and net-proton multiplicity distributions as a function of collision centrality and energy. The energy and the centrality dependence of higher moments and their products have been compared with different models

  8. Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

    Directory of Open Access Journals (Sweden)

    Ding Fang

    2013-06-01

    Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

  9. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Application and Theory of Petri Nets

    DEFF Research Database (Denmark)

    This volume contains the proceedings of the 13th International Conference onApplication and Theory of Petri Nets, held in Sheffield, England, in June 1992. The aim of the Petri net conferences is to create a forum for discussing progress in the application and theory of Petri nets. Typically....... Balbo and W. Reisig, 18 submitted papers, and seven project papers. The submitted papers and project presentations were selectedby the programme committee and a panel of referees from a large number of submissions....

  11. TwiddleNet: Smartphones as Personal Servers

    OpenAIRE

    Gurminder, Singh; Center for the Study of Mobile Devices and Communications

    2012-01-01

    TwiddleNet uses smartphones as personal servers to enable instant content capture and dissemination for firstresponders. It supports the information sharing needs of first responders in the early stages of an emergency response operation. In TwiddleNet, content, once captured, is automatically tagged and disseminated using one of the several networking channels available in smartphones. TwiddleNet pays special attention to minimizing the equipment, network set-up time, and content...

  12. Professional Visual Basic 2010 and .NET 4

    CERN Document Server

    Sheldon, Bill; Sharkey, Kent

    2010-01-01

    Intermediate and advanced coverage of Visual Basic 2010 and .NET 4 for professional developers. If you've already covered the basics and want to dive deep into VB and .NET topics that professional programmers use most, this is your book. You'll find a quick review of introductory topics-always helpful-before the author team of experts moves you quickly into such topics as data access with ADO.NET, Language Integrated Query (LINQ), security, ASP.NET web programming with Visual Basic, Windows workflow, threading, and more. You'll explore all the new features of Visual Basic 2010 as well as all t

  13. NASA Net Zero Energy Buildings Roadmap

    Energy Technology Data Exchange (ETDEWEB)

    Pless, S.; Scheib, J.; Torcellini, P.; Hendron, B.; Slovensky, M.

    2014-10-01

    In preparation for the time-phased net zero energy requirement for new federal buildings starting in 2020, set forth in Executive Order 13514, NASA requested that the National Renewable Energy Laboratory (NREL) to develop a roadmap for NASA's compliance. NASA detailed a Statement of Work that requested information on strategic, organizational, and tactical aspects of net zero energy buildings. In response, this document presents a high-level approach to net zero energy planning, design, construction, and operations, based on NREL's first-hand experience procuring net zero energy construction, and based on NREL and other industry research on net zero energy feasibility. The strategic approach to net zero energy starts with an interpretation of the executive order language relating to net zero energy. Specifically, this roadmap defines a net zero energy acquisition process as one that sets an aggressive energy use intensity goal for the building in project planning, meets the reduced demand goal through energy efficiency strategies and technologies, then adds renewable energy in a prioritized manner, using building-associated, emission- free sources first, to offset the annual energy use required at the building; the net zero energy process extends through the life of the building, requiring a balance of energy use and production in each calendar year.

  14. Pro Agile NET Development with Scrum

    CERN Document Server

    Blankenship, Jerrel; Millett, Scott

    2011-01-01

    Pro Agile .NET Development with SCRUM guides you through a real-world ASP.NET project and shows how agile methodology is put into practice. There is plenty of literature on the theory behind agile methodologies, but no book on the market takes the concepts of agile practices and applies these in a practical manner to an end-to-end ASP.NET project, especially the estimating, requirements and management aspects of a project. Pro Agile .NET Development with SCRUM takes you through the initial stages of a project - gathering requirements and setting up an environment - through to the development a

  15. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  16. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-01

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

  17. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  18. 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)

  19. Experiments and simulation of a net closing mechanism for tether-net capture of space debris

    Science.gov (United States)

    Sharf, Inna; Thomsen, Benjamin; Botta, Eleonora M.; Misra, Arun K.

    2017-10-01

    This research addresses the design and testing of a debris containment system for use in a tether-net approach to space debris removal. The tether-net active debris removal involves the ejection of a net from a spacecraft by applying impulses to masses on the net, subsequent expansion of the net, the envelopment and capture of the debris target, and the de-orbiting of the debris via a tether to the chaser spacecraft. To ensure a debris removal mission's success, it is important that the debris be successfully captured and then, secured within the net. To this end, we present a concept for a net closing mechanism, which we believe will permit consistently successful debris capture via a simple and unobtrusive design. This net closing system functions by extending the main tether connecting the chaser spacecraft and the net vertex to the perimeter and around the perimeter of the net, allowing the tether to actuate closure of the net in a manner similar to a cinch cord. A particular embodiment of the design in a laboratory test-bed is described: the test-bed itself is comprised of a scaled-down tether-net, a supporting frame and a mock-up debris. Experiments conducted with the facility demonstrate the practicality of the net closing system. A model of the net closure concept has been integrated into the previously developed dynamics simulator of the chaser/tether-net/debris system. Simulations under tether tensioning conditions demonstrate the effectiveness of the closure concept for debris containment, in the gravity-free environment of space, for a realistic debris target. The on-ground experimental test-bed is also used to showcase its utility for validating the dynamics simulation of the net deployment, and a full-scale automated setup would make possible a range of validation studies of other aspects of a tether-net debris capture mission.

  20. Feature to prototype transition in neural networks

    Science.gov (United States)

    Krotov, Dmitry; Hopfield, John

    Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

  1. Neural network segmentation of magnetic resonance images

    International Nuclear Information System (INIS)

    Frederick, B.

    1990-01-01

    Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover, once trained, they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network; by varying imaging parameters, MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. This paper reports that a neural network classifier for image segmentation was implanted on a Sun 4/60, and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter, white matter, cerebrospinal fluid, bone, and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities, and the image was subsequently segmented by the classifier

  2. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  3. Price smarter on the Net.

    Science.gov (United States)

    Baker, W; Marn, M; Zawada, C

    2001-02-01

    Companies generally have set prices on the Internet in two ways. Many start-ups have offered untenably low prices in a rush to capture first-mover advantage. Many incumbents have simply charged the same prices on-line as they do off-line. Either way, companies are missing a big opportunity. The fundamental value of the Internet lies not in lowering prices or making them consistent but in optimizing them. After all, if it's easy for customers to compare prices on the Internet, it's also easy for companies to track customers' behavior and adjust prices accordingly. The Net lets companies optimize prices in three ways. First, it lets them set and announce prices with greater precision. Different prices can be tested easily, and customers' responses can be collected instantly. Companies can set the most profitable prices, and they can tap into previously hidden customer demand. Second, because it's so easy to change prices on the Internet, companies can adjust prices in response to even small fluctuations in market conditions, customer demand, or competitors' behavior. Third, companies can use the clickstream data and purchase histories that it collects through the Internet to segment customers quickly. Then it can offer segment-specific prices or promotions immediately. By taking full advantage of the unique possibilities afforded by the Internet to set prices with precision, adapt to changing circumstances quickly, and segment customers accurately, companies can get their pricing right. It's one of the ultimate drivers of e-business success.

  4. HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity

    Data.gov (United States)

    National Aeronautics and Space Administration — The Human Appropriation of Net Primary Productivity (HANPP) as a Percentage of Net Primary Product (NPP) portion of the HANPP Collection represents a map identifying...

  5. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  6. Reduction rules for reset/inhibitor nets

    NARCIS (Netherlands)

    Verbeek, H.M.W.; Wynn, M.T.; Aalst, van der W.M.P.; Hofstede, ter A.H.M.

    2010-01-01

    Reset/inhibitor nets are Petri nets extended with reset arcs and inhibitor arcs. These extensions can be used to model cancellation and blocking. A reset arc allows a transition to remove all tokens from a certain place when the transition fires. An inhibitor arc can stop a transition from being

  7. Verifying generalized soundness for workflow nets

    NARCIS (Netherlands)

    Hee, van K.M.; Oanea, O.I.; Sidorova, N.; Voorhoeve, M.; Virbitskaite, I.; Voronkov, A.

    2007-01-01

    We improve the decision procedure from [10] for the problem of generalized soundness of workflow nets. A workflow net is generalized sound iff every marking reachable from an initial marking with k tokens on the initial place terminates properly, i.e. it can reach a marking with k tokens on the

  8. A Brief Introduction to Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1997-01-01

    Coloured Petri Nets (CP-nets or CPN) is a graphical oriented language for design, specification, simulation and verification of systems. It is in particular well- suited for systems in which communication, synchronisation and resource sharing are important. Typical examples of application areas a...

  9. Net analyte signal based statistical quality control

    NARCIS (Netherlands)

    Skibsted, E.T.S.; Boelens, H.F.M.; Westerhuis, J.A.; Smilde, A.K.; Broad, N.W.; Rees, D.R.; Witte, D.T.

    2005-01-01

    Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is

  10. 47 CFR 69.302 - Net investment.

    Science.gov (United States)

    2010-10-01

    ...) Investment in Accounts 2002, 2003 and to the extent such inclusions are allowed by this Commission, Account... Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES (CONTINUED) ACCESS CHARGES Apportionment of Net Investment § 69.302 Net investment. (a) Investment in Accounts 2001, 1220 and Class B Rural...

  11. Asynchronous stream processing with S-Net

    NARCIS (Netherlands)

    Grelck, C.; Scholz, S.-B.; Shafarenko, A.

    2010-01-01

    We present the rationale and design of S-Net, a coordination language for asynchronous stream processing. The language achieves a near-complete separation between the application code, written in any conventional programming language, and the coordination/communication code written in S-Net. Our

  12. Dynamic response of the thermometric net radiometer

    Science.gov (United States)

    J. D. Wilson; W. J. Massman; G. E. Swaters

    2009-01-01

    We computed the dynamic response of an idealized thermometric net radiometer, when driven by an oscillating net longwave radiation intended roughly to simulate rapid fluctuations of the radiative environment such as might be expected during field use of such devices. The study was motivated by curiosity as to whether non-linearity of the surface boundary conditions...

  13. 78 FR 72451 - Net Investment Income Tax

    Science.gov (United States)

    2013-12-02

    ... Net Investment Income Tax AGENCY: Internal Revenue Service (IRS), Treasury. ACTION: Withdrawal of... computation of net investment income. The regulations affect individuals, estates, and trusts whose incomes meet certain income thresholds. DATES: The proposed rule published December 5, 2012 (77 FR 72612), is...

  14. 10 CFR 436.20 - Net savings.

    Science.gov (United States)

    2010-01-01

    ... ENERGY ENERGY CONSERVATION FEDERAL ENERGY MANAGEMENT AND PLANNING PROGRAMS Methodology and Procedures for Life Cycle Cost Analyses § 436.20 Net savings. For a retrofit project, net savings may be found by subtracting life cycle costs based on the proposed project from life cycle costs based on not having it. For a...

  15. Net Neutrality and Inflation of Traffic

    NARCIS (Netherlands)

    Peitz, M.; Schütt, F.

    2015-01-01

    Under strict net neutrality Internet service providers (ISPs) are required to carry data without any differentiation and at no cost to the content provider. We provide a simple framework with a monopoly ISP to evaluate different net neutrality rules. Content differs in its sensitivity to delay.

  16. The Net Neutrality Debate: The Basics

    Science.gov (United States)

    Greenfield, Rich

    2006-01-01

    Rich Greenfield examines the basics of today's net neutrality debate that is likely to be an ongoing issue for society. Greenfield states the problems inherent in the definition of "net neutrality" used by Common Cause: "Network neutrality is the principle that Internet users should be able to access any web content they choose and…

  17. Net neutrality and inflation of traffic

    NARCIS (Netherlands)

    Peitz, M.; Schütt, Florian

    Under strict net neutrality Internet service providers (ISPs) are required to carry data without any differentiation and at no cost to the content provider. We provide a simple framework with a monopoly ISP to evaluate the short-run effects of different net neutrality rules. Content differs in its

  18. 27 CFR 4.37 - Net contents.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Net contents. 4.37 Section 4.37 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU, DEPARTMENT OF THE TREASURY LIQUORS LABELING AND ADVERTISING OF WINE Labeling Requirements for Wine § 4.37 Net...

  19. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  20. Net energy gain from DT fusion

    International Nuclear Information System (INIS)

    Buende, R.

    1985-01-01

    The net energy which can be gained from an energy raw material by means of a certain conversion system is deduced as the figure-of-merit which adequately characterizes the net energy balance of utilizing an energy source. This potential net energy gain is determined for DT fusion power plants. It is represented as a function of the degree of exploitation of the energy raw material lithium ore and is compared with the net energy which can be gained with LW and FBR power plants by exploiting uranium ore. The comparison clearly demonstrates the net energetic advantage of DT fusion. A sensitivity study shows that this holds even if the energy expenditure for constructing and operating is drastically increased

  1. Discrete, continuous, and hybrid petri nets

    CERN Document Server

    David, René

    2004-01-01

    Petri nets do not designate a single modeling formalism. In fact, newcomers to the field confess sometimes to be a little puzzled by the diversity of formalisms that are recognized under this "umbrella". Disregarding some extensions to the theoretical modeling capabilities, and looking at the level of abstraction of the formalisms, Condition/Event, Elementary, Place/Transition, Predicate/Transition, Colored, Object Oriented... net systems are frequently encountered in the literature. On the other side, provided with appropriate interpretative extensions, Controled Net Systems, Marking Diagrams (the Petri net generalization of State Diagrams), or the many-many variants in which time can be explicitly incorporated -Time(d), Deterministic, (Generalized) Stochastic, Fuzzy...- are defined. This represents another way to define practical formalisms that can be obtained by the "cro- product" of the two mentioned dimensions. Thus Petri nets constitute a modeling paradigm, understandable in a broad sense as "the total...

  2. Net charge fluctuations and local charge compensation

    International Nuclear Information System (INIS)

    Fu Jinghua

    2006-01-01

    We propose net charge fluctuation as a measure of local charge correlation length. It is demonstrated that, in terms of a schematic multiperipheral model, net charge fluctuation satisfies the same Quigg-Thomas relation as satisfied by charge transfer fluctuation. Net charge fluctuations measured in finite rapidity windows depend on both the local charge correlation length and the size of the observation window. When the observation window is larger than the local charge correlation length, the net charge fluctuation only depends on the local charge correlation length, while forward-backward charge fluctuations always have strong dependence on the observation window size. Net charge fluctuations and forward-backward charge fluctuations measured in the present heavy ion experiments show characteristic features similar to those from multiperipheral models. But the data cannot all be understood within this simple model

  3. Net energy from nuclear power

    International Nuclear Information System (INIS)

    Rotty, R.M.; Perry, A.M.; Reister, D.B.

    1975-11-01

    An analysis of net energy from nuclear power plants is dependent on a large number of variables and assumptions. The energy requirements as they relate to reactor type, concentration of uranium in the ore, enrichment tails assays, and possible recycle of uranium and plutonium were examined. Specifically, four reactor types were considered: pressurized water reactor, boiling water reactor, high temperature gas-cooled reactor, and heavy water reactor (CANDU). The energy requirements of systems employing both conventional (current) ores with uranium concentration of 0.176 percent and Chattanooga Shales with uranium concentration of 0.006 percent were determined. Data were given for no recycle, uranium recycle only, and uranium plus plutonium recycle. Starting with the energy requirements in the mining process and continuing through fuel reprocessing and waste storage, an evaluation of both electrical energy requirements and thermal energy requirements of each process was made. All of the energy, direct and indirect, required by the processing of uranium in order to produce electrical power was obtained by adding the quantities for the individual processes. The energy inputs required for the operation of a nuclear power system for an assumed life of approximately 30 years are tabulated for nine example cases. The input requirements were based on the production of 197,100,000 MWH(e), i.e., the operation of a 1000 MW(e) plant for 30 years with an average plant factor of 0.75. Both electrical requirements and thermal energy requirements are tabulated, and it should be emphasized that both quantities are needed. It was found that the electricity generated far exceeded the energy input requirements for all the cases considered

  4. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  5. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  6. Net energy benefits of carbon nanotube applications

    International Nuclear Information System (INIS)

    Zhai, Pei; Isaacs, Jacqueline A.; Eckelman, Matthew J.

    2016-01-01

    Highlights: • Life cycle net energy benefits are examined. • CNT-enabled and the conventional technologies are compared. • Flash memory with CNT switches show significant positive net energy benefit. • Lithium-ion batteries with MWCNT cathodes show positive net energy benefit. • Lithium-ion batteries with SWCNT anodes tend to exhibit negative net energy benefit. - Abstract: Implementation of carbon nanotubes (CNTs) in various applications can reduce material and energy requirements of products, resulting in energy savings. However, processes for the production of carbon nanotubes (CNTs) are energy-intensive and can require extensive purification. In this study, we investigate the net energy benefits of three CNT-enabled technologies: multi-walled CNT (MWCNT) reinforced cement used as highway construction material, single-walled CNT (SWCNT) flash memory switches used in cell phones and CNT anodes and cathodes used in lithium-ion batteries used in electric vehicles. We explore the avoided or additional energy requirement in the manufacturing and use phases and estimate the life cycle net energy benefits for each application. Additional scenario analysis and Monte Carlo simulation of parameter uncertainties resulted in probability distributions of net energy benefits, indicating that net energy benefits are dependent on the application with confidence intervals straddling the breakeven line in some cases. Analysis of simulation results reveals that SWCNT switch flash memory and MWCNT Li-ion battery cathodes have statistically significant positive net energy benefits (α = 0.05) and SWCNT Li-ion battery anodes tend to have negative net energy benefits, while positive results for MWCNT-reinforced cement were significant only under an efficient CNT production scenario and a lower confidence level (α = 0.1).

  7. Pro visual C++/CLI and the net 35 platform

    CERN Document Server

    Fraser, Stephen

    2008-01-01

    Pro Visual C++/CLI and the .NET 3.5 Platform is about writing .NET applications using C++/CLI. While readers are learning the ins and outs of .NET application development, they will also be learning the syntax of C++, both old and new to .NET. Readers will also gain a good understanding of the .NET architecture. This is truly a .NET book applying C++ as its development language not another C++ syntax book that happens to cover .NET.

  8. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  9. Application of deconvolution interferometry with both Hi-net and KiK-net data

    Science.gov (United States)

    Nakata, N.

    2013-12-01

    Application of deconvolution interferometry to wavefields observed by KiK-net, a strong-motion recording network in Japan, is useful for estimating wave velocities and S-wave splitting in the near surface. Using this technique, for example, Nakata and Snieder (2011, 2012) found changed in velocities caused by Tohoku-Oki earthquake in Japan. At the location of the borehole accelerometer of each KiK-net station, a velocity sensor is also installed as a part of a high-sensitivity seismograph network (Hi-net). I present a technique that uses both Hi-net and KiK-net records for computing deconvolution interferometry. The deconvolved waveform obtained from the combination of Hi-net and KiK-net data is similar to the waveform computed from KiK-net data only, which indicates that one can use Hi-net wavefields for deconvolution interferometry. Because Hi-net records have a high signal-to-noise ratio (S/N) and high dynamic resolution, the S/N and the quality of amplitude and phase of deconvolved waveforms can be improved with Hi-net data. These advantages are especially important for short-time moving-window seismic interferometry and deconvolution interferometry using later coda waves.

  10. KONVERGENSI DALAM PROGRAM NET CITIZEN JOURNALISM

    Directory of Open Access Journals (Sweden)

    Rhafidilla Vebrynda

    2017-06-01

    Full Text Available Di dalam artikel ini, peneliti ingin melihat perkembangan teknologi di Indonesia sebagai sebuah peluang untuk menjalankan sebuah program berita berbasis video kiriman masyarakat. Perkembangan teknologi tersebut adalah teknologi penyiaran, teknologi sosial media dan teknologi dalam proses produksi sebuah video. Di Indonesia, jumlah televisi semakin banyak. Setiap stasiun televisi harus bersaing untuk dapat bertahan hidup. Net TV merupakan sebuah stasiun televisi baru di Indonesia yang harus memiliki berbagai program unggulan baru agar dapat bersaing dengan televisi lainnya yang sudah ada. Net TV menggunakan berbagai platform media untuk menjalankan program Net Citizen Journalism (Net CJ. Penggunaan berbagai platform media dikenal dengan istilah multiplatform dan secara teoritis dikenal dengan istilah konvergensi. Konvergensi yaitu saat meleburnya domain-domain dalam berbagai media komunikasi. Artikel ini menggunakan metode studi kasus untuk melihat bagaimana konvergensi terjadi dalam proses pengelolaan program Net CJ. Teknik pengumpulan data adalah dengan wawancara mendalam, observasi dan studi dokumen. Wawancara mendalam dilakukan dari tiga sudut pandang yaitu dari pengelola program, pengguna/audience dan pengamat media. Penelitian ini menemukan bahwa dengan menggunakan berbagai platform media yang fungsinya berbeda, memiliki satu tujuan yang sama yaitu untuk menjalankan program Net CJ. Adapun berbagai platform dalam proses produksi program yaitu tayangan TV konvensional, streaming TV, website, aplikasi Net CJ, facebook, twitter, instagram dan path. Konvergensi media dijalankan dalam dua proses, yaitu proses produksi dan proses promosi program berita.

  11. Net Neutrality: Media Discourses and Public Perception

    Directory of Open Access Journals (Sweden)

    Christine Quail

    2010-01-01

    Full Text Available This paper analyzes media and public discourses surrounding net neutrality, with particular attention to public utility philosophy, from a critical perspective. The article suggests that further public education about net neutrality would be beneficial. The first portion of this paper provides a survey of the existing literature surrounding net neutrality, highlighting the contentious debate between market-based and public interest perspectives. In order to contextualize the debate, an overview of public utility philosophy is provided, shedding light on how the Internet can be conceptualized as a public good. Following this discussion, an analysis of mainstream media is presented, exploring how the media represents the issue of net neutrality and whether or not the Internet is discussed through the lens of public utility. To further examine how the net neutrality debate is being addressed, and to see the potential impacts of media discourses on the general public, the results of a focus group are reported and analyzed. Finally, a discussion assesses the implications of the net neutrality debate as presented through media discourses, highlighting the future of net neutrality as an important policy issue.

  12. Visualizing deep neural network by alternately image blurring and deblurring.

    Science.gov (United States)

    Wang, Feng; Liu, Haijun; Cheng, Jian

    2018-01-01

    Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Construction of a Piezoresistive Neural Sensor Array

    Science.gov (United States)

    Carlson, W. B.; Schulze, W. A.; Pilgrim, P. M.

    1996-01-01

    The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors

  14. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  15. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  16. Symmetric Cryptosystem Based on Petri Net

    Directory of Open Access Journals (Sweden)

    Hussein ‎ A. Lafta

    2017-12-01

    Full Text Available In this wok, a novel approach based on ordinary Petri net is used to generate private key . The reachability marking  of petri net is used as encryption/decryption key to provide more complex key . The same ordinary Petri Nets models  are used for the sender(encryption and  the receiver(decryption.The plaintext has been permutated  using  look-up table ,and XOR-ed with key to generate cipher text

  17. Characterizing behavioural congruences for Petri nets

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Priese, Lutz; Sassone, Vladimiro

    1995-01-01

    We exploit a notion of interface for Petri nets in order to design a set of net combinators. For such a calculus of nets, we focus on the behavioural congruences arising from four simple notions of behaviour, viz., traces, maximal traces, step, and maximal step traces, and from the corresponding...... four notions of bisimulation, viz., weak and weak step bisimulation and their maximal versions. We characterize such congruences via universal contexts and via games, providing in such a way an understanding of their discerning powers....

  18. Model and calculations for net infiltration

    International Nuclear Information System (INIS)

    Childs, S.W.; Long, A.

    1992-01-01

    In this paper a conceptual model for calculating net infiltration is developed and implemented. It incorporates the following important factors: viability of climate for the next 10,000 years, areal viability of net infiltration, and important soil/plant factors that affect the soil water budget of desert soils. Model results are expressed in terms of occurrence probabilities for time periods. In addition the variability of net infiltration is demonstrated both for change with time and differences among three soil/hydrologic units present at the site modeled

  19. Mars MetNet Mission Payload Overview

    Science.gov (United States)

    Harri, A.-M.; Haukka, H.; Alexashkin, S.; Guerrero, H.; Schmidt, W.; Genzer, M.; Vazquez, L.

    2012-09-01

    A new kind of planetary exploration mission for Mars is being developed in collaboration between the Finnish Meteorological Institute (FMI), Lavochkin Association (LA), Space Research Institute (IKI) and Institutio Nacional de Tecnica Aerospacial (INTA). The Mars MetNet mission [1] is based on a new semi-hard landing vehicle called MetNet Lander (MNL). The scientific payload of the Mars MetNet Precursor mission is divided into three categories: Atmospheric instruments, Optical devices and Composition and structure devices. Each of the payload instruments will provide crucial scientific data about the Martian atmospheric phenomena.

  20. The net neutrality debate on Twitter

    Directory of Open Access Journals (Sweden)

    Wolf J. Schünemann

    2015-12-01

    Full Text Available The internet has been seen as a medium that empowers individual political actors in relation to established political elites and media gatekeepers. The present article discusses this “net empowerment hypothesis” and tests it empirically by analysing Twitter communication on the regulation of net neutrality. We extracted 503.839 tweets containing #NetNeutrality posted between January and March 2015 and analysed central developments and the network structure of the debate. The empirical results show that traditional actors from media and politics still maintain a central role.

  1. Visual Studio 2010 and NET 4 Six-in-One

    CERN Document Server

    Novak, Istvan; Granicz, Adam

    2010-01-01

    Complete coverage of all key .NET 4 and Visual Studio 2010 languages and technologies. .NET 4 is Microsoft's latest version of their core programming platform, and Visual Studio 2010 is the toolset that helps write .NET 4 applications. This comprehensive resource offers one-stop shopping for all you need to know to get productive with .NET 4. Experienced author and .NET guru Mitchel Sellers reviews all the important new features of .NET 4, including .NET charting and ASP.NET charting, ASP.NET dynamic data and jQuery, and the addition of F# as a supported package language. The expansive coverag

  2. Automated Understanding of Financial Statements Using Neural Networks and Semantic Grammars

    OpenAIRE

    Markovitch, J. S.

    1995-01-01

    This article discusses how neural networks and semantic grammars may be used to locate and understand financial statements embedded in news stories received from on-line news wires. A neural net is used to identify where in the news story a financial statement appears to begin. A grammar then is applied to this text in an effort to extract specific facts from the financial statement. Applying grammars to financial statements presents unique parsing problems since the dollar amounts of financi...

  3. 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...

  4. Net Pay Estimator | Alaska Division of Retirement and Benefits

    Science.gov (United States)

    Benefits > Net Pay Estimator Online Counselor Scheduler Empower Retirement Account Info Online myRnB Accessibility Net Pay Estimator Click here for the Retiree Net Pay Estimator? The net pay estimator is a useful tool to estimate your net pay under different salaries, federal withholding tax exemptions, and

  5. Studies on the suitability of HDPE material for gill nets

    OpenAIRE

    Subramania Pillai, N.; Boopendranath, M.R.; Kunjipalu, K.K.

    1989-01-01

    The suitability of HDPE yarn and HDPE twine in place of nylon for gill nets has been studied. As regards total catch nylon gill net is found to be better than HDPE nets. However, statistical analysis of the catch in respect of quality fishes shows that HDPE yarn nets are equally efficient as nylon nets.

  6. The Petri Net Markup Language : concepts, technology, and tools

    NARCIS (Netherlands)

    Billington, J.; Christensen, S.; Hee, van K.M.; Kindler, E.; Kummer, O.; Petrucci, L.; Post, R.D.J.; Stehno, C.; Weber, M.; Aalst, van der W.M.P.; Best, E.

    2003-01-01

    The Petri Net Markup Language (PNML) is an XML-based interchange format for Petri nets. In order to support different versions of Petri nets and, in particular, future versions of Petri nets, PNML allows the definition of Petri net types.Due to this flexibility, PNML is a starting point for a

  7. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    Kolanoski, H.

    1995-04-01

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  8. Learning and Generalisation in Neural Networks with Local Preprocessing

    OpenAIRE

    Kutsia, Merab

    2007-01-01

    We study learning and generalisation ability of a specific two-layer feed-forward neural network and compare its properties to that of a simple perceptron. The input patterns are mapped nonlinearly onto a hidden layer, much larger than the input layer, and this mapping is either fixed or may result from an unsupervised learning process. Such preprocessing of initially uncorrelated random patterns results in the correlated patterns in the hidden layer. The hidden-to-output mapping of the net...

  9. High-Performance Neural Networks for Visual Object Classification

    OpenAIRE

    Cireşan, Dan C.; Meier, Ueli; Masci, Jonathan; Gambardella, Luca M.; Schmidhuber, Jürgen

    2011-01-01

    We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better ...

  10. Deep nets vs expert designed features in medical physics: An IMRT QA case study.

    Science.gov (United States)

    Interian, Yannet; Rideout, Vincent; Kearney, Vasant P; Gennatas, Efstathios; Morin, Olivier; Cheung, Joey; Solberg, Timothy; Valdes, Gilmer

    2018-03-30

    The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features. Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06. Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts. © 2018 American Association of Physicists in Medicine.

  11. Mars MetNet Mission Status

    Science.gov (United States)

    Harri, A.-M.; Aleksashkin, S.; Arruego, I.; Schmidt, W.; Genzer, M.; Vazquez, L.; Haukka, H.; Palin, M.; Nikkanen, T.

    2015-10-01

    New kind of planetary exploration mission for Mars is under development in collaboration between the Finnish Meteorological Institute (FMI), Lavochkin Association (LA), Space Research Institute (IKI) and Institutio Nacional de Tecnica Aerospacial (INTA). The Mars MetNet mission is based on a new semihard landing vehicle called MetNet Lander (MNL). The scientific payload of the Mars MetNet Precursor [1] mission is divided into three categories: Atmospheric instruments, Optical devices and Composition and structure devices. Each of the payload instruments will provide significant insights in to the Martian atmospheric behavior. The key technologies of the MetNet Lander have been qualified and the electrical qualification model (EQM) of the payload bay has been built and successfully tested.

  12. Mars MetNet Precursor Mission Status

    Science.gov (United States)

    Harri, A.-M.; Aleksashkin, S.; Guerrero, H.; Schmidt, W.; Genzer, M.; Vazquez, L.; Haukka, H.

    2013-09-01

    We are developing a new kind of planetary exploration mission for Mars in collaboration between the Finnish Meteorological Institute (FMI), Lavochkin Association (LA), Space Research Institute (IKI) and Institutio Nacional de Tecnica Aerospacial (INTA). The Mars MetNet mission is based on a new semi-hard landing vehicle called MetNet Lander (MNL). The scientific payload of the Mars MetNet Precursor [1] mission is divided into three categories: Atmospheric instruments, Optical devices and Composition and structure devices. Each of the payload instruments will provide significant insights in to the Martian atmospheric behavior. The key technologies of the MetNet Lander have been qualified and the electrical qualification model (EQM) of the payload bay has been built and successfully tested.

  13. RadNet Air Quality (Deployable) Data

    Data.gov (United States)

    U.S. Environmental Protection Agency — RadNet Deployable Monitoring is designed to collect radiological and meteorological information and data asset needed to establish the impact of radiation levels on...

  14. Versatile Wireless Data Net, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed R many will be MEMS devices. The net enables coordinated, efficient transmission of measurement signals; self test metrics, and environmental metrics to...

  15. Integrating phenotype ontologies with PhenomeNET

    KAUST Repository

    Rodriguez-Garcia, Miguel Angel; Gkoutos, Georgios V.; Schofield, Paul N.; Hoehndorf, Robert

    2017-01-01

    in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization

  16. The Uniframe .Net Web Service Discovery Service

    National Research Council Canada - National Science Library

    Berbeco, Robert W

    2003-01-01

    ...) and registered with Internet Information Server (IIS), and can be applied in numerous fashions This project uses the NET capabilities to create a distributed discovery service (called as UNWSDS...

  17. Elliptic net and its cryptographic application

    Science.gov (United States)

    Muslim, Norliana; Said, Mohamad Rushdan Md

    2017-11-01

    Elliptic net is a generalization of elliptic divisibility sequence and in cryptography field, most cryptographic pairings that are based on elliptic curve such as Tate pairing can be improved by applying elliptic nets algorithm. The elliptic net is constructed by using n dimensional array of values in rational number satisfying nonlinear recurrence relations that arise from elliptic divisibility sequences. The two main properties hold in the recurrence relations are for all positive integers m>n, hm +nhm -n=hm +1hm -1hn2-hn +1hn -1hm2 and hn divides hm whenever n divides m. In this research, we discuss elliptic divisibility sequence associated with elliptic nets based on cryptographic perspective and its possible research direction.

  18. A Lightweight TwiddleNet Portal

    National Research Council Canada - National Science Library

    Rimikis, Antonios M

    2008-01-01

    TwiddleNet is a distributed architecture of personal servers that harnesses the power of the mobile devices, enabling real time information and file sharing of multiple data types from commercial-off-the-shelf platforms...

  19. MMPM - Mars MetNet Precursor Mission

    Science.gov (United States)

    Harri, A.-M.; Schmidt, W.; Pichkhadze, K.; Linkin, V.; Vazquez, L.; Uspensky, M.; Polkko, J.; Genzer, M.; Lipatov, A.; Guerrero, H.; Alexashkin, S.; Haukka, H.; Savijarvi, H.; Kauhanen, J.

    2008-09-01

    We are developing a new kind of planetary exploration mission for Mars - MetNet in situ observation network based on a new semi-hard landing vehicle called the Met-Net Lander (MNL). The eventual scope of the MetNet Mission is to deploy some 20 MNLs on the Martian surface using inflatable descent system structures, which will be supported by observations from the orbit around Mars. Currently we are working on the MetNet Mars Precursor Mission (MMPM) to deploy one MetNet Lander to Mars in the 2009/2011 launch window as a technology and science demonstration mission. The MNL will have a versatile science payload focused on the atmospheric science of Mars. Detailed characterization of the Martian atmospheric circulation patterns, boundary layer phenomena, and climatology cycles, require simultaneous in-situ measurements by a network of observation posts on the Martian surface. The scientific payload of the MetNet Mission encompasses separate instrument packages for the atmospheric entry and descent phase and for the surface operation phase. The MetNet mission concept and key probe technologies have been developed and the critical subsystems have been qualified to meet the Martian environmental and functional conditions. Prototyping of the payload instrumentation with final dimensions was carried out in 2003-2006.This huge development effort has been fulfilled in collaboration between the Finnish Meteorological Institute (FMI), the Russian Lavoschkin Association (LA) and the Russian Space Research Institute (IKI) since August 2001. Currently the INTA (Instituto Nacional de Técnica Aeroespacial) from Spain is also participating in the MetNet payload development. To understand the behavior and dynamics of the Martian atmosphere, a wealth of simultaneous in situ observations are needed on varying types of Martian orography, terrain and altitude spanning all latitudes and longitudes. This will be performed by the Mars MetNet Mission. In addition to the science aspects the

  20. Portable Rule Extraction Method for Neural Network Decisions Reasoning

    Directory of Open Access Journals (Sweden)

    Darius PLIKYNAS

    2005-08-01

    Full Text Available Neural network (NN methods are sometimes useless in practical applications, because they are not properly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN decisions explanatory power. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophistication of the rule extraction technique etc. The authors propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool in the financial problems' domain and SOM (input data space clusterization. The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

  1. Processing of chromatic information in a deep convolutional neural network.

    Science.gov (United States)

    Flachot, Alban; Gegenfurtner, Karl R

    2018-04-01

    Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

  2. .NET 4.5 parallel extensions

    CERN Document Server

    Freeman, Bryan

    2013-01-01

    This book contains practical recipes on everything you will need to create task-based parallel programs using C#, .NET 4.5, and Visual Studio. The book is packed with illustrated code examples to create scalable programs.This book is intended to help experienced C# developers write applications that leverage the power of modern multicore processors. It provides the necessary knowledge for an experienced C# developer to work with .NET parallelism APIs. Previous experience of writing multithreaded applications is not necessary.

  3. Mastering AngularJD for .NET developers

    CERN Document Server

    Majid, Mohammad Wadood

    2015-01-01

    This book is envisioned for traditional developers and programmers who want to develop client-side applications using the AngularJS framework and ASP.NET Web API 2 with Visual Studio. .NET developers who have already built web applications or web services and who have a fundamental knowledge of HTML, JavaScript, and CSS and want to explore single-page applications will also find this guide useful. Basic knowledge of AngularJS would be helpful.

  4. CCS - and its relationship to net theory

    DEFF Research Database (Denmark)

    Nielsen, Mogens

    1987-01-01

    In this paper we give a short introduction to Milner's Calculus for Communicating Systems - a paradigm for concurrent computation. We put special emphasis on the basic concepts and tools from the underlying "algebraic approach", and their relationship to the approach to concurrency within net the...... theory. Furthermore, we provide an operational version of the language CCS with "true concurrency" in the sense of net theory, and a discussion of the possible use of such a marriage of the two theories of concurrency....

  5. Control of Petri Nets by finite automata

    Energy Technology Data Exchange (ETDEWEB)

    Burkhard, H D

    1983-01-01

    Petri Nets are considered where the firings are controlled by finite automata. The control may be distributed to various automata working over disjoint sets of transitions. To avoid deadlocks and conflicts for the whole system the distribution of control must be organised in an appropriate manner. The existence of deadlocks and conflicts is shown to be undecidable in general, but conflict resolving and deadlock free controls can be constructed for given nets. 10 references.

  6. Inferring Phylogenetic Networks Using PhyloNet.

    Science.gov (United States)

    Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

    2018-07-01

    PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

  7. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  8. Visual Studio 2013 and .NET 4.5 expert cookbook

    CERN Document Server

    Sur, Abhishek

    2014-01-01

    If you are a Visual Studio 2013 or .NET developer who would like to sharpen your existing skill set and adapt to new .NET technologies, this is the book for you. A basic understanding of .NET and C# is required.

  9. HANPP Collection: Global Patterns in Net Primary Productivity (NPP)

    Data.gov (United States)

    National Aeronautics and Space Administration — The Global Patterns in Net Primary Productivity (NPP) portion of the Human Appropriation of Net Primary Productivity (HANPP) Collection maps the net amount of solar...

  10. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  11. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  12. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  13. Radio frequency interference mitigation using deep convolutional neural networks

    Science.gov (United States)

    Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

    2017-01-01

    We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

  14. ASP.NET web API build RESTful web applications and services on the .NET framework

    CERN Document Server

    Kanjilal, Joydip

    2013-01-01

    This book is a step-by-step, practical tutorial with a simple approach to help you build RESTful web applications and services on the .NET framework quickly and efficiently.This book is for ASP.NET web developers who want to explore REST-based services with C# 5. This book contains many real-world code examples with explanations whenever necessary. Some experience with C# and ASP.NET 4 is expected.

  15. Fuzzy-neural network in the automatic detection and volumetry of the spleen on spiral CT scans

    International Nuclear Information System (INIS)

    Heitmann, K.R.; Mainz Univ.; Rueckert, S.; Heussel, C.P.; Thelen, M.; Kauczor, H.U.; Uthmann, T.

    2000-01-01

    Purpose: To assess spleen segmentation and volumetry in spiral CT scans with and without pathological changes of splenic tissue. Methods: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. Results: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding>100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. Conclusions: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans. (orig.) [de

  16. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  17. SCYNet. Testing supersymmetric models at the LHC with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Bechtle, Philip; Belkner, Sebastian; Hamer, Matthias [Universitaet Bonn, Bonn (Germany); Dercks, Daniel [Universitaet Hamburg, Hamburg (Germany); Keller, Tim; Kraemer, Michael; Sarrazin, Bjoern; Schuette-Engel, Jan; Tattersall, Jamie [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany)

    2017-10-15

    SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. (orig.)

  18. Pro ASP.NET 4 in VB 2010

    CERN Document Server

    MacDonald, Matthew; Freeman, Adam; Szpuszta, Mario; Agarwal, Vidya Vrat

    2010-01-01

    ASP.NET 4 is the latest version of Microsoft's revolutionary ASP.NET technology. It is the principal standard for creating dynamic web pages on the Windows platform. Pro ASP.NET 4 in VB 2010 raises the bar for high-quality, practical advice on learning and deploying Microsoft's dynamic web solution. This new edition is updated with everything you need to come to grips with the latest version of ASP.NET, including coverage of ASP.NET MVC, ASP.NET AJAX 4, ASP.NET Dynamic Data, and Silverlight 3. Seasoned .NET professionals Matthew MacDonald and Mario Szpuszta explain how you can get the most fro

  19. 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.

  20. 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.

  1. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  2. Active Neural Localization

    OpenAIRE

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

    2018-01-01

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

  3. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  4. Near-Net Forging Technology Demonstration Program

    Science.gov (United States)

    Hall, I. Keith

    1996-01-01

    Significant advantages in specific mechanical properties, when compared to conventional aluminum (Al) alloys, make aluminum-lithium (Al-Li) alloys attractive candidate materials for use in cryogenic propellant tanks and dry bay structures. However, the cost of Al-Li alloys is typically five times that of 2219 aluminum. If conventional fabrication processes are employed to fabricate launch vehicle structure, the material costs will restrict their utilization. In order to fully exploit the potential cost and performance benefits of Al-Li alloys, it is necessary that near-net manufacturing methods be developed to off-set or reduce raw material costs. Near-net forging is an advanced manufacturing method that uses elevated temperature metal movement (forging) to fabricate a single piece, near-net shape, structure. This process is termed 'near-net' because only a minimal amount of post-forge machining is required. The near-net forging process was developed to reduce the material scrap rate (buy-to-fly ratio) and fabrication costs associated with conventional manufacturing methods. The goal for the near-net forging process, when mature, is to achieve an overall cost reduction of approximately 50 percent compared with conventional manufacturing options for producing structures fabricated from Al-Li alloys. This NASA Marshall Space Flight Center (MSFC) sponsored program has been a part of a unique government / industry partnership, coordinated to develop and demonstrate near-net forging technology. The objective of this program was to demonstrate scale-up of the near-net forging process. This objective was successfully achieved by fabricating four integrally stiffened, 170- inch diameter by 20-inch tall, Al-Li alloy 2195, Y-ring adapters. Initially, two 2195 Al-Li ingots were converted and back extruded to produce four cylindrical blockers. Conventional ring rolling of the blockers was performed to produce ring preforms, which were then contour ring rolled to produce

  5. Ontological Annotation with WordNet

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Tratz, Stephen C.; Gregory, Michelle L.; Chappell, Alan R.; Whitney, Paul D.; Posse, Christian; Paulson, Patrick R.; Baddeley, Bob; Hohimer, Ryan E.; White, Amanda M.

    2006-06-06

    Semantic Web applications require robust and accurate annotation tools that are capable of automating the assignment of ontological classes to words in naturally occurring text (ontological annotation). Most current ontologies do not include rich lexical databases and are therefore not easily integrated with word sense disambiguation algorithms that are needed to automate ontological annotation. WordNet provides a potentially ideal solution to this problem as it offers a highly structured lexical conceptual representation that has been extensively used to develop word sense disambiguation algorithms. However, WordNet has not been designed as an ontology, and while it can be easily turned into one, the result of doing this would present users with serious practical limitations due to the great number of concepts (synonym sets) it contains. Moreover, mapping WordNet to an existing ontology may be difficult and requires substantial labor. We propose to overcome these limitations by developing an analytical platform that (1) provides a WordNet-based ontology offering a manageable and yet comprehensive set of concept classes, (2) leverages the lexical richness of WordNet to give an extensive characterization of concept class in terms of lexical instances, and (3) integrates a class recognition algorithm that automates the assignment of concept classes to words in naturally occurring text. The ensuing framework makes available an ontological annotation platform that can be effectively integrated with intelligence analysis systems to facilitate evidence marshaling and sustain the creation and validation of inference models.

  6. Automating Ontological Annotation with WordNet

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Tratz, Stephen C.; Gregory, Michelle L.; Chappell, Alan R.; Whitney, Paul D.; Posse, Christian; Paulson, Patrick R.; Baddeley, Bob L.; Hohimer, Ryan E.; White, Amanda M.

    2006-01-22

    Semantic Web applications require robust and accurate annotation tools that are capable of automating the assignment of ontological classes to words in naturally occurring text (ontological annotation). Most current ontologies do not include rich lexical databases and are therefore not easily integrated with word sense disambiguation algorithms that are needed to automate ontological annotation. WordNet provides a potentially ideal solution to this problem as it offers a highly structured lexical conceptual representation that has been extensively used to develop word sense disambiguation algorithms. However, WordNet has not been designed as an ontology, and while it can be easily turned into one, the result of doing this would present users with serious practical limitations due to the great number of concepts (synonym sets) it contains. Moreover, mapping WordNet to an existing ontology may be difficult and requires substantial labor. We propose to overcome these limitations by developing an analytical platform that (1) provides a WordNet-based ontology offering a manageable and yet comprehensive set of concept classes, (2) leverages the lexical richness of WordNet to give an extensive characterization of concept class in terms of lexical instances, and (3) integrates a class recognition algorithm that automates the assignment of concept classes to words in naturally occurring text. The ensuing framework makes available an ontological annotation platform that can be effectively integrated with intelligence analysis systems to facilitate evidence marshaling and sustain the creation and validation of inference models.

  7. Review of FEWS NET Biophysical Monitoring Requirements

    Science.gov (United States)

    Ross, K. W.; Brown, Molly E.; Verdin, J.; Underwood, L. W.

    2009-01-01

    The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users: it focused upon operational requirements to determine additional useful remote sensing data and; subsequently, beneficial FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world, enabling a robust set of professional perspectives to be gathered and analyzed rapidly. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard: that high resolution remote sensing products continue to be in demand, and that rainfall and vegetation products were valued as data that provide actionable food security information.

  8. The net charge at interfaces between insulators

    International Nuclear Information System (INIS)

    Bristowe, N C; Littlewood, P B; Artacho, Emilio

    2011-01-01

    The issue of the net charge at insulating oxide interfaces is briefly reviewed with the ambition of dispelling myths of such charges being affected by covalency and related charge density effects. For electrostatic analysis purposes, the net charge at such interfaces is defined by the counting of discrete electrons and core ion charges, and by the definition of the reference polarization of the separate, unperturbed bulk materials. The arguments are illustrated for the case of a thin film of LaAlO 3 over SrTiO 3 in the absence of free carriers, for which the net charge is exactly 0.5e per interface formula unit, if the polarization response in both materials is referred to zero bulk values. Further consequences of the argument are extracted for structural and chemical alterations of such interfaces, in which internal rearrangements are distinguished from extrinsic alterations (changes of stoichiometry, redox processes), only the latter affecting the interfacial net charge. The arguments are reviewed alongside the proposal of Stengel and Vanderbilt (2009 Phys. Rev. B 80 241103) of using formal polarization values instead of net interfacial charges, based on the interface theorem of Vanderbilt and King-Smith (1993 Phys. Rev. B 48 4442-55). Implications for non-centrosymmetric materials are discussed, as well as for interfaces for which the charge mismatch is an integer number of polarization quanta. (viewpoint)

  9. Activation of PAD4 in NET formation

    Directory of Open Access Journals (Sweden)

    Amanda eRohrbach

    2012-11-01

    Full Text Available Peptidyl arginine deiminases, or PADs, convert arginine residues to the non-ribosomally encoded amino acid citrulline in a variety of protein substrates. PAD4 is expressed in granulocytes and is essential for the formation of neutrophil extracellular traps (NETs via PAD4-mediated histone citrullination. Citrullination of histones is thought to promote NET formation by inducing chromatin decondensation and facilitating the expulsion of chromosomal DNA that is coated with antimicrobial molecules. Numerous stimuli have been reported to lead to PAD4 activation and NET formation. However, how this signaling process proceeds and how PAD4 becomes activated in cells is largely unknown. Herein, we describe the various stimuli and signaling pathways that have been implicated in PAD4 activation and NET formation, including the role of reactive oxygen species generation. To provide a foundation for the above discussion, we first describe PAD4 structure and function, and how these studies led to the development of PAD-specific inhibitors. A comprehensive survey of the receptors and signaling pathways that regulate PAD4 activation will be important for our understanding of innate immunity, and the identification of signaling intermediates in PAD4 activation may also lead to the generation of pharmaceuticals to target NET-related pathogenesis.

  10. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  11. Generating Seismograms with Deep Neural Networks

    Science.gov (United States)

    Krischer, L.; Fichtner, A.

    2017-12-01

    The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

  12. StarNet: An application of deep learning in the analysis of stellar spectra

    Science.gov (United States)

    Kielty, Collin; Bialek, Spencer; Fabbro, Sebastien; Venn, Kim; O'Briain, Teaghan; Jahandar, Farbod; Monty, Stephanie

    2018-06-01

    In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact. These surveys provide a homogeneous database of stellar spectra that are ideal for machine learning applications. In this poster, we present StarNet: a convolutional neural network model applied to the analysis of both SDSS-III APOGEE DR13 and synthetic stellar spectra. When trained on synthetic spectra alone, the calculated stellar parameters (temperature, surface gravity, and metallicity) are of excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. While StarNet was developed using the APOGEE observed spectra and corresponding ASSeT synthetic grid, we suggest that this technique is applicable to other spectral resolutions, spectral surveys, and wavelength regimes. As a demonstration of this, we present a StarNet model trained on lower resolution, R=6000, IR synthetic spectra, describing the spectra delivered by Gemini/NIFS and the forthcoming Gemini/GIRMOS instrument (PI Sivanandam, UToronto). Preliminary results suggest that the stellar parameters determined from this low resolution StarNet model are comparable in precision to the high-resolution APOGEE results. The success of StarNet at lower resolution can be attributed to (1) a large training set of synthetic spectra (N ~200,000) with a priori stellar labels, and (2) the use of the entire spectrum in the solution rather than a few weighted windows, which are common methods in other spectral analysis tools (e.g. FERRE or The Cannon). Remaining challenges in our StarNet applications include rectification, continuum normalization, and wavelength coverage. Solutions to these problems could be used to guide decisions made in the development of future spectrographs, spectroscopic surveys, and data reduction pipelines, such as for the future MSE.

  13. Adaptive Near-Optimal Multiuser Detection Using a Stochastic and Hysteretic Hopfield Net Receiver

    Directory of Open Access Journals (Sweden)

    Gábor Jeney

    2003-01-01

    Full Text Available This paper proposes a novel adaptive MUD algorithm for a wide variety (practically any kind of interference limited systems, for example, code division multiple access (CDMA. The algorithm is based on recently developed neural network techniques and can perform near optimal detection in the case of unknown channel characteristics. The proposed algorithm consists of two main blocks; one estimates the symbols sent by the transmitters, the other identifies each channel of the corresponding communication links. The estimation of symbols is carried out either by a stochastic Hopfield net (SHN or by a hysteretic neural network (HyNN or both. The channel identification is based on either the self-organizing feature map (SOM or the learning vector quantization (LVQ. The combination of these two blocks yields a powerful real-time detector with near optimal performance. The performance is analyzed by extensive simulations.

  14. .net core application lifecycle on Openshift

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    # .net core application lifecycle on Openshift I will show an example of a lifecycle of an OpenShift application with an emphasis on the continuous integration and deployment. The application compatible with [.net Standard](https://docs.microsoft.com/en-us/dotnet/standard/net-standard) can be easily deployed on OpenShift using [Source2Image](https://docs.openshift.com/enterprise/3.0/architecture/core_concepts/builds_and_image_streams.html#source-build) functionality, which doesn't require developers to maintain docker images of the application. I will also present how to efficiently integrate this feature into GitLab pipelines with an automated deployment of the "review" environment, as one its parts.

  15. Towards Self-Managed Executable Petri Nets

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius; Zhang, Weishan; Ingstrup, Mads

    2008-01-01

    An issue in self-managed systems is that different abstractions and programming models are used on different architectural layers, leading to systems that are harder to build and understand. To alleviate this, we introduce a self-management approach which combines high-level Petri nets...... with the capability of distributed communication among nets. Organized in a three-layer goal management, change management, and component control architecture this allows for self-management in distributed systems. We validate the approach through the Flamenco/CPN middleware that allows for self-management of service......-oriented pervasive computing systems through the runtime interpretation of colored Petri nets. The current work focuses on the change management and component control layers....

  16. Implementation of CLP4NET in Bulgaria

    International Nuclear Information System (INIS)

    Naydenova, I.; Pironkov, L.; Filipov, A.; Petrova, T.; Tsochev, G.; Ganev, I.

    2016-01-01

    Full text: Networking solutions (networks of excellence, communities of practice, knowledge portals, etc.) are recognized as effective tools for nuclear training and education services, transfer of good practices, knowledge and programmes, and knowledge management. In addition, the e-learning is recommended as a state of the art and cost effective approach for supplementing the traditional face to face training and education programmes. Thus, the Cyber Learning Platform for Nuclear Education and Training (CLP4NET) was implemented into the Kozloduy Nuclear Power Plant (KNPP) Training System. Based on the experience of KNPP, the CLP4NET was implemented also at the College of Energy and Electronics (CEE), Technical University of Sofia (TU-Sofia), providing an appropriate tool for further establishment of a National Nuclear Network of Competency. The current study is focused mainly on specific issues and lessons learned during the installation of CLP4NET at the CEE, TU-Sofia. (author

  17. Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT.

    Science.gov (United States)

    Han, Yoseob; Ye, Jong Chul

    2018-06-01

    X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

  18. ObamaNet: Photo-realistic lip-sync from text

    OpenAIRE

    Kumar, Rithesh; Sotelo, Jose; Kumar, Kundan; de Brebisson, Alexandre; Bengio, Yoshua

    2017-01-01

    We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to ...

  19. Figure-Ground Organization Emerges in a Deep Net with a Feedback Loop

    OpenAIRE

    Zipser, Karl

    2015-01-01

    We used a deep net to model how object-specific activation at the high levels of a hierarchical neural network could be fed back to modify representations at lower levels. We first identified a subset of nodes in the uppermost hidden layer that were preferentially activated by images of people. We then ran a procedure to recursively modify an image so as to increase activation of the 'person-selective' nodes. The image was modified by choosing a rectangular region (of random size and position...

  20. Neural estimation of kinetic rate constants from dynamic PET-scans

    DEFF Research Database (Denmark)

    Fog, Torben L.; Nielsen, Lars Hupfeldt; Hansen, Lars Kai

    1994-01-01

    A feedforward neural net is trained to invert a simple three compartment model describing the tracer kinetics involved in the metabolism of [18F]fluorodeoxyglucose in the human brain. The network can estimate rate constants from positron emission tomography sequences and is about 50 times faster ...

  1. Deep Belief Nets for Topic Modeling

    DEFF Research Database (Denmark)

    Maaløe, Lars; Arngren, Morten; Winther, Ole

    2015-01-01

    -formative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep be-lief nets (DBN) that both find low-dimensional latent representations for documents....... Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an on-line publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance...

  2. State Space Methods for Timed Petri Nets

    DEFF Research Database (Denmark)

    Christensen, Søren; Jensen, Kurt; Mailund, Thomas

    2001-01-01

    it possible to condense the usually infinite state space of a timed Petri net into a finite condensed state space without loosing analysis power. The second method supports on-the-fly verification of certain safety properties of timed systems. We discuss the application of the two methods in a number......We present two recently developed state space methods for timed Petri nets. The two methods reconciles state space methods and time concepts based on the introduction of a global clock and associating time stamps to tokens. The first method is based on an equivalence relation on states which makes...

  3. A Lightweight TwiddleNet Portal

    Science.gov (United States)

    2008-03-01

    dimensions of 2.8 x 0.7 x 4.6 inches make for a very good tool for TwiddleNet missions. Its Lithium -ion battery with 1200 mAh energy gives approximately...Wireless Connectivity IrDA, Bluetooth, IEEE 802.11b Battery Lithium ion Approximate Dimensions (in) 2.8 x 0.7 x 4.6 Weight (oz) 6.2 ROM 128 MB...designed to exploit the multiple networking modalities available in the current generation of smartphones . TwiddleNet enables well-organized and well

  4. Introducing NET 40 With Visual Studio 2010

    CERN Document Server

    Mackey, A

    2010-01-01

    Microsoft is introducing a large number of changes to the way that the .NET Framework operates. Familiar technologies are being altered, best practices replaced, and developer methodologies adjusted. Many developers find it hard to keep up with the pace of change across .NET's ever-widening array of technologies. You may know what's happening in C#, but how about the Azure cloud? How is that going to affect your work? What are the limitations of the new pLINQ syntax? What you need is a roadmap. A guide to help you see the innovations that matter and to give you a head start on the opportunitie

  5. The Schmehausen cable net cooling tower

    International Nuclear Information System (INIS)

    Schlaich, J.; Mayr, G.; Weber, P.; Jasch, E.

    1976-01-01

    The prototype of a large cable net shell as a natural-draught cooling tower for the THTR-300 is presented. Results of wind tunnel tests and calculations are given, and the capacity is discussed. Design features of the main components are presented in illustrations and are described with regard to the construction process of the cooling tower. Finally, it is shown that the cable net cooling tower is a suitable construction for large dimensions and caving-in or seismic areas. (orig./HP) [de

  6. Pro ASP.NET 4 CMS

    CERN Document Server

    Harris, Alan

    2010-01-01

    To be a successful ASP.NET 4 developer, you need to know how to apply the vast array of new functionality available in the latest release of the .NET 4 Framework and Visual Studio 2010. This book will immerse you in a variety of advanced topics, including architecting different application data tiers, memory caching paradigms, data mining, and search engine optimization. Working through step-by-step exercises using P/LINQ, DLR, MEF, MVC, IronPython, Axum, and Ajax, you will learn a variety of approaches to building each of the key application tiers common to all web solutions. Using a proven t

  7. Net shape powder processing of aluminium

    International Nuclear Information System (INIS)

    Schaffer, G.B.

    2000-01-01

    The increasing interest in light weight materials coupled to the need for cost-effective processing have combined to create a significant opportunity for aluminium powder metallurgy. Net shape processing of aluminium using the classical press-and-sinter powder metallurgy technique is a unique and important metal-forming method which is cost effective in producing complex parts at, or very close to, final dimensions. This paper provides an overview of the net shape powder processing of aluminium. Current research is critically reviewed and the future potential is briefly considered

  8. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

    International Nuclear Information System (INIS)

    Willoughby, Twyla R.; Starkschall, George; Janjan, Nora A.; Rosen, Isaac I.

    1996-01-01

    Purpose: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Methods and Materials: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Results: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. Conclusions: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit

  9. Net Gain: A New Method for Preventing Malaria Deaths | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    A finely spun net could prevent as many as one-third of all child deaths in Africa, reports IDRC's new publication, Net Gain. Studies conducted in Gambia, Ghana, and Kenya show that the insecticide-treated mosquito net reduced the mortality rate of children under 5 years of age by up to 63 percent. Net Gain reviews and ...

  10. Mars MetNet Mission Status

    Science.gov (United States)

    Harri, Ari-Matti; Aleksashkin, Sergei; Arruego, Ignacio; Schmidt, Walter; Genzer, Maria; Vazquez, Luis; Haukka, Harri

    2015-04-01

    New kind of planetary exploration mission for Mars is under development in collaboration between the Finnish Meteorological Institute (FMI), Lavochkin Association (LA), Space Research Institute (IKI) and Institutio Nacional de Tecnica Aerospacial (INTA). The Mars MetNet mission is based on a new semi-hard landing vehicle called MetNet Lander (MNL). The scientific payload of the Mars MetNet Precursor [1] mission is divided into three categories: Atmospheric instruments, Optical devices and Composition and structure devices. Each of the payload instruments will provide significant insights in to the Martian atmospheric behavior. The key technologies of the MetNet Lander have been qualified and the electrical qualification model (EQM) of the payload bay has been built and successfully tested. 1. MetNet Lander The MetNet landing vehicles are using an inflatable entry and descent system instead of rigid heat shields and parachutes as earlier semi-hard landing devices have used. This way the ratio of the payload mass to the overall mass is optimized. The landing impact will burrow the payload container into the Martian soil providing a more favorable thermal environment for the electronics and a suitable orientation of the telescopic boom with external sensors and the radio link antenna. It is planned to deploy several tens of MNLs on the Martian surface operating at least partly at the same time to allow meteorological network science. 2. Scientific Payload The payload of the two MNL precursor models includes the following instruments: Atmospheric instruments: 1. MetBaro Pressure device 2. MetHumi Humidity device 3. MetTemp Temperature sensors Optical devices: 1. PanCam Panoramic 2. MetSIS Solar irradiance sensor with OWLS optical wireless system for data transfer 3. DS Dust sensor The descent processes dynamic properties are monitored by a special 3-axis accelerometer combined with a 3-axis gyrometer. The data will be sent via auxiliary beacon antenna throughout the

  11. Markets, voucher subsidies and free nets combine to achieve high bed net coverage in rural Tanzania

    Directory of Open Access Journals (Sweden)

    Gerrets Rene PM

    2008-06-01

    Full Text Available Abstract Background Tanzania has a well-developed network of commercial ITN retailers. In 2004, the government introduced a voucher subsidy for pregnant women and, in mid 2005, helped distribute free nets to under-fives in small number of districts, including Rufiji on the southern coast, during a child health campaign. Contributions of these multiple insecticide-treated net delivery strategies existing at the same time and place to coverage in a poor rural community were assessed. Methods Cross-sectional household survey in 6,331 members of randomly selected 1,752 households of 31 rural villages of Demographic Surveillance System in Rufiji district, Southern Tanzania was conducted in 2006. A questionnaire was administered to every consenting respondent about net use, treatment status and delivery mechanism. Findings Net use was 62.7% overall, 87.2% amongst infants (0 to1 year, 81.8% amongst young children (>1 to 5 years, 54.5% amongst older children (6 to 15 years and 59.6% amongst adults (>15 years. 30.2% of all nets had been treated six months prior to interview. The biggest source of nets used by infants was purchase from the private sector with a voucher subsidy (41.8%. Half of nets used by young children (50.0% and over a third of those used by older children (37.2% were obtained free of charge through the vaccination campaign. The largest source of nets amongst the population overall was commercial purchase (45.1% use and was the primary means for protecting adults (60.2% use. All delivery mechanisms, especially sale of nets at full market price, under-served the poorest but no difference in equity was observed between voucher-subsidized and freely distributed nets. Conclusion All three delivery strategies enabled a poor rural community to achieve net coverage high enough to yield both personal and community level protection for the entire population. Each of them reached their relevant target group and free nets only temporarily

  12. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  13. CM Net heterogeneous local network of microcomputers

    International Nuclear Information System (INIS)

    Gutche, R.Kh.

    1988-01-01

    Realization of the CM Net heterogeneous local network for the PDP-11 type computers with the RT-11 operational system control is described. The main advantage of the network is the realization of virtual disks in general form including a system unit. the network can be used everywhere, where the problem on computer connection by simple interfaces is stated. 6 refs

  14. NetBench. Automated Network Performance Testing

    CERN Document Server

    Cadeddu, Mattia

    2016-01-01

    In order to evaluate the operation of high performance routers, CERN has developed the NetBench software to run benchmarking tests by injecting various traffic patterns and observing the network devices behaviour in real-time. The tool features a modular design with a Python based console used to inject traffic and collect the results in a database, and a web user

  15. 77 FR 72611 - Net Investment Income Tax

    Science.gov (United States)

    2012-12-05

    ... avoid double taxation of net investment income and the taxation of amounts distributed to charities. The.... The proposed regulations affect individuals, estates, and trusts. This document also contains a notice... calculated correctly. The likely respondents are individuals, estates, and trusts. Estimated total annual...

  16. 78 FR 72393 - Net Investment Income Tax

    Science.gov (United States)

    2013-12-02

    ... of Article 23 (Relief from Double Taxation) of the 2006 United States Model Income Tax Convention... taxation of ANSTs at the lowest individual tax rate. Furthermore, section 646 treats all distributions, to... of Net Investment Income. The regulations affect individuals, estates, and trusts whose incomes meet...

  17. Teaching Petri Nets Using P3

    Science.gov (United States)

    Gasevic, Dragan; Devedzic, Vladan

    2004-01-01

    This paper presents Petri net software tool P3 that is developed for training purposes of the Architecture and organization of computers (AOC) course. The P3 has the following features: graphical modeling interface, interactive simulation by single and parallel (with previous conflict resolution) transition firing, two well-known Petri net…

  18. Petri Nets as Models of Linear Logic

    DEFF Research Database (Denmark)

    Engberg, Uffe Henrik; Winskel, Glynn

    1990-01-01

    The chief purpose of this paper is to appraise the feasibility of Girad's linear logic as a specification language for parallel processes. To this end we propose an interpretation of linear logic in Petri nets, with respect to which we investigate the expressive power of the logic...

  19. Analysis of Squid Net Fisheries Business Production

    Directory of Open Access Journals (Sweden)

    Herna Octivia Damayanti

    2017-03-01

    Full Text Available Squid net is one of alternatives to replace trawl net in Pati regency. The purposes of the research are 1 to determine the influence factors, 2 to analyze the return to scale, 3 to analyze cost and return.The research location in Juwana Subdistrict particularly Bakaran Kulon, Dukutalit, Bajomulyo and Bendar Villages. The research conducted on October 2015 to June 2016. The number of final samples was 36, while the formulation of management strategies used 15 samples by snowball sampling. Data analysis techniques used 1 Cobb Douglas production function, 2 revenue-cost ratio analysis. The results of the research are 1 significant inputs for production factor are long trip, Solar fuel, the number of crew and lights. 2 the return to scale of squid net bussiness in Juwana subdistrict Pati regency is -0.231 means decreasing to scale. 3 the R/C ratio of scenario II more profitable for squid net crews than  scenario I.

  20. Distributed S-Net: design and implementation

    NARCIS (Netherlands)

    Grelck, C.; Julku, J.; Penczek, F.; Morazan, M.

    2009-01-01

    S-Net is a declarative coordination language and component technology aimed at modern multi-core/many-core architectures and systems-on-chip. It builds on the concept of stream processing to structure networks of communicating asynchronous components, which can be implemented using a conventional

  1. Distributed evaluation of stochastic Petri nets

    NARCIS (Netherlands)

    Bell, A.; Buchholz, Peter; Lehnert, Ralf; Pioro, Micha

    2004-01-01

    In this paper we present on the distributed performance evaluation and model checking of systems specified by stochastic Petri nets. The approaches discussed rely on an explicit state-space generation and target at the usage of clusters of workstations. We present results for systems with several

  2. Verification of Timed-Arc Petri Nets

    DEFF Research Database (Denmark)

    Jacobsen, Lasse; Jacobsen, Morten; Møller, Mikael Harkjær

    2011-01-01

    of interesting theoretical properties distinguishing them from other time extensions of Petri nets. We shall give an overview of the recent theory developed in the verification of TAPN extended with features like read/transport arcs, timed inhibitor arcs and age invariants. We will examine in detail...

  3. 29 CFR 1926.105 - Safety nets.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 8 2010-07-01 2010-07-01 false Safety nets. 1926.105 Section 1926.105 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR (CONTINUED) SAFETY AND HEALTH REGULATIONS FOR CONSTRUCTION Personal Protective and Life Saving Equipment § 1926.105...

  4. Integrating phenotype ontologies with PhenomeNET

    KAUST Repository

    Rodriguez-Garcia, Miguel Angel

    2017-12-19

    Background Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Results Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies. Conclusions PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.

  5. Average Costs versus Net Present Value

    NARCIS (Netherlands)

    E.A. van der Laan (Erwin); R.H. Teunter (Ruud)

    2000-01-01

    textabstractWhile the net present value (NPV) approach is widely accepted as the right framework for studying production and inventory control systems, average cost (AC) models are more widely used. For the well known EOQ model it can be verified that (under certain conditions) the AC approach gives

  6. BioNet Digital Communications Framework

    Science.gov (United States)

    Gifford, Kevin; Kuzminsky, Sebastian; Williams, Shea

    2010-01-01

    BioNet v2 is a peer-to-peer middleware that enables digital communication devices to talk to each other. It provides a software development framework, standardized application, network-transparent device integration services, a flexible messaging model, and network communications for distributed applications. BioNet is an implementation of the Constellation Program Command, Control, Communications and Information (C3I) Interoperability specification, given in CxP 70022-01. The system architecture provides the necessary infrastructure for the integration of heterogeneous wired and wireless sensing and control devices into a unified data system with a standardized application interface, providing plug-and-play operation for hardware and software systems. BioNet v2 features a naming schema for mobility and coarse-grained localization information, data normalization within a network-transparent device driver framework, enabling of network communications to non-IP devices, and fine-grained application control of data subscription band width usage. BioNet directly integrates Disruption Tolerant Networking (DTN) as a communications technology, enabling networked communications with assets that are only intermittently connected including orbiting relay satellites and planetary rover vehicles.

  7. OK-Net Arable online knowledge platform

    DEFF Research Database (Denmark)

    Rasmussen, Ilse Ankjær; Jensen, Allan Leck; Jørgensen, Margit Styrbæk

    2017-01-01

    The complexity of organic farming requires farmers to have a very high level of knowledge and skills, but exchange on organic farming techniques remains limited. In order to increase productivity and quality in organic arable cropping in Europe, the thematic network OK-Net Arable under Horizon 20...

  8. Musings on Sketches, Artists, and Mosquito Nets

    Centers for Disease Control (CDC) Podcasts

    2014-09-23

    Byron Breedlove reads his essay Musings on Sketches, Artists, and Mosquito Nets about the art of James Whistler and the transmission of vector borne diseases.  Created: 9/23/2014 by National Center for Emerging and Zoonotic Infectious Diseases (NCEZID).   Date Released: 10/20/2014.

  9. 25 CFR 502.16 - Net revenues.

    Science.gov (United States)

    2010-04-01

    ... 25 Indians 2 2010-04-01 2010-04-01 false Net revenues. 502.16 Section 502.16 Indians NATIONAL INDIAN GAMING COMMISSION, DEPARTMENT OF THE INTERIOR GENERAL PROVISIONS DEFINITIONS OF THIS CHAPTER § 502... consistent with professional accounting pronouncements, excluding management fees. [74 FR 36932, July 27...

  10. Net escapement of Antartic krill in trawls

    DEFF Research Database (Denmark)

    Krafft, B.A.; Krag, Ludvig Ahm; Herrmann, Bent

    This document describes the aims and methodology of a three year project (commenced in 2012) entitled Net Escapement of Antarctic krill in Trawls (NEAT). The study will include a morphology based mathematical modeling (FISHSELECT) of different sex and maturity groups of Antarctic krill (Euphausia...

  11. 27 CFR 5.38 - Net contents.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Net contents. 5.38 Section 5.38 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU, DEPARTMENT OF THE TREASURY LIQUORS LABELING AND ADVERTISING OF DISTILLED SPIRITS Labeling Requirements for Distilled...

  12. Statistical physics, neural networks, brain studies

    International Nuclear Information System (INIS)

    Toulouse, G.

    1999-01-01

    An overview of some aspects of a vast domain, located at the crossroads of physics, biology and computer science is presented: (1) During the last fifteen years, physicists advancing along various pathways have come into contact with biology (computational neurosciences) and engineering (formal neural nets). (2) This move may actually be viewed as one component in a larger picture. A prominent trend of recent years, observable over many countries, has been the establishment of interdisciplinary centers devoted to the study of: cognitive sciences; natural and artificial intelligence; brain, mind and behaviour; perception and action; learning and memory; robotics; man-machine communication, etc. What are the promising lines of development? What opportunities for physicists? An attempt will be made to address such questions and related issues

  13. A neural network to identify neutral mesons

    International Nuclear Information System (INIS)

    Lefevre, F.; Lautridou, P.; Marques, M.; Matulewicz, T.; Ostendorf, R.; Schutz, Y.

    1994-01-01

    Both π 0 and η mesons decay long before they can reach a detector. They predominantly decay by emission of two photons, and are identified by constructing the invariant mass of the photons. Misidentified mesons result from ambiguity in associating photons. Our work tries to select which pair is the most likely to be a physical one rather than a chance one. We first designed a Hopfield neural net, but all the activities converged rapidly towards zero except the highest one. To improve the solution we slew down the computation in order to let the network explore several states and to impose activities to converge towards one for all selected pairs. This was achieved by adding links connecting each cell to itself. The network performance is all the more interesting that the solid angle covered by the detector is greater than 15%. (D.L.). 5 refs

  14. PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

    Science.gov (United States)

    Lutich, Andrey

    2017-07-01

    This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

  15. RadNet Radiological Air Monitoring Network

    International Nuclear Information System (INIS)

    Scott Telofski, J.; Askren, D.R.; Petko, Ch.M.; Fraass, R.G.

    2010-01-01

    The United States Environmental Protection Agency operates a national environmental radiation monitoring program called RadNet. RadNet monitors airborne particulates, precipitation, milk, and drinking water for radiation levels. The primary purpose of the original program in the 1950's and 1960's was to collect and analyze samples in various media to assess the effects of radioactive fallout from above-ground nuclear weapon testing. As above-ground testing diminished in the 1970's, the program, especially the air network, became critical in evaluating effects of other types of nuclear incidents, such as the nuclear reactor accident at Chernobyl, as well as monitoring trends in environmental radioactive contamination. The value of rapid data collection subsequent to such incidents led to the consideration of developing air monitors with radiation detectors and telecommunication equipment for real-time radiation measurement. The strengthened United States homeland security posture after 2001 led to production and installation of the current real-time RadNet air monitors. There are now 118 stationary, continuously operating air monitoring stations and 40 mobile air monitors for site specific monitoring. The stationary air monitors include radiation detectors, meteorological sensors, a high-volume air sampler, and communication devices for hourly data transfers. When unusual levels are detected, scientists download a full sodium iodide detector spectrum for analysis. The real-time data collected by RadNet stationary systems permit rapid identification and quantification of airborne nuclides with sufficient sensitivity to provide critical information to help determine protective actions. The data also may help to rapidly refine long-range radioactive plume models and estimate exposure to the population. This paper provides an overview of the airborne particulate monitoring conducted during above-ground nuclear weapon testing, summarizes the uses of data from the program

  16. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  17. Petri neural network model for the effect of controlled thermomechanical process parameters on the mechanical properties of HSLA steels

    Energy Technology Data Exchange (ETDEWEB)

    Datta, S.

    1999-10-01

    The effect of composition and controlled thermomechanical process parameters on the mechanical properties of HSLA steels is modelled using the Widrow-Hoff's concept of training a neural net with feed-forward topology by applying Rumelhart's back propagation type algorithm for supervised learning, using a Petri like net structure. The data used are from laboratory experiments as well as from the published literature. The results from the neural network are found to be consistent and in good agreement with the experimented results. (author)

  18. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  19. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  20. 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....

  1. Use of bed nets and factors that influence bed net use among Jinuo Ethnic Minority in southern China.

    Science.gov (United States)

    Xu, Jian-wei; Liao, Yuan-mei; Liu, Hui; Nie, Ren-hua; Havumaki, Joshua

    2014-01-01

    Insecticide-treated nets (ITNs) are an integral part of vector control recommendations for malaria elimination in China. This study investigated the extent to which bed nets were used and which factors influence bed net use among Jinuo Ethnic Minority in China-Myanmar-Laos border areas. This study combined a quantitative household questionnaire survey and qualitative semi-structured in-depth interviews (SDI). Questionnaires were administered to 352 heads of households. SDIs were given to 20 key informants. The bed net to person ratio was 1∶2.1 (i.e., nearly one net for every two people), however only 169 (48.0%) households owned at least one net and 623 (47.2%) residents slept under bed nets the prior night. The percentages of residents who regularly slept under nets (RSUN) and slept under nets the prior night (SUNPN) were similar (48.0% vs. 47.2%, P>0.05), however the percentage correct use of nets (CUN) was significantly lower (34.5%, Pcash income per person (ACIP) was an independent factor that influenced bed net use (PHigh bed net availability does not necessarily mean higher coverage or bed net use. Household income, house type and knowledge of the ability of bed nets to prevent malaria are all independent factors that influence bed net use among Jinuo Ethnic Minority.

  2. 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

  3. State-of-the-art of applications of neural networks in the nuclear industry

    International Nuclear Information System (INIS)

    Zwingelstein, G.; Masson, M.H.

    1990-01-01

    Artificial neural net models have been extensively studied for many years in various laboratories to try to simulate with computer programs the human brain performances. The first applications were developed in the fields of speech and image recognition. The aims of these studies were mainly to classify rapidly patterns corrupted by noises or partly missing. Neural networks with the development of new net topologies and algorithms and parallel computing hardwares and softwares are to-day very promising for applications in many industries. In the introduction, this paper presents the anticipated benefits of the uses of neural networks for industrial applications. Then a brief overview of the main neural networks is provided. Finally a short review of neural networks applications in the nuclear industry is given. It covers domains such as: predictive maintenance for vibratory surveillance of rotating machinery, signal processing, operator guidance and eddy current inspection. In conclusion recommendations are made to use with efficiency neural networks for practical applications. In particular the need for supercomputing will be pinpointed. (author)

  4. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  5. Modelling of current loads on aquaculture net cages

    Science.gov (United States)

    Kristiansen, Trygve; Faltinsen, Odd M.

    2012-10-01

    In this paper we propose and discuss a screen type of force model for the viscous hydrodynamic load on nets. The screen model assumes that the net is divided into a number of flat net panels, or screens. It may thus be applied to any kind of net geometry. In this paper we focus on circular net cages for fish farms. The net structure itself is modelled by an existing truss model. The net shape is solved for in a time-stepping procedure that involves solving a linear system of equations for the unknown tensions at each time step. We present comparisons to experiments with circular net cages in steady current, and discuss the sensitivity of the numerical results to a set of chosen parameters. Satisfactory agreement between experimental and numerical prediction of drag and lift as function of the solidity ratio of the net and the current velocity is documented.

  6. Axial design of fuel for BWRs using neural networks

    International Nuclear Information System (INIS)

    Ortiz, J.J.; Castillo, A.; Montes, J.L.; Perusquia, R.

    2007-01-01

    In this work a new system of axial optimization of fuel is presented based on a recurrent multi state neural net called RENODC. They are described with detail the main characteristics of this type of neural net (architecture, energy function and actualization of neural states) and like was adapted to the assemble design of nuclear fuel. The fuel design is proven by means of a fuel recharge and pre determined control rod patterns. By this way a good axial fuel design one has, when the thermal limits are fulfilled along the cycle, the reactor stays critic and at least the wanted longitude of the cycle is reached; also the margin of in cold turned off is verified. The assemble of fuel created with RENODC it is substituted by a recharge assemble and it is sought to verify that the energy requirements and aspects of safety are completed. The used cycle corresponds to a balance cycle of 18 months that it can be applied to the Laguna Verde Nucleo electric Central. The tests demonstrate the effectiveness of the system to reach satisfactory results in times of CPU of around 4 hours. This way, it could be proven that the design proposed with a lightly superior enrichment to that of the substituted design, fulfills the energy requirements. In later stages of this project this system will be coupled to the other optimization modules that are already had. (Author)

  7. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

    Science.gov (United States)

    Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

    2018-03-01

    Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

  8. Control of 12-Cylinder Camless Engine with Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

    Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

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

    OpenAIRE

    Gollier, Christian

    2010-01-01

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

  10. A dosimetry based on artificial neural net theory in treatment of Graves disease

    International Nuclear Information System (INIS)

    Yang Ping; Luo Dongyun; Lin Yongxia; Zeng Shiquan

    2002-01-01

    Objective: To study the feasibility of radioactive treatment of Graves disease with dosimetry of 131 I in novel method by computer. Methods: The database comprised 206 selected Graves disease cases and according to this database the nonlinear functions could be inferred consequently, then 206 Graves disease patients were divided into two groups. The group one, 109 patients, was treated with back-propagation (BP) dosimetry method. The group two was treated with traditional method. The results were analyzed. Results: Group one had an effective rate of 93.5%, cure rate of 90.8%, recurrent rate of 3.6% and early stage of hypothyroidism rate of 3.6%. Group two had an effective rate of 90.7%, cure rate of 85.5%, recurrent rate of 5.1% and early stage of hypothyroidism rate of 5.1%. Conclusion: The BP method is efficient and the results reflect that this method might increase the accuracy of the dosimetry of 131 I and reduce recurrent rate and early stage of hypothyroidism occurrence

  11. Prediction of power system frequency response after generator outages using neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M B; Popovic, D P [Electrotechnicki Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States)

    1993-09-01

    A new methodology is presented for estimating the frequency behaviour of power systems necessary for an indication of under-frequency load shedding in steady-state security assessment. It is well known that large structural disturbances such as generator tripping or load outages can initiate cascading outages, system separation into islands, and even the complete breakup. The approach provides a fairly accurate method of estimating the system average frequency response without making simplifications or neglecting non-linearities and small time constants in the equations of generating units, voltage regulators and turbines. The efficiency of the new procedure is demonstrated using the New England power system model for a series of characteristic perturbations. The validity of the proposed approach is verified by comparison with the simulation of short-term dynamics including effects of control and automatic devices. (author)

  12. Drag &Drop, Multiphysics & Neural Net-based Lab-on-Chip Optimization Software, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The overall objective of this project is to develop a drag and drop, component library (fluidic lego) based, system simulation and optimization software for entire...

  13. Where the thoughts dwell: The physiology of neuronal-glial "diffuse neural net"

    Czech Academy of Sciences Publication Activity Database

    Verkhratsky, Alexei; Parpura, V.; Rodríguez Arellano, Jose Julio

    2011-01-01

    Roč. 66, 1-2 (2011), s. 133-151 ISSN 0165-0173 R&D Projects: GA ČR GA309/09/1696; GA ČR GA305/08/1384 Institutional research plan: CEZ:AV0Z50390703 Keywords : human brain * glia * neurone Subject RIV: FH - Neurology Impact factor: 10.342, year: 2011

  14. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  15. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  16. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  17. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  18. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  19. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  20. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  1. NET test blanket design and remote maintenance

    International Nuclear Information System (INIS)

    Holloway, C.; Hubert, P.

    1991-01-01

    The NET machine has three horizontal ports reserved for testing tritium breeding blanket designs during the physics phase and possibly five during the technology phase. The design of the ports and test blankets are modular to accept a range of blanket options, provide radiation shielding and allow routine replacement. Radiation levels during replacement or maintenance require that all operations must be carried out remotely. The paper describes the problems overcome in providing a port design which includes attachment to the vacuum vessel with double vacuum seals, an integrated cooled first wall and support guides for the test blanket module. The method selected to remotely replace the test module whilst controlling the spread of contamination is also adressed. The paper concludes that the provisions of a test blanket facility based on the NET machine design is feasible. (orig.)

  2. Fuzzy net present value for engineering analysis

    Directory of Open Access Journals (Sweden)

    Ali Nazeri

    2012-10-01

    Full Text Available Cash flow analysis is one of the most popular methods for investigating the outcome of an economical project. The costs and benefits of a construction project are often involved with uncertainty and it is not possible to find a precise value for a particular project. In this paper, we present a simple method to calculate the net present value of a cash flow when both costs and benefits are given as triangular numbers. The proposed model of this paper uses Delphi method to figure out the fair values of all costs and revenues and then using fizzy programming techniques, it calculates the fuzzy net present value. The implementation of the proposed model is demonstrated using a simple example.

  3. AstroNet-II International Final Conference

    CERN Document Server

    Masdemont, Josep

    2016-01-01

    These are the proceedings of the "AstroNet-II International Final Conference". This conference was one of the last milestones of the Marie-Curie Research Training Network on Astrodynamics "AstroNet-II", that has been funded by the European Commission under the Seventh Framework Programme. The aim of the conference, and thus this book, is to communicate work on astrodynamics problems to an international and specialised audience. The results are presented by both members of the network and invited specialists. The topics include: trajectory design and control, attitude control, structural flexibility of spacecraft and formation flying. The book addresses a readership across the traditional boundaries between mathematics, engineering and industry by offering an interdisciplinary and multisectorial overview of the field.

  4. Automatic pipeline operation using Petri Nets

    Energy Technology Data Exchange (ETDEWEB)

    Moreira, Guilherme O. [PETROBRAS TRANSPORTE S.A., Rio de Janeiro, RJ (Brazil)

    2009-07-01

    A pipeline operation requires several actions, attention and time from the control room operator in each of its operating phases. This article proposition is to use automation as something more than a remote control, drastically reducing the number of repetitive and routine actions needed from the operator to start and stop the system, granting more time for system supervision, decision making during critical conditions and avoiding errors caused due to the need of several actions being executed in a short period of time. To achieve these objectives the pipeline operation will be modeled as a Petri Net consisting of states, event and actions. A methodology for converting this Petri Net into a Ladder controller code will also be proposed. (author)

  5. Net accumulation of the Greenland ice sheet

    DEFF Research Database (Denmark)

    Kiilsholm, Sissi; Christensen, Jens Hesselbjerg; Dethloff, Klaus

    2003-01-01

    High-resolution (50 km) climate change simulations for an area covering the entire Arctic have been conducted with the regional climate model (RCM) HIRHAM. The experiments were forced at the lateral boundary by large-scale atmospheric conditions from transient climate change scenario simulations...... with HIRHAM for periods representing present-day (1961-1990) and the future (2071-2100) in the two scenarios. We find that due to a much better representation of the surface topography in the RCM, the geographical distribution of present-day accumulation rates simulated by the RCM represents a substantial...... improvement compared to the driving OAGCM. Estimates of the regional net balance are also better represented by the RCM. In the future climate the net balance for the Greenland Ice Sheet is reduced in all the simulation, but discrepancies between the amounts when based on ECHAM4/OPYC3 and HIRHAM are found...

  6. Multi-net optimization of VLSI interconnect

    CERN Document Server

    Moiseev, Konstantin; Wimer, Shmuel

    2015-01-01

    This book covers layout design and layout migration methodologies for optimizing multi-net wire structures in advanced VLSI interconnects. Scaling-dependent models for interconnect power, interconnect delay and crosstalk noise are covered in depth, and several design optimization problems are addressed, such as minimization of interconnect power under delay constraints, or design for minimal delay in wire bundles within a given routing area. A handy reference or a guide for design methodologies and layout automation techniques, this book provides a foundation for physical design challenges of interconnect in advanced integrated circuits.  • Describes the evolution of interconnect scaling and provides new techniques for layout migration and optimization, focusing on multi-net optimization; • Presents research results that provide a level of design optimization which does not exist in commercially-available design automation software tools; • Includes mathematical properties and conditions for optimal...

  7. Gastroenteropancreatic neuroendocrine tumors (GEP-NETS)

    International Nuclear Information System (INIS)

    Vargas Martinez, Cristian Camilo; Castano Llano, Rodrigo

    2010-01-01

    Gastroenteropancreatic neuroendocrine tumors (GEP-NETS) are rare neoplasms which can occur anywhere in the gastrointestinal tract. Their particular characteristics include uptake of silver salts, neuroendocrine cell marker expression and hormonal secretory granules. Depending on their size, anatomical location and upon whether or not metastasis has occurred, these tumors can show different clinical patterns and have different prognoses. Early diagnosis is essential for treating these lesions and improving the patients' prognoses, but it requires a high degree of suspicion and confirmation by special testing. Surgical treatment is the first choice, but other medical therapy can be helpful for patients who have unresectable disease. This review presents the most relevant aspects of classification, morphology, methods of locating tumors, diagnosis and treatment of GEP-NETS. It presents only the Colombian experience in the epidemiology and management of these tumors.

  8. Thermal transmission of camouflage nets revisited

    Science.gov (United States)

    Jersblad, Johan; Jacobs, Pieter

    2016-10-01

    In this article we derive, from first principles, the correct formula for thermal transmission of a camouflage net, based on the setup described in the US standard for lightweight camouflage nets. Furthermore, we compare the results and implications with the use of an incorrect formula that have been seen in several recent tenders. It is shown that the incorrect formulation not only gives rise to large errors, but the result also depends on the surrounding room temperature, which in the correct derivation cancels out. The theoretical results are compared with laboratory measurements. The theoretical results agree with the laboratory results for the correct derivation. To summarize we discuss the consequences for soldiers on the battlefield if incorrect standards and test methods are used in procurement processes.

  9. Performance Analysis using Coloured Petri Nets

    DEFF Research Database (Denmark)

    Wells, Lisa Marie

    Performance is often a central issue in the design, development, and configuration of systems. It is not always enough to know that systems work properly, they must also work effectively. There are numerous studies, e.g. in the areas of computer and telecommunication systems, manufacturing......, military, health care, and transportation, that have shown that time, money, and even lives can be saved if the performance of a system is improved. Performance analysis studies are conducted to evaluate existing or planned systems, to compare alternative configurations, or to find an optimal configuration...... of a system. There are three alternative techniques for analysing the performance of a system: measurement, analytical models, and simulation models. This dissertation focuses on the the use of coloured Petri nets for simulationbased performance analysis of industrial-sized systems. Coloured Petri nets...

  10. Determinization of timed Petri nets behaviors

    Czech Academy of Sciences Publication Activity Database

    Komenda, Jan; Lahaye, S.; Boimond, J.-L.

    2016-01-01

    Roč. 26, č. 3 (2016), s. 413-437 ISSN 0924-6703 R&D Projects: GA MŠk LH13012; GA ČR GA15-02532S Institutional support: RVO:67985840 Keywords : timed Petri nets * (max,+) automata * determinization Subject RIV: BA - General Mathematics Impact factor: 1.660, year: 2016 http://link.springer.com/article/10.1007%2Fs10626-015-0214-1

  11. Liquid metal cooled blanket concept for NET

    International Nuclear Information System (INIS)

    Malang, S.; Casal, V.; Arheidt, K.; Fischer, U.; Link, W.; Rust, K.

    1986-01-01

    A blanket concept for NET using liquid lithium-lead both as breeder material and as coolant is described. The need for inboard breeding is avoided by using beryllium as neutron multiplier in the outboard blanket. Novel flow channel inserts are employed in all poloidal ducts to reduce the MHD pressure drop. The concept offers a simple mechanical design and a higher tritium breeding ratio compared to water- and gas-cooled blankets. (author)

  12. EVo: Net Shape RTM Production Line

    Directory of Open Access Journals (Sweden)

    Sven Torstrick

    2016-04-01

    Full Text Available EVo research platform is operated by the Center for Lightweight-Production-Technology of the German Aerospace Center in Stade. Its objective is technology demonstration of a fully automated RTM (Resin Transfer Molding production line for composite parts in large quantities. Process steps include cutting and ply handling, draping, stacking, hot-forming, preform-trimming to net shape, resin injection, curing and demolding.

  13. New control system: net communications on VAX

    International Nuclear Information System (INIS)

    David, L.; Maugeais, C.

    1992-01-01

    The control system is made of five different types of processors: a server (VAX 3800), stations (VS4000) as setting interfaces for operators, CAMAC VANTAGE controllers, VME controllers and industrial programmable automates. These automates are treated with the IMAGIN supervision software and with the OSI communication protocol. All the processors are of the VAX family and use DECNET communication protocol on ETHERNET net. (A.B.). 3 figs

  14. AREVA net income: 649 million euros

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2007-03-15

    This document presents the financial statements for 2006 of Areva group: net income: 649 million euros; backlog up by 24.6% to 25.6 billion euros; steady growth of sales revenue: + 7.3%1 to 10.863 billion euros; operating income of 407 million euros: excellent divisional performance and constitution of a significant provision for the OL3 project in Finland; dividend proposed to Annual General Meeting of Shareholders: 8.46 euros per share.

  15. AREVA net income: 649 million euros

    International Nuclear Information System (INIS)

    2007-03-01

    This document presents the financial statements for 2006 of Areva group: net income: 649 million euros; backlog up by 24.6% to 25.6 billion euros; steady growth of sales revenue: + 7.3%1 to 10.863 billion euros; operating income of 407 million euros: excellent divisional performance and constitution of a significant provision for the OL3 project in Finland; dividend proposed to Annual General Meeting of Shareholders: 8.46 euros per share

  16. SpecNet: Spectrum Sensing Sans Frontieres

    OpenAIRE

    Iyer, Anand Padmanabha; Chintalapudi, Krishna; Navda, Vishnu; Ramjee, Ramachandran; Padmanabhan, Venkata N; Murthy, Chandra R

    2011-01-01

    While the under-utilization of licensed spectrum based on measurement studies conducted in a few developed countries has spurred lots of interest in opportunistic spectrum access, there exists no infrastructure today for measuring real-time spectrum occupancy across vast geographical regions. In this paper, we present the design and implementation of SpecNet, a first-of-its-kind platform that allows spectrum analyzers around the world to be networked and efficiently used in a coordinated manner...

  17. A Petri Nets Model for Blockchain Analysis

    OpenAIRE

    Pinna, Andrea; Tonelli, Roberto; Orrú, Matteo; Marchesi, Michele

    2017-01-01

    A Blockchain is a global shared infrastructure where cryptocurrency transactions among addresses are recorded, validated and made publicly available in a peer- to-peer network. To date the best known and important cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency and in particular on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism. The proposed model allows us to quickly collect information about identities owning Bitcoin addresses and to re...

  18. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  19. GOLD NETTING TO STRENGTHEN FINANCIAL SYSTEM STABILITY

    Directory of Open Access Journals (Sweden)

    Ahamed Kameel Mydin Meera

    2017-08-01

    Full Text Available Economic and financial crises seem to occur with increased frequency. Indeed now most countries, including several advanced economies like the US, Europe and Japan, are in serious economic recession. Employment and business opportunities have been much dampened. Inflation seems to be soaring globally and nations are witnessing widening gaps in income and wealth distribution. Many of these advanced economies are also facing shrinking population sizes that translate into aging problems and labor shortages. On top of those, there are environmental issues, including global warming. All these, in turn, have caused regional and global political conflicts and turmoil. The Arab Spring and the sovereign debt problems faced by some European countries like Greece are examples of this. Sustainability of economics and environment is thus of paramount concern of today. This paper considers those problems and suggests Interest-free Gold-based Electronic Netting System (IGENS as an effective way of injecting liquidity into the economy, practically free, that can spur business and employment while bringing about structural stability, inflation checked with both economic and environmental sustainability. Netting or muqassah is a transaction allowed in shari’ah and is practiced worldwide in different forms. Examples include the highly successful WIR Bank of Switzerland, various Local Exchange Trading Schemes (LETS and Bilateral and Multilateral Payment Arrangements between central banks.   Keywords:  Global crisis, Payment system, Gold, Netting, Muqassah, Liquidity, Sustainability, Financial system stability JEL Classification: E40, E42, E51

  20. WE-NET: Japanese hydrogen program

    International Nuclear Information System (INIS)

    Mitsugi, Chiba; Harumi, Arai; Kenzo, Fukuda

    1998-01-01

    The Agency of Industrial Science and Technology (AIST), in the Ministry of International Trade and Industry (MITI), started the New Sunshine Program in 1993 by unifying the Sunshine Program (R and D on new energy technology), the Moonlight Program (R and D on energy conservation technology), and the Research and Development Program for Environmental Technology. The objective of the new program is to develop innovative technologies to allow sustainable growth while solving energy and environmental issues. One of the new projects in this program is the ''International Clean Energy System Technology Utilizing Hydrogen (World Energy Network)'': WE-NET. The goal of WE-NET is to construct a worldwide energy network for effective supply, transportation and utilization of renewable energy using hydrogen. The WE-NET program extends over 28 years from 1993 to 2020. In Phase 1, we started core research in areas such as development of high efficiency technologies including hydrogen production using polymer electrolyte membrane water electrolysis, hydrogen combustion turbines, etc. (author)