Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

International Nuclear Information System (INIS)

Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

2015-01-01

This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

Modelling a variable valve timing spark ignition engine using different neural networks

Energy Technology Data Exchange (ETDEWEB)

Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group

2004-10-01

In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)

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

OpenAIRE

Shrivastava, Piyush

2012-01-01

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

Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

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Wei-Chin Lin

2009-04-01

Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

Parity violating NN forcES in the quark compound bag model

International Nuclear Information System (INIS)

Simonov, Yu.A.

1982-01-01

Parity violation (PV) in the interaction is considered as due to the Weinberg-Salam quark-quark interaction inside the six-quark bag. The initial and final strong interaction is described within the same quark compound bag (QCB) model, where the NN coupling to the six quark QCB is defined from the NN experimental data. The resulting PV amplitude contains no free parameters and allows therefore an unambiguous test of the QCB model. An estimate of the 1 S 0 → 3 P 0 contribution to the proton-proton asymmetry is in a rough agreement with experimental data [ru

Improved NN-GM(1,1 for Postgraduates’ Employment Confidence Index Forecasting

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Lu Wang

2014-01-01

Full Text Available Postgraduates’ employment confidence index (ECI forecasting can help the university to predict the future trend of postgraduates’ employment. However, the common forecast method based on the grey model (GM has unsatisfactory performance to a certain extent. In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1 model is combined with the improved neural network (NN in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1 model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.

Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

Science.gov (United States)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Alkhasawneh, Mutasem Sh.; Aziz, Hamidi Abdul

2017-10-01

Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.

Hybrid NN/SVM Computational System for Optimizing Designs

Science.gov (United States)

Rai, Man Mohan

2009-01-01

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily

Direct nn-scattering at the YAGUAR reactor

Energy Technology Data Exchange (ETDEWEB)

Crawford, B.E. [Gettysburg College, Department of Physics, Gettysburg, PA 17325 (United States)]. E-mail: bcrawfor@gettysburg.edu; Furman, W.I. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Howell, C.R. [Duke University and Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States); Lychagin, E.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Levakov, B.G. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Litvin, V.I. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Lyzhin, A.E. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Magda, E.P. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Mitchell, G.E. [North Carolina State University, Raleigh, NC 27695-8202 (United States); Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States); Muzichka, A.Yu. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Nekhaev, G.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Sharapov, E.I. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Shvetsov, V.N. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Stephenson, S.L. [Gettysburg College, Department of Physics, Gettysburg, PA 17325 (United States); Strelkov, A.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Tornow, W. [Duke University and Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States)

2005-12-15

The Direct Investigation of a {sub nn} Association (DIANNA) is finalizing the design of a direct measurement of the nn-scattering length to be performed at the YAGUAR reactor in Snezhinsk, Russia. Extensive modeling of the neutron field, nn-scattering kinematics, and sources of detector background have verified the plan for a 3% measurement of a {sub nn}. Measurements of the neutron flux support the neutron field modeling. Initial test measurements of the neutron field inside the underground channel have confirmed calculations of the thermal neutron background.

Relativistic scalar-vector models of the N-N and N-nuclear interactions

International Nuclear Information System (INIS)

Green, A.E.S.

1985-01-01

This paper for the Proceedings of Conference an Anti-Nucleon and Nucleon-Nucleus Interactions summarizes work by the principal investigator and his collaborators on the nucleon-nucleon (N-N) and nucleon-nuclear (N-eta) interactions. It draws heavily on a paper presented at the Many Body Conference in Rome in 1972 but also includes a brief review of our phenomenological N-eta interaction studies. We first summarize our 48-49 generalized scalar-vector meson field theory model of the N-N interactions. This is followed by a brief description of our phenomenological work in the 50's on the N-eta interaction sponsored by the Atomic Energy Commission (the present DOE). This work finally led to strong velocity dependent potentials with spin orbit and isospin terms for shell and optical model applications. This is followed by a section on the Emergence of One-Boson Exchange Models describing developments in the 60's of quantitative generalized one boson exchange potentials (GOBEP) including our purely relativistic N-N analyses. Then follows a section on the application of this meson field model to the N-eta interaction, in particular to spherical closed shell nuclei. This work was sponsored by AFOSR but funding was halted with the Mansfield amendment. We conclude with a discussion of subsequent collateral work by former colleagues and by others who have converged upon scalar-vector relativistic models of N-N, antiN-N, N-eta and antiN-eta interactions and some lessons learned from this extended endeavor. 61 refs

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

Science.gov (United States)

Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

2018-04-01

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

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.

Local TEC Modelling and Forecasting using Neural Networks

Science.gov (United States)

Tebabal, A.; Radicella, S. M.; Nigussie, M.; Damtie, B.; Nava, B.; Yizengaw, E.

2017-12-01

Abstract Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, satellite navigation and technologies. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as driver previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.

Two-component dressed-bag model for NN interaction: deuteron structure and phase shifts up to 1 GeV

International Nuclear Information System (INIS)

Kukulin, V.I.; Obukhovsky, I.T.; Pomerantsev, V.N.; Faessler, A.

2002-01-01

A two-component model is developed for the intermediate-range NN interaction based on a new mechanism with an intermediate symmetric six-quark bag 'dressed' by σ and other fields. To calculate the transition amplitude, the microscopic six-quark shell-model in combination with the 3 P 0 -quark pion production mechanism is used. As a result, an effective energy-dependent NN interaction is constructed. The new quark-meson model for the NN interaction has been demonstrated to result in a new type of NN tensor force at intermediate ranges, which is crucially important for the treatment of tensor mixing at intermediate energies. The suggested model is able to describe NN phase shifts in a broad energy range from low energy up to 1 GeV, and the deuteron structure. The generalization of the model results in new spin-orbit 2N and 3N forces and new meson-exchange currents induced by intermediate dressed bag components, and also in the enhancement of a collective σ-field in nuclei. (author)

Important configurations in six-quark N-N states. II. Current quark model

International Nuclear Information System (INIS)

Stancu, F.; Wilets, L.

1989-01-01

Quark basis states constructed from molecular-type orbitals were shown previously to be more convenient to use than cluster model states for N-N processes. The usual cluster model representation omits configurations which emerge naturally in a molecular basis which contains the same number of spatial functions. The importance of the omitted states was demonstrated for a constituent quark model. The present work extends the study to the prototypical current quark model, namely the MIT bag. In order to test the expansion for short-range N-N interactions, the eigenstates and eigenenergies of six quarks in a spherical bag, including one-gluon exchange, are calculated. The lowest eigenenergies are lowered significantly with respect to the usual cluster model. This reaffirms the importance of dynamics for obtaining the needed short-range repulsion

Present status of the theory of πNN systems

International Nuclear Information System (INIS)

Mizutani, T.

1987-01-01

In the present discussion the existing data are compared with various model calculations within the πNN theory to assess the appropriateness of the latter. Globally the models in their present form reproduce the data quite well in view of the number of channels in which the comparison was made. From the quantitative point of view the models must be upgraded in several different ingredients. First, it is the parameters of the NN heavy meson exchange potentials when combined with the explicit pion and nucleon isobar degrees of freedom. A right choice of their values will lead to a better account of such observables like the beam asymmetries and vector analyzing power in a number of channels like NN in equilibrium πd, NN → πNN, etc. The next one may be the model for the Δ resonance (or the πN P 33 off-shell amplitude). A simple monopole or dipole form factor for the πNΔ vertex had difficulty in reproducing the major NN partial wave phase parameters. In view of the fact that dσ/dΩ for the deuteron photodisintegration in the Δ resonance region cannot be explained at all by the coupled NN-NΔ model, one should seriously consider a better model for the Δ. 39 refs., 10 figs

Dynamic neural network modeling of HF radar current maps for forecasting oil spill trajectories

International Nuclear Information System (INIS)

Tissot, P.; Perez, J.; Kelly, F.J.; Bonner, J.; Michaud, P.

2001-01-01

This paper examined the concept of dynamic neural network (NN) modeling for short-term forecasts of coastal high-frequency (HF) radar current maps offshore of Galveston Texas. HF radar technology is emerging as a viable and affordable way to measure surface currents in real time and the number of users applying the technology is increasing. A 25 megahertz, two site, Seasonde HF radar system was used to map ocean and bay surface currents along the coast of Texas where wind and river discharge create complex and rapidly changing current patters that override the weaker tidal flow component. The HF radar system is particularly useful in this type of setting because its mobility makes it a good marine spill response tool that could provide hourly current maps. This capability helps improve deployment of response resources. In addition, the NN model recently developed by the Conrad Blucher Institute can be used to forecast water levels during storm events. Forecasted currents are based on time series of current vectors from HF radar plus wind speed, wind direction, and water levels, as well as tidal forecasts. The dynamic NN model was tested to evaluate its performance and the results were compared with a baseline model which assumes the currents do not change from the time of the forecast up to the forecasted time. The NN model showed improvements over the baseline model for forecasting time equal or greater than 3 hours, but the difference was relatively small. The test demonstrated the ability of the dynamic NN model to link meteorological forcing functions with HF radar current maps. Development of the dynamic NN modeling is still ongoing. 18 refs., 1 tab., 5 figs

Prototype-Incorporated Emotional Neural Network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

Directory of Open Access Journals (Sweden)

Ahmed Shamsul Arefin

Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

Science.gov (United States)

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Application of CA and NN for event recognition in experiments DISTO and STREAMER

International Nuclear Information System (INIS)

Bussa, M.P.; Ivanov, V.V.; Kisel, I.V.; Pontecorvo, G.B.

1997-01-01

An algorithm for charged particle recognition and event identification applying a cellular automaton (CA) model and a multi-layer neural network (NN) has been developed for the DISTO experiment under way at Saturne (Saclay, France). A further development of the model will be applied for particle recognition in the Dubna Streamer Chamber Spectrometer (DSCS) for studying pion-nucleus absorption (experiments DISTO and STREAMER). (orig.)

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

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Manjunath Patel Gowdru Chandrashekarappa

2014-01-01

Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

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Lund Ole

2009-09-01

Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

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S. N. Naikwad

2009-01-01

Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

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Yuan Pu

2015-01-01

Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

Signal peptide discrimination and cleavage site identification using SVM and NN.

Science.gov (United States)

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

Science.gov (United States)

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

On the theory of coupled πNN-NN systems

International Nuclear Information System (INIS)

Machavariani, A.I.

1982-01-01

On the basis of the deuteron and δ asobar as a one-particle state, a version of relativistic equations for coupled πNN and NN systems is obtained. It is demonstrated that, if one neglects the non-pole term of the pion-nucleon Green function in the (3.3) resonance region, the three-body equations reduce to a set of equations for the two-body amplitudes of transitions between the πd, NN and Nδ channels

Modeling ionospheric foF 2 response during geomagnetic storms using neural network and linear regression techniques

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Tshisaphungo, Mpho; Habarulema, John Bosco; McKinnell, Lee-Anne

2018-06-01

In this paper, the modeling of the ionospheric foF 2 changes during geomagnetic storms by means of neural network (NN) and linear regression (LR) techniques is presented. The results will lead to a valuable tool to model the complex ionospheric changes during disturbed days in an operational space weather monitoring and forecasting environment. The storm-time foF 2 data during 1996-2014 from Grahamstown (33.3°S, 26.5°E), South Africa ionosonde station was used in modeling. In this paper, six storms were reserved to validate the models and hence not used in the modeling process. We found that the performance of both NN and LR models is comparable during selected storms which fell within the data period (1996-2014) used in modeling. However, when validated on storm periods beyond 1996-2014, the NN model gives a better performance (R = 0.62) compared to LR model (R = 0.56) for a storm that reached a minimum Dst index of -155 nT during 19-23 December 2015. We also found that both NN and LR models are capable of capturing the ionospheric foF 2 responses during two great geomagnetic storms (28 October-1 November 2003 and 6-12 November 2004) which have been demonstrated to be difficult storms to model in previous studies.

CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

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Smita K Magdum

2017-10-01

Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia

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Wichaipanich, Noraset; Hozumi, Kornyanat; Supnithi, Pornchai; Tsugawa, Takuya

2017-06-01

This paper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.3°E, dip angle 10.1°S) are at the conjugate points while Chumphon (10.72°N, 99.37°E, dip angle 3.0°N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004-2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (θ), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 ≤ RMSE ≤ 1.0 for Chumphon and Kototabang, 0.7 ≤ RMSE ≤ 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 ≤ RMSE ≤ 1.5 for Chumphon and Kototabang, 1.2 ≤ RMSE ≤ 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the

Artificial neural network modeling of DDGS flowability with varying process and storage parameters

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Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....

Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study

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Puddu Paolo

2012-07-01

Full Text Available Abstract Background Projection pursuit regression, multilayer feed-forward networks, multivariate adaptive regression splines and trees (including survival trees have challenged classic multivariable models such as the multiple logistic function, the proportional hazards life table Cox model (Cox, the Poisson’s model, and the Weibull’s life table model to perform multivariable predictions. However, only artificial neural networks (NN have become popular in medical applications. Results We compared several Cox versus NN models in predicting 45-year all-cause mortality (45-ACM by 18 risk factors selected a priori: age; father life status; mother life status; family history of cardiovascular diseases; job-related physical activity; cigarette smoking; body mass index (linear and quadratic terms; arm circumference; mean blood pressure; heart rate; forced expiratory volume; serum cholesterol; corneal arcus; diagnoses of cardiovascular diseases, cancer and diabetes; minor ECG abnormalities at rest. Two Italian rural cohorts of the Seven Countries Study, made up of men aged 40 to 59 years, enrolled and first examined in 1960 in Italy. Cox models were estimated by: a forcing all factors; b a forward-; and c a backward-stepwise procedure. Observed cases of deaths and of survivors were computed in decile classes of estimated risk. Forced and stepwise NN were run and compared by C-statistics (ROC analysis with the Cox models. Out of 1591 men, 1447 died. Model global accuracies were extremely high by all methods (ROCs > 0.810 but there was no clear-cut superiority of any model to predict 45-ACM. The highest ROCs (> 0.838 were observed by NN. There were inter-model variations to select predictive covariates: whereas all models concurred to define the role of 10 covariates (mainly cardiovascular risk factors, family history, heart rate and minor ECG abnormalities were not contributors by Cox models but were so by forced NN. Forced expiratory volume and arm

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

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Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS

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Marijana Zekić-Sušac

2012-07-01

Full Text Available After production and operations, finance and investments are one of the mostfrequent areas of neural network applications in business. The lack of standardizedparadigms that can determine the efficiency of certain NN architectures in a particularproblem domain is still present. The selection of NN architecture needs to take intoconsideration the type of the problem, the nature of the data in the model, as well as somestrategies based on result comparison. The paper describes previous research in that areaand suggests a forward strategy for selecting best NN algorithm and structure. Since thestrategy includes both parameter-based and variable-based testings, it can be used forselecting NN architectures as well as for extracting models. The backpropagation, radialbasis,modular, LVQ and probabilistic neural network algorithms were used on twoindependent sets: stock market and credit scoring data. The results show that neuralnetworks give better accuracy comparing to multiple regression and logistic regressionmodels. Since it is model-independant, the strategy can be used by researchers andprofessionals in other areas of application.

Determination of nn scattering length from data on nn final state interaction in nd-breakup reaction

International Nuclear Information System (INIS)

Konobeevski, E.S.; Mordovskoy, M.V.; Sergeev, V.A.; Potashev, S.I.; Zuev, S.V.

2006-01-01

Full text: An experiment is proposed for the high-precision determination of the neutron-neutron scattering length investigating the nn final state interaction in the nd breakup reaction. The singlet pp and nn scattering lengths are very sensitive probes of the NN-interaction, and their difference is a direct measure of charge-symmetry breaking (CSB) of the nuclear force. However CSB is a small effect, and accurate values of the scattering lengths are needed for a theoretical analysis. The proton-proton scattering length is well known from pp-scattering data (a pp = -17.3± 0.4 fm), and its uncertainty is mainly due to a model-dependent procedure of removing Coulomb effects. The neutron-neutron scattering length is determined from the following processes n+d→p+n+n, π - + d → γ +n+n, d+d→ 2 He+n+n by investigating the kinematic region of the nn final-state interaction (FSI) where two neutrons fly with low relative energy. The results obtained by now are characterized by a significant uncertainty in values of a nn ; they are grouped near -16 and -19 fm [1,2], so even the sign of the difference a nn - a pp is uncertain. In this experiment neutron-neutron scattering length is determined by measuring the yield of the nd breakup reaction as a function of the relative energy ε nn =(E 1 +E 2 -2(E 1 E 2 ) 1/2 cosθ)/2 of two neutrons in the FSI region (two neutrons fly in a narrow angular cone) where nn-interaction is strongly revealed. The theory of reactions in 3N system predicts the ε nn dependence of the FSI cross section being sensitive to the value of a nn . The measurements will be made using the neutron channel RADEX at Moscow meson factory of the Institute for Nuclear Research. The momenta and angles of the two emitted neutrons and the energy of the proton will be measured for each breakup event. The measured dependence of the reaction yield on the relative energy of the two neutrons will be compared to results of the Monte Carlo simulation that includes

A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

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P.-Y. Chen

2009-01-01

Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

PERAMALAN KONSUMSI LISTRIK JANGKA PENDEK DENGAN ARIMA MUSIMAN GANDA DAN ELMAN-RECURRENT NEURAL NETWORK

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Suhartono Suhartono

2009-07-01

Full Text Available Neural network (NN is one of many method used to predict the electricity consumption per hour in many countries. NN method which is used in many previous studies is Feed-Forward Neural Network (FFNN or Autoregressive Neural Network(AR-NN. AR-NN model is not able to capture and explain the effect of moving average (MA order on a time series of data. This research was conducted with the purpose of reviewing the application of other types of NN, that is Elman-Recurrent Neural Network (Elman-RNN which could explain MA order effect and compare the result of prediction accuracy with multiple seasonal ARIMA (Autoregressive Integrated Moving Average models. As a case study, we used data electricity consumption per hour in Mengare Gresik. Result of analysis showed that the best of double seasonal Arima models suited to short-term forecasting in the case study data is ARIMA([1,2,3,4,6,7,9,10,14,21,33],1,8(0,1,124 (1,1,0168. This model produces a white noise residuals, but it does not have a normal distribution due to suspected outlier. Outlier detection in iterative produce 14 innovation outliers. There are 4 inputs of Elman-RNN network that were examined and tested for forecasting the data, the input according to lag Arima, input such as lag Arima plus 14 dummy outlier, inputs are the lag-multiples of 24 up to lag 480, and the inputs are lag 1 and lag multiples of 24+1. All of four network uses one hidden layer with tangent sigmoid activation function and one output with a linear function. The result of comparative forecast accuracy through value of MAPE out-sample showed that the fourth networks, namely Elman-RNN (22, 3, 1, is the best model for forecasting electricity consumption per hour in short term in Mengare Gresik.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

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Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Consistency requirements on Δ contributions to the NN potential

International Nuclear Information System (INIS)

Rinat, A.S.

1982-04-01

We discuss theories leading to intermediate state NΔ and ΔΔ contributions to Vsub(NN). We focus on the customary addition of Lsub(ΔNπ)' to Lsub(πNN)' in a conventional field theory and argue that overcounting of contributions to tsub(πN) and Vsub(NN) will be the rule. We then discuss the cloudy bag model where a similar interaction naturally arises and which leads to a consistent theory. (author)

Construction of high-quality NN potential models

International Nuclear Information System (INIS)

Stoks, V.G.J.; Klomp, R.A.M.; Terheggen, C.P.F.; de Swart, J.J.

1994-01-01

We present an updated version (Nijm93) of the Nijmegen soft-core potential, which gives a much better description of the np data than the older version (Nijm78). The χ 2 per datum is 1.87. The configuration-space and momentum-space versions of this potential are exactly equivalent, a unique feature among meson-theoretical potentials. We also present three new NN potential models: a nonlocal Reid-like Nijmegen potential (Nijm I), a local version (Nijm II), and an updated regularized version (Reid 93) of the Reid soft-core potential. These three potentials all have a nearly optimal χ 2 per datum and can therefore be considered as alternative partial-wave analyses. All potentials contain the proper charge-dependent one-pion-exchange tail

Optimasi parameter neural network pada data time series

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Muzakir Hi Sultan

2014-05-01

Full Text Available Gempa bumi merupakan suatu pergerakan tanah yang terjadi secara tiba-tiba hingga menimbulkan getaran, besarnya kekuatan gempa dapat mengakibatkan bencana baik kerusakan maupun korban jiwa. Untuk mengantisipasi bencana yang akan datang maka diperlukan suatu model khususnya untuk meramalkan besarnya kekuatan gempa. Pada penelitian ini, digunakan model ARIMA dan model kombinasi dari Neural Network-Algoritma Genetik (NN-GA untuk memprediksi rata-rata kekuatan gempa bumi setiap bulan khususnya yang terjadi di wilayah Maluku Utara. Data yang digunakan adalah data kekuatan gempa berdasarkan skala richter yang diperoleh dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG kota Ternate. Sebagai input pada model ARIMA dan NN-GA digunakan rata-rata kekuatan gempa bumi 36 bulan dan rata-rata kekuatan gempa 36 bulan berikutnya digunakan sebagai target untuk prediksi. Untuk meng-update parameter (bobot dari Neural Network digunakan metode Gradient Descent dan untuk mendapatkan parameter yang lebih optimal pada layer Output, maka di diterapkan Algoritma Genetik. Hasil peramalan dari kedua model kemudian dibandingkan dan model terbaik ditentukan dari nilai Mean square Error (MSE yang terkecil. dari hasil peramalan dengan model ARIMA diperoleh MSE sebesar 1.0125, sedangkan pada model NN-GA diperoleh MSE sebesar 0.9196. Nilai tersebut, menunjukkan bahwa model NN-GA lebih baik dari model ARIMA untuk peramalan rata-rata kekuatan gempa bumi beberapa bulan ke depan

π-exchange NN interaction model with overlapping nucleon form factors

International Nuclear Information System (INIS)

Bagnoud, X.

1986-01-01

The nucleon-nucleon (NN) interaction model includes a π-exchange and takes into account the first excited state Δ(1232) of the nucleon. It is supplemented by a short-range repulsion which has been derived from the nucleon form factor (rms radius b/sub f/) combined with the three-quark wave function (rms radius b/sub q/). The optimization of the model on empirical scattering phase shifts below 300 MeV gives, for a minimum chi 2 , the root-mean-square radii b/sub f/ = b/sub q/ = 0.51 fm and a coupling constant G/sub π/ 2 /4π = 13

Prediction of Classroom Reverberation Time using Neural Network

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Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

2018-04-01

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

Heat control in HVDC resistive divider by PID and NN controllers

International Nuclear Information System (INIS)

Yilmaz, S.; Dincer, H.; Eksin, I.; Kalenderli, O.

2007-01-01

In this study, a control system is presented that is devised to increase measurement precisions within a prototype high voltage DC resistive divider (HVDC-RD). Since one of the major sources of measurement errors in such devices is the self heating effect, a system controlling the temperature within the high voltage DC resistive divider is devised so that suitable and stable temperature conditions are maintained that, in return, will decrease the measurement errors. The resistive divider system is cooled by oil, and PID and neural network (NN) controllers try to keep the temperature within the prescribed limits. The system to be controlled exhibits a nonlinear character, and therefore, a control approach based on NN controllers is proposed. Thus, a system that can fulfill the various requirements dictated by the designer is constructed. The performance of the NN controller is compared with that of the PID controller developed for the same purpose, and the values of the performance indices indicate the superiority of the NN controller over that of the classical PID controller

An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

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Ming-Chang Wu

2015-10-01

Full Text Available Floods, one of the most significant natural hazards, often result in loss of life and property. Accurate hourly streamflow forecasting is always a key issue in hydrology for flood hazard mitigation. To improve the performance of hourly streamflow forecasting, a methodology concerning the development of neural network (NN based models with an enforced learning strategy is proposed in this paper. Firstly, four different NNs, namely back propagation network (BPN, radial basis function network (RBFN, self-organizing map (SOM, and support vector machine (SVM, are used to construct streamflow forecasting models. Through the cross-validation test, NN-based models with superior performance in streamflow forecasting are detected. Then, an enforced learning strategy is developed to further improve the performance of the superior NN-based models, i.e., SOM and SVM in this study. Finally, the proposed flow forecasting model is obtained. Actual applications are conducted to demonstrate the potential of the proposed model. Moreover, comparison between the NN-based models with and without the enforced learning strategy is performed to evaluate the effect of the enforced learning strategy on model performance. The results indicate that the NN-based models with the enforced learning strategy indeed improve the accuracy of hourly streamflow forecasting. Hence, the presented methodology is expected to be helpful for developing improved NN-based streamflow forecasting models.

Three-body ΛNN force due to Λ-Σ coupling

International Nuclear Information System (INIS)

Myint, Khin Swe; Akaishi, Yoshinori

2003-01-01

The ΛNN three - body force due to coherent Λ - Σ Coupling effect was derived from realistic Nijmegen model D potential. Repulsive and attractive three - body ΛNN forces were reconcilably accounted. For 5 He, within one - channel description, ΛNN force is largely repulsive and its origin comes from Pauli forbidden terms. Within two - channel description, attractive Pauli allowed terms exist and resulting three - body force is always attractive. Large attractive ΛNN force effect due to coherent Λ - Σ coupling effect is predicted in neutron - rich nuclei. The attractive coherent Λ - Σ coupling effect is largely enhanced at high density neutron matter. The attractive three - body ΛNN force effect is essential dynamics of Λ - Σ coupling while the repulsive Nogami three - body effect arises from Pauli forbidden diagrams. (Y. Kazumata)

NN → NN π: the new frontier in nucleon-nucleon interactions

International Nuclear Information System (INIS)

Silbar, R.R.

1986-01-01

The torch in nucleon-nucleon scattering has been passed to experimental and theoretical studies of pion production. Comparing two unitary models shows that most of the structures predicted for spin observables in NN → NNπ are model independent and roughly in agreement with the data. The contribution of rho- exchange is small, indicating the reaction is largely ''peripheral''. The energy dependence of these isobar models is smooth. The largely unstudied reactions producing neutral and negatively-charged pions show richer structure than positively-charged pion production. 6 refs

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

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Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Further investigations of the NN interaction in the Skyrme model

International Nuclear Information System (INIS)

Kaelbermann, G.; Eisenberg, J.M.

1989-01-01

We examine the influence of the coupling to NΔ and ΔΔ degrees of freedom for the NN interaction as derived in the Skyrme model, carrying out an extensive search for parameters in the basic Lagrangian that will yield both reasonable single-baryon results and appreciable attraction. Separately the free one-body skyrmeon solution and an improved two-body solution are inserted in the product ansatz for the two-body system both with and without time-dependent dynamical terms. No appreciable central attraction between nucleons is found with either of these approaches. (author)

One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

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Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

2015-04-01

Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

A unitary approach to the coupling between the NN and πNN channels

International Nuclear Information System (INIS)

Blankleider, B.

1980-11-01

Some basic properties of the πNN system, in particular its coupling to the NN channel, are investigated. A set of linear integral equations that couple the N-N to the π-d channel, and satisfy two- and three-body unitarity is derived. By including the π-N amplitude in the P 11 channel, and retaining certain disconnected diagrams, it is found that the propagators for the nucleons, and form factors for the vertices, become dressed without changing the basic structure of the equations. For the numerical solution relativistic kinematics for the pion and non-relativistic kinematics for the nucleons are used. There is uncertainty about the importance of real pion absorption in the π-d elastic scattering reaction. Although the effect of absorption can be very large, its influence is cancelled to a large extent by the further inclusion of P 11 rescattering. The inclusion of absorption signnificantly lowers the dips in the π-d differential cross sections at higher energies. The model is able to reproduce the sole experimental value of the tensor polarization t 20 at 180 deg. so far available. Numerical results for the reaction NN→πd are in excellent agreement with the differential cross sections at all but the very high energies

Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

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Lei La

2012-01-01

Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

Projection of the six-quark wave function onto the NN channel and the problem of the repulsive core in the NN interaction

International Nuclear Information System (INIS)

Kusainov, A.M.; Neudatchin, V.G.; Obukhovsky, I.T.

1991-01-01

A modification of the resonating-group method (RGM) is proposed which includes the multiquark shell-model configurations in the nucleon overlap region. The instanton, gluon, and π,σ exchange is taken into account, the interaction constants being consistent with the baryon spectrum. This enables one to cover a wide interval of NN scattering energies up to E lab =2 GeV. The projection of the six-quark wave function onto the NN and other baryon channels is discussed in detail in our approach and in other RGM versions as well, and in this context the problem of repulsive core in the NN forces is discussed

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

Science.gov (United States)

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

Science.gov (United States)

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

A consistent meson-theoretic description of the NN-interaction

International Nuclear Information System (INIS)

Machleidt, R.

1985-01-01

In this paper, the meson-theory of the NN-interaction is performed consistently. All irreducible diagrams up to a total exchanged mass of about 1 GeV (i. e. up to the cutoff region) are taken into account. These diagrams contain in particular an explicit field theoretic model for the 2π-exchange taking into account virtual Δ-excitation and direct π π-interaction. This part of the model agrees quantitatively with results obtained from dispersion theory which in turn are based on the analysis of πN- and π π-scattering data. A detailed description of the lower partial wave phase-shifts of NN-scattering requires the introduction of irreducible diagrams containing also heavy boson exchange, in particular the combination of π and rho. In the framework of this consistent meson theory an accurate description of the NN-scattering data below 300 MeV laboratory energy as well as the deuteron data is achieved; the numerical results are superior to those of simplified boson exchange models

A consistent analysis of (p,p`) and (n,n`) reactions using the Feshbach-Kerman-Koonin model

Energy Technology Data Exchange (ETDEWEB)

Yoshioka, S.; Watanabe, Y.; Harada, M. [Kyushu Univ., Fukuoka (Japan)] [and others

1997-03-01

Double-differential proton emission cross sections were measured for proton-induced reactions on several medium-heavy nuclei ({sup 54,56}Fe, {sup 60}Ni, {sup 90}Zr, and {sup 93}Nb) at two incident energies of 14.1 and 26 MeV. The (p,p`) data for {sup 56}Fe and {sup 93}Nb were compared with available data of (n,n`) scattering for the same target nuclei and incident energies, and both data were analyzed using the Feshbach-Kerman-Koonin model to extract the strength V{sub 0} of the effective N-N interaction which is the only free parameter used in multistep direct calculations. (author)

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Directory of Open Access Journals (Sweden)

Santosh Kumar Nanda

2011-01-01

Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

Dihadron Azimuthal Correlations in p-p Collisions at sNN=7 TeV and p-Pb Collisions at sNN=5.02 TeV

Directory of Open Access Journals (Sweden)

Ting Bai

2015-01-01

Full Text Available The dihadron azimuthal correlations in p-p collisions at sNN=7 TeV and p-Pb collisions at sNN=5.02 TeV are investigated in the framework of a multisource thermal model. The model can approximately describe the experimental results measured in the Large Hadron Collider. We find the px amplitude of the source is magnified and the source translates along the direction.

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

OpenAIRE

Naikwad, S. N.; Dudul, S. V.

2009-01-01

A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes tempora...

ABOUT PROBABILITY OF RESEARCH OF THE NN Ser SPECTRUM BY MODEL ATMOSPHERES METHOD

OpenAIRE

Sakhibullin, N. A.; Shimansky, V. V.

2017-01-01

The spectrum of close binary system NN Ser is investigated by a models atmospheres method. It is show that the atmosphere near the centrum of a hot spot on surface of red dwarf has powerful chromospheres, arising from heating in Laiman continua. Four models of binary system with various of parameters are constructed and their theoretical spectra are obtained. Temperature of white dwarf Tef = 62000 K, radius of the red dwarf RT = 0.20139 and angle inclination of system i = 82“ are determined. ...

Bankruptcy prediction for credit risk using neural networks: a survey and new results.

Science.gov (United States)

Atiya, A F

2001-01-01

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).

The role of neural networks in nuclear power plant safety systems

International Nuclear Information System (INIS)

Boger, Z.

1993-01-01

Neural networks (NN) techniques have been applied in recent years to many systems by researchers in the nuclear power industry, mainly for modeling and sensor validation. Recent results are reviewed, including new directions in applications to control systems, safety analysis, and ''virtual'' instruments. As new fast learning algorithms become available, large systems may be learned effectively, even with few training examples. The nuclear industry hesitates to include NN in safety related systems, but it seems that the obstacles could be overcome with the demonstration of successful applications, even from other industries. Coupling of full-scale reactor simulators, as fault database generators, with neural networks learning should be explored. The integration of Expert System technology with NN should improve the Validation and Verification tasks, and also help overcome psychological barriers. It may prove that the potential of NN to help operators, compared with the existing and proposed alternatives, outweigh the risks. (author). 58 refs, 2 figs

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Science.gov (United States)

Hesthaven, J. S.; Ubbiali, S.

2018-06-01

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and employs artificial neural networks (ANNs), particularly multi-layer perceptrons (MLPs), to accurately approximate the coefficients of the reduced model. The search for the optimal number of neurons and the minimum amount of training samples to avoid overfitting is carried out in the offline phase through an automatic routine, relying upon a joint use of the Latin hypercube sampling (LHS) and the Levenberg-Marquardt (LM) training algorithm. This guarantees a complete offline-online decoupling, leading to an efficient RB method - referred to as POD-NN - suitable also for general nonlinear problems with a non-affine parametric dependence. Numerical studies are presented for the nonlinear Poisson equation and for driven cavity viscous flows, modeled through the steady incompressible Navier-Stokes equations. Both physical and geometrical parametrizations are considered. Several results confirm the accuracy of the POD-NN method and show the substantial speed-up enabled at the online stage as compared to a traditional RB strategy.

Neural Network Based Models for Fusion Applications

Science.gov (United States)

Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff

2017-10-01

Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-­network (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.

Formation of q bar q resonances in the bar NN system

International Nuclear Information System (INIS)

Ivanov, N.Ya.

1995-01-01

The formation of q bar q resonances lying on the leading Regge trajectories in the bar NN system is studied in the quark-gluon string model. The model predicts strong suppression of the decays of q bar q states into bar NN pairs in relation to two-meson modes. The author's analysis shows that the contributions of the resonances f 4 (2050) (I G J PC = 0 + 4 ++ ), ρ 5 (2240) (I G J PC = 1 + 5 -- ), and f 6 (2510) (I G J PC = 0 + 6 ++ ) to the processes of two-meson bar NN annihilation (bar pp → ππ, bar KK, hor-ellipsis) are about 1% of the corresponding experimental integrated cross sections. 30 refs., 2 figs., 1 tab

Classification in medical images using adaptive metric k-NN

Science.gov (United States)

Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

2010-03-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

Deep attractive NN potential as a potential for the N(1440)-N system

International Nuclear Information System (INIS)

Glozman, L.Y.; Kukulin, V.I.; Pomerantsev, V.N.

1992-01-01

This work demonstrates that the model of a deep attractive NN potential (the Moscow NN potential) with a deep lying extra state may be interpreted as a potential in the N(1440)-N system. Under such an interpretation, the deep-lying level forbidden for the NN system describes the N(1440)-N component in deuteron. This conclusion is used to obtain the momentum distribution for the N(1440)-N component in deuteron

A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

Directory of Open Access Journals (Sweden)

Parisa Bazmi

2016-11-01

Full Text Available Named Data Networking (NDN is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN Based Traffic-aware Forwarding strategy (NNTF is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

NΔ-NN interaction in the pionic disintegration of the deuteron

International Nuclear Information System (INIS)

Tanabe, H.; Ohta, K.

1987-07-01

The cross sections for the pionic disintegration of the deuteron in the Δ-resonance region are calculated based on a unitary three-body model. The NΔ-NN transition potential is constructed from the πN P 11 and P 33 scattering amplitudes extrapolated to the off-shell region, and from the πNN three-body propagator. The idea of the two-potential model for the P 11 wave is extended to the P 33 wave. The parameters of the model are deduced from the fits to the πN scattering phase shifts. It is found that the off-shell P 11 and P 33 scattering amplitudes behave quite similarly to the monopole form factor with a cut-off momentum Λ = 600 MeV/c as obtained earlier in the perturbation model by Gibbs, Gibson, and Stephenson. It is also found that the backward-propagating-pion component of the πNN propagator, which is often ignored in three-body calculations, is crucial to reproduce the magnitude of the total cross section. The three-body calculation is compared to the perturbation calculations. The second-order perturbation gives the results which closely approximate the full-order three-body calculation. (author)

Meson-exchange Hamiltonian for NN scattering and isobar-nucleus dynamics

International Nuclear Information System (INIS)

Lee, T.S.H.

1983-01-01

We have constructed a meson-exchange Hamiltonian for π, N, Δ and N* for NN scattering up to 2 GeV. The model gives good descriptions of the Arndt phase-shifts up to 1 GeV in both the T = 0 and T = 1 channels. The calculated total cross sections sigma/sup tot/, Δsigma/sub L//sup tot/ and Δsigma/sub T//sup tot/ agree to a large extent with the data in both the magnitudes and the signs. The present calculation gives a sound starting point for future refinements. Among them, a large-scale three-body calculation could be needed to investigate the energy dependence of the effect due to NN interactions in the πNN channel. Until this effect is carefully studied, it is premature to extract information on dibaryon resonances, if they exist, from the data. Our model also gives definite predictions of np scattering. Precise np polarization measurements at higher energy > 0.6 GeV are needed to have a complete test of our model. Finally, the present model Hamiltonian can be used to carry our many-body calculations

Artificial Astrocytes Improve Neural Network Performance

Science.gov (United States)

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Artificial astrocytes improve neural network performance.

Directory of Open Access Journals (Sweden)

Ana B Porto-Pazos

Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial astrocytes improve neural network performance.

Science.gov (United States)

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

New approach to the theory of coupled πNN-NN system. III. A three-body limit

International Nuclear Information System (INIS)

Avishai, Y.; Mizutani, T.

1980-01-01

In the limit where the pion is restricted to be emitted only by the nucleon that first absorbed it, it is shown that the equations previously developed to describe the couple πNN (πd) - NN system reduce to conventional three-body equations. Specifically, it is found in this limit that the input πN p 11 amplitude which, put on-shell, is directly related to the experimental phase shift, contrary to the original equations where the direct (dressed) nucleon pole term and the non-pole part of this partial wave enter separately. The present study clarifies the limitation of pure three-body approach to the πNN-NN problems as well as suggests a rare opportunity of observing a possible resonance behavior in the non-pole part of the πN P 11 amplitude through πd experiments

Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

Directory of Open Access Journals (Sweden)

Chunling DU

2012-03-01

Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

Prediction of the electron redundant SinNn fullerenes

Science.gov (United States)

Yang, Huihui; Song, Yan; Zhang, Yan; Chen, Hongshan

2018-05-01

The stabilities and electronic structures of SimAln-mNn and SinNn (n = 16, 20, m = 12 and n = 24, m = 16) fullerene-like cages have been investigated using density functional method B3LYP and the second-order perturbation theory MP2. The results show that the SimAln-mNn and SinNn fullerenes are more stable than the AlN counterparts. Comparing with the corresponding AlnNn cages, one silicon atom in each Si2N2 square protrudes and the excess electrons reside as lone pair electrons at the outside of the protrudent Si atoms. Analyses on the electronic structures suggest that the Sisbnd N bonds are covalent bonding with strong polarity. The ELF (electron localization function) shows large electron pair probability between Si and N atoms. The orbital interactions between Si and N are stronger than that between Al and N atoms; the overlap integral is 0.40 per Sisbnd N bond in SinNn and 0.34 per Alsbnd N bond in AlnNn. The AIM (atoms in molecule) charges on the Al atoms in AlnNn and SimAln-mNn are 2.37 and 2.40. The charges on the in-plane and protrudent Si atoms are about 2.88 and 1.50 respectively. Considering the large local dipole moments around the protrudent Si atoms, the electrostatic interactions are also favorable to the SiN cages.

What quark theory gives for the potential description of the parity violation in NN interactions

International Nuclear Information System (INIS)

Dubovik, V.M.; Zenkin, S.V.

1982-01-01

The constants of the parity violating (PV) πNN, rhoNN and #betta#NN interactions are calculated in the framework of quark picture based on the standard SU(2)sub(L)xU(1) electroweak model with account for the QCD corrections. The constants are close to the well-known ''best values'', which provide a successful fit to the low-energy PV experimental data

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Science.gov (United States)

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

Predicting Diameter Distributions of Longleaf Pine Plantations: A Comparison Between Artificial Neural Networks and Other Accepted Methodologies

Science.gov (United States)

Daniel J. Leduc; Thomas G. Matney; Keith L. Belli; V. Clark Baldwin

2001-01-01

Artificial neural networks (NN) are becoming a popular estimation tool. Because they require no assumptions about the form of a fitting function, they can free the modeler from reliance on parametric approximating functions that may or may not satisfactorily fit the observed data. To date there have been few applications in forestry science, but as better NN software...

Modified GMDH-NN algorithm and its application for global sensitivity analysis

International Nuclear Information System (INIS)

Song, Shufang; Wang, Lu

2017-01-01

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. - Highlights: • The GMDH-NN is improved to construct the explicit polynomial model of optimal complexity by self-organization. • The paper aims at combining improved GMDH-NN with HDMR expansions and using it to compute Sobol' indices directly. • The method can be applied in uniform, normal and exponential distribution by using suitable orthogonal polynomials. • Engineering examples, e.g., electronic circuit models can be solved by the presented method.

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

Directory of Open Access Journals (Sweden)

Yuzheng Yang

2014-01-01

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

Forecasting water demand using back propagation networks in the operation of reservoirs in the citarum cascade, west java, indonesia

Directory of Open Access Journals (Sweden)

Mulya R. Mashudi

2017-11-01

Full Text Available This study investigates the use of Neural Networks (NN as a potential means of more accurately forecasting water demand in the Citarum River basin cascade. Neural Networks have the ability to recognise nonlinear patterns when sufficiently trained with historical data. The study constructs a NN model of the cascade, based on Back Propagation Networks (BPN. Data representing physical characteristics and meteorological conditions in the Citarum River basin from 1989 through 1995 were used to train the BPN. Nonlinear activation functions (sigmoid, tangent, and gaussian functions and hidden layers in the BPN were chosen for the study.

Neural network retrieval of soil moisture: application to SMOS

Science.gov (United States)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Kolasssa, Jana; Jimenez, Carlos; Cabot, Francois; Mahmoodi, Ali

2014-05-01

We present an efficient statistical soil moisture (SM) retrieval method using SMOS brightness temperatures (BTs) complemented with MODIS NDVI and ASCAT backscattering data. The method is based on a feed-forward neural network (hereafter NN) trained with SM from ECMWF model predictions or from the SMOS operational algorithm. The best compromise to retrieve SM with NNs from SMOS brightness temperatures in a large fraction of the swath (~ 670 km) is to use incidence angles from 25 to 60 degrees (in 7 bins of 5 deg width) for both H and V polarizations. The correlation coefficient (R) of the SM retrieved by the NN and the reference SM dataset (ECMWF or SMOS L3) is 0.8. The correlation coefficient increases to 0.91 when adding as input MODIS NDVI, ECOCLIMAP sand and clay fractions and one of the following data: (i) active microwaves observations (ASCAT backscattering coefficient at 40 deg incidence angle), (ii) ECMWF soil temperature. Finally, the correlation coefficient increases to R=0.94 when using a normalization index computed locally for each latitude-longitude point with the maximum and minimum BTs and the associated SM values from the local time series. Global maps of SM obtained with NNs reproduce well the spatial structures present in the reference SM datasets, implying that the NN works well for a wide range of ecosystems and physical conditions. In addition, the results of the NNs have been evaluated at selected locations for which in situ measurements are available such as the USDA-ARS watersheds (USA), the OzNet network (AUS) and USDA-NRCS SCAN network (USA). The time series of SM obtained with NNs reproduce the temporal behavior measured with in situ sensors. For well known sites where the in situ measurement is representative of a 40 km scale like the Little Washita watershed, the NN models show a very high correlation of (R = 0.8-0.9) and a low standard deviation of 0.02-0.04 m3/m3 with respect to the in situ measurements. When comparing with all the in

Communication: Fitting potential energy surfaces with fundamental invariant neural network

Energy Technology Data Exchange (ETDEWEB)

Shao, Kejie; Chen, Jun; Zhao, Zhiqiang; Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn [State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China and University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China. (China)

2016-08-21

A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

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Jae Kwon Kim

2017-01-01

Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Medium energy inelastic proton-nucleus scattering with spin dependent NN interaction

International Nuclear Information System (INIS)

Ahmad, I.; Auger, J.P.

1981-12-01

The previously proposed effective profile expansion method for the Glauber multiple scattering model calculation has been extended to the case of proton-nucleus inelastic scattering with spin dependent NN interaction. Using the method which turns out to be computationally simple and of relatively wider applicability, a study of sensitivity of proton-nucleus inelastic scattering calculation to the sometimes neglected momentum transfer dependence of the NN scattering amplitude has been made. We find that the calculated polarization is particularly sensitive in this respect. (author)

New approach to the theory of coupled πNN-NN systems. 4. Completion of formal development

International Nuclear Information System (INIS)

Avishai, Y.; Mizutani, T.

1980-05-01

We complete our discussion pertaining to the coupled πNN-NN system in succession of our previous works and approach the problem in two independent ways. The first one starts from a Hamiltonian formalism and coupled Schroedinger equations whereas the second one employs an off-mass-shell relativistic theory of classifying perturbation diagrams. Both ways lead to connected equations among transition operators in which πNN vertices as well as nucleon propagators are completely dressed and renormalized. Furthermore, the physical amplitudes obey two and three body unitarity relations. The resultant equations form a sound theoretical basis for subsequent numerical calculations leading to the evaluation of physical observables in the reactions π+d→π+d, π+d→N+N and N+N→N+N

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

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Jingli Yang

2016-12-01

Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

Science.gov (United States)

Yang, Jingli; Sun, Zhen; Chen, Yinsheng

2016-01-01

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

Folding model analysis of Λ binding energies and three-body ΛNN force

International Nuclear Information System (INIS)

Mian, M.; Rahman Khan, M.Z.

1988-02-01

Working within the framework of the folding model, we analyze the Λ binding energy data of light hypernuclei with effective two-body ΛN plus three-body ΛNN interaction. The two-body density for the core nucleus required for evaluating the three-body force contribution is obtained in terms of the centre of mass pair correlation. It is found that except for Λ 5 He the data are fairly well explained. The three-body force seems to account for the density dependence of the effective two-body ΛN interaction proposed earlier. (author). 13 refs, 2 tabs

DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive

International Nuclear Information System (INIS)

Akkaya, R.; Kulaksiz, A.A.; Aydogdu, O.

2007-01-01

This paper presents a brushless dc motor drive for heating, ventilating and air conditioning fans, which is utilized as the load of a photovoltaic system with a maximum power point tracking (MPPT) controller. The MPPT controller is based on a genetic assisted, multi-layer perceptron neural network (GA-MLP-NN) structure and includes a DC-DC boost converter. Genetic assistance in the neural network is used to optimize the size of the hidden layer. Also, for training the network, a genetic assisted, Levenberg-Marquardt (GA-LM) algorithm is utilized. The off line GA-MLP-NN, trained by this hybrid algorithm, is utilized for online estimation of the voltage and current values in the maximum power point. A brushless dc (BLDC) motor drive system that incorporates a motor controller with proportional integral (PI) speed control loop is successfully implemented to operate the fans. The digital signal processor (DSP) based unit provides rapid achievement of the MPPT and current control of the BLDC motor drive. The performance results of the system are given, and experimental results are presented for a laboratory prototype of 120 W

Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

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Yussouf Nahayo

2016-04-01

Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

Neural networks for genetic epidemiology: past, present, and future

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Motsinger-Reif Alison A

2008-07-01

Full Text Available Abstract During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes. In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN and Grammatical Evolution Neural Networks (GENN, for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work.

Experimental calibration of forward and inverse neural networks for rotary type magnetorheological damper

DEFF Research Database (Denmark)

Bhowmik, Subrata; Weber, Felix; Høgsberg, Jan Becker

2013-01-01

This paper presents a systematic design and training procedure for the feed-forward backpropagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output...

Issues in the use of neural networks in information retrieval

CERN Document Server

Iatan, Iuliana F

2017-01-01

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.

Write up of the codes for microscopic models of NN and NA scattering

International Nuclear Information System (INIS)

Amos, K.

1998-01-01

This report documents the essential details of the NN and NA computer programs that culminate in the prediction of elastic and inelastic nucleon scattering observables form optical potentials generated by full folding and effective NN interaction within the nuclear medium. That same (energy and density dependent) effective interaction is used as the transition operator in the distorted wave approximation (DWA) for inelastic (and charge exchange) nucleon scattering from nuclei. The report consists of four sections: 1) general remarks and program locations, 2) the t- and g-matrix codes and how to use them, 3) the effective interaction codes and how to use them, and 4) the NA codes, DWBA97 and DWBB97 and how to use them. (author)

Analysis of NN amplitudes up to 2.5 GeV: an optical model and geometric interpretation

International Nuclear Information System (INIS)

Geramb, H.V. von; Universitaet Hamburg, Hamburg,; Amos, K.A.; Labes, H.; Sander, M.

1998-01-01

We analyse the SM97 partial wave amplitudes for nucleon-nucleon (NN) scattering to 2.5 GeV, in which resonance and meson production effects are evident for energies above pion production threshold. Our analyses are based upon boson exchange or quantum inversion potentials with which the sub-threshold data are fit perfectly. Above 300 MeV they are extrapolations, to which complex short ranged Gaussian potentials are added in the spirit of the optical models of nuclear physics and of diffraction models of high energy physics. The data to 2.5 GeV are all well fit. The energy dependences of these Gaussians are very smooth save for precise effects caused by the known Δ and N* resonances. With this approach, we confirm that the geometrical implications of the profile function found from diffraction scattering are pertinent in the regime 300 MeV to 2.5 GeV and that the overwhelming part of meson production comes from the QCD sector of the nucleons when they have a separation of their centres of 1 to 1.2 fm. This analysis shows that the elastic NN scattering data above 300 MeV can be understood with a local potential operator as well as has the data below 300 MeV

Spectroscopy of light nuclei with realistic NN interaction JISP

International Nuclear Information System (INIS)

Shirokov, A. M.; Vary, J. P.; Mazur, A. I.; Weber, T. A.

2008-01-01

Recent results of our systematic ab initio studies of the spectroscopy of s- and p-shell nuclei in fully microscopic large-scale (up to a few hundred million basis functions) no-core shell-model calculations are presented. A new high-quality realistic nonlocal NN interaction JISP is used. This interaction is obtained in the J-matrix inverse-scattering approach (JISP stands for the J-matrix inverse-scattering potential) and is of the form of a small-rank matrix in the oscillator basis in each of the NN partial waves, providing a very fast convergence in shell-model studies. The current purely two-body JISP model of the nucleon-nucleon interaction JISP16 provides not only an excellent description of two-nucleon data (deuteron properties and np scattering) with χ 2 /datum = 1.05 but also a better description of a wide range of observables (binding energies, spectra, rms radii, quadrupole moments, electromagnetic-transition probabilities, etc.) in all s-and p-shell nuclei than the best modern interaction models combining realistic nucleon-nucleon and three-nucleon interactions.

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks.

Science.gov (United States)

Han, Yongkoo; Park, Kisung; Hong, Jihye; Ulamin, Noor; Lee, Young-Koo

2015-07-27

The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l-κNN query, we must discover all sets of κNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l-κNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.

Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

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Ibnkahla Mohamed

2003-01-01

Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

Neural network potential for Al-Mg-Si alloys

Science.gov (United States)

Kobayashi, Ryo; Giofré, Daniele; Junge, Till; Ceriotti, Michele; Curtin, William A.

2017-10-01

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

Can the Σ-nn system be bound?

International Nuclear Information System (INIS)

Stadler, A.; Gibson, B.F.

1994-01-01

Motivated by the Σ-hypernuclear states reported in (K - ,π ± ) experiments, we have explored the possibility that there exists a particle-stable Σ - nn bound state. For the Juelich A hyperon-nucleon, realistic-force model, our calculations yield little reason to expect a positive-parity bound state or resonance in either the J=1/2 or the J=3/2 channels

A meson-theoretical model for the πρinteraction and the πNN form factor

International Nuclear Information System (INIS)

Janssen, G.

1993-02-01

Based on the successful description of ππ interaction within the meson exchange framework we extend our model to a further meson-meson process, the πρ interaction. In comparison with ππ interaction several new aspects appear in the πρ system. Besides other points the main new aspect is due to the instability of the ρ-meson, which induces a transition of the πρ system into a 3π system. A realistic model for the πρ interaction should take into account coupling to this three-particle channel and thus requires application of the complicated three-body formalism. Having obtained the πρ T-matrix we first investigate the L JT = S 11 partial wave, i.e. the A 1 -channel. Here, ρ-exchange provides the dominant exchange contribution; however, it is definitely necessary to include the genuine A 1 -pole term. The detailed investigation of the structure of the resulting T-matrix provides an explanation for discrepancies existing in the interpretation of different experimental data sets. As a first application of our πρ interaction model we investigate the πNN vertex. We consider the meson cloud part of the vertex extension by calculating loop corrections to the pointlike vertex. It turns out that the formfactor is rather soft in our calculation (Λ πNN ≅ 1.0GeV; monopole parametrization). For this result the inclusion of the πρ interaction is quite important, because it leads to a remarkable shift in the formfactor towards lower cut-off masses. (orig.) [de

Identification and control of plasma vertical position using neural network in Damavand tokamak

International Nuclear Information System (INIS)

Rasouli, H.; Rasouli, C.; Koohi, A.

2013-01-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Identification and control of plasma vertical position using neural network in Damavand tokamak

Energy Technology Data Exchange (ETDEWEB)

Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

2013-02-15

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Detection and classification of power quality disturbances using S-transform and modular neural network

Energy Technology Data Exchange (ETDEWEB)

Bhende, C.N.; Mishra, S.; Panigrahi, B.K. [Department of Electrical Engineering, Indian Institute of Technology, New Delhi 110016 (India)

2008-01-15

This paper presents an S-transform based modular neural network (NN) classifier for recognition of power quality disturbances. The excellent time - frequency resolution characteristics of the S-transform makes it an attractive candidate for the analysis of power quality (PQ) disturbances under noisy condition and has the ability to detect the disturbance correctly. On the other hand, the performance of wavelet transform (WT) degrades while detecting and localizing the disturbances in the presence of noise. Features extracted by using the S-transform are applied to a modular NN for automatic classification of the PQ disturbances that solves a relatively complex problem by decomposing it into simpler subtasks. Modularity of neural network provides better classification, model complexity reduction and better learning capability, etc. Eleven types of PQ disturbances are considered for the classification. The simulation results show that the combination of the S-transform and a modular NN can effectively detect and classify different power quality disturbances. (author)

Statistical model calculation of fission isomer excitation functions in (n,n') and (n,γ) reactions

International Nuclear Information System (INIS)

Chatterjee, A.; Athougies, A.L.; Mehta, M.K.

1977-01-01

A statistical model developed by Britt and others (1971, 1973) to analyze isomer excitation functions in spallation type reactions like (α,2n) has been adopted in fission isomer calculations for (n,n') and (n,γ) reactions. Calculations done for 235 U(n,n')sup(238m)U and 235 U(n,γ)sup(236m)U reactions have been compared with experimental measurements. A listing of the computer program ISOMER using FORTRAN IV to calculate the isomer to prompt ratios is given. (M.G.B.)

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

Energy Technology Data Exchange (ETDEWEB)

Mottaghitalab, M.; Nikkhah, N.; Darmani-Kuhi, H.; López, S.; France, J.

2015-07-01

Artificial neural network models offer an alternative to linear regression analysis for predicting the amino acid content of feeds from their chemical composition. A group method of data handling-type neural network (GMDH-type NN), with an evolutionary method of genetic algorithm, was used to predict methionine (Met) and lysine (Lys) contents of soybean meal (SBM) and fish meal (FM) from their proximate analyses (i.e. crude protein, crude fat, crude fibre, ash and moisture). A data set with 119 data lines for Met and 116 lines for Lys was used to develop GMDH-type NN models with two hidden layers. The data lines were divided into two groups to produce training and validation sets. The data sets were imported into the GEvoM software for training the networks. The predictive capability of the constructed models was evaluated by their abilities to estimate the validation data sets accurately. A quantitative examination of goodness of fit for the predictive models was made using a number of precision, concordance and bias statistics. The statistical performance of the models developed revealed close agreement between observed and predicted Met and Lys contents for SBM and FM. The results of this study clearly illustrate the validity of GMDH-type NN models to estimate accurately the amino acid content of poultry feed ingredients from their chemical composition . (Author)

An artificial neural network approach and sensitivity analysis in predicting skeletal muscle forces.

Science.gov (United States)

Vilimek, Miloslav

2014-01-01

This paper presents the use of an artificial neural network (NN) approach for predicting the muscle forces around the elbow joint. The main goal was to create an artificial NN which could predict the musculotendon forces for any general muscle without significant errors. The input parameters for the network were morphological and anatomical musculotendon parameters, plus an activation level experimentally measured during a flexion/extension movement in the elbow. The muscle forces calculated by the 'Virtual Muscle System' provide the output. The cross-correlation coefficient expressing the ability of an artificial NN to predict the "true" force was in the range 0.97-0.98. A sensitivity analysis was used to eliminate the less sensitive inputs, and the final number of inputs for a sufficient prediction was nine. A variant of an artificial NN for a single specific muscle was also studied. The artificial NN for one specific muscle gives better results than a network for general muscles. This method is a good alternative to other approaches to calculation of muscle force.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

Science.gov (United States)

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Investigation of the charge exchange process π-d→π0nn

International Nuclear Information System (INIS)

Il-Tong Cheon.

1978-02-01

The transition rate has been calculated for the charge exchange of stopped π - in the deuteron. The present result is hω(π - d→π 0 nn)=0.695x10 -4 eV. Making use of the value hω(π - d→nn)=0.682 eV, which was previously obtained, estimated the branching ratio ω(π - d→π 0 nn)|ω(π - d→nn)=1.02x10 -4 has been estimated

Static Voltage Stability Analysis by Using SVM and Neural Network

Directory of Open Access Journals (Sweden)

Mehdi Hajian

2013-01-01

Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

Spin correlations in NN-NNπ reactions

International Nuclear Information System (INIS)

Davison, N.E.

1990-01-01

This paper reports that even after years of intensive work on the coupled NN-NNπ reactions, there are still some remarkable simple things that we do not know about the NN interactions at a few hundred MeV notably: Do dibaryon resonances exist, and if so, what are their masses and widths? How much isospin I = O interaction is there in the np channel? Why is the microscopic description of such a basic process as single pion production so elusive?

Pion, Kaon, and Proton Production in Central Pb--Pb Collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

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Abelev, Betty; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agocs, Andras Gabor; Agostinelli, Andrea; Aguilar Salazar, Saul; Ahammed, Zubayer; Ahmad, Arshad; Ahmad, Nazeer; Ahn, Sang Un; Akindinov, Alexander; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Almaraz Avina, Erick Jonathan; Alme, Johan; Alt, Torsten; Altini, Valerio; Altinpinar, Sedat; Altsybeev, Igor; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshauser, Harald; Arbor, Nicolas; Arcelli, Silvia; Armesto, Nestor; Arnaldi, Roberta; Aronsson, Tomas Robert; Arsene, Ionut Cristian; Arslandok, Mesut; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Aysto, Juha Heikki; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bailhache, Raphaelle Marie; Bala, Renu; Baldini Ferroli, Rinaldo; Baldisseri, Alberto; Baldit, Alain; Baltasar Dos Santos Pedrosa, Fernando; Ban, Jaroslav; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batyunya, Boris; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont-Moreno, Ernesto; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bergognon, Anais Annick Erica; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhati, Ashok Kumar; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Bjelogrlic, Sandro; Blanco, F; Blanco, Francesco; Blau, Dmitry; Blume, Christoph; Bock, Nicolas; Boettger, Stefan; Bogdanov, Alexey; Boggild, Hans; Bogolyubsky, Mikhail; Boldizsar, Laszlo; Bombara, Marek; Book, Julian; Borel, Herve; Borissov, Alexander; Bose, Suvendu Nath; Bossu, Francesco; Botje, Michiel; Botta, Elena; Boyer, Bruno Alexandre; Braidot, Ermes; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Browning, Tyler Allen; Broz, Michal; Brun, Rene; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Busch, Oliver; Buthelezi, Edith Zinhle; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calvo Villar, Ernesto; Camerini, Paolo; Canoa Roman, Veronica; Cara Romeo, Giovanni; Carena, Francesco; Carena, Wisla; Carminati, Federico; Casanova Diaz, Amaya Ofelia; Castillo Castellanos, Javier Ernesto; Casula, Ester Anna Rita; Catanescu, Vasile; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Charvet, Jean-Luc Fernand; Chattopadhyay, Sukalyan; Chattopadhyay, Subhasis; Chawla, Isha; Cherney, Michael Gerard; Cheshkov, Cvetan; Cheynis, Brigitte; Chiavassa, Emilio; Chibante Barroso, Vasco Miguel; Chinellato, David; Chochula, Peter; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Coccetti, Fabrizio; Colamaria, Fabio; Colella, Domenico; Conesa Balbastre, Gustavo; Conesa del Valle, Zaida; Constantin, Paul; Contin, Giacomo; Contreras, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Cotallo, Manuel Enrique; Crochet, Philippe; Cuautle, Eleazar; Cunqueiro, Leticia; D'Erasmo, Ginevra; Dainese, Andrea; Dalsgaard, Hans Hjersing; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Kushal; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; de Barros, Gabriel; De Caro, Annalisa; de Cataldo, Giacinto; de Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; de Rooij, Raoul Stefan; 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Knichel, Michael Linus; Knospe, Anders Garritt; Koch, Kathrin; Kohler, Markus; Kollegger, Thorsten; Kolojvari, Anatoly; Kondratiev, Valery; Kondratyeva, Natalia; Konevskih, Artem; Korneev, Andrey; Kour, Ravjeet; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kramer, Frederick; Kraus, Ingrid Christine; Krawutschke, Tobias; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Krus, Miroslav; Kryshen, Evgeny; Krzewicki, Mikolaj; Kucheriaev, Yury; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paul; Kulakov, Igor; Kumar, Jitendra; Kurashvili, Podist; Kurepin, A; Kurepin, AB; Kuryakin, Alexey; Kushpil, Svetlana; Kushpil, Vasily; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron de Guevara, Pedro; Lakomov, Igor; Langoy, Rune; Lara, Camilo Ernesto; Lardeux, Antoine Xavier; Lazzeroni, Cristina; Le Bornec, Yves; Lea, Ramona; Lechman, Mateusz; Lee, Graham Richard; Lee, Ki Sang; Lee, Sung Chul; Lefevre, Frederic; 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2012-01-01

In this Letter we report the first results on $\\pi^\\pm$, K$^\\pm$, p and pbar production at mid-rapidity (|y|<0.5) in central Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV, measured by the ALICE experiment at the LHC. The pT distributions and yields are compared to previous results at $\\sqrt{s_{NN}}$ = 200 GeV and expectations from hydrodynamic and thermal models. The spectral shapes indicate a strong increase of the radial flow velocity with $\\sqrt{s_{NN}}$, which in hydrodynamic models is expected as a consequence of the increasing particle density. While the K/$\\pi$ ratio is in line with predictions from the thermal model, the p/$\\pi$ ratio is found to be lower by a factor of about 1.5. This deviation from thermal model expectations is still to be understood.

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

Science.gov (United States)

Shen, Lin; Yang, Weitao

2018-03-13

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of

Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm Optimization

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Chih-Hong Lin

2015-01-01

Full Text Available Because the V-belt continuously variable transmission (CVT system driven by permanent magnet synchronous motor (PMSM has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network (NN control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization (PSO is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

Stress Assignment in N+N Combinations in Arabic

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Abdel Rahman Mitib Altakhaineh

2017-09-01

Full Text Available The validity of stress as a criterion to distinguish between compounds and phrases has been investigated in many languages, including English (see e.g. Lieber 2005: 376; Booij 2012: 84. However, the possibility of using stress as a criterion in this way has not been investigated for Arabic. Siloni (1997: 21 claims that in N+N combinations in Semitic languages, stress always falls on the second element. However, the results of a study using PRAAT reveal that, in Modern Standard Arabic (MSA and Jordanian Arabic (JA, stress plays no role in distinguishing between various N+N combinations, i.e. compounds and phrases, e.g.ˈmuʕallim lfiizyaaʔ ‘the physics teacher’ vs.ˈbayt lwalad ‘the boy’s house’, respectively. Analysis shows that the default position of stress in N+N combinations in MSA and JA is on the first element. There is only one systematic exception, which is phonetically conditioned: in N+N combinations with assimilated geminates on the word boundary, a secondary stress or perhaps double stress is assigned.

Centrality determination using the Glauber model in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV

CERN Document Server

2018-01-01

This document describes the methods used by the ALICE Collaboration for determining the centrality of Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV at the LHC within the Glauber model. The half-density radius $R$ and the diffusivity $a$ describing the Xe-129 nucleus with a 2-parameter Fermi distribution have been derived from a recent electron scattering measurement for the Xe-132 nucleus. The deformation parameter $\\beta$ has been derived by an interpolation between measured deformation parameters for the even-$A$ Xe isotopes. The inelastic nucleon-nucleon cross-section at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV has been estimated by interpolation of pp data at different center-of-mass energies. The particle multiplicity per nucleon-nucleon collision has been parameterized by a negative binomial distribution (NBD) and a number of independently emitting sources (ancestors) given by a linear combination of $N_{\\rm part}$ and $N_{\\rm coll}$. The NBD parameters $\\mu$ and $k$ and the ancestor parameter $f$ are then...

Architecture and performance of neural networks for efficient A/C control in buildings

International Nuclear Information System (INIS)

Mahmoud, Mohamed A.; Ben-Nakhi, Abdullatif E.

2003-01-01

The feasibility of using neural networks (NNs) for optimizing air conditioning (AC) setback scheduling in public buildings was investigated. The main focus is on optimizing the network architecture in order to achieve best performance. To save energy, the temperature inside public buildings is allowed to rise after business hours by setting back the thermostat. The objective is to predict the time of the end of thermostat setback (EoS) such that the design temperature inside the building is restored in time for the start of business hours. State of the art building simulation software, ESP-r, was used to generate a database that covered the years 1995-1999. The software was used to calculate the EoS for two office buildings using the climate records in Kuwait. The EoS data for 1995 and 1996 were used for training and testing the NNs. The robustness of the trained NN was tested by applying them to a 'production' data set (1997-1999), which the networks have never 'seen' before. For each of the six different NN architectures evaluated, parametric studies were performed to determine the network parameters that best predict the EoS. External hourly temperature readings were used as network inputs, and the thermostat end of setback (EoS) is the output. The NN predictions were improved by developing a neural control scheme (NC). This scheme is based on using the temperature readings as they become available. For each NN architecture considered, six NNs were designed and trained for this purpose. The performance of the NN analysis was evaluated using a statistical indicator (the coefficient of multiple determination) and by statistical analysis of the error patterns, including ANOVA (analysis of variance). The results show that the NC, when used with a properly designed NN, is a powerful instrument for optimizing AC setback scheduling based only on external temperature records

Handling limited datasets with neural networks in medical applications: A small-data approach.

Science.gov (United States)

Shaikhina, Torgyn; Khovanova, Natalia A

2017-01-01

Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Study of production and cold nuclear matter effects in pPb collisions at s NN √sNN = 5 TeV

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Aaij, R.; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Cartelle, P. Alvarez; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; Everse, LA; Anderlini, L.; Anderson, J.; Andreassen, P.R.; Andreotti, M.; Andrews, J.E.; Appleby, R. B.; Gutierrez, O. Aquines; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Balagura, V.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bettler, M-O.; Van Beuzekom, Martin; Bien, A.; Bifani, S.; Bird, T.D.; Bizzeti, A.; Bjørnstad, P. M.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Borsato, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Brambach, T.; Van Den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N. H.; Brown, H.; Bursche, A.; Busetto, G.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Gomez, M. Calvo; Camboni, A.; Campana, P.; Perez, D. H. Campora; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carranza-Mejia, H.; Carson, L.; Akiba, K. Carvalho; Casse, G.; Cassina, L.; Garcia, L. Castillo; Cattaneo, M.; Cauet, Ch; Cenci, R.; Charles, M.; Charpentier, Ph; Chen, S.; Cheung, S-F.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Vidal, X. Cid; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Counts, I.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Torres, M. Cruz; Cunliffe, S.; Currie, C.R.; D'Ambrosio, C.; Dalseno, J.; David, P.; Davis, A.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J. M.; Paula, L.E.; da-Silva, W.S.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Déléage, N.; Derkach, D.; Deschamps, O.; Dettori, F.; Di Canto, A.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Suárez, A. Dosil; Dossett, D.; Dovbnya, A.; Dujany, G.; Dupertuis, F.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, T. M.; Falabella, A.; Färber, C.; Farinelli, C.; Farley, N.; Farry, S.; Ferguson, D.; Albor, V. Fernandez; Rodrigues, F. Ferreira; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fontana, Mark; Fontanelli, F.; Forty, R.; De Aguiar Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Torreira, A. Gallas; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; Garofoli, J.; Tico, J. Garra; Garrido, L.; Carvalho-Gaspar, M.; Gauld, Rhorry; Gavardi, L.; Gersabeck, E.; Gersabeck, M.; Gershon, T. J.; Ghez, Ph; Gianelle, A.; Giani, S.; Gibson, V.; Giubega, L.; Gligorov, V. V.; Göbel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.Q.; Head-Gordon, Teresa; Gotti, C.; Gándara, M. Grabalosa; Diaz, R. Graciani; Cardoso, L. A.Granado; Graugés, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Griffith, P.; Grillo, L.; Grünberg, O.; Gui, B.; Gushchin, E.; Guz, Yu; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Hampson, T.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; Hartmann, T.; He, J.; Head, T.; Heijne, V.; Hennessy, K.; Henrard, P.; Henry, L.; Morata, J. A.Hernando; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hulsbergen, W.; Hunt, P.; Hussain, N.; Hutchcroft, D. E.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jaton, P.; Jawahery, A.; Jezabek, M.; Jing, F.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kaballo, M.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T. M.; Kelsey, M. H.; Kenyon, I. R.; Ketel, T.; Khanji, B.; Khurewathanakul, C.; Klaver, S.M.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kucharczyk, M.; Kudryavtsev, V.; Kurek, K.; Kvaratskheliya, T.; La Thi, V. N.; Lacarrere, D.; Lafferty, G. D.; Lai, A.; Lambert, D.M.; Lambert, R. W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T. E.; Lazzeroni, C.; Le Gac, R.; Van Leerdam, J.; Lees, J. P.; Lefèvre, R.; Leflat, A.; Lefrançois, J.; Di Leo, S.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Liles, M.; Lindner, R.; Linn, S.C.; Lionetto, F.; Liu, B.; Liu, G.; Lohn, S.; Longstaff, I.; Lopes, J. H.; Lopez-March, N.; Lowdon, P.; Lu, H.; Lucchesi, D.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Machefert, F.; Machikhiliyan, I. V.; Maciuc, F.; Maev, O.; Malde, S.; Manca, G.; Mancinelli, G.; Manzali, M.; Maratas, J.; Marchand, J. F.; Marconi, U.; Benito, C. Marin; Marino, P.; Märki, R.; Marks, J.; Martellotti, G.; Martens, A.; Sánchez, A. Martín; Martinelli-Boneschi, F.; Santos, D. Martinez; Vidal, F. Martinez; Tostes, D. Martins; Massafferri, A.; Matev, R.; Mathe, Z.; Matteuzzi, C.; Mazurov, A.; McCann, M.; McCarthy, J.; Mcnab, A.; McNulty, R.; McSkelly, B.; Meadows, B. T.; Meier, F.; Meissner, M.; Merk, M.; Milanes, D. A.; Minard, M. N.; Moggi, N.; Rodriguez, J. Molina; Monteil, S.; Moran-Zenteno, D.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Müller, Karl; Muresan, R.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen, T. D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, G.; Orlandea, M.; Goicochea, J. M.Otalora; Owen, R.P.; Oyanguren, A.; Pal, B. K.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C. J.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrignani, C.; Alvarez, A. Pazos; Pearce, D.A.; Pellegrino, A.; Altarelli, M. Pepe; Perazzini, S.; Trigo, E. Perez; Perret, P.; Perrin-Terrin, M.; Pescatore, L.; Pesen, E.; Petridis, K.; Petrolini, A.; Olloqui, E. Picatoste; Pietrzyk, B.; Pilař, T.; Pinci, D.; Pistone, A.; Playfer, S.; Casasus, M. Plo; Polci, F.; Poluektov, A.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Powell, A.; Prisciandaro, J.; Pritchard, C.A.; Prouve, C.; Pugatch, V.; Navarro, A. Puig; Punzi, G.; Qian, Y.W.; Rachwal, B.; Rademacker, J. H.; Rakotomiaramanana, B.; Rama, M.; Rangel, M. S.; Raniuk, I.; Rauschmayr, N.; Raven, G.; Reichert, S.; Reid, M.; dos Reis, A. C.; Ricciardi, S.; Richards, Al.; Rihl, M.; Rinnert, K.; Molina, V. Rives; Romero, D. A.Roa; Robbe, P.; Rodrigues, A. B.; Rodrigues, L.E.T.; Perez, P. Rodriguez; Roiser, S.; Romanovsky, V.; Vidal, A. Romero; Rotondo, M.; Rouvinet, J.; Ruf, T.; Ruffini, F.; Ruiz, van Hapere; Valls, P. Ruiz; Sabatino, G.; Silva, J. J.Saborido; Sagidova, N.; Sail, P.; Saitta, B.; Guimaraes, V. Salustino; Mayordomo, C. Sanchez; Sedes, B. Sanmartin; Santacesaria, R.; Rios, C. Santamarina; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Savrina, D.; Schiller, M.; Schindler, R. H.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M. H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Senderowska, K.; Sepp, I.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Coutinho, R. Silva; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, N. A.; Smith, E.; Smith, E.; Smith, J; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Soomro, F.; de Souza, D.K.; De Paula, B. Souza; Spaan, B.; Sparkes, A.; Spinella, F.; Spradlin, P.; Stagni, F.; Stahl, S.; Steinkamp, O.; Stenyakin, O.; Stevenson-Moore, P.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Stroili, R.; Subbiah, V. K.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szczypka, P.; Szilard, D.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; Van Tilburg, J.; Tisserand, V.; Tobin, M. N.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, N.T.M.T.; Tresch, M.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Garcia, M. Ubeda; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vallier, A.; Gomez, R. Vazquez; Regueiro, P. Vazquez; Sierra, C. Vázquez; Vecchi, S.; Velthuis, M.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Diaz, M. Vieites; Vilasis-Cardona, X.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voß, C.; Voss, H.; De Vries, J. A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, John; Wandernoth, S.; Wang, J.; Ward, D. R.; Watson, N. K.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wilkinson, G.; Williams, M.P.; Williams, M.; Wilson, James F; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wright, S.J.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, Z.; Xu, Z.; Yang, Z.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, F.; Zhang, L.; Zhang, W. C.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zvyagin, A.

2014-01-01

Production of mesons in proton-lead collisions at a nucleon-nucleon centre-of-mass energy s NN √sNN = 5 TeV is studied with the LHCb detector. The analysis is based on a data sample corresponding to an integrated luminosity of 1.6 nb-1. The mesons of transverse momenta up to 15 GeV/c are

Effective field theory for NN interactions

International Nuclear Information System (INIS)

Tran Duy Khuong; Vo Hanh Phuc

2003-01-01

The effective field theory of NN interactions is formulated and the power counting appropriate to this case is reviewed. It is more subtle than in most effective field theories since in the limit that the S-wave NN scattering lengths go to infinity. It is governed by nontrivial fixed point. The leading two body terms in the effective field theory for nucleon self interactions are scale invariant and invariant under Wigner SU(4) spin-isospin symmetry in this limit. Higher body terms with no derivatives (i.e. three and four body terms) are automatically invariant under Wigner symmetry. (author)

More about the comparison of local and non-local NN interaction models

International Nuclear Information System (INIS)

Amghar, A.; Desplanques, B.

2003-01-01

The effect of non-locality in the NN interaction with an off-energy shell character has been studied in the past in relation with the possibility that some models could be approximately phase-shifts equivalent. This work is extended to a non-locality implying terms that involve an anticommutator with the operator p 2 . It includes both scalar and tensor components. The most recent 'high accuracy' models are considered in the analysis. After studying the deuteron wave functions, electromagnetic properties of various models are compared with the idea that these ones differ by their non-locality but are equivalent up to a unitary transformation. It is found that the extra non-local tensor interaction considered in this work tends to re-enforce the role of the term considered in previous works, allowing one to explain almost completely the difference in the deuteron D-state probabilities evidenced by the comparison of the Bonn-QB and Paris models for instance. Conclusions for the effect of the non-local scalar interaction are not so clear. In many cases, it was found that these terms could explain part of the differences that the comparison of predictions for various models evidences but cases where they could not were also found. Some of these last ones have been analyzed in order to pointing out the origin of the failure

Neural network radiative transfer solvers for the generation of high resolution solar irradiance spectra parameterized by cloud and aerosol parameters

International Nuclear Information System (INIS)

Taylor, M.; Kosmopoulos, P.G.; Kazadzis, S.; Keramitsoglou, I.; Kiranoudis, C.T.

2016-01-01

This paper reports on the development of a neural network (NN) model for instantaneous and accurate estimation of solar radiation spectra and budgets geared toward satellite cloud data using a ≈2.4 M record, high-spectral resolution look up table (LUT) generated with the radiative transfer model libRadtran. Two NN solvers, one for clear sky conditions dominated by aerosol and one for cloudy skies, were trained on a normally-distributed and multiparametric subset of the LUT that spans a very broad class of atmospheric and meteorological conditions as inputs with corresponding high resolution solar irradiance target spectra as outputs. The NN solvers were tested by feeding them with a large (10 K record) “off-grid” random subset of the LUT spanning the training data space, and then comparing simulated outputs with target values provided by the LUT. The NN solvers demonstrated a capability to interpolate accurately over the entire multiparametric space. Once trained, the NN solvers allow for high-speed estimation of solar radiation spectra with high spectral resolution (1 nm) and for a quantification of the effect of aerosol and cloud optical parameters on the solar radiation budget without the need for a massive database. The cloudy sky NN solver was applied to high spatial resolution (54 K pixel) cloud data extracted from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat Second Generation 3 (MSG3) satellite and demonstrated that coherent maps of spectrally-integrated global horizontal irradiance at this resolution can be produced on the order of 1 min. - Highlights: • Neural network radiative transfer solvers for generation of solar irradiance spectra. • Sensitivity analysis of irradiance spectra with respect to aerosol and cloud parameters. • Regional maps of total global horizontal irradiance for cloudy sky conditions. • Regional solar radiation maps produced directly from MSG3/SEVIRI satellite inputs.

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

Directory of Open Access Journals (Sweden)

Ahcene Farah

2002-06-01

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

A biologically inspired neural network model to transformation invariant object recognition

Science.gov (United States)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

Directory of Open Access Journals (Sweden)

Ja’fari A.

2014-01-01

Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

A kNN method that uses a non-natural evolutionary algorithm for ...

African Journals Online (AJOL)

We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

International Nuclear Information System (INIS)

Guerra, Fabio A.; Coelho, Leandro dos S.

2008-01-01

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

Directory of Open Access Journals (Sweden)

Xingang Fu

2016-04-01

Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

Energy Technology Data Exchange (ETDEWEB)

Olyaee, Saeed; Hamedi, Samaneh, E-mail: s_olyaee@srttu.edu [Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran (Iran, Islamic Republic of)

2011-02-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

International Nuclear Information System (INIS)

Olyaee, Saeed; Hamedi, Samaneh

2011-01-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

Directory of Open Access Journals (Sweden)

S. N. Kale

2009-01-01

Full Text Available Electromyography (EMG signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors developed an optimal FTLRNN model, which removes the noise from the EMG signal. Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. It is also noticed that the output of the estimated FTLRNN model closely follows the real one. This network is indeed robust as EMG signal tolerates the noise variance from 0.1 to 0.4 for uniform noise and 0.30 for Gaussian noise. It is clear that the training of the network is independent of specific partitioning of dataset. It is seen that the performance of the proposed FTLRNN model clearly outperforms the best Multilayer perceptron (MLP and Radial Basis Function NN (RBF models. The simple NN model such as the FTLRNN with single-hidden layer can be employed to remove noise from EMG signal.

Classification in medical image analysis using adaptive metric k-NN

DEFF Research Database (Denmark)

Chen, Chen; Chernoff, Konstantin; Karemore, Gopal

2010-01-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier...

A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

OpenAIRE

Parisa Bazmi; Manijeh Keshtgary

2016-01-01

Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding ...

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

Directory of Open Access Journals (Sweden)

Hossein Foroozand

2018-03-01

Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.

Hidden neural networks: application to speech recognition

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1998-01-01

We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

Science.gov (United States)

Li, Wenwen; Ando, Yasunobu; Minamitani, Emi; Watanabe, Satoshi

2017-12-01

To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.

Toward automatic time-series forecasting using neural networks.

Science.gov (United States)

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

NN resonance and the corrections to Goldberger-Treiman relation

International Nuclear Information System (INIS)

Bhamathi, G.; Raghavan, S.

1977-01-01

The relevance of the recent experimental observation of possible bound and resonant states in NN scattering to the Goldberger-Treiman (GT) relation is examined. It is pointed out that an S-wave resonance in NN scattering goes a long way towards accounting for the corrections to the GT relations. Values of the mass and width of the resonance capable of giving a reasonable fit for the GT relation are presented. (author)

Modeling the citation network by network cosmology.

Science.gov (United States)

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

Science.gov (United States)

Safi’ie, M. A.; Utami, E.; Fatta, H. A.

2018-03-01

Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

Modelling computer networks

International Nuclear Information System (INIS)

Max, G

2011-01-01

Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

Science.gov (United States)

Pukrittayakamee, A.; Malshe, M.; Hagan, M.; Raff, L. M.; Narulkar, R.; Bukkapatnum, S.; Komanduri, R.

2009-04-01

An improved neural network (NN) approach is presented for the simultaneous development of accurate potential-energy hypersurfaces and corresponding force fields that can be utilized to conduct ab initio molecular dynamics and Monte Carlo studies on gas-phase chemical reactions. The method is termed as combined function derivative approximation (CFDA). The novelty of the CFDA method lies in the fact that although the NN has only a single output neuron that represents potential energy, the network is trained in such a way that the derivatives of the NN output match the gradient of the potential-energy hypersurface. Accurate force fields can therefore be computed simply by differentiating the network. Both the computed energies and the gradients are then accurately interpolated using the NN. This approach is superior to having the gradients appear in the output layer of the NN because it greatly simplifies the required architecture of the network. The CFDA permits weighting of function fitting relative to gradient fitting. In every test that we have run on six different systems, CFDA training (without a validation set) has produced smaller out-of-sample testing error than early stopping (with a validation set) or Bayesian regularization (without a validation set). This indicates that CFDA training does a better job of preventing overfitting than the standard methods currently in use. The training data can be obtained using an empirical potential surface or any ab initio method. The accuracy and interpolation power of the method have been tested for the reaction dynamics of H+HBr using an analytical potential. The results show that the present NN training technique produces more accurate fits to both the potential-energy surface as well as the corresponding force fields than the previous methods. The fitting and interpolation accuracy is so high (rms error=1.2 cm-1) that trajectories computed on the NN potential exhibit point-by-point agreement with corresponding

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Directory of Open Access Journals (Sweden)

Hyung-Ju Cho

2012-01-01

Full Text Available Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Particle identification using artificial neural networks at BESIII

International Nuclear Information System (INIS)

Qin Gang; Lv Junguang; Bian Jianming; Chinese Academy of Sciences, Beijing

2008-01-01

A multilayered perceptrons' neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. (authors)

A 10 nN resolution thrust-stand for micro-propulsion devices

Energy Technology Data Exchange (ETDEWEB)

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert, E-mail: herbert.shea@epfl.ch [Microsystems for Space Technologies Laboratory (LMTS), Ecole Polytechnique Federale de Lausanne (EPFL), Neuchatel (Switzerland)

2015-11-15

We report on the development of a nano-Newton thrust-stand that can measure up to 100 μN thrust from different types of microthrusters with 10 nN resolution. The compact thrust-stand measures the impingement force of the particles emitted from a microthruster onto a suspended plate of size 45 mm × 45 mm and with a natural frequency over 50 Hz. Using a homodyne (lock-in) readout provides strong immunity to facility vibrations, which historically has been a major challenge for nano-Newton thrust-stands. A cold-gas thruster generating up to 50 μN thrust in air was first used to validate the thrust-stand. Better than 10 nN resolution and a minimum detectable thrust of 10 nN were achieved. Thrust from a miniature electrospray propulsion system generating up to 3 μN of thrust was measured with our thrust-stand in vacuum, and the thrust was compared with that computed from beam diagnostics, obtaining agreement within 50 nN to 150 nN. The 10 nN resolution obtained from this thrust-stand matches that from state-of-the-art nano-Newton thrust-stands, which measure thrust directly from the thruster by mounting it on a moving arm (but whose natural frequency is well below 1 Hz). The thrust-stand is the first of its kind to demonstrate less than 3 μN resolution by measuring the impingement force, making it capable of measuring thrust from different types of microthrusters, with the potential of easy upscaling for thrust measurement at much higher levels, simply by replacing the force sensor with other force sensors.

Solution of combinatorial optimization problems by an accelerated hopfield neural network. Kobai kasokugata poppu firudo nyuraru netto ni yoru kumiawase saitekika mondai no kaiho

Energy Technology Data Exchange (ETDEWEB)

Ohori, T.; Yamamoto, H.; Setsu, Nenso; Watanabe, K. (Hokkaido Inst. of Technology, Hokkaido (Japan))

1994-04-20

The accelerated approximate solution of combinatorial optimization problems by symmetry integrating hopfield neural network (NN) has been applied to many combinatorial problems such as the traveling salesman problem, the network planning problem, etc. However, the hopfield NN converges to local minimum solutions very slowly. In this paper, a general inclination model composed by introducing an accelerated parameter to the hopfield model is proposed, and it has been shown that the acceleration parameter can make the model converge to the local minima more quickly. Moreover, simulation experiments for random quadratic combinatorial problems with two and twenty-five variables were carried out. The results show that the acceleration of convergence makes the attraction region of the local minimum change and the accuracy of solution worse. If an initial point is selected around the center of unit hyper cube, solutions with high accuracy not affected by the acceleration parameter can be obtained. 9 refs., 8 figs., 3 tabs.

A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising

DEFF Research Database (Denmark)

Kærgaard, Kevin; Jensen, Søren Hjøllund; Puthusserypady, Sadasivan

2016-01-01

Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of theheart. These signals, however, are often obscured by artifacts/noises from various sources and mini-mization of these artifacts is of paramount importance for detecting anomalies. This paper presents...... athorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble EmpiricalMode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS)adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neu-ral Network (NN...

Sum rules for the ed - NN scattering reactions and microscopic potential field-theoretical approach

International Nuclear Information System (INIS)

Machivariani, A.I.

1996-01-01

The connections between the equal-time commutators of nucleon and photon field-operators and relativistic potential approach of ed - NN scattering equations is established. Namely, it is demonstrated that: 1) equal-time commutator between nucleon field operators generated completeness condition for NN interaction functions, 2) the off-mass shell contributions in γd - NN exchange currents or in microscopic NN potential are determined by equal time commutator between nucleon field operator and photon or nucleon source operators, and 3) equal-time commutators between source operators produce sum rules for same vertex functions and effective potentials [ru

Influence of six-quark bags on the NN interaction in a resonating group scattering calculation

International Nuclear Information System (INIS)

Zhang Zongye; Braeuer, K.; Faessler, A.; Shimizu, K.

1985-01-01

The influence of six-quark bags oin the nucleon-nucleon (NN) interaction is studied in a dynamical calculation of the NN scattering process. The NN interaction is described by the exchange of gluons and pions between quarks and a phenomenological sigma-meson exchange between nucleons. The quark wave functions are harmonic oscillators and the relative wave function between the two nucleons is determined by the resonating group method. At short distances the NN system is allowed to fuse to a six-quark bag where all six quarks are in a ground state or where two quarks are in excited Op states. The sizes of these six-quark bags are dynamical parameters in the resonating group calculation allowing for spatial polarisation effects during the interaction. The S-wave NN scattering data can be reproduced by adjusting the sigma-coupling strength. The main result is that the six-quark bags with an increased radius have a large influence on the NN scattering process. (orig.)

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

Science.gov (United States)

Jamei, Mehdi; Nisnevich, Aleksandr; Wetchler, Everett; Sudat, Sylvia; Liu, Eric

2017-01-01

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Directory of Open Access Journals (Sweden)

Lihe Xi

2017-11-01

Full Text Available The extended range electric vehicle (EREV can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Short term and medium term power distribution load forecasting by neural networks

International Nuclear Information System (INIS)

Yalcinoz, T.; Eminoglu, U.

2005-01-01

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

Science.gov (United States)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Parameter extraction with neural networks

Science.gov (United States)

Cazzanti, Luca; Khan, Mumit; Cerrina, Franco

1998-06-01

In semiconductor processing, the modeling of the process is becoming more and more important. While the ultimate goal is that of developing a set of tools for designing a complete process (Technology CAD), it is also necessary to have modules to simulate the various technologies and, in particular, to optimize specific steps. This need is particularly acute in lithography, where the continuous decrease in CD forces the technologies to operate near their limits. In the development of a 'model' for a physical process, we face several levels of challenges. First, it is necessary to develop a 'physical model,' i.e. a rational description of the process itself on the basis of know physical laws. Second, we need an 'algorithmic model' to represent in a virtual environment the behavior of the 'physical model.' After a 'complete' model has been developed and verified, it becomes possible to do performance analysis. In many cases the input parameters are poorly known or not accessible directly to experiment. It would be extremely useful to obtain the values of these 'hidden' parameters from experimental results by comparing model to data. This is particularly severe, because the complexity and costs associated with semiconductor processing make a simple 'trial-and-error' approach infeasible and cost- inefficient. Even when computer models of the process already exists, obtaining data through simulations may be time consuming. Neural networks (NN) are powerful computational tools to predict the behavior of a system from an existing data set. They are able to adaptively 'learn' input/output mappings and to act as universal function approximators. In this paper we use artificial neural networks to build a mapping from the input parameters of the process to output parameters which are indicative of the performance of the process. Once the NN has been 'trained,' it is also possible to observe the process 'in reverse,' and to extract the values of the inputs which yield outputs

Brain Network Modelling

DEFF Research Database (Denmark)

Andersen, Kasper Winther

Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

Photon production in Pb + Pb collisions at √{sNN } = 2.76 TeV

Science.gov (United States)

Fu, Yong-Ping; Xi, Qin

2018-02-01

We calculate the high energy photon production from the Pb + Pb collisions for different centrality classes at √{sNN } = 2.76 TeV Large Hadron Collider (LHC) energy. The jet energy loss in the jet fragmentation, jet-photon conversion and jet bremsstrahlung is considered by using the Wang-Huang-Sarcevic (WHS) and Baier-Dokshitzer-Mueller-Peigne-Schiff (BDMPS) models. We use the (1 + 1)-dimensional ideal relativistic hydrodynamics to study the collective transverse flow and space-time evolution of the quark gluon plasma (QGP). The numerical results agree well with the ALICE data of the direct photons from the Pb + Pb collisions (√{sNN } = 2.76 TeV) for 0-20%, 20-40% and 40-80% centrality classes.

Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

Science.gov (United States)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Discrimination of panti p → tanti t events by a neural network classifier

International Nuclear Information System (INIS)

Cherubini, A.; Odorico, R.

1992-01-01

Neural network and conventional statistical techniques are compared in the problem of discriminating panti p→tanti t events, with top quarks decaying into anything, from the associated hadronic background at the energy of the Fermilab collider. The NN we develop for this sake is an improved version of Kohonen's learning vector quantization scheme. Performance of the NN as a tanti t event classifier is found to be less satisfactory than that achievable by statistical methods. We conclude that the probable reasons for that are: i) The NN approach presents advantages only when dealing with event distributions in the feature space which substantially differ from Gaussians; ii) NN's require much larger training sets of events than statistical discrimination in order to give comparable results. (orig.)

A recurrent neural network for solving bilevel linear programming problem.

Science.gov (United States)

He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian

2014-04-01

In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.

Failure detection studies by layered neural network

International Nuclear Information System (INIS)

Ciftcioglu, O.; Seker, S.; Turkcan, E.

1991-06-01

Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs

Using neural networks to represent potential surfaces as sums of products.

Science.gov (United States)

Manzhos, Sergei; Carrington, Tucker

2006-11-21

By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.

Resonance particle production in inelastic N-N and π-N interactions

International Nuclear Information System (INIS)

Amelin, N.S.; Barashenkov, V.S.; Slavin, N.V.

1984-01-01

Using the considerations connected with the scaling hypothesis and the Regge pole model the phenomenological expressions are obtained for differential single-particle inclusive production cross sections (Δ + , Δ 0 , Δ ++ ) and (p +- , p 0 , ω) resonances in inelastic π-N and N-N collisions at high energies. These expressions describe the known experimental data in a wide region of kinematic variables from 10 to several thousand GeV

Electric dipole moment of the deuteron in the standard model with NN - ΛN - ΣN coupling

Science.gov (United States)

Yamanaka, Nodoka

2017-07-01

We calculate the electric dipole moment (EDM) of the deuteron in the standard model with | ΔS | = 1 interactions by taking into account the NN - ΛN - ΣN channel coupling, which is an important nuclear level systematics. The two-body problem is solved with the Gaussian Expansion Method using the realistic Argonne v18 nuclear force and the YN potential which can reproduce the binding energies of Λ3H, Λ3He, and Λ4He. The | ΔS | = 1 interbaryon potential is modeled by the one-meson exchange process. It is found that the deuteron EDM is modified by less than 10%, and the main contribution to this deviation is due to the polarization of the hyperon-nucleon channels. The effect of the YN interaction is small, and treating ΛN and ΣN channels as free is a good approximation for the EDM of the deuteron.

A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

Science.gov (United States)

Lahmiri, Salim

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

Science.gov (United States)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

International Nuclear Information System (INIS)

Vitela, J.E.; Martinell, J.J.

1998-01-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN. (author)

Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network

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MUHAMMAD RUSWANDI DJALAL

2017-07-01

Full Text Available ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN. Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN, NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN. The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130

Segmentation of isolated MR images: development and comparison of neuronal networks

International Nuclear Information System (INIS)

Paredes, R.; Robles, M.; Marti-Bonmati, L.; Masia, L.

1998-01-01

Segmentation defines the capacity to differentiate among types of tissues. In MR. it is frequently applied to volumetric determinations. Digital images can be segmented in a number of ways; neuronal networks (NN) can be employed for this purpose. Our objective was to develop algorithms for automatic segmentation using NN and apply them to central nervous system MR images. The segmentation obtained with NN was compared with that resulting from other procedures (region-growing and K means). Each NN consisted of two layers: one based on unsupervised training, which was utilized for image segmentation in sets of K, and a second layer associating each set obtained by the preceding layer with the real set corresponding to the previously segmented objective image. This NN was trained with previously segmented images with supervised regions-growing algorithms and automatic K means. Thus, 4 different segmentation were obtained: region-growing, K means, NN with region-growing and NN with K means. The tissue volumes corresponding to cerebrospinal fluid, gray matter and white matter obtained with the 4 techniques were compared and the most representative segmented image was selected qualitatively by averaging the visual perception of 3 radiologists. The segmentation that best corresponded to the visual perception of the radiologists was that consisting of trained NN with region-growing. In comparison, the other 3 algorithms presented low percentage differences (mean, 3.44%). The mean percentage error for the 3 tissues from these algorithms was lower for region-growing segmentation (2.34%) than for trained NN with K means (3.31%) and for automatic K-means segmentation (4.66%). Thus, NN are reliable in the automation of isolated MR image segmentation. (Author) 12 refs

Measurements on NN → dπ very near threshold. II

International Nuclear Information System (INIS)

Korkmaz, E.; Li, J.; Hutcheon, D.A.; Abegg, R.; Elliott, J.B.; Greeniaus, L.G.; Mack, D.J.; Miller, C.A.; Rodning, N.L.

1991-04-01

We have measured analyzing powers for the reaction pp → dπ + at beam energies 3 and 7 MeV above pion production threshold. With the use of Watson's Theorem to fix phases and effective range estimates of pion d-wave strength, we have been able to determine the NN → dπ s-wave amplitude and the two p-wave amplitudes at these energies. The results are compared to the Faddeev model predictions of Blankleider. (Author) 11 refs., 2 tabs., 11 figs

Energy dependence of acceptance-corrected dielectron excess mass spectrum at mid-rapidity in Au+Au collisions at sNN=19.6 and 200 GeV

Directory of Open Access Journals (Sweden)

L. Adamczyk

2015-11-01

Full Text Available The acceptance-corrected dielectron excess mass spectra, where the known hadronic sources have been subtracted from the inclusive dielectron mass spectra, are reported for the first time at mid-rapidity |yee|<1 in minimum-bias Au+Au collisions at sNN=19.6 and 200 GeV. The excess mass spectra are consistently described by a model calculation with a broadened ρ spectral function for Mee<1.1 GeV/c2. The integrated dielectron excess yield at sNN=19.6 GeV for 0.4NN=17.3 GeV. For sNN=200 GeV, the normalized excess yield in central collisions is higher than that at sNN=17.3 GeV and increases from peripheral to central collisions. These measurements indicate that the lifetime of the hot, dense medium created in central Au+Au collisions at sNN=200 GeV is longer than those in peripheral collisions and at lower energies.

Recognition of decays of charged tracks with neural network techniques

International Nuclear Information System (INIS)

Stimpfl-Abele, G.

1991-01-01

We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

Science.gov (United States)

Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

2018-01-04

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

Science.gov (United States)

Azami, Hamed; Escudero, Javier

2015-08-01

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

Ratios of charged antiparticles to particles near midrapidity in Au+Au collisions at (sNN)=200 GeV

Science.gov (United States)

Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; Garcia, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2003-02-01

The ratios of charged antiparticles to particles have been obtained for pions, kaons, and protons near midrapidity in central Au+Au collisions at (sNN)=200 GeV. Ratios of /=1.025±0.006(stat.)±0.018(syst.), /=0.95±0.03(stat.)±0.03(syst.), and / =0.73±0.02(stat.)±0.03(syst.) have been observed. The / and / ratios are consistent with a baryochemical potential μB of 27 MeV, roughly a factor of 2 smaller than in (sNN)=130 GeV collisions. The data are compared to results from lower energies and model calculations. Our accurate measurements of the particle ratios impose stringent constraints on current and future models dealing with baryon production and transport.

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

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Mehdi Jamei

Full Text Available Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.

Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud

Directory of Open Access Journals (Sweden)

Jun Hong

2017-01-01

Full Text Available As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate k-nearest neighbors (kNN query and kNN query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient kNN query and kNN query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower kNN query processing and verification overhead than the MR-tree.

Collaborative networks: Reference modeling

NARCIS (Netherlands)

Camarinha-Matos, L.M.; Afsarmanesh, H.

2008-01-01

Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

CONTROLLED CONDENSATION IN K-NN AND ITS APPLICATION FOR REAL TIME COLOR IDENTIFICATION

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Carmen Villar Patiño

2017-04-01

Full Text Available k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging.

Hypernuclear weak decay experiments at KEK: n-n and n-p coincidence measurement

International Nuclear Information System (INIS)

Outa, H.; Ajimura, S.; Aoki, K.; Banu, A.; Bhang, H.C.; Fukuda, T.; Hashimoto, O.; Hwang, J.I.; Kameoka, S.; Kang, B.H.; Kim, E.H.; Kim, J.H.; Kim, M.J.; Maruta, T.; Miura, Y.; Miyake, Y.; Nagae, T.; Nakamura, M.; Nakamura, S.N.; Noumi, H.; Okada, S.; Okayasu, Y.; Park, H.; Saha, P.K.; Sato, Y.; Sekimoto, M.; Takahashi, T.; Tamura, H.; Tanida, K.; Toyoda, A.; Tsukada, K.; Watanabe, T.; Yim, H.J.

2005-01-01

We performed a coincidence measurement of two nucleons emitted from the nonmesonic weak decay (NMWD) of 5 Λ He and 12 Λ C formed via the (π+,K+) reaction. In both of n+p and n+n pair coincidence spectra, we observed a clean back-to-back correlation coming from the two-body decay of Λp->np and Λn->nn, respectively. We obtained the ratio of the nucleon pair numbers, Nnn/Nnp ( 5 Λ He)=0.45-bar +/--bar 0.11-bar (stat)-bar +/--bar 0.03-bar (syst) in the kinematic region of cosθNN-0.8. Since each decay mode was exclusively detected, the measured ratio should be close to the ratio of Γ(Λp->nn)/Γ(Λn->np). The Γn/Γp ratio was measured also for the NMWD of 12 Λ C. It is also close to 0.5. Those ratios are consistent with recent theoretical calculations based on the heavy meson/direct quark exchange picture

NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER

Directory of Open Access Journals (Sweden)

B.I. Kuznetsov

2015-08-01

Full Text Available The aim of this work is the synthesis of neural network aiming and stabilization system for the special equipment of moving objects with neuro-controller on the basis of standard model and performance comparison of the neural network system with the neural network predictive control. Build a block diagram of the neural network aiming and stabilization system, based on the subject control principle with PD-regulator in the position loop and with neuro-controller on the basis of standard model in the in the velocity loop. The neuro-controller on the basis of standard model Model Reference Controller is synthesized in the MATLAB Neural Network Toolbox and system simulation is performed. The studies show that the transient state variables of the system are oscillatory. Therefore, the neuro-controller with the prediction NN Predictive Controller should be used for aiming and stabilizing system to provide high dynamic characteristics achieved at the cost of higher complexity and computational cost.

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

Science.gov (United States)

2016-01-01

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

Remote sensing of nutrient deficiency in Lactuca sativa using neural networks for terrestrial and advanced life support applications

Science.gov (United States)

Sears, Edie Seldon

2000-12-01

A remote sensing study using reflectance and fluorescence spectra of hydroponically grown Lactuca sativa (lettuce) canopies was conducted. An optical receiver was designed and constructed to interface with a commercial fiber optic spectrometer for data acquisition. Optical parameters were varied to determine effects of field of view and distance to target on vegetation stress assessment over the test plant growth cycle. Feedforward backpropagation neural networks (NN) were implemented to predict the presence of canopy stress. Effects of spatial and spectral resolutions on stress predictions of the neural network were also examined. Visual inspection and fresh mass values failed to differentiate among controls, plants cultivated with 25% of the recommended concentration of phosphorous (P), and those cultivated with 25% nitrogen (N) based on fresh mass and visual inspection. The NN's were trained on input vectors created using reflectance and test day, fluorescence and test day, and reflectance, fluorescence, and test day. Four networks were created representing four levels of spectral resolution: 100-nm NN, 10-nm NN, 1-nm NN, and 0.1-nm NN. The 10-nm resolution was found to be sufficient for classifying extreme nitrogen deficiency in freestanding hydroponic lettuce. As a result of leaf angle and canopy structure broadband scattering intensity in the 700-nm to 1000-nm range was found to be the most useful portion of the spectrum in this study. More subtle effects of "greenness" and fluorescence emission were believed to be obscured by canopy structure and leaf orientation. As field of view was not as found to be as significant as originally believed, systems implementing higher repetitions over more uniformly oriented, i.e. smaller, flatter, target areas would provide for more discernible neural network input vectors. It is believed that this technique holds considerable promise for early detection of extreme nitrogen deficiency. Further research is recommended using

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

Science.gov (United States)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

Energy dependence of acceptance-corrected dielectron excess mass spectrum at mid-rapidity in Au +Au collisions at √{sNN} = 19.6 and 200 GeV

Science.gov (United States)

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Alford, J.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Averichev, G. S.; Banerjee, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandin, A. V.; Bunzarov, I.; Burton, T. P.; Butterworth, J.; Caines, H.; Calder'on de la Barca S'anchez, M.; Campbell, J. M.; Cebra, D.; Cervantes, M. C.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Codrington, M. J. M.; Contin, G.; Crawford, H. J.; Das, S.; De Silva, L. C.; Debbe, R. R.; Dedovich, T. G.; Deng, J.; Derevschikov, A. A.; di Ruzza, B.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Du, C. M.; Dunkelberger, L. E.; Dunlop, J. C.; Efimov, L. G.; Engelage, J.; Eppley, G.; Esha, R.; Evdokimov, O.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Feng; Filip, P.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Garand, D.; Geurts, F.; Gibson, A.; Girard, M.; Greiner, L.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, S.; Gupta, A.; Guryn, W.; Hamad, A.; Hamed, A.; Haque, R.; Harris, J. W.; He, L.; Heppelmann, S.; Hirsch, A.; Hoffmann, G. W.; Hofman, D. J.; Horvat, S.; Huang, H. Z.; Huang, X.; Huang, B.; Huck, P.; Humanic, T. J.; Igo, G.; Jacobs, W. W.; Jang, H.; Jiang, K.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z. H.; Kikola, D. P.; Kisel, I.; Kisiel, A.; Klein, S. R.; Koetke, D. D.; Kollegger, T.; Kosarzewski, L. K.; Kotchenda, L.; Kraishan, A. F.; Kravtsov, P.; Krueger, K.; Kulakov, I.; Kumar, L.; Kycia, R. A.; Lamont, M. A. C.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, X.; Li, X.; Li, W.; Li, Z. M.; Li, Y.; Li, C.; Lisa, M. A.; Liu, F.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, X.; Ma, L.; Ma, R.; Ma, G. L.; Ma, Y. G.; Magdy, N.; Majka, R.; Manion, A.; Margetis, S.; Markert, C.; Masui, H.; Matis, H. S.; McDonald, D.; Meehan, K.; Minaev, N. G.; Mioduszewski, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nandi, B. K.; Nasim, Md.; Nayak, T. K.; Nigmatkulov, G.; Nogach, L. V.; Noh, S. Y.; Novak, J.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V.; Olvitt, D. L.; Page, B. S.; Pan, Y. X.; Pandit, Y.; Panebratsev, Y.; Pawlak, T.; Pawlik, B.; Pei, H.; Perkins, C.; Peterson, A.; Pile, P.; Planinic, M.; Pluta, J.; Poljak, N.; Poniatowska, K.; Porter, J.; Posik, M.; Poskanzer, A. M.; Pruthi, N. K.; Putschke, J.; Qiu, H.; Quintero, A.; Ramachandran, S.; Raniwala, R.; Raniwala, S.; Ray, R. L.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roy, A.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Sakrejda, I.; Salur, S.; Sandacz, A.; Sandweiss, J.; Sarkar, A.; Schambach, J.; Scharenberg, R. P.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Seger, J.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Sharma, M. K.; Sharma, B.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Skoby, M. J.; Smirnov, N.; Smirnov, D.; Solanki, D.; Song, L.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stock, R.; Strikhanov, M.; Stringfellow, B.; Sumbera, M.; Summa, B. J.; Sun, Y.; Sun, Z.; Sun, X. M.; Sun, X.; Surrow, B.; Svirida, D. N.; Szelezniak, M. A.; Takahashi, J.; Tang, A. H.; Tang, Z.; Tarnowsky, T.; Tawfik, A. N.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vandenbroucke, M.; Varma, R.; Vasiliev, A. N.; Vertesi, R.; Videbaek, F.; Viyogi, Y. P.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, Y.; Wang, F.; Wang, H.; Wang, J. S.; Wang, G.; Wang, Y.; Webb, J. C.; Webb, G.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y. F.; Xiao, Z.; Xie, W.; Xin, K.; Xu, Z.; Xu, Q. H.; Xu, N.; Xu, H.; Xu, Y. F.; Yang, Y.; Yang, C.; Yang, S.; Yang, Q.; Yang, Y.; Ye, Z.; Yepes, P.; Yi, L.; Yip, K.; Yoo, I.-K.; Yu, N.; Zbroszczyk, H.; Zha, W.; Zhang, J. B.; Zhang, X. P.; Zhang, S.; Zhang, J.; Zhang, Z.; Zhang, Y.; Zhang, J. L.; Zhao, F.; Zhao, J.; Zhong, C.; Zhou, L.; Zhu, X.; Zoulkarneeva, Y.; Zyzak, M.

2015-11-01

The acceptance-corrected dielectron excess mass spectra, where the known hadronic sources have been subtracted from the inclusive dielectron mass spectra, are reported for the first time at mid-rapidity |yee | < 1 in minimum-bias Au +Au collisions at √{sNN} = 19.6 and 200 GeV. The excess mass spectra are consistently described by a model calculation with a broadened ρ spectral function for Mee < 1.1 GeV /c2. The integrated dielectron excess yield at √{sNN} = 19.6 GeV for 0.4 NN} = 17.3 GeV. For √{sNN} = 200 GeV, the normalized excess yield in central collisions is higher than that at √{sNN} = 17.3 GeV and increases from peripheral to central collisions. These measurements indicate that the lifetime of the hot, dense medium created in central Au +Au collisions at √{sNN} = 200 GeV is longer than those in peripheral collisions and at lower energies.

Prediction and Optimization of Key Performance Indicators in the Production of Stator Core Using a GA-NN Approach

Science.gov (United States)

Rajora, M.; Zou, P.; Xu, W.; Jin, L.; Chen, W.; Liang, S. Y.

2017-12-01

With the rapidly changing demands of the manufacturing market, intelligent techniques are being used to solve engineering problems due to their ability to handle nonlinear complex problems. For example, in the conventional production of stator cores, it is relied upon experienced engineers to make an initial plan on the number of compensation sheets to be added to achieve uniform pressure distribution throughout the laminations. Additionally, these engineers must use their experience to revise the initial plans based upon the measurements made during the production of stator core. However, this method yields inconsistent results as humans are incapable of storing and analysing large amounts of data. In this article, first, a Neural Network (NN), trained using a hybrid Levenberg-Marquardt (LM) - Genetic Algorithm (GA), is developed to assist the engineers with the decision-making process. Next, the trained NN is used as a fitness function in an optimization algorithm to find the optimal values of the initial compensation sheet plan with the aim of minimizing the required revisions during the production of the stator core.

Inclusive J/$\\psi$ production in Xe-Xe collisions at $\\sqrt{s_{NN}}$ = 5.44 TeV

CERN Document Server

Acharya, Shreyasi; The ALICE collaboration; Adamova, Dagmar; Adolfsson, Jonatan; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Al-turany, Mohammad; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Ali, Yasir; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altenkamper, Lucas; Altsybeev, Igor; Anaam, Mustafa Naji; Andrei, Cristian; Andreou, Dimitra; Andrews, Harry Arthur; Andronic, Anton; Angeletti, Massimo; Anguelov, Venelin; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Anwar, Rafay; Apadula, Nicole; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; 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Wilkinson, Jeremy John; Willems, Guido Alexander; Williams, Crispin; Willsher, Emily; Windelband, Bernd Stefan; Witt, William Edward; Xu, Ran; Yalcin, Serpil; Yamakawa, Kosei; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correa Zanoli, Henrique Jose; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zhalov, Mikhail; Zhang, Xiaoming; Zhang, Yonghong; Zhang, Zuman; Zhao, Chengxin; Zherebchevskii, Vladimir; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Ya; Zichichi, Antonino; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zmeskal, Johann; Zou, Shuguang

2018-01-01

Inclusive J/$\\psi$ production is studied in Xe-Xe interactions at a centre-of-mass energy per nucleon pair of $\\sqrt{s_{NN}}$ = 5.44 TeV, using the ALICE detector at the CERN LHC. The J/$\\psi$ meson is reconstructed via its decay into a muon pair, in the centre-of-mass rapidity interval 2.5 < y < 4 and down to zero transverse momentum. In this Letter, the nuclear modification factors $R_{AA}$ for inclusive J/$\\psi$, measured in the centrality range 0-90% as well as in the centrality intervals 0-20% and 20-90% are presented. The $R_{AA}$ values are compared to previously published results for Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV and to the calculation of a transport model. A good agreement is found between Xe-Xe and Pb-Pb results as well as between data and the model.

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

Science.gov (United States)

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2016-06-03

The pseudorapidity density of charged particles, dN_{ch}/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. For the 5% most central collisions, we measure a value of 1943±54. The rise in dN_{ch}/dη as a function of sqrt[s_{NN}] is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of dN_{ch}/dη as a function of the average number of participant nucleons, ⟨N_{part}⟩, calculated in a Glauber model, is compared with the previous measurement at sqrt[s_{NN}]=2.76  TeV. A constant factor of about 1.2 describes the increase in dN_{ch}/dη from sqrt[s_{NN}]=2.76 to 5.02 TeV for all centrality classes, within the measured range of 0%-80% centrality. The results are also compared to models based on different mechanisms for particle production in nuclear collisions.

Delay-Dependent Exponential Optimal Synchronization for Nonidentical Chaotic Systems via Neural-Network-Based Approach

Directory of Open Access Journals (Sweden)

Feng-Hsiag Hsiao

2013-01-01

Full Text Available A novel approach is presented to realize the optimal exponential synchronization of nonidentical multiple time-delay chaotic (MTDC systems via fuzzy control scheme. A neural-network (NN model is first constructed for the MTDC system. Then, a linear differential inclusion (LDI state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, a delay-dependent exponential stability criterion of the error system derived in terms of Lyapunov's direct method is proposed to guarantee that the trajectories of the slave system can approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI. According to the LMI, a fuzzy controller is synthesized not only to realize the exponential synchronization but also to achieve the optimal performance by minimizing the disturbance attenuation level at the same time. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach.

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

Science.gov (United States)

Habarulema, J. B.; McKinnell, L.-A.

2012-05-01

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

Science.gov (United States)

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

Nonlinear forecasting of stream flows using a chaotic approach and artificial neural networks

Directory of Open Access Journals (Sweden)

Hakan Tongal

2013-07-01

Full Text Available This paper evaluates the forecasting performance of two nonlinear models, k-nearest neighbor (kNN and feed-forward neural networks (FFNN, using stream flow data of the Kızılırmak River, the longest river in Turkey. For the kNN model, the required parameters are delay time, number of nearest neigh- bors and embedding dimension. The optimal delay time was obtained with the mutual information function; the number of nearest neighbors was obtained with the optimization process that minimi- zes RMSE as a function of the neighbor number and the embedding dimension was obtained with the correlation dimension method. The correlation dimension of the Kızılırmak River was d = 2.702, which was used in forming the input structure of the FFNN. The nearest integer above the correlation dimension (i.e., 3 provided the minimal number of required variables to characterize the system, and the maximum number of required variables was obtained with the nearest integer above the value 2d + 1 (Takens, 1981 (i.e., 7. Two FFNN models were developed that incorporate 3 and 7 lagged discharge values and the predicted performance compared to that of the kNN model. The results showed that the kNN model was superior to the FFNN model in stream flow forecasting. However, as a result from the kNN model structure, the model failed in the prediction of peak values. Additionally, it was found that the correlation dimension (if it existed could successfully be used in time series where the determina- tion of the input structure is difficult because of high inter-dependency, as in stream flow time series.  Resumen Este trabajo evalúa el desempeño de pronóstico de dos modelos no lineares, de método de clasificación no paramétrico kNN y de redes neuronales con alimentación avanzada (FNNN, usando datos de flujo del río Kizilirmak, el mayor de Turquía. Para el modelo kNN, los parámetros requeridos son tiempo de retraso, número de vecindarios cercanos y dimensión de

Output feedback control of a quadrotor UAV using neural networks.

Science.gov (United States)

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

Science.gov (United States)

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

MR fingerprinting Deep RecOnstruction NEtwork (DRONE).

Science.gov (United States)

Cohen, Ouri; Zhu, Bo; Rosen, Matthew S

2018-09-01

Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods. A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T. Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T 1 and 1.9 ms for T 2 . The reconstruction error in the presence of noise was less than 10% for both T 1 and T 2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R 2  = 0.99/0.99 for MRF EPI T 1 /T 2 and 0.94/0.98 for MRF FISP T 1 /T 2 ) between the T 1 and T 2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching. © 2018 International Society for Magnetic Resonance in Medicine.

Pseudorapidity density of charged particles p-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV

CERN Document Server

Abelev, Betty; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agocs, Andras Gabor; Agostinelli, Andrea; Ahammed, Zubayer; Ahmad, Nazeer; Ahmad, Arshad; Ahn, Sang Un; Ahn, Sul-Ah; Ajaz, Muhammad; Akindinov, Alexander; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Almaraz Avina, Erick Jonathan; Alme, Johan; Alt, Torsten; Altini, Valerio; Altinpinar, Sedat; Altsybeev, Igor; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshauser, Harald; Arbor, Nicolas; Arcelli, Silvia; Arend, Andreas; Armesto, Nestor; Arnaldi, Roberta; Aronsson, Tomas Robert; Arsene, Ionut Cristian; Arslandok, Mesut; Asryan, Andzhey; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Aysto, Juha Heikki; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bailhache, Raphaelle Marie; Bala, Renu; Baldini Ferroli, Rinaldo; Baldisseri, Alberto; Baltasar Dos Santos Pedrosa, Fernando; Ban, Jaroslav; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batyunya, Boris; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont-Moreno, Ernesto; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bergognon, Anais Annick Erica; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhati, Ashok Kumar; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Bjelogrlic, Sandro; Blanco, Francesco; Blanco, F; Blau, Dmitry; Blume, Christoph; Boccioli, Marco; Boettger, Stefan; Bogdanov, Alexey; Boggild, Hans; Bogolyubsky, Mikhail; Boldizsar, Laszlo; Bombara, Marek; Book, Julian; Borel, Herve; Borissov, Alexander; Bossu, Francesco; Botje, Michiel; Botta, Elena; Braidot, Ermes; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Browning, Tyler Allen; Broz, Michal; Brun, Rene; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Busch, Oliver; Buthelezi, Edith Zinhle; Caballero Orduna, Diego; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calvo Villar, Ernesto; Camerini, Paolo; Canoa Roman, Veronica; Cara Romeo, Giovanni; Carena, Wisla; Carena, Francesco; Carlin Filho, Nelson; Carminati, Federico; Casanova Diaz, Amaya Ofelia; Castillo Castellanos, Javier Ernesto; Castillo Hernandez, Juan Francisco; Casula, Ester Anna Rita; Catanescu, Vasile; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chawla, Isha; Cherney, Michael Gerard; Cheshkov, Cvetan; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Chinellato, David; Chochula, Peter; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Coccetti, Fabrizio; Colamaria, Fabio; Colella, Domenico; Collu, Alberto; Conesa Balbastre, Gustavo; Conesa del Valle, Zaida; Connors, Megan Elizabeth; Contin, Giacomo; Contreras, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cortes Maldonado, Ismael; Cosentino, Mauro Rogerio; Costa, Filippo; Cotallo, Manuel Enrique; Crescio, Elisabetta; Crochet, Philippe; Cruz Alaniz, Emilia; Cuautle, Eleazar; Cunqueiro, Leticia; Dainese, Andrea; Dalsgaard, Hans Hjersing; Danu, Andrea; Das, Kushal; Das, Indranil; Das, Supriya; Das, Debasish; Dash, Sadhana; Dash, Ajay Kumar; De, Sudipan; de Barros, Gabriel; De Caro, Annalisa; de Cataldo, Giacinto; de Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; Delagrange, Hugues; Deloff, Andrzej; De Marco, Nora; Denes, Ervin; De Pasquale, Salvatore; Deppman, Airton; D'Erasmo, Ginevra; de Rooij, Raoul Stefan; Diaz Corchero, Miguel Angel; Di Bari, Domenico; Dietel, Thomas; Di Giglio, Carmelo; Di Liberto, Sergio; Di Mauro, Antonio; Di Nezza, Pasquale; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Donigus, Benjamin; Dordic, Olja; Driga, Olga; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Dutta Majumdar, Mihir Ranjan; Dutta Majumdar, AK; Elia, Domenico; Emschermann, David Philip; Engel, Heiko; Erazmus, Barbara; Erdal, Hege Austrheim; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Evans, David; Eyyubova, Gyulnara; Fabris, Daniela; Faivre, Julien; Falchieri, Davide; Fantoni, Alessandra; Fasel, Markus; Fearick, Roger Worsley; Fehlker, Dominik; Feldkamp, Linus; Felea, Daniel; Feliciello, Alessandro; Fenton-Olsen, Bo; Feofilov, Grigory; Fernandez Tellez, Arturo; Ferretti, Alessandro; Festanti, Andrea; Figiel, Jan; Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Fusco Girard, Mario; Gaardhoje, Jens Joergen; Gagliardi, Martino; Gago, Alberto; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Garabatos, Jose; Garcia-Solis, Edmundo; Garishvili, Irakli; Gerhard, Jochen; Germain, Marie; Geuna, Claudio; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Gianotti, Paola; Girard, Martin Robert; Giubellino, Paolo; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez, Ramon; Gonzalez Ferreiro, Elena; Gonzalez-Trueba, Laura Helena; Gonzalez-Zamora, Pedro; Gorbunov, Sergey; Goswami, Ankita; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Grajcarek, Robert; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoriev, Vladislav; Grigoryan, Smbat; Grigoryan, Ara; Grinyov, Boris; Grion, Nevio; Gros, Philippe; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerra Gutierrez, Cesar; Guerzoni, Barbara; Guilbaud, Maxime Rene Joseph; Gulbrandsen, Kristjan Herlache; Gulkanyan, Hrant; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Han, Byounghee; Hanratty, Luke David; Hansen, Alexander; Harmanova, Zuzana; Harris, John William; Hartig, Matthias; Harton, Austin; Hasegan, Dumitru; Hatzifotiadou, Despoina; Hayashi, Shinichi; Hayrapetyan, Arsen; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Herrmann, Norbert; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hicks, Bernard; Hippolyte, Boris; Hori, Yasuto; Hristov, Peter Zahariev; Hrivnacova, Ivana; Huang, Meidana; Humanic, Thomas; Hwang, Dae Sung; Ichou, Raphaelle; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Incani, Elisa; Innocenti, Gian Michele; Innocenti, Pier Giorgio; Ippolitov, Mikhail; Irfan, Muhammad; Ivan, Cristian George; Ivanov, Vladimir; Ivanov, Andrey; Ivanov, Marian; Ivanytskyi, Oleksii; Jacholkowski, Adam Wlodzimierz; Jacobs, Peter; Jang, Haeng Jin; Janik, Rudolf; Janik, Malgorzata Anna; Jayarathna, Sandun; Jena, Satyajit; Jha, Deeptanshu Manu; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jung, Hyung Taik; Jusko, Anton; Kaidalov, Alexei; Kalcher, Sebastian; Kalinak, Peter; Kalliokoski, Tuomo Esa Aukusti; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karpechev, Evgeny; Kazantsev, Andrey; Kebschull, Udo Wolfgang; Keidel, Ralf; Khan, Kamal Hussain; Khan, Palash; Khan, Mohisin Mohammed; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Taesoo; Kim, Beomkyu; Kim, Jonghyun; Kim, Jin Sook; Kim, Mimae; Kim, Minwoo; Kim, Se Yong; Kim, Dong Jo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Jochen; Klein-Bosing, Christian; Kliemant, Michael; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kohler, Markus; Kollegger, Thorsten; Kolojvari, Anatoly; Kondratiev, Valery; Kondratyeva, Natalia; Konevskih, Artem; Kour, Ravjeet; Kovalenko, Vladimir; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kramer, Frederick; Kravcakova, Adela; Krawutschke, Tobias; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Krus, Miroslav; Kryshen, Evgeny; Krzewicki, Mikolaj; Kucheriaev, Yury; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paul; Kulakov, Igor; Kumar, Jitendra; Kurashvili, Podist; Kurepin, A; Kurepin, AB; Kuryakin, Alexey; Kushpil, Vasily; Kushpil, Svetlana; Kvaerno, Henning; Kweon, Min Jung; Kwon, Youngil; Ladron de Guevara, Pedro; Lakomov, Igor; Langoy, Rune; La Pointe, Sarah Louise; Lara, Camilo Ernesto; Lardeux, Antoine Xavier; La Rocca, Paola; Lea, Ramona; Lechman, Mateusz; Lee, Ki Sang; Lee, Sung Chul; Lee, Graham Richard; Legrand, Iosif; Lehnert, Joerg Walter; Lenhardt, Matthieu Laurent; Lenti, Vito; Leon, Hermes; Leoncino, Marco; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Levai, Peter; Lien, Jorgen; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Loenne, Per-Ivar; Loggins, Vera; Loginov, Vitaly; Lohner, Daniel; Loizides, Constantinos; Loo, Kai Krister; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lovhoiden, Gunnar; Lu, Xianguo; Luettig, Philipp; Lunardon, Marcello; Luo, Jiebin; Luparello, Grazia; Luzzi, Cinzia; Ma, Ke; Ma, Rongrong; Madagodahettige-Don, Dilan Minthaka; Maevskaya, Alla; Mager, Magnus; Mahapatra, Durga Prasad; Maire, Antonin; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Ludmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manceau, Loic Henri Antoine; Mangotra, Lalit Kumar; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martashvili, Irakli; Martin, Nicole Alice; Martinengo, Paolo; Martinez, Mario Ivan; Martinez Davalos, Arnulfo; Martinez Garcia, Gines; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastroserio, Annalisa; Matthews, Zoe Louise; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel; Mazzoni, Alessandra Maria; Meddi, Franco; Menchaca-Rocha, Arturo Alejandro; Mercado Perez, Jorge; Meres, Michal; Miake, Yasuo; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz; Mitu, Ciprian Mihai; Mizuno, Sanshiro; Mlynarz, Jocelyn; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Monteno, Marco; Montes, Esther; Moon, Taebong; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhuri, Sanjib; Mukherjee, Maitreyee; Muller, Hans; Munhoz, Marcelo; Musa, Luciano; Musso, Alfredo; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Nattrass, Christine; Navin, Sparsh; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nicassio, Maria; Niculescu, Mihai; Nielsen, Borge Svane; Niida, Takafumi; Nikolaev, Sergey; Nikolic, Vedran; Nikulin, Vladimir; Nikulin, Sergey; Nilsen, Bjorn Steven; Nilsson, Mads Stormo; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Novitzky, Norbert; Nyanin, Alexandre; Nyatha, Anitha; Nygaard, Casper; Nystrand, Joakim Ingemar; Ochirov, Alexander; Oeschler, Helmut Oskar; Oh, Sun Kun; Oh, Saehanseul; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oppedisano, Chiara; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Ostrowski, Piotr Krystian; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozawa, Kyoichiro; Pachmayer, Yvonne Chiara; Pachr, Milos; Padilla, Fatima; Pagano, Paola; Paic, Guy; Painke, Florian; Pajares, Carlos; Pal, Susanta Kumar; Palaha, Arvinder Singh; Palmeri, Armando; Papikyan, Vardanush; Pappalardo, Giuseppe; Park, Woo Jin; Passfeld, Annika; Pastircak, Blahoslav; Patalakha, Dmitri Ivanovich; Paticchio, Vincenzo; Paul, Biswarup; Pavlinov, Alexei; Pawlak, Tomasz Jan; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitri; Perez Lara, Carlos Eugenio; Perini, Diego; Perrino, Davide; Peryt, Wiktor Stanislaw; Pesci, Alessandro; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petran, Michal; Petris, Mariana; Petrov, Plamen Rumenov; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Piccotti, Anna; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Pitz, Nora; Piyarathna, Danthasinghe; Planinic, Mirko; Ploskon, Mateusz Andrzej; Pluta, Jan Marian; Pocheptsov, Timur; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polak, Karel; Polichtchouk, Boris; Pop, Amalia; Porteboeuf-Houssais, Sarah; Pospisil, Vladimir; Potukuchi, Baba; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puddu, Giovanna; Punin, Valery; Putis, Marian; Putschke, Jorn Henning; Quercigh, Emanuele; Qvigstad, Henrik; Rachevski, Alexandre; Rademakers, Alphonse; Raiha, Tomi Samuli; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Ramirez Reyes, Abdiel; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick; Reicher, Martijn; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riccati, Lodovico; Ricci, Renato Angelo; Richert, Tuva; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roed, Ketil; Rohr, David; Rohrich, Dieter; Romita, Rosa; Ronchetti, Federico; Rosnet, Philippe; Rossegger, Stefan; Rossi, Andrea; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Rybicki, Andrzej; Sadovsky, Sergey; Safarik, Karel; Sahoo, Raghunath; Sahu, Pradip Kumar; Saini, Jogender; Sakaguchi, Hiroaki; Sakai, Shingo; Sakata, Dosatsu; Salgado, Carlos Albert; Salzwedel, Jai; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sano, Satoshi; Santagati, Gianluca; Santoro, Romualdo; Sarkamo, Juho Jaako; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schreiner, Steffen; Schuchmann, Simone; Schukraft, Jurgen; Schuster, Tim; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Patrick Aaron; Scott, Rebecca; Segato, Gianfranco; Selyuzhenkov, Ilya; Senyukov, Serhiy; Seo, Jeewon; Serci, Sergio; Serradilla, Eulogio; Sevcenco, Adrian; Shabetai, Alexandre; Shabratova, Galina; Shahoyan, Ruben; Sharma, Satish; Sharma, Natasha; Sharma, Rohini; Shigaki, Kenta; Shtejer, Katherin; Sibiriak, Yury; Siciliano, Melinda; Siddhanta, Sabyasachi; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Skjerdal, Kyrre; Smakal, Radek; Smirnov, Nikolai; Snellings, Raimond; Sogaard, Carsten; Soltz, Ron Ariel; Son, Hyungsuk; Song, Jihye; Song, Myunggeun; Soos, Csaba; Soramel, Francesca; Sputowska, Iwona; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stan, Ionel; Stefanek, Grzegorz; 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Usai, Gianluca; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Vande Vyvre, Pierre; van Leeuwen, Marco; Vannucci, Luigi; Vargas, Aurora Diozcora; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vechernin, Vladimir; Veldhoen, Misha; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Yury; Vinogradov, Leonid; Virgili, Tiziano; Viyogi, Yogendra; Vodopianov, Alexander; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Vladimir; Wagner, Boris; Wan, Renzhuo; Wang, Yaping; Wang, Mengliang; Wang, Dong; Wang, Yifei; Watanabe, Kengo; Weber, Michael; Wessels, Johannes; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilk, Alexander; Williams, Crispin; Windelband, Bernd Stefan; Xaplanteris Karampatsos, Leonidas; Yaldo, Chris G; Yamaguchi, Yorito; Yang, Shiming; Yang, Hongyan; Yasnopolsky, Stanislav; Yi, JunGyu; Yin, Zhongbao; Yoo, In-Kwon; Yoon, Jongik; Yu, Weilin; Yuan, Xianbao; Yushmanov, Igor; Zaccolo, Valentina; Zach, Cenek; Zampolli, Chiara; Zaporozhets, Sergey; Zarochentsev, Andrey; Zavada, Petr; Zaviyalov, Nikolai; Zbroszczyk, Hanna Paulina; Zelnicek, Pierre; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhou, Fengchu; Zhou, Daicui; Zhou, You; Zhu, Jianhui; Zhu, Hongsheng; Zhu, Jianlin; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zinovjev, Gennady; Zoccarato, Yannick Denis; Zynovyev, Mykhaylo; Zyzak, Maksym

2013-01-18

The charged-particle pseudorapidity density measured over 4 units of pseudorapidity in non-single-diffractive (NSD) p-Pb collisions at a centre-of-mass energy per nucleon pair $\\sqrt{s_{NN}}$ = 5.02 TeV is presented. The average value at midrapidity is measured to be 16.81 $\\pm$ 0.71 (syst.), which corresponds to 2.14 $\\pm$ 0.17 (syst.) per participating nucleon. This is 16% lower than in NSD pp collisions interpolated to the same collision energy, and 84% higher than in d-Au collisions at $\\sqrt{s_{NN}}$ = 0.2 TeV. The measured pseudorapidity density in p-Pb collisions is compared to model predictions, and provides new constraints on the description of particle production in high-energy nuclear collisions.

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

Science.gov (United States)

Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

1994-03-01

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks

Energy Technology Data Exchange (ETDEWEB)

Garcia, Silvia R; Romo, Miguel P; Mayoral, Juan M [Instituto de Ingenieria, Universidad Nacional Autonoma de Mexico, Mexico D.F. (Mexico)

2007-01-15

An extensive analysis of the strong ground motion Mexican data base was conducted using Soft Computing (SC) techniques. A Neural Network NN is used to estimate both orthogonal components of the horizontal (PGAh) and vertical (PGAv) peak ground accelerations measured at rock sites during Mexican subduction zone earthquakes. The work discusses the development, training, and testing of this neural model. Attenuation phenomenon was characterized in terms of magnitude, epicentral distance and focal depth. Neural approximators were used instead of traditional regression techniques due to their flexibility to deal with uncertainty and noise. NN predictions follow closely measured responses exhibiting forecasting capabilities better than those of most established attenuation relations for the Mexican subduction zone. Assessment of the NN, was also applied to subduction zones in Japan and North America. For the database used in this paper the NN and the-better-fitted- regression approach residuals are compared. [Spanish] Un analisis exhaustivo de la base de datos mexicana de sismos fuertes se llevo a cabo utilizando tecnicas de computo aproximado, SC (soft computing). En particular, una red neuronal, NN, es utilizada para estimar ambos componentes ortogonales de la maxima aceleracion horizontal del terreno, PGAh, y la vertical, PGAv, medidas en sitios en roca durante terremotos generados en la zona de subduccion de la Republica Mexicana. El trabajo discute el desarrollo, entrenamiento, y prueba de este modelo neuronal. El fenomeno de atenuacion fue caracterizado en terminos de la magnitud, la distancia epicentral y la profundidad focal. Aproximaciones neuronales fueron utilizadas en lugar de tecnicas de regresion tradicionales por su flexibilidad para tratar con incertidumbre y ruido en los datos. La NN sigue de cerca la respuesta medida exhibiendo capacidades predictivas mejores que las mostradas por muchas de las relaciones de atenuacion establecidas para la zona de

A neighbourhood evolving network model

International Nuclear Information System (INIS)

Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

2006-01-01

Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

Proceedings of the BRNS sponsored theme meeting on artificial neural network

International Nuclear Information System (INIS)

Dubey, B.P.; Varde, P.V.

2003-09-01

Neural network (NN) has emerged as a very powerful calculation tool. It doesn't follow the traditional algorithmic approach for solving a problem. It has to be trained first for solving a specific problem - like human beings. NNs are generally used for solving complex problems including ones' in nuclear industry, which are very difficult to express in algorithmic manner. They learn from examples from the given problem domain and become expert. Later, if an input set is given to the trained NN, which was never used during training, the NN can correctly predict the corresponding output. NNs have been used for solving many complex problems in nuclear industry, speech recognition, pattern matching, classification, optimisation, time series analysis, control, noise filtering etc. Papers relevant INIS are indexed separately

Neural networks, cellular automata, and robust approach applications for vertex localization in the opera target tracker detector

International Nuclear Information System (INIS)

Dmitrievskij, S.G.; Gornushkin, Yu.A.; Ososkov, G.A.

2005-01-01

A neural-network (NN) approach for neutrino interaction vertex reconstruction in the OPERA experiment with the help of the Target Tracker (TT) detector is described. A feed-forward NN with the standard back propagation option is used. The energy functional minimization of the network is performed by the method of conjugate gradients. Data preprocessing by means of cellular automaton algorithm is performed. The Hough transform is applied for muon track determination and the robust fitting method is used for shower axis reconstruction. A comparison of the proposed approach with earlier studies, based on the use of the neural network package SNNS, shows their similar performance. The further development of the approach is underway

Design of FPGA Based Neural Network Controller for Earth Station Power System

Directory of Open Access Journals (Sweden)

Hassen T. Dorrah

2012-06-01

Full Text Available Automation of generating hardware description language code from neural networks models can highly decrease time of implementation those networks into a digital devices, thus significant money savings. To implement the neural network into hardware designer, it is required to translate generated model into device structure. VHDL language is used to describe those networks into hardware. VHDL code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic of the earth station and the satellite power systems using ModelSim PE 6.6 simulator tool. Integration between MATLAB and VHDL is used to save execution time of computation. The results shows that a good agreement between MATLAB and VHDL and a fast/flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance. FPGA synthesis results are obtained with view RTL schematic and technology schematic from Xilinix tool. Minimum number of utilized resources is obtained by using Xilinix VERTIX5.

Physics of flow in weighted complex networks

Science.gov (United States)

Wu, Zhenhua

This thesis uses concepts from statistical physics to understand the physics of flow in weighted complex networks. The traditional model for random networks is the Erdoḧs-Renyi (ER.) network, where a network of N nodes is created by connecting each of the N(N - 1)/2 pairs of nodes with a probability p. The degree distribution, which is the probability distribution of the number of links per node, is a Poisson distribution. Recent studies of the topology in many networks such as the Internet and the world-wide airport network (WAN) reveal a power law degree distribution, known as a scale-free (SF) distribution. To yield a better description of network dynamics, we study weighted networks, where each link or node is given a number. One asks how the weights affect the static and the dynamic properties of the network. In this thesis, two important dynamic problems are studied: the current flow problem, described by Kirchhoff's laws, and the maximum flow problem, which maximizes the flow between two nodes. Percolation theory is applied to these studies of the dynamics in complex networks. We find that the current flow in disordered media belongs to the same universality class as the optimal path. In a randomly weighted network, we identify the infinite incipient percolation cluster as the "superhighway", which contains most of the traffic in a network. We propose an efficient strategy to improve significantly the global transport by improving the superhighways, which comprise a small fraction of the network. We also propose a network model with correlated weights to describe weighted networks such as the WAN. Our model agrees with WAN data, and provides insight into the advantages of correlated weights in networks. Lastly, the upper critical dimension is evaluated using two different numerical methods, and the result is consistent with the theoretical prediction.

Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

Science.gov (United States)

Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

2012-01-01

This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

Science.gov (United States)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Telecommunications network modelling, planning and design

CERN Document Server

Evans, Sharon

2003-01-01

Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

Directory of Open Access Journals (Sweden)

J. Uwamahoro

2012-06-01

Full Text Available Estimating the geoeffectiveness of solar events is of significant importance for space weather modelling and prediction. This paper describes the development of a neural network-based model for estimating the probability occurrence of geomagnetic storms following halo coronal mass ejection (CME and related interplanetary (IP events. This model incorporates both solar and IP variable inputs that characterize geoeffective halo CMEs. Solar inputs include numeric values of the halo CME angular width (AW, the CME speed (Vcme, and the comprehensive flare index (cfi, which represents the flaring activity associated with halo CMEs. IP parameters used as inputs are the numeric peak values of the solar wind speed (Vsw and the southward Z-component of the interplanetary magnetic field (IMF or Bs. IP inputs were considered within a 5-day time window after a halo CME eruption. The neural network (NN model training and testing data sets were constructed based on 1202 halo CMEs (both full and partial halo and their properties observed between 1997 and 2006. The performance of the developed NN model was tested using a validation data set (not part of the training data set covering the years 2000 and 2005. Under the condition of halo CME occurrence, this model could capture 100% of the subsequent intense geomagnetic storms (Dst ≤ −100 nT. For moderate storms (−100 < Dst ≤ −50, the model is successful up to 75%. This model's estimate of the storm occurrence rate from halo CMEs is estimated at a probability of 86%.

Statistical techniques to extract information during SMAP soil moisture assimilation

Science.gov (United States)

Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

2017-12-01

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

PREFACE: 11th International Conference on Nucleus-Nucleus Collisions (NN2012)

Science.gov (United States)

Li, Bao-An; Natowitz, Joseph B.

2013-03-01

The 11th International Conference on Nucleus-Nucleus Collisions (NN2012) was held from 27 May to 1 June 2012, in San Antonio, Texas, USA. It was jointly organized and hosted by The Cyclotron Institute at Texas A&M University, College Station and The Department of Physics and Astronomy at Texas A&M University-Commerce. Among the approximately 300 participants were a large number of graduate students and post-doctoral fellows. The Keynote Talk of the conference, 'The State of Affairs of Present and Future Nucleus-Nucleus Collision Science', was given by Dr Robert Tribble, University Distinguished Professor and Director of the TAMU Cyclotron Institute. During the conference a very well-received public lecture on neutrino astronomy, 'The ICEcube project', was given by Dr Francis Halzen, Hilldale and Gregory Breit Distinguished Professor at the University of Wisconsin, Madison. The Scientific program continued in the general spirit and intention of this conference series. As is typical of this conference a broad range of topics including fundamental areas of nuclear dynamics, structure, and applications were addressed in 42 plenary session talks, 150 parallel session talks, and 21 posters. The high quality of the work presented emphasized the vitality and relevance of the subject matter of this conference. Following the tradition, the NN2012 International Advisory Committee selected the host and site of the next conference in this series. The 12th International Conference on Nucleus-Nucleus Collisions (NN2015) will be held 21-26 June 2015 in Catania, Italy. It will be hosted by The INFN, Laboratori Nazionali del Sud, INFN, Catania and the Dipartimento di Fisica e Astronomia of the University of Catania. The NN2012 Proceedings contains the conference program and 165 articles organized into the following 10 sections 1. Heavy and Superheavy Elements 2. QCD and Hadron Physics 3. Relativistic Heavy-Ion Collisions 4. Nuclear Structure 5. Nuclear Energy and Applications of

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

Science.gov (United States)

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim

2014-12-03

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

2014-01-01

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

Modeling and optimization of kerf taper and surface roughness in laser cutting of titanium alloy sheet

Energy Technology Data Exchange (ETDEWEB)

Pandey, Arun Kumar; Dubey, Avanish Kumar [Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh (India)

2013-07-15

Laser cutting of titanium and its alloys is difficult due to it's poor thermal conductivity and chemical reactivity at elevated temperatures. But demand of these materials in different advanced industries such as aircraft, automobile and space research, require accurate geometry with high surface quality. The present research investigates the laser cutting process behavior of titanium alloy sheet (Ti-6Al-4V) with the aim to improve geometrical accuracy and surface quality by minimizing the kerf taper and surface roughness. The data obtained from L{sub 27} orthogonal array experiments have been used for developing neural network (NN) based models of kerf taper and surface roughness. A hybrid approach of neural network and genetic algorithm has been proposed and applied for the optimization of different quality characteristics. The optimization results show considerable improvements in both the quality characteristics. The results predicted by NN models are well in agreement with the experimental data.

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

Science.gov (United States)

Kasap, Nihat; Agarwal, Anurag

2012-08-01

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

Modeling online social signed networks

Science.gov (United States)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Northern Edge Navajo Casino, Fruitland, NM: NN0030343

Science.gov (United States)

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030343) to the Navajo Tribal Utility Authority Northern Edge Navajo Casino Wastewater Treatment Facility, 2752 Indian Service Road 36, Fruitland, NM.

Defining Higher-Order Turbulent Moment Closures with an Artificial Neural Network and Random Forest

Science.gov (United States)

McGibbon, J.; Bretherton, C. S.

2017-12-01

Unresolved turbulent advection and clouds must be parameterized in atmospheric models. Modern higher-order closure schemes depend on analytic moment closure assumptions that diagnose higher-order moments in terms of lower-order ones. These are then tested against Large-Eddy Simulation (LES) higher-order moment relations. However, these relations may not be neatly analytic in nature. Rather than rely on an analytic higher-order moment closure, can we use machine learning on LES data itself to define a higher-order moment closure?We assess the ability of a deep artificial neural network (NN) and random forest (RF) to perform this task using a set of observationally-based LES runs from the MAGIC field campaign. By training on a subset of 12 simulations and testing on remaining simulations, we avoid over-fitting the training data.Performance of the NN and RF will be assessed and compared to the Analytic Double Gaussian 1 (ADG1) closure assumed by Cloudy Layers Unified By Binormals (CLUBB), a higher-order turbulence closure currently used in the Community Atmosphere Model (CAM). We will show that the RF outperforms the NN and the ADG1 closure for the MAGIC cases within this diagnostic framework. Progress and challenges in using a diagnostic machine learning closure within a prognostic cloud and turbulence parameterization will also be discussed.

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...

Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator

Directory of Open Access Journals (Sweden)

Saeed Pezeshki

2012-11-01

Full Text Available The performance of robot manipulators with nonadaptive controllers might degrade significantly due to the open loop unstable system and the effect of some uncertainties on the robot model or environment. A novel Neural Network PID controller (NNP is proposed in order to improve the system performance and its robustness. The Neural Network (NN technique is applied to compensate for the effect of the uncertainties of the robot model. With the NN compensator introduced, the system errors and the NN weights with large dispersion are guaranteed to be bounded in the Lyapunov sense. The weights of the NN compensator are adaptively tuned. The simulation results show the effectiveness of the model validation approach and its efficiency to guarantee a stable and accurate trajectory tracking process in the presence of uncertainties.

Relativistic three-body approach to NN scattering at intermediate energies

International Nuclear Information System (INIS)

van Faassen, E.; Tjon, J.A.

1986-01-01

The Bethe-Salpeter equation for coupled-channel N-Δ scattering is extended to satisfy unitarity in the NN and NNπ sectors. The procedure eliminates the unitarity violations characteristic of the standard ladder Bethe-Salpeter equation in the inelastic region, and improves the description of pion production near threshold. Results are presented for the NN phase shift and a number of observables up to 1 GeV. In particular, the 1D 2 inelasticity is found to be considerably smaller than found from phase shift analysis. In this context, the importance of the pion deuteron channel for the inelasticity parameter of is pointed out. 33 refs., 16 figs., 4 tabs

Driving profile modeling and recognition based on soft computing approach.

Science.gov (United States)

Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

2009-04-01

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Directory of Open Access Journals (Sweden)

Mehmet Şahin

2017-11-01

Full Text Available The main purpose of this study was to develop and apply a neural network (NN approach and an adaptive neuro-fuzzy inference system (ANFIS model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD and mean absolute percent error (MAPE, were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

Detection of Two-Level Inverter Open-Circuit Fault Using a Combined DWT-NN Approach

Directory of Open Access Journals (Sweden)

Bilal Djamel Eddine Cherif

2018-01-01

Full Text Available Three-phase static converters with voltage structure are widely used in many industrial systems. In order to prevent the propagation of the fault to other components of the system and ensure continuity of service in the event of a failure of the converter, efficient and rapid methods of detection and localization must be implemented. This paper work addresses a diagnostic technique based on the discrete wavelet transform (DWT algorithm and the approach of neural network (NN, for the detection of an inverter IGBT open-circuit switch fault. To illustrate the merits of the technique and validate the results, experimental tests are conducted using a built voltage inverter fed induction motor. The inverter is controlled by the SVM control strategy.

Comparative Analysis of Logistic Regression, Support Vector Machine and Artificial Neural Network for the Differential Diagnosis of Benign and Malignant Solid Breast Tumors by the Use of Three-Dimensional Power Doppler Imaging

International Nuclear Information System (INIS)

Chen, Shou Tung; Hsiao, Yi Hsuan; Kuo, Shou Jen; Tseng, Hsin Shun; Wu, Hwa Koon; Chen, Dar Ren; Huang, Yu Len

2009-01-01

Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computeraided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass. A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized. The virtual organ computer-aided analysis-imaging program was used to analyze the stored volumes of the 183 solid breast tumors. LRA, an SVM and NN were employed in comparative analyses for the characterization of benign and malignant solid breast masses from the database. The values of area under receiver operating characteristic (ROC) curve, referred to as Az values for the use of non-harmonic 3D power Doppler US with LRA, SVM and NN were 0.9341, 0.9185 and 0.9086, respectively. The Az values for the use of harmonic 3D power Doppler US with LRA, SVM and NN were 0.9286, 0.8979 and 0.9009, respectively. The Az values of six ROC curves for the use of LRA, SVM and NN for non-harmonic or harmonic 3D power Doppler imaging were similar. The diagnostic performances of these three models (LRA, SVM and NN) are not different as demonstrated by ROC curve analysis. Depending on user emphasis for the use of ROC curve findings, the use of LRA appears to provide better sensitivity as compared to the other statistical models

Repellent Activity of TRIG (N-N Diethyl Benzamide) against Man-Biting Mosquitoes

OpenAIRE

Msangi, Shandala; Kweka, Eliningaya; Mahande, Aneth

2018-01-01

A study was conducted to assess efficacy of a new repellent brand TRIG (15% N-N Diethyl Benzamide) when compared to DEET (20% N-N Methyl Toluamide). The repellents were tested in laboratory and field. In the laboratory, the repellence was tested on human volunteers, by exposing their repellent-treated arms on starved mosquitoes in cages for 3 minutes at hourly intervals, while counting the landing and probing attempts. Anopheles gambiae and Aedes aegypti mosquitoes were used. Field evaluation...

KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

Directory of Open Access Journals (Sweden)

R. Hadapiningradja Kusumodestoni

2017-09-01

Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

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

The long-acting GLP-1 derivative NN2211 ameliorates glycemia and increases beta-cell mass in diabetic mice

DEFF Research Database (Denmark)

Rolin, Bidda; Larsen, Marianne O; Gotfredsen, Carsten F

2002-01-01

in food intake, there were no significant differences between NN2211 and vehicle treatment, and body weight was not affected. Histological examination revealed that beta-cell proliferation and mass were not increased significantly in ob/ob mice with NN2211, although there was a strong tendency...... for increased proliferation. In db/db mice, exendin-4 and NN2211 decreased blood glucose compared with vehicle, but NN2211 had a longer duration of action. Food intake was lowered only on day 1 with both compounds, and body weight was unaffected. beta-Cell proliferation rate and mass were significantly...

Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method.

Science.gov (United States)

Barkaoui, Abdelwahed; Chamekh, Abdessalem; Merzouki, Tarek; Hambli, Ridha; Mkaddem, Ali

2014-03-01

The complexity and heterogeneity of bone tissue require a multiscale modeling to understand its mechanical behavior and its remodeling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained NN simulation. Finite element calculation is performed at nanoscopic levels to provide a database to train an in-house NN program; and (iii) in steps 2-10 from fibril to continuum cortical bone tissue, homogenization equations are used to perform the computation at the higher scales. The NN outputs (elastic properties of the microfibril) are used as inputs for the homogenization computation to determine the properties of mineralized collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen, and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modeling. Good agreement was obtained between our predicted results and literature data. Copyright © 2013 John Wiley & Sons, Ltd.

Investigation of tt in the full hadronic final state at CDF with a neural network approach

CERN Document Server

Sidoti, A; Busetto, G; Castro, A; Dusini, S; Lazzizzera, I; Wyss, J

2001-01-01

In this work we present the results of a neural network (NN) approach to the measurement of the tt production cross-section and top mass in the all-hadronic channel, analyzing data collected at the Collider Detector at Fermilab (CDF) experiment. We have used a hardware implementation of a feedforward neural network, TOTEM, the product of a collaboration of INFN (Istituto Nazionale Fisica Nucleare)-IRST (Istituto per la Ricerca Scientifica e Tecnologica)-University of Trento, Italy. Particular attention has been paid to the evaluation of the systematics specifically related to the NN approach. The results are consistent with those obtained at CDF by conventional data selection techniques. (38 refs).

The European Narcolepsy Network (EU-NN) database

DEFF Research Database (Denmark)

Khatami, Ramin; Luca, Gianina; Baumann, Christian R

2016-01-01

Narcolepsy with cataplexy is a rare disease with an estimated prevalence of 0.02% in European populations. Narcolepsy shares many features of rare disorders, in particular the lack of awareness of the disease with serious consequences for healthcare supply. Similar to other rare diseases, only a ......, identification of post-marketing medication side-effects, and will contribute to improve clinical trial designs and provide facilities to further develop phase III trials....... Narcolepsy Network is introduced. The database structure, standardization of data acquisition and quality control procedures are described, and an overview provided of the first 1079 patients from 18 European specialized centres. Due to its standardization this continuously increasing data pool is most...

Wave forecasting in near real time basis by neural network

Digital Repository Service at National Institute of Oceanography (India)

Rao, S.; Mandal, S.; Prabaharan, N.

., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

Short-Term Wind Speed Forecasting Study and Its Application Using a Hybrid Model Optimized by Cuckoo Search

Directory of Open Access Journals (Sweden)

Xuejun Chen

2015-01-01

Full Text Available The support vector regression (SVR and neural network (NN are both new tools from the artificial intelligence field, which have been successfully exploited to solve various problems especially for time series forecasting. However, traditional SVR and NN cannot accurately describe intricate time series with the characteristics of high volatility, nonstationarity, and nonlinearity, such as wind speed and electricity price time series. This study proposes an ensemble approach on the basis of 5-3 Hanning filter (5-3H and wavelet denoising (WD techniques, in conjunction with artificial intelligence optimization based SVR and NN model. So as to confirm the validity of the proposed model, two applicative case studies are conducted in terms of wind speed series from Gansu Province in China and electricity price from New South Wales in Australia. The computational results reveal that cuckoo search (CS outperforms both PSO and GA with respect to convergence and global searching capacity, and the proposed CS-based hybrid model is effective and feasible in generating more reliable and skillful forecasts.

Agent-based modeling and network dynamics

CERN Document Server

Namatame, Akira

2016-01-01

The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

Electrical localization of weakly electric fish using neural networks

International Nuclear Information System (INIS)

Kiar, Greg; Mamatjan, Yasin; Adler, Andy; Jun, James; Maler, Len

2013-01-01

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.

Structural vascular disease in Africans: performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: the SABPA study

OpenAIRE

Botha, J.; De Ridder, J.H.; Potgieter, J.C.; Steyn, H.S.; Malan, L.

2013-01-01

A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fa...

Resonances in the ΣNN system

International Nuclear Information System (INIS)

Gibson, B.F.; Afnan, I.R.

1993-01-01

We first review certain unique aspects of few-body Α- hypernuclei and then explore the physics of summation threshold in few-body elastic scattering and reactions. In particular, we discuss a predicted enhancement in the Αd cross section near the summation NN threshold in terms of poles in the Τ=0YNN amplitude. A brief discussion of anticipated poles in the Τ=1 amplitudes is also given

Isobar contributions to the NN interaction

International Nuclear Information System (INIS)

Bagnoud, X.; Holinde, K.; Machleidt, R.

1981-01-01

The fourth-order noniterative 2π-exchange diagrams involving double-isobar intermediate states are evaluated in momentum space, in the framework of noncovariant perturbation theory. The role of these diagrams is studied by making a detailed comparison with (i) the corresponding iterative diagrams and (ii) diagrams involving NN or NΔ intermediate states. Current prescriptions for replacing all time-orderings of those diagrams by (simple) pion-range transition potentials are tested

Old Torreon Navajo Day School, Cuba, NM: NN0030341

Science.gov (United States)

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030341 to Bureau of Indian Affairs Old Torreon Navajo Day School Wastewater Treatment Lagoon.

Modeling Epidemic Network Failures

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Fagertun, Anna Manolova

2013-01-01

This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

A Comparison between a Minijet Model and a Glasma Flux Tube Model for Central Au-Au Collisions at √(ovr sNN)=200 GeV

International Nuclear Information System (INIS)

Longacre, R.S.

2011-01-01

In this paper we compare two models with central Au-Au collisions at √(ovr s NN )=200 GeV. The first model is a minijet model which assumes that around ∼50 minijets are produced in back-to-back pairs and have an altered fragmentation functions. It is also assumed that the fragments are transparent and escape the collision zone and are detected. The second model is a glasma flux tube model which leads to flux tubes on the surface of a radial expanding fireball driven by interacting flux tubes near the center of the fireball through plasma instabilities. This internal fireball becomes an opaque hydro fluid which pushes the surface flux tubes outward. Around ∼12 surface flux tubes remain and fragment with ∼1/2 the produced particles escaping the collision zone and are detected. Both models can reproduce two particle angular correlations in the different p t1 p t2 bins. We also compare the two models for three additional effects: meson baryon ratios; the long range nearside correlation called the ridge; and the so-called mach cone effect when applied to three particle angular correlations.

Construction of six-quark states from parity eigenfunctions for n-n processes

International Nuclear Information System (INIS)

Stancu, F.; Wilets, L.

1987-01-01

The work presented is to classify and construct six-quark states as totally antisymmetric states of six fermions, each described by orbital, spin, isospin, and color degrees of freedom. A classification scheme is proposed based on parity eigenfunctions. The single-particle hamiltonian is assumed to be reflectionally and axially symmetric and can be obtained, for example, from constrained Hartree-Fock or solition mean field theories. The ultimate aim is to study N-N processes in the context of the (relativistic) soliton bag model

Exploring the Role of Genetic Algorithms and Artificial Neural Networks for Interpolation of Elevation in Geoinformation Models

Science.gov (United States)

Bagheri, H.; Sadjadi, S. Y.; Sadeghian, S.

2013-09-01

One of the most significant tools to study many engineering projects is three-dimensional modelling of the Earth that has many applications in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling (DTM). DTM has numerous applications in the fields of sciences, engineering, design and various project administrations. One of the most significant events in DTM technique is the interpolation of elevation to create a continuous surface. There are several methods for interpolation, which have shown many results due to the environmental conditions and input data. The usual methods of interpolation used in this study along with Genetic Algorithms (GA) have been optimised and consisting of polynomials and the Inverse Distance Weighting (IDW) method. In this paper, the Artificial Intelligent (AI) techniques such as GA and Neural Networks (NN) are used on the samples to optimise the interpolation methods and production of Digital Elevation Model (DEM). The aim of entire interpolation methods is to evaluate the accuracy of interpolation methods. Universal interpolation occurs in the entire neighbouring regions can be suggested for larger regions, which can be divided into smaller regions. The results obtained from applying GA and ANN individually, will be compared with the typical method of interpolation for creation of elevations. The resulting had performed that AI methods have a high potential in the interpolation of elevations. Using artificial networks algorithms for the interpolation and optimisation based on the IDW method with GA could be estimated the high precise elevations.

Hydrography-driven coarsening of grid digital elevation models

Science.gov (United States)

Moretti, G.; Orlandini, S.

2017-12-01

A new grid coarsening strategy, denoted as hydrography-driven (HD) coarsening, is developed in the present study. The HD coarsening strategy is designed to retain the essential hydrographic features of surface flow paths observed in high-resolution digital elevation models (DEMs): (1) depressions are filled in the considered high-resolution DEM, (2) the obtained topographic data are used to extract a reference grid network composed of all surface flow paths, (3) the Horton order is assigned to each link of the reference grid network, and (4) within each coarse grid cell, the elevation of the point lying along the highest-order path of the reference grid network and displaying the minimum distance to the cell center is assigned to this coarse grid cell center. The capabilities of the HD coarsening strategy to provide consistent surface flow paths with respect to those observed in high-resolution DEMs are evaluated over a synthetic valley and two real drainage basins located in the Italian Alps and in the Italian Apennines. The HD coarsening is found to yield significantly more accurate surface flow path profiles than the standard nearest neighbor (NN) coarsening. In addition, the proposed strategy is found to reduce drastically the impact of depression-filling procedures in coarsened topographic data. The HD coarsening strategy is therefore advocated for all those cases in which the relief of the extracted drainage network is an important hydrographic feature. The figure below reports DEMs of a synthetic valley and extracted surface flow paths. (a) 10-m grid DEM displaying no depressions and extracted surface flow path (gray line). (b) 1-km grid DEM obtained from NN coarsening. (c) 1-km grid DEM obtained from NN coarsening plus depression-filling and extracted surface flow path (light blue line). (d) 1-km grid DEM obtained from HD coarsening and extracted surface flow path (magenta line).

Narrow coherent effects in πNN-dynamics

International Nuclear Information System (INIS)

Kudryavtsev, A.E.; Obrant, G.Z.

1990-01-01

Coherent effect production is considered in πNN-dynamics with resonant pion-nucleon interaction via Brueckner theory and Faddev equations. It is shown that the narrow energy and final momentum dependence can arise in the inelastic S-wave πd-scattering. The energy dependence peculiarities can have a width an order magnitude less than πN-resonance one

Current approaches to gene regulatory network modelling

Directory of Open Access Journals (Sweden)

Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wucherl; Sim, Alex

2014-07-07

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Network bandwidth utilization forecast model on high bandwidth networks

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-03-30

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

RMBNToolbox: random models for biochemical networks

Directory of Open Access Journals (Sweden)

Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Anion induced conformational preference of Cα NN motif residues in functional proteins.

Science.gov (United States)

Patra, Piya; Ghosh, Mahua; Banerjee, Raja; Chakrabarti, Jaydeb

2017-12-01

Among different ligand binding motifs, anion binding C α NN motif consisting of peptide backbone atoms of three consecutive residues are observed to be important for recognition of free anions, like sulphate or biphosphate and participate in different key functions. Here we study the interaction of sulphate and biphosphate with C α NN motif present in different proteins. Instead of total protein, a peptide fragment has been studied keeping C α NN motif flanked in between other residues. We use classical force field based molecular dynamics simulations to understand the stability of this motif. Our data indicate fluctuations in conformational preferences of the motif residues in absence of the anion. The anion gives stability to one of these conformations. However, the anion induced conformational preferences are highly sequence dependent and specific to the type of anion. In particular, the polar residues are more favourable compared to the other residues for recognising the anion. © 2017 Wiley Periodicals, Inc.

An inclusive study of the reaction anti NN → K01 + anything below 1.0 GeV/c

International Nuclear Information System (INIS)

Angelini, C.

1975-01-01

Some preliminary results are presented on an inclusive analysis of anti NN annihilations between 0. and 1.0 GeV/c incident anti p momentum. Inclusive K 0 1 distributions are considered and discussed in the framework of the thermodynamical model. (L.M.K.)

Ionospheric scintillation forecasting model based on NN-PSO technique

Science.gov (United States)

Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

2017-09-01

The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.

Measurement of electrons from beauty-hadron decays in p-Pb collisions at √sNN=5.02 TeV and Pb-Pb collisions at √sNN=2.76 TeV

NARCIS (Netherlands)

The ALICE collaboration, ALICE collaboration; Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Janssen, M M; Andrei, C.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C. D.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont-Moreno, E.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Iga Buitron, S. A.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A R; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, Sukhee; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crkovská, J.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; Dasgupta, S. S.; De Caro, A.; De Cataldo, G.; De Conti, C.; De Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; De Souza, R. Derradi; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, O.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A S; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; De Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Hughes, C.W.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Isakov, V.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H S Y; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L.D.; Keil, M.; Mohisin Khan, M.; Khan, P.M.; Khan, Shfaqat A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.-S.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.L.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, Seema; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Strunz-Lehner, Christine; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal’Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, Alicia; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, Isabel M.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Mishra, T.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, Rajiv; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal Da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao De Oliveira, R. A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Palni, P.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, J.-W.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira Da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L M; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q. Y.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J.M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A P; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; tripathy, S.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C S; Windelband, B.; Winn, M.; Yalcin, S.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I. K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.

2017-01-01

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1NN=5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √sNN=2.76

Investigation of tt-bar in the full hadronic final state at CDF with a neural network approach

International Nuclear Information System (INIS)

Sidoti, A.; Azzi, P.; Busetto, G.; Castro, A.; Dusini, S.; Lazzizzera, I.; Wyss, J.L.

2001-01-01

In this work we present the results of a neural network (NN) approach to the measurement of the tt-bar production cross-section and top mass in the all-hadronic channel, analyzing data collected at the Collider Detector at Fermilab (CDF) experiment. We have used a hardware implementation of a feed forward neural network, TOTEM, the product of a collaboration of INFN (Istituto Nazionale Fisica Nucleare) - IRST (Istituto per la Ricerca Scientifica e Tecnologica) - University of Trento, Italy. Particular attention has been paid to the evaluation of the systematics specifically related to the NN approach. The results are consistent with those obtained at CDF by conventional data selection techniques

Alkali metal control over N-N cleavage in iron complexes.

Science.gov (United States)

Grubel, Katarzyna; Brennessel, William W; Mercado, Brandon Q; Holland, Patrick L

2014-12-03

Though N2 cleavage on K-promoted Fe surfaces is important in the large-scale Haber-Bosch process, there is still ambiguity about the number of Fe atoms involved during the N-N cleaving step and the interactions responsible for the promoting ability of K. This work explores a molecular Fe system for N2 reduction, particularly focusing on the differences in the results obtained using different alkali metals as reductants (Na, K, Rb, Cs). The products of these reactions feature new types of Fe-N2 and Fe-nitride cores. Surprisingly, adding more equivalents of reductant to the system gives a product in which the N-N bond is not cleaved, indicating that the reducing power is not the most important factor that determines the extent of N2 activation. On the other hand, the results suggest that the size of the alkali metal cation can control the number of Fe atoms that can approach N2, which in turn controls the ability to achieve N2 cleavage. The accumulated results indicate that cleaving the triple N-N bond to nitrides is facilitated by simultaneous approach of least three low-valent Fe atoms to a single molecule of N2.

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo

2015-09-15

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

Three-quark forces and the role of meson exchanges in weak NN interaction

International Nuclear Information System (INIS)

Grach, I.; Shmatikov, M.

1989-01-01

The contribution of weak three-quark forces involving meson exchanges to the longitudinal analyzing power A L in the low-energy pp-scattering is calculated. The nonrelativistic potential model is used for the desorption of strong quark interactions while their weak coupling is described by the Weinberg-Salam lagrangian. The dominant mechanism of parity violation in the NN system (provided the one-pion exchange is forbidden by selection rules) is the contact interaction of quarks. 17 refs.; 3 figs

Prospects for N-N* Transitions from Lattice QCD

International Nuclear Information System (INIS)

Richards, David

2008-01-01

I describe the status of calculations of N-N* transition form factors in lattice QCD, and the prospects for future calculations. I emphasise the need to reliably delineate the states in the spectrum, and the progress that has been made by the Hadron Spectrum Collaboration in so doing.

Effects of short range ΔN interaction on observables of the πNN system

International Nuclear Information System (INIS)

Alexandrou, C.; Blankleider, B.

1990-01-01

The inadequacy of standard few-body approaches in describing the πNN system has motivated searches for the responsible missing mechanism. In the case of πd scattering, it has recently been asserted that an additional short range ΔN interaction can account for essentially all the discrepancies between a few-body calculation and experimental data. This conclusion, however, has been based on calculations where a phenomenological ΔN interaction is added only in Born term to background few-body amplitudes. In the present work we investigate the effect of including such a ΔN interaction to all orders within a unitary few-body calculation of the πNN system. Besides testing the validity of adding the ΔN interaction in Born term in πd scattering, our fully coupled approach also enables us to see the influence of the same ΔN interaction on the processes NN→πd and NN→NN. For πd elastic scattering, we find that the higher order ΔN interaction terms can have as much influence on πd observables as the lowest order contribution alone. Moreover, we find that the higher order contributions tend to cancel the effect obtained by adding the ΔN interaction in Born term only. The effect of the same ΔN interaction on NN→πd and NN→NN appears to be as significant as in πd→πd, suggesting that future investigations of the short range ΔN interaction should be done in the context of the fully coupled πNN system

Generalized Network Psychometrics : Combining Network and Latent Variable Models

NARCIS (Netherlands)

Epskamp, S.; Rhemtulla, M.; Borsboom, D.

2017-01-01

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

Science.gov (United States)

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Development of neural network driven fuzzy controller for outlet sodium temperature of DHX

International Nuclear Information System (INIS)

Okusa, Kyoichi; Endou, Akira; Yoshikawa, Shinji; Ozawa, Kenji

1996-01-01

Fuzzy controls are capable to exquisitely control non-linear dynamic systems in wide operating range, using linguistic description to define the control law. However the selection and the definition of the fuzzy rules and sets require a tedious trial and error process based on experience. As a method to overcome this limitation, a neural network driven fuzzy control (NDF), where the learning capability of the neural network (NN) is used to build the fuzzy rules and sets, is presented in this paper. In the NDF control the IF part of a fuzzy control is represented by a multilayer NN while the THEN part is represented by a series of multilayer NNs which calculate the desirable control action. In this work the usual stepwise variable reduction method, used for the selection of the input variable in the THEN part NN, is replaced with a learning algorithm with forgetting mechanism that realizes the automatic reduction of the variables and the tuning up of all the fuzzy control law i.e. the membership function. The NDF has been successfully applied to control the outlet sodium temperature of a dump heat exchanger (DHX) of a FBR plant

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

Science.gov (United States)

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Eight challenges for network epidemic models

Directory of Open Access Journals (Sweden)

Lorenzo Pellis

2015-03-01

Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

Complex networks-based energy-efficient evolution model for wireless sensor networks

Energy Technology Data Exchange (ETDEWEB)

Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

2009-08-30

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

Complex networks-based energy-efficient evolution model for wireless sensor networks

International Nuclear Information System (INIS)

Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

2009-01-01

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

Directory of Open Access Journals (Sweden)

Weiwei Liu

2017-01-01

Full Text Available University spin-outs (USOs, creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to this distribution, a radial basis function (RBF neural network (NN model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university’s equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs.

Brand Marketing Model on Social Networks

Directory of Open Access Journals (Sweden)

Jolita Jezukevičiūtė

2014-04-01

Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Directory of Open Access Journals (Sweden)

Sharif Uddin

2016-01-01

Full Text Available An enhanced k-nearest neighbor (k-NN classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed k-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.

Viittomakielisten muistiasiantuntijoiden näkemyksiä CERAD-tehtäväsarjan viittomakielisestä käännöksestä

OpenAIRE

Näyrä, Taru

2015-01-01

Suomessa laajasti käytössä olevasta muistitestistä, CERAD-tehtäväsarjasta, tuotettiin vuonna 2010 käännös suomalaiselle viittomakielelle. Käännöksen tuotti Kuurojen Palvelusäätiön Memo-projekti Ra-ha-automaattiyhdistyksen tuella. Projektin jatkona toimiva niin ikään RAY:n tukema Memo-ohjelma tuottaa käännöksestä uutta versiota, jossa pyritään korjaamaan ensimmäisessä käännöksessä havaitut puutteet. Opinnäytetyöni tehtävänä on tukea käännöksen tekemistä kokoamalla tietoa siitä, millaisia näkem...

Centrality dependence of bulk fireball properties in √(sNN)=200 GeV Au-Au collisions

International Nuclear Information System (INIS)

Rafelski, Johann; Letessier, Jean; Torrieri, Giorgio

2005-01-01

We explore the centrality dependence of the properties of the dense hadronic matter created in √(s NN )=200 GeV Au-Au collisions at the Relativistic Heavy Ion Collider. Using the statistical hadronization model, we fit particle yields known for 11 centrality bins. We present the resulting model parameters, rapidity yields of physical quantities, and the physical properties of bulk matter at hadronization as function of centrality. We discuss the production of strangeness and entropy

The πNN coupling from high precision np charge exchange at 162 MeV

International Nuclear Information System (INIS)

Nilsson, J.; Blomgren, J.; Conde, H.; Elmgren, K.; Olsson, N.; Ericson, T.E.O.; Uppsala Univ.; Jonsson, O.; Nilsson, L.; Loiseau, B.; Ringbom, A.

1995-02-01

Differential cross sections for unpolarized neutrons of 162 MeV have been measured to high precision with particular attention to the absolute normalisation. These data can be extrapolated precisely and model-independently to the pion pole and give a πNN coupling constant g 2 =14.6±0.3 or f 2 =0.0808±0.0017. This is higher than recently suggested values. (author) 24 refs.; 3 figs.; 1 tab

Introducing Synchronisation in Deterministic Network Models

DEFF Research Database (Denmark)

Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.

2006-01-01

The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

Derivation of the parity-violating NN potential

International Nuclear Information System (INIS)

Zenkin, S.

1981-01-01

The parity-violating NN potential due to vector-meson exchange is considered in the framework of current algebra. A method to calculate the effective P-odd NNV vertex, free of uncertainties due to the existence of the Schwinger and seagull terms, is presented. The final result coincides with the result of the factorization approximation and may be considered as a justification of the approximation

Three-body charmful baryonic B decays B-bar→D(D*)NN-bar

International Nuclear Information System (INIS)

Cheng Haiyang; Yang Kweichou

2002-01-01

We study the charmful three-body baryonic B decays B-bar→D ( * ) NN-bar: the color-allowed modes B-bar 0 →D ( * )+ np-bar and the color-suppressed ones B-bar 0 →D ( * )0 pp-bar. While the D* + /D + production ratio is predicted to be of order 3, it is found that D 0 pp-bar has a similar rate as D* 0 pp-bar. It is pointed out that B-bar 0 →D(D*)NN-bar are dominated by the nucleon vector current or by vector meson intermediate states, whereas B-bar 0 →D 0 pp-bar proceeds mainly via the exchange of the axial-vector intermediate state a 1 (1260). The study of the NN-bar invariant mass distribution in general indicates a threshold baryon pair production; that is, a recoil charmed meson accompanied by a low mass baryon pair except that the spectrum of D 0 pp-bar has a hump at large pp-bar invariant mass m pp-bar ∼3.0 GeV

A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

Directory of Open Access Journals (Sweden)

Zisheng Wang

2018-02-01

Full Text Available We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN to focus the distorted inverse synthetic aperture radar (ISAR image. In this paper, a coefficient estimator based on the artificial neural network (ANN is firstly developed to solve the time-consuming rotational motion compensation (RMC polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs. Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

Medium energy measurements of N-N parameters

International Nuclear Information System (INIS)

Ambrose, D.; Bachman, M.; Coffey, P.; Glass, G.; Jobst, B.; McNaughton, K.H.; Nguyen, C.; Riley, P.J.

1993-01-01

The authors report here progress made during the three year period January 1, 1990, to December 31, 1993, for the Department of Energy Three-Year Grant No. DE-FG05-88ER40446, third year. A major part of the work has been associated with nucleon-nucleon (N-N) research carried out at the Nucleon Physics Laboratory (NPL) at the Los Alamos Meson Physics Facility (LAMPF). During this period they also completed data acquisition and analyses of a TRIUMF experiment, but they have no further plans for experimental work at TRIUMF. Other research has been and will be continued to be carried out at BNL, and involves two rare kaon decay experiments, BNL E791, now completed, and a second generation rare kaon decay experiment, E871, which has just this summer completed an engineering test run. The authors are now also members of a proposed experiment, STAR, (Solenoidal Tracker at RHIC) to be carried out at the Relativistic Heavy Ion Collider facility, RHIC, at BNL. The past three years have been a time of rapid change in the focus of the experimental program. A LAMPF experiment, E1097, in which they spent a large amount of effort during the past three years, was terminated due to funding shortages after they had fabricated the detector, but before data acquisition, and consequently they increased their participation in the rare kaon experiment at BNL, E871. It now appears that there will be no LAMPF N-N program after 1993, so that the research efforts will concentrate on the BNL rare kaon decay measurement, E871, and on STAR. The authors expect that STAR, which requires the fabrication of a large colliding beam detector facility, will use an increasing amount of their research efforts during the next few years. In what follows they describe recent progress on the LAMPF and TRIUMF N-N measurements, on the BNL rare kaon decay work, and on the initial work with the STAR group

Modeling Network Interdiction Tasks

Science.gov (United States)

2015-09-17

118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.

Science.gov (United States)

Briceño, Javier; Cruz-Ramírez, Manuel; Prieto, Martín; Navasa, Miguel; Ortiz de Urbina, Jorge; Orti, Rafael; Gómez-Bravo, Miguel-Ángel; Otero, Alejandra; Varo, Evaristo; Tomé, Santiago; Clemente, Gerardo; Bañares, Rafael; Bárcena, Rafael; Cuervas-Mons, Valentín; Solórzano, Guillermo; Vinaixa, Carmen; Rubín, Angel; Colmenero, Jordi; Valdivieso, Andrés; Ciria, Rubén; Hervás-Martínez, César; de la Mata, Manuel

2014-11-01

There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival. 64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores. Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48). ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights

Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

CERN Document Server

Santi, Paolo

2012-01-01

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

Complex Networks in Psychological Models

Science.gov (United States)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Optimization of Gas Flow Network using the Traveling Salesman ...

African Journals Online (AJOL)

The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...

Artificial neural network modelling

CERN Document Server

Samarasinghe, Sandhya

2016-01-01

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network.

Science.gov (United States)

Roffman, David; Hart, Gregory; Girardi, Michael; Ko, Christine J; Deng, Jun

2018-01-26

Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The objective of this study was to develop and validate a multi-parameterized artificial neural network based on available personal health information for early detection of NMSC with high sensitivity and specificity, even in the absence of known UVR exposure and family history. The 1997-2015 NHIS adult survey data used to train and validate our neural network (NN) comprised of 2,056 NMSC and 460,574 non-cancer cases. We extracted 13 parameters for our NN: gender, age, BMI, diabetic status, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. This study yielded an area under the ROC curve of 0.81 and 0.81 for training and validation, respectively. Our results (training sensitivity 88.5% and specificity 62.2%, validation sensitivity 86.2% and specificity 62.7%) were comparable to a previous study of basal and squamous cell carcinoma prediction that also included UVR exposure and family history information. These results indicate that our NN is robust enough to make predictions, suggesting that we have identified novel associations and potential predictive parameters of NMSC.

Effect of training algorithms on neural networks aided pavement ...

African Journals Online (AJOL)

Especially, the use of Finite Element (FE) based pavement modeling results for training the NN aided inverse analysis is considered to be accurate in realistically characterizing the non-linear stress-sensitive response of underlying pavement layers in real-time. Efficient NN learning algorithms have been developed and ...

Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

Science.gov (United States)

Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

2007-07-01

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Gossip spread in social network Models

Science.gov (United States)

Johansson, Tobias

2017-04-01

Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

Investigation of {sup 136} Ba gamma-Transitions in the (n,n` gamma) Reaction

Energy Technology Data Exchange (ETDEWEB)

Sergiwa, S M [University of Garyuonis, Benghazi, (Libyan Arab Jamahiriya); Rateb, G M; Zleetni, S M; dufani, M M; Shermit, A M; Al Hamidi, M M [Tajoura Nuclear Research Center, Tripoli, (Libyan Arab Jamahiriya); El-Ahrash, M S [7th of April University, Zawia, (Libyan Arab Jamahiriya)

1995-10-01

Using the reactor fast neutron beam, angular distribution and linear polarization of gamma-rays emitted from the {sup 136} Ba (n,n` gamma) reaction were measured. From these measurements, a decay scheme of {sup 1}36{sup B}a has been constructed. New spin and parity (J{pi}) assignments as well as resolving ambiguities in previous assignments for some levels were done. In addition, multipole mixing rations ({delta} - values, important for model comparison) have been unambiguously determined for many gamma-transitions. 1 fig., 2 tabs.

Narrowing of the balance function with centrality in Au + Au collisions at √sNN

International Nuclear Information System (INIS)

Adams, J.; Alder, C.; Ahammed, Z.; Allgower, C.; Amonett, J.; Anderson, B.D.; Anderson, M.; Averichev, G.S.; Balewski, J.; Barannikova, O.; Barnby, L.S.; Baudot, J.; Bekele, S.; Belaga, V.V.; Bellwied, R.; Berger, J.; Bichsel, H.; Billmeier, A.; Bland, L.C.; Blyth, C.O.; Bonner, B.E.; Boucham, A.; Brandin, A.; Bravar, A.; Cadman, R.V.; Caines, H.; Calderonde la Barca Sanchez, M.; Cardenas, A.; Carroll, J.; Castillo, J.; Castro, M.; Cebra, D.; Chaloupka, P.; Chattopadhyay, S.; Chen, Y.; Chernenko, S.P.; Cherney, M.; Chikanian, A.; Choi, B.; Christie, W.; Coffin, J.P.; Cormier, T.M.; Corral, M.M.; Cramer, J.G.; Crawford, H.J.; Derevschikov, A.A.; Didenko, L.; Dietel, T.; Draper, J.E.; Dunin, V.B.; Dunlop, J.C.; Eckardt, V.; Efimov, L.G.; Emelianov, V.; Engelage, J.; Eppley, G.; Erazmus, B.; Fachini, P.; Faine, V.; Faivre, J.; Fatemi, R.; Filimonov, K.; Finch, E.; Fisyak, Y.; Flierl, D.; Foley, K.J.; Fu, J.; Gagliardi, C.A.; Gagunashvili, N.; Gans, J.; Gaudichet, L.; Germain, M.; Geurts, F.; Ghazikhanian, V.; Grachov, O.; Grigoriev, V.; Guedon, M.; Guertin, S.M.; Gushin, E.; Hallman, T.J.; Hardtke, D.; Harris, J.W.; Heinz, M.; Henry, T.W.; Heppelmann, S.; Herston, T.; Hippolyte, B.; Hirsch, A.; Hjort, E.; Hoffmann, G.W.; Horsley, M.; Huang, H.Z.; Humanic, T.J.; Igo, G.; Ishihara, A.; Ivanshin, Yu.I.; Jacobs, P.; Jacobs, W.W.; Janik, M.; Johnson, I.; Jones, P.G.; Judd, E.G.; Kaneta, M.; Kaplan, M.; Keane, D.; Kiryluk, J.; Kisiel, A.; Klay, J.; Klein, S.R.; Klyachko, A.; Kollegger, T.; Konstantinov, A.S.; Kopytine, M.; Kotchenda, L.; Kovalenko, A.D.; Kramer, M.; Kravtsov, P.; Krueger, K.; Kuhn, C.; Kulikov, A.I.; Kunde, G.J.; Kunz, C.L.; Kutuev, R.Kh.; Kuznetsov, A.A.; Lamont, M.A.C.; Landgraf, J.M.; Lange, S.; Lansdell, C.P.; Lasiuk, B.; Laue, F.; Lauret, J.; Lebedev, A.; Lednicky, R.; Leontiev, V.M.; LeVine, M.J.; Li, Q.; Lindenbaum, S.J.; Lisa, M.A.; Liu, F.; Liu, L.; Liu, Z.; Liu, Q.J.; Ljubicic, T.; Llope, W.J.; Long, H.

2003-01-01

The balance function is a new observable based on the principle that charge is locally conserved when particles are pair produced. Balance functions have been measured for charged particle pairs and identified charged pion pairs in Au + Au collisions at √sNN = 130 GeV at the Relativistic Heavy Ion Collider using STAR. Balance functions for peripheral collisions have widths consistent with model predictions based on a superposition of nucleon-nucleon scattering. Widths in central collisions are smaller, consistent with trends predicted by models incorporating late hadronization

In-medium NN interactions and nucleon and meson masses studied with nucleon knockout reactions

CERN Document Server

Noro, T; Akiyoshi, H; Daito, I; Fujimura, H; Hatanaka, K; Ihara, F; Ishikawa, T; Ito, M; Kawabata, M; Kawabata, T; Maeda, Y; Matsuoka, N; Morinobu, S; Nakamura, M; Obayashi, E; Okihana, A; Sagara, K; Sakaguchi, H; Takeda, H; Taki, T; Tamii, A; Tamura, K; Yamazaki, H; Yoshida, H; Yoshimura, M; Yosoi, M

2000-01-01

Spin observables have been measured for (p, 2p) reactions aiming at studying medium effects on NN interactions in nuclear field. Observed strong density-dependent reduction of the analyzing power is consistent with a model calculation where reduction of nucleon and meson masses are taken into account. On the other hand, calculations with g-matrices in the Shroedinger framework does not predict the reduction. The spin-transfer coefficients, which data are not reproduced by the model calculation, are found to be sensitive to reduction rate of each meson mass and have a possibility to test scaling lows in mass reductions.

A Formal Method for Verification and Validation of Neural Network High Assurance Systems, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Our proposed innovation is to develop neural network (NN) rule extraction technology to a level where it can be incorporated into a software tool, we are calling...

Material discovery by combining stochastic surface walking global optimization with a neural network.

Science.gov (United States)

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Matkustussäännön toteuttaminen ja kehittäminen - Case Norilsk Nickel Harjavalta Oy

OpenAIRE

Sihvonen, Aino

2015-01-01

Tämä opinnäytetyö tehtiin toimeksiantona Norilsk Nickel Harjavalta Oy:lle. Opinnäytetyön tarkoituksena oli tutkia toimeksiantajan matkustussäännön vahvuuksia ja puutteita. Tutkimustehtävänä oli selvittää, miten yhtiön henkilöstö kokee nykyisen matkustussäännön sekä mistä johtuu, että matkustusasioissa ilmenee erilaisia käytänteitä. Tutkimuksen tavoitteena oli luoda kehitysehdotuksia yhtiön matkustussääntöön. Matkustussäännön kehittämisellä pyritään kustannustehokkuuden ja ympäristönäkökulmien...

Forecasting of electricity prices with neural networks

Energy Technology Data Exchange (ETDEWEB)

Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

2006-08-15

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

Forecasting of electricity prices with neural networks

International Nuclear Information System (INIS)

Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

2006-01-01

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

Optimization of composite panels using neural networks and genetic algorithms

NARCIS (Netherlands)

Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries

2003-01-01

The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a

Brand Marketing Model on Social Networks

OpenAIRE

Jolita Jezukevičiūtė; Vida Davidavičienė

2014-01-01

The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...

Neural network based adaptive control for nonlinear dynamic regimes

Science.gov (United States)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Modified GMDH-NN algorithm and its application for global sensitivity analysis

Science.gov (United States)

Song, Shufang; Wang, Lu

2017-11-01

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages.

A model of coauthorship networks

Science.gov (United States)

Zhou, Guochang; Li, Jianping; Xie, Zonglin

2017-10-01

A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

The crossing phenomenon and power of the 1-NN rule

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Krayem, S.

submitted 2017 (2018) ISSN 0176-4268 R&D Projects: GA MŠk(CZ) LG15047 Institutional support: RVO:67985807 Keywords : kNN rule * multivariate data * classification * distance * nearest neighbor * distribution mapping function Impact factor: 3.083, year: 2016

A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

Directory of Open Access Journals (Sweden)

Sajad Sabzi

2018-03-01

Full Text Available Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied. This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges (Citrus sinensis L., namely Bam, Payvandi and Thomson. A total of 300 color images were used for the experiments, 100 samples for each orange variety, which are publicly available. After segmentation, 263 parameters, including texture, color and shape features, were extracted from each sample using image processing. Among them, the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm (ANN-PSO. Then, three different classifiers were applied and compared: hybrid artificial neural network – artificial bee colony (ANN-ABC; hybrid artificial neural network – harmony search (ANN-HS; and k-nearest neighbors (kNN. The experimental results show that the hybrid approaches outperform the results of kNN. The average correct classification rate of ANN-HS was 94.28%, while ANN-ABS achieved 96.70% accuracy with the available data, contrasting with the 70.9% baseline accuracy of kNN. Thus, this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties, which can be easily implemented in processing factories. The main contribution of this work is that the method can be directly adapted to other use cases, since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems

Energy Technology Data Exchange (ETDEWEB)

Li, Jun; Jiang, Bin; Guo, Hua, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States)

2013-11-28

A rigorous, general, and simple method to fit global and permutation invariant potential energy surfaces (PESs) using neural networks (NNs) is discussed. This so-called permutation invariant polynomial neural network (PIP-NN) method imposes permutation symmetry by using in its input a set of symmetry functions based on PIPs. For systems with more than three atoms, it is shown that the number of symmetry functions in the input vector needs to be larger than the number of internal coordinates in order to include both the primary and secondary invariant polynomials. This PIP-NN method is successfully demonstrated in three atom-triatomic reactive systems, resulting in full-dimensional global PESs with average errors on the order of meV. These PESs are used in full-dimensional quantum dynamical calculations.

Neural Network-Based Receiver in Band-Limited Communication System with MPPSK Modulation

Directory of Open Access Journals (Sweden)

Wang Zixin

2018-01-01

Full Text Available As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI. Therefore, a single hidden-layer neural network (NN-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.

Database and prediction model for CANDU pressure tube diameter

Energy Technology Data Exchange (ETDEWEB)

Jung, J.Y.; Park, J.H. [Korea Atomic Energy Research Inst., Daejeon (Korea, Republic of)

2014-07-01

The pressure tube (PT) diameter is basic data in evaluating the CCP (critical channel power) of a CANDU reactor. Since the CCP affects the operational margin directly, an accurate prediction of the PT diameter is important to assess the operational margin. However, the PT diameter increases by creep owing to the effects of irradiation by neutron flux, stress, and reactor operating temperatures during the plant service period. Thus, it has been necessary to collect the measured data of the PT diameter and establish a database (DB) and develop a prediction model of PT diameter. Accordingly, in this study, a DB for the measured PT diameter data was established and a neural network (NN) based diameter prediction model was developed. The established DB included not only the measured diameter data but also operating conditions such as the temperature, pressure, flux, and effective full power date. The currently developed NN based diameter prediction model considers only extrinsic variables such as the operating conditions, and will be enhanced to consider the effect of intrinsic variables such as the micro-structure of the PT material. (author)

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

Science.gov (United States)

Patel, Maulika S; Mazumdar, Himanshu S

2014-11-21

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

Brand marketing model on social networks

OpenAIRE

Jezukevičiūtė, Jolita; Davidavičienė, Vida

2014-01-01

Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

Wind turbine condition monitoring based on SCADA data using normal behavior models

DEFF Research Database (Denmark)

Schlechtingen, Meik; Santos, Ilmar; Achiche, Sofiane

2013-01-01

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior...... the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze...

Retrieving Single Scattering Albedos and Temperatures from CRISM Hyperspectral Data Using Neural Networks

Science.gov (United States)

He, L.; Arvidson, R. E.; O'Sullivan, J. A.

2018-04-01

We use a neural network (NN) approach to simultaneously retrieve surface single scattering albedos and temperature maps for CRISM data from 1.40 to 3.85 µm. It approximates the inverse of DISORT which simulates solar and emission radiative streams.

Target-Centric Network Modeling

DEFF Research Database (Denmark)

Mitchell, Dr. William L.; Clark, Dr. Robert M.

In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

A Complex Network Approach to Distributional Semantic Models.

Directory of Open Access Journals (Sweden)

Akira Utsumi

Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Modeling Renewable Penertration Using a Network Economic Model

Science.gov (United States)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Transverse momentum spectra and nuclear modification factors of charged particles in Xe–Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV

CERN Document Server

Acharya, Shreyasi; The ALICE collaboration; Adamova, Dagmar; Adolfsson, Jonatan; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Al-turany, Mohammad; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Ali, Yasir; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altenkamper, Lucas; Altsybeev, Igor; Andrei, Cristian; Andreou, Dimitra; Andrews, Harry Arthur; Andronic, Anton; Angeletti, Massimo; Anguelov, Venelin; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Anwar, Rafay; Apadula, Nicole; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Ball, Markus; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barioglio, Luca; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartsch, Esther; Bastid, Nicole; Basu, Sumit; Batigne, Guillaume; Batyunya, Boris; Batzing, Paul Christoph; Bazo Alba, Jose Luis; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Espinoza Beltran, Lucina Gabriela; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhaduri, Partha Pratim; Bhasin, Anju; Bhat, Inayat Rasool; Bhatt, Himani; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Antonio; Bianchi, Livio; Bianchi, Nicola; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Boca, Gianluigi; Bock, Friederike; Bogdanov, Alexey; Boldizsar, Laszlo; Bombara, Marek; Bonomi, Germano; Bonora, Matthias; Borel, Herve; Borissov, Alexander; Borri, Marcello; Botta, Elena; Bourjau, Christian; Bratrud, Lars; Braun-munzinger, Peter; Bregant, Marco; Broker, Theo Alexander; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buhler, Paul; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Caines, Helen Louise; Caliva, Alberto; Calvo Villar, Ernesto; Soto Camacho, Rabi; Camerini, Paolo; Capon, Aaron Allan; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Chandra, Sinjini; Chang, Beomsu; Chang, Wan; Chapeland, Sylvain; Chartier, Marielle; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Chowdhury, Tasnuva; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Concas, Matteo; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Costanza, Susanna; Crkovska, Jana; Crochet, Philippe; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Derradi De Souza, Rafael; Franz Degenhardt, Hermann; Deisting, Alexander; Deloff, Andrzej; Delsanto, Silvia; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Ruzza, Benedetto; Arteche Diaz, Raul; Dietel, Thomas; Dillenseger, Pascal; Ding, Yanchun; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Van Doremalen, Lennart Vincent; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dudi, Sandeep; Duggal, Ashpreet Kaur; Dukhishyam, Mallick; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erhardt, Filip; Ersdal, Magnus Rentsch; Espagnon, Bruno; Eulisse, Giulio; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Fabbietti, Laura; Faggin, Mattia; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Fernandez Tellez, Arturo; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiorenza, Gabriele; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Francisco, Audrey; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gajdosova, Katarina; Gallio, Mauro; Duarte Galvan, Carlos; Ganoti, Paraskevi; Garabatos Cuadrado, Jose; Garcia-solis, Edmundo Javier; Garg, Kunal; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; De Leone Gay, Maria Beatriz; Germain, Marie; Ghosh, Jhuma; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Greiner, Leo Clifford; Grelli, Alessandro; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Gronefeld, Julius Maximilian; Grosa, Fabrizio; Grosse-oetringhaus, Jan Fiete; Grosso, Raffaele; Guernane, Rachid; Guerzoni, Barbara; Guittiere, Manuel; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Bautista Guzman, Irais; Haake, Rudiger; Habib, Michael Karim; Hadjidakis, Cynthia Marie; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Hannigan, Ryan; Haque, Md Rihan; Harris, John William; Harton, Austin Vincent; Hassan, Hadi; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Gonzalez Hernandez, Emma; Herrera Corral, Gerardo Antonio; Herrmann, Florian; Hetland, Kristin Fanebust; Hilden, Timo Eero; Hillemanns, Hartmut; Hills, Christopher; Hippolyte, Boris; Hohlweger, Bernhard; Horak, David; Hornung, Sebastian; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Huang, Chun-lu; Hughes, Charles; Huhn, Patrick; Humanic, Thomas; Hushnud, Hushnud; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Iddon, James Philip; Iga Buitron, Sergio Arturo; Ilkaev, Radiy; Inaba, Motoi; Ippolitov, Mikhail; Islam, Md Samsul; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacak, Barbara; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jaelani, Syaefudin; Jahnke, Cristiane; Jakubowska, Monika Joanna; Janik, Malgorzata Anna; Jena, Chitrasen; Jercic, Marko; Jimenez Bustamante, Raul Tonatiuh; Jin, Muqing; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karczmarczyk, Przemyslaw; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; 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Luettig, Philipp Johannes; Luhder, Jens Robert; Lunardon, Marcello; Luparello, Grazia; Lupi, Matteo; Maevskaya, Alla; Mager, Magnus; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Malik, Qasim Waheed; Malinina, Liudmila; Mal'kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-garcia, Gines; Martinez Pedreira, Miguel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Masson, Erwann; Mastroserio, Annalisa; Mathis, Andreas Michael; Toledo Matuoka, Paula Fernanda; Matyja, Adam Tomasz; Mayer, Christoph; Mazzilli, Marianna; Mazzoni, Alessandra Maria; Meddi, Franco; Melikyan, Yuri; Menchaca-rocha, Arturo Alejandro; Meninno, Elisa; Mercado-perez, Jorge; 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Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Peng, Xinye; Pereira, Luis Gustavo; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrovici, Mihai; Petta, Catia; Peretti Pezzi, Rafael; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Pisano, Silvia; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pliquett, Fabian; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Poppenborg, Hendrik; Porteboeuf, Sarah Julie; Pozdniakov, Valeriy; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Punin, Valery; Putschke, Jorn Henning; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Ratza, Viktor; Ravasenga, Ivan; Read, Kenneth Francis; Redlich, Krzysztof; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reshetin, Andrey; Revol, Jean-pierre; Reygers, Klaus Johannes; Riabov, Viktor; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-lucian; Rode, Sudhir Pandurang; Rodriguez Cahuantzi, Mario; Roeed, Ketil; Rogalev, Roman; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Rokita, Przemyslaw Stefan; Ronchetti, Federico; Dominguez Rosas, Edgar; Roslon, Krystian; Rosnet, Philippe; Rossi, Andrea; Rotondi, Alberto; Roukoutakis, Filimon; Roy, Christelle Sophie; Roy, Pradip Kumar; Vazquez Rueda, Omar; Rui, Rinaldo; Rumyantsev, Boris; Rustamov, Anar; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Saha, Sumit Kumar; Sahoo, Baidyanath; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandoval, Andres; Sarkar, Amal; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Sas, Mike Henry Petrus; Scapparone, Eugenio; Scarlassara, Fernando; Schaefer, Brennan; Scheid, Horst Sebastian; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schmidt, Marten Ole; Schmidt, Martin; Schmidt, Nicolas Vincent; Schukraft, Jurgen; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sett, Priyanka; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shahoyan, Ruben; Shaikh, Wadut; Shangaraev, Artem; Sharma, Anjali; Sharma, Ankita; Sharma, Meenakshi; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shimomura, Maya; Shirinkin, Sergey; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singh, Randhir; Singhal, Vikas; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Soramel, Francesca; Sorensen, Soren Pontoppidan; Sozzi, Federica; Sputowska, Iwona Anna; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Stocco, Diego; Storetvedt, Maksim Melnik; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Suzuki, Ken; Swain, Sagarika; Szabo, Alexander; Szarka, Imrich; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Teyssier, Boris; Thakur, Dhananjaya; Thakur, Sanchari; Thomas, Deepa; Thoresen, Freja; Tieulent, Raphael Noel; Tikhonov, Anatoly; Timmins, Anthony Robert; Toia, Alberica; Topilskaya, Nataliya; Toppi, Marco; Rojas Torres, Solangel; Tripathy, Sushanta; Trogolo, Stefano; Trombetta, Giuseppe; Tropp, Lukas; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Trzcinski, Tomasz Piotr; Trzeciak, Barbara Antonina; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Umaka, Ejiro Naomi; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vazquez Doce, Oton; Vechernin, Vladimir; Veen, Annelies Marianne; Vercellin, Ermanno; Vergara Limon, Sergio; Vermunt, Luuk; Vernet, Renaud; Vertesi, Robert; Vickovic, Linda; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Virgili, Tiziano; Vislavicius, Vytautas; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Voscek, Dominik; Vranic, Danilo; Vrlakova, Janka; Wagner, Boris; Wang, Hongkai; Wang, Mengliang; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Wegrzynek, Adam; Weiser, Dennis Franz; Wenzel, Sandro Christian; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Willems, Guido Alexander; Williams, Crispin; Willsher, Emily; Windelband, Bernd Stefan; Witt, William Edward; Xu, Ran; Yalcin, Serpil; Yamakawa, Kosei; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correa Zanoli, Henrique Jose; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zhalov, Mikhail; Zhang, Xiaoming; Zhang, Yonghong; Zhang, Zuman; Zhao, Chengxin; Zherebchevskii, Vladimir; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Ya; Zichichi, Antonino; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zmeskal, Johann; Zou, Shuguang

2018-01-01

Transverse momentum ($p_{\\rm T}$ ) spectra of charged particles at mid-pseudorapidity in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV measured with the ALICE apparatus at the Large Hadron Collider are reported. The kinematic range $0.15 10$ GeV/$c$. This similarity is qualitatively consistent with the expected quadratic path length dependence of medium-induced radiative energy loss of a parton propagating in the medium. The centrality dependence of the ratio of the average transverse momentum $p_{\\rm T}$ in Xe-Xe collisions over Pb-Pb collisions at $\\sqrt{s_{\\rm NN}} = 5.02$ TeV is compared to hydrodynamical model calculations.

An evolving network model with community structure

International Nuclear Information System (INIS)

Li Chunguang; Maini, Philip K

2005-01-01

Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

Safro, I. M. (Mathematics and Computer Science)

2012-02-24

Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

Research on the model of home networking

Science.gov (United States)

Yun, Xiang; Feng, Xiancheng

2007-11-01

It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

On prognostic models, artificial intelligence and censored observations.

Science.gov (United States)

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Breakdown of NpNn scheme in very heavy nuclei

International Nuclear Information System (INIS)

Varshney, A.K.; Singh, M.; Kumar, Rajesh; Gupta, K.K.; Gupta, D.K.

2016-01-01

The proton neutron interaction has been considered the key ingredient in the development of configuration mixing, collectivity and ultimately deformation in atomic nuclei for over five decades. Phenomenologically, the correlation of the integrated valance p - n interaction with the onset of collectivity and deformation has been described in terms of NpNn scheme

Network models in economics and finance

CERN Document Server

Pardalos, Panos; Rassias, Themistocles

2014-01-01

Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

Science.gov (United States)

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

Event-by-event fluctuations in mean pT and mean eT in √(sNN)=130 GeV Au+Au collisions

International Nuclear Information System (INIS)

Adcox, K.; El Chenawi, K.; Ghosh, T.K.; Greene, S.V.; Maguire, C.F.; Miller, T.E.; Rose, A.A.; Adler, S.S.; Aronson, S.H.; David, G.; Desmond, E.J.; Ewell, L.; Franz, A.; Guryn, W.; Haggerty, J.S.; Johnson, B.M.; Kistenev, E.; Kroon, P.J.; Mahon, J.; Makdisi, Y.I.

2002-01-01

Distributions of event-by-event fluctuations of the mean transverse momentum and mean transverse energy near mid-rapidity have been measured in Au+Au collisions at √(s NN )=130 GeV at the Relativistic Heavy-Ion Collider. By comparing the distributions to what is expected for statistically independent particle emission, the magnitude of nonstatistical fluctuations in mean transverse momentum is determined to be consistent with zero. Also, no significant nonrandom fluctuations in mean transverse energy are observed. By constructing a fluctuation model with two event classes that preserve the mean and variance of the semi-inclusive p T or e T spectra, we exclude a region of fluctuations in √(s NN )=130 GeV Au+Au collisions

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

Science.gov (United States)

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Precise strength of the $\\pi$NN coupling constant

CERN Document Server

Ericson, Torleif Eric Oskar; Rahm, J; Blomgren, J; Olsson, N; Thomas, A W

1998-01-01

We report here a preliminary value for the piNN coupling constant deduced from the GMO sumrule for forward piN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives $g^2_c$(GMO) = 13.99(24).

Complex networks under dynamic repair model

Science.gov (United States)

Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

2018-01-01

Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

Adaptive-network models of collective dynamics

Science.gov (United States)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

Charged-particle multiplicity density at mid-rapidity in central Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

CERN Document Server

Aamodt, K; Abrahantes Quintana, A; Adamova, D; Adare, A M; Aggarwal, M M; Aglieri Rinella, G; Agocs, A G; Aguilar Salazar, S; Ahammed, Z; Ahmad Masoodi, A; Ahmad, N; Ahn, S U; Akindinov, A; Aleksandrov, D; Alessandro, B; Alfaro Molina, R; Alici, A; Alkin, A; Almaraz Avina, E; Alt, T; Altini, V; Altinpinar, S; Altsybeev, I; Andrei, C; Andronic, A; Anguelov, V; Anson, C; Anticic, T; Antinori, F; Antonioli, P; Aphecetche, L; Appelshauser, H; Arbor, N; Arcelli, S; Arend, A; Armesto, N; Arnaldi, R; Aronsson, T; Arsene, I C; Asryan, A; Augustinus, A; Averbeck, R; Awes, T C; Aysto, J; Azmi, M D; Bach, M; Badala, A; Baek, Y W; Bagnasco, S; Bailhache, R; Bala, R; Baldini-Ferroli, R; Baldisseri, A; Baldit, A; Baltasar Dos Santos Pedrosa, F; Ban, J; Barbera, R; Barile, F; Barnafoldi, G G; Barnby, L S; Barret, V; Bartke, J; Basile, M; Bastid, N; Bathen, B; Batigne, G; Batyunya, B; Baumann, C; Bearden, I G; Beck, H; Belikov, I; Bellini, F; Bellwied, R; Belmont-Moreno, E; Beole, S; Berceanu, I; Bercuci, A; Berdermann, E; Berdnikov, Y; Bergmann, C; Betev, L; Bhasin, A; Bhati, A K; Bianchi, L; Bianchi, N; Bianchin, C; Bielcik, J; Bielcikova, J; Bilandzic, A; Biolcati, E; Blanc, A; Blanco, F; Blanco, F; Blau, D; Blume, C; Boccioli, M; Bock, N; Bogdanov, A; Boggild, H; Bogolyubsky, M; Boldizsar, L; Bombara, M; Bombonati, C; Book, J; Borel, H; Borissov, A; Bortolin, C; Bose, S; Bossu, F; Botje, M; Bottger, S; Boyer, B; Braun-Munzinger, P; Bravina, L; Bregant, M; Breitner, T; Broz, M; Brun, R; Bruna, E; Bruno, G E; Budnikov, D; Buesching, H; Bugaiev, K; Busch, O; Buthelezi, Z; Caffarri, D; Cai, X; Caines, H; Calvo Villar, E; Camerini, P; Canoa Roman, V; Cara Romeo, G; Carena, F; Carena, W; Carminati, F; Casanova Diaz, A; Caselle, M; Castillo Castellanos, J; Catanescu, V; Cavicchioli, C; Cepila, J; Cerello, P; Chang, B; Chapeland, S; Charvet, J L; Chattopadhyay, S; Chattopadhyay, S; Cherney, M; Cheshkov, C; Cheynis, B; Chiavassa, E; Chibante Barroso, V; Chinellato, D D; Chochula, P; Chojnacki, M; Christakoglou, P; Christensen, C H; Christiansen, P; Chujo, T; Cicalo, C; Cifarelli, L; Cindolo, F; Cleymans, J; Coccetti, F; Coffin, J P; Coli, S; Conesa Balbastre, G; Conesa del Valle, Z; Constantin, P; Contin, G; Contreras, J G; Cormier, T M; Corrales Morales, Y; Cortes Maldonado, I; Cortese, P; Cosentino, M R; Costa, F; Cotallo, M E; Crescio, E; Crochet, P; Cuautle, E; Cunqueiro, L; D'Erasmo, G; Dainese, A; Dalsgaard, H H; Danu, A; Das, D; Das, I; Das, K; Dash, A; Dash, S; De, S; De Azevedo Moregula, A; de Barros, G O V; De Caro, A; de Cataldo, G; de Cuveland, J; De Falco, A; De Gruttola, D; De Marco, N; De Pasquale, S; De Remigis, R; de Rooij, R; Debski, P R; Del Castillo Sanchez, E; Delagrange, H; Delgado Mercado, Y; Dellacasa, G; Deloff, A; Demanov, V; Denes, E; Deppman, A; Di Bari, D; Di Giglio, C; Di Liberto, S; Di Mauro, A; Di Nezza, P; Dietel, T; Divia, R; Djuvsland, O; Dobrin, A; Dobrowolski, T; Dominguez, I; Donigus, B; Dordic, O; Driga, O; Dubey, A K; Dubuisson, J; Ducroux, L; Dupieux, P; Dutta Majumdar, A K; Dutta Majumdar, M R; Elia, D; Emschermann, D; Engel, H; Erdal, H A; Espagnon, B; Estienne, M; Esumi, S; Evans, D; Evrard, S; Eyyubova, G; Fabjan, C W; Fabris, D; Faivre, J; Falchieri, D; Fantoni, A; Fasel, M; Fearick, R; Fedunov, A; Fehlker, D; Fekete, V; Felea, D; Feofilov, G; Fernandez Tellez, A; Ferretti, A; Ferretti, R; Figiel, J; Figueredo, M A S; Filchagin, S; Fini, R; Finogeev, D; Fionda, F M; Fiore, E M; Floris, M; Foertsch, S; Foka, P; Fokin, S; Fragiacomo, E; Fragkiadakis, M; Frankenfeld, U; Fuchs, U; Furano, F; Furget, C; Fusco Girard, M; Gaardhoje, J J; Gadrat, S; Gagliardi, M; Gago, A; Gallio, M; Gangadharan, D R; Ganoti, P; Ganti, M S; Garabatos, C; Garcia-Solis, E; Garishvili, I; Gemme, R; Gerhard, J; Germain, M; Geuna, C; Gheata, A; Gheata, M; Ghidini, B; Ghosh, P; Gianotti, P; Girard, M R; Giraudo, G; Giubellino, P; Gladysz-Dziadus, E; Glassel, P; Gomez, R; Ferreiro, E G; Gonzalez Santos, H; González-Trueba, L H; González-Zamora, P; Gorbunov, S; Gotovac, S; Grabski, V; Grajcarek, R; Grelli, A; Grigoras, A; Grigoras, C; Grigoriev, V; Grigoryan, A; Grigoryan, S; Grinyov, B; Grion, N; Gros, P; Grosse-Oetringhaus, J F; Grossiord, J Y; Grosso, R; Guber, F; Guernane, R; Guerra Gutierrez, C; Guerzoni, B; Gulbrandsen, K; Gunji, T; Gupta, A; Gupta, R; Gutbrod, H; Haaland, O; Hadjidakis, C; Haiduc, M; Hamagaki, H; Hamar, G; Harris, J W; Hartig, M; Hasch, D; Hasegan, D; Hatzifotiadou, D; Hayrapetyan, A; Heide, M; Heinz, M; Helstrup, H; Herghelegiu, A; Hernandez, C; Herrera Corral, G; Herrmann, N; Hetland, K F; Hicks, B; Hille, P T; Hippolyte, B; Horaguchi, T; Hori, Y; Hristov, P; Hrivnacova, I; Huang, M; Huber, S; Humanic, T J; Hwang, D S; Ichou, R; Ilkaev, R; Ilkiv, I; Inaba, M; Incani, E; Innocenti, G M; Innocenti, P G; Ippolitov, M; Irfan, M; Ivan, C; Ivanov, A; Ivanov, M; Ivanov, V; Jacholkowski, A; Jacobs, P M; Jancurova, L; Jangal, S; Janik, R; Jena, S; Jirden, L; Jones, G T; Jones, P G; Jovanovic, P; Jung, H; Jung, W; Jusko, A; Kalcher, S; Kalinak, P; Kalisky, M; Kalliokoski, T; Kalweit, A; Kamermans, R; Kanaki, K; Kang, E; Kang, J H; Kaplin, V; Karavichev, O; Karavicheva, T; Karpechev, E; Kazantsev, A; Kebschull, U; Keidel, R; Khan, M M; Khan, S A; Khanzadeev, A; Kharlov, Y; Kileng, B; Kim, D J; Kim, D S; Kim, D W; Kim, H N; Kim, J H; Kim, J S; Kim, M; Kim, M; Kim, S; Kim, S H; Kirsch, S; Kisel, I; Kiselev, S; Kisiel, A; Klay, J L; Klein, J; Klein-Bosing, C; Kliemant, M; Klovning, A; Kluge, A; Knichel, M L; Koch, K; Kohler, M; Kolevatov, R; Kolojvari, A; Kondratiev, V; Kondratyeva, N; Konevskih, A; Kornas, E; Kottachchi Kankanamge Don, C; Kour, R; Kowalski, M; Kox, S; Koyithatta Meethaleveedu, G; Kozlov, K; Kral, J; Kralik, I; Kramer, F; Kraus, I; Krawutschke, T; Kretz, M; Krivda, M; Krizek, F; Krumbhorn, D; Krus, M; Kryshen, E; Krzewicki, M; Kucheriaev, Y; Kuhn, C; Kuijer, P G; Kurashvili, P; Kurepin, A; Kurepin, A B; Kuryakin, A; Kushpil, S; Kushpil, V; Kweon, M J; Kwon, Y; La Rocca, P; Ladron de Guevara, P; Lafage, V; Lara, C; Lardeux, A; Larsen, D T; Lazzeroni, C; Le Bornec, Y; Lea, R; Lee, K S; Lee, S C; Lefevre, F; Lehnert, J; Leistam, L; Lenhardt, M; Lenti, V; Leon Monzon, I; Leon Vargas, H; Levai, P; Li, X; Lien, J; Lietava, R; Lindal, S; Lindenstruth, V; Lippmann, C; Lisa, M A; Liu, L; Loenne, P I; Loggins, V R; Loginov, V; Lohn, S; Loizides, C; Loo, K K; Lopez, X; Lopez Noriega, M; Lopez Torres, E; Lovhoiden, G; Lu, X G; Luettig, P; Lunardon, M; Luparello, G; Luquin, L; Luzzi, C; Ma, K; Ma, R; Madagodahettige-Don, D M; Maevskaya, A; Mager, M; Mahapatra, D P; Maire, A; Mal'Kevich, D; Malaev, M; Maldonado Cervantes, I; Malinina, L; Malzacher, P; Mamonov, A; Manceau, L; Mangotra, L; Manko, V; Manso, F; Manzari, V; Mao, Y; Mares, J; Margagliotti, G V; Margotti, A; Marin, A; Markert, C; Martashvili, I; Martinengo, P; Martinez, M I; Martinez Davalos, A; Martinez Garcia, G; Martynov, Y; Masciocchi, S; Masera, M; Masoni, A; Massacrier, L; Mastromarco, M; Mastroserio, A; Matthews, Z L; Matyja, A; Mayani, D; Mayer, C; Mazza, G; Mazzoni, M A; Meddi, F; Menchaca-Rocha, A; Mendez Lorenzo, P; Menis, I; Mercado Perez, J; Meres, M; Mereu, P; Miake, Y; Midori, J; Milano, L; Milosevic, J; Mischke, A; Miskowiec, D; Mitu, C; Mlynarz, J; Mohanty, A K; Mohanty, B; Molnar, L; Montano Zetina, L; Monteno, M; Montes, E; Morando, M; Moreira De Godoy, D A; Moretto, S; Morsch, A; Muccifora, V; Mudnic, E; Muhuri, S; Muller, H; Munhoz, M G; Munoz, J; Musa, L; Musso, A; Nandi, B K; Nania, R; Nappi, E; Nattrass, C; Navach, F; Navin, S; Nayak, T K; Nazarenko, S; Nazarov, G; Nedosekin, A; Nendaz, F; Newby, J; Nicassio, M; Nielsen, B S; Niida, T; Nikolaev, S; Nikolic, V; Nikulin, S; Nikulin, V; Nilsen, B S; Nilsson, M S; Noferini, F; Nooren, G; Novitzky, N; Nyanin, A; Nyatha, A; Nygaard, C; Nystrand, J; Obayashi, H; Ochirov, A; Oeschler, H; Oh, S K; Oleniacz, J; Oppedisano, C; Ortiz Velasquez, A; Ortona, G; Oskarsson, A; Ostrowski, P; Otterlund, I; Otwinowski, J; Oyama, K; Ozawa, K; Pachmayer, Y; Pachr, M; Padilla, F; Pagano, P; Jayarathna, S P; Paic, G; Painke, F; Pajares, C; Pal, S; Pal, S K; Palaha, A; Palmeri, A; Pappalardo, G S; Park, W J; Patalakha, D I; Paticchio, V; Pavlinov, A; Pawlak, T; Peitzmann, T; Peresunko, D; Perez Lara, C E; Perini, D; Perrino, D; Peryt, W; Pesci, A; Peskov, V; Pestov, Y; Peters, A J; Petracek, V; Petran, M; Petris, M; Petrov, P; Petrovici, M; Petta, C; Piano, S; Piccotti, A; Pikna, M; Pillot, P; Pinazza, O; Pinsky, L; Pitz, N; Piuz, F; Piyarathna, D B; Platt, R; Ploskon, M; Pluta, J; Pocheptsov, T; Pochybova, S; Podesta-Lerma, P L M; Poghosyan, M G; Polak, K; Polichtchouk, B; Pop, A; Porteboeuf, S; Pospisil, V; Potukuchi, B; Prasad, S K; Preghenella, R; Prino, F; Pruneau, C A; Pshenichnov, I; Puddu, G; Pulvirenti, A; Punin, V; Putis, M; Putschke, J; Quercigh, E; Qvigstad, H; Rachevski, A; Rademakers, A; Rademakers, O; Radomski, S; Raiha, T S; Rak, J; Rakotozafindrabe, A; Ramello, L; Ramirez Reyes, A; Rammler, M; Raniwala, R; Raniwala, S; Rasanen, S S; Read, K F; Real, J; Redlich, K; Renfordt, R; Reolon, A R; Reshetin, A; Rettig, F; Revol, J P; Reygers, K; Ricaud, H; Riccati, L; Ricci, R A; Richter, M; Riedler, P; Riegler, W; Riggi, F; Rodriguez Cahuantzi, M; Rohr, D; Rohrich, D; Romita, R; Ronchetti, F; Rosinsky, P; Rosnet, P; Rossegger, S; Rossi, A; Roukoutakis, F; Rousseau, S; Roy, C; Roy, P; Rubio Montero, A J; Rui, R; Rivetti, A; Rusanov, I; Ryabinkin, E; Rybicki, A; Sadovsky, S; Safarik, K; Sahoo, R; Sahu, P K; Saini, J; Saiz, P; Sakai, S; Sakata, D; Salgado, C A; Samanta, T; Sambyal, S; Samsonov, V; Sanchez Castro, X; Sandor, L; Sandoval, A; Sano, M; Sano, S; Santo, R; Santoro, R; Sarkamo, J; Saturnini, P; Scapparone, E; Scarlassara, F; Scharenberg, R P; Schiaua, C; Schicker, R; Schmidt, C; Schmidt, H R; Schreiner, S; Schuchmann, S; Schukraft, J; Schutz, Y; Schwarz, K; Schweda, K; Scioli, G; Scomparin, E; Scott, P A; Scott, R; Segato, G; Selyuzhenkov, I; Senyukov, S; Seo, J; Serci, S; Serradilla, E; Sevcenco, A; Sgura, I; Shabratova, G; Shahoyan, R; Sharma, N; Sharma, S; Shigaki, K; Shimomura, M; Shtejer, K; Sibiriak, Y; Siciliano, M; Sicking, E; Siemiarczuk, T; Silenzi, A; Silvermyr, D; Simonetti, G; Singaraju, R; Singh, R; Singhal, V; Sinha, B C; Sinha, T; Sitar, B; Sitta, M; Skaali, T B; Skjerdal, K; Smakal, R; Smirnov, N; Snellings, R; Sogaard, C; Soloviev, A; Soltz, R; Son, H; Song, J; Song, M; Soos, C; Soramel, F; Spyropoulou-Stassinaki, M; Srivastava, B K; Stachel, J; Stan, I; Stefanek, G; Stefanini, G; Steinbeck, T; Steinpreis, M; Stenlund, E; Steyn, G; Stocco, D; Stock, R; Stokkevag, C H; Stolpovskiy, M; Strmen, P; Suaide, A A P; Subieta Vasquez, M A; Sugitate, T; Suire, C; Sukhorukov, M; Sumbera, M; Susa, T; Swoboda, D; Symons, T J M; Szanto de Toledo, A; Szarka, I; Szostak, A; Tagridis, C; Takahashi, J; Tapia Takaki, J D; Tauro, A; Tavlet, M; Tejeda Munoz, G; Telesca, A; Terrevoli, C; Thader, J; Thomas, D; Thomas, J H; Tieulent, R; Timmins, A R; Tlusty, D; Toia, A; Torii, H; Toscano, L; Tosello, F; Traczyk, T; Truesdale, D; Trzaska, W H; Tsuji, T; Tumkin, A; Turrisi, R; Turvey, A J; Tveter, T S; Ulery, J; Ullaland, K; Uras, A; Urban, J; Urciuoli, G M; Usai, G L; Vacchi, A; Vajzer, M; Vala, M; Valencia Palomo, L; Vallero, S; van der Kolk, N; van Leeuwen, M; Vande Vyvre, P; Vannucci, L; Vargas, A; Varma, R; Vasileiou, M; Vasiliev, A; Vechernin, V; Veldhoen, M; Venaruzzo, M; Vercellin, E; Vergara, S; Vernekohl, D C; Vernet, R; Verweij, M; Vickovic, L; Viesti, G; Vikhlyantsev, O; Vilakazi, Z; Villalobos Baillie, O; Vinogradov, A; Vinogradov, L; Vinogradov, Y; Virgili, T; Viyogi, Y P; Vodopyanov, A; Voloshin, K; Voloshin, S; Volpe, G; von Haller, B; Vranic, D; Ovrebekk, G; Vrlakova, J; Vulpescu, B; Vyushin, A; Wagner, B; Wagner, V; Wan, R; Wang, D; Wang, Y; Wang, Y; Watanabe, K; Wessels, J P; Westerhoff, U; Wiechula, J; Wikne, J; Wilde, M; Wilk, A; Wilk, G; Williams, M C S; Windelband, B; Xaplanteris Karampatsos, L; Yang, H; Yang, S; Yasnopolskiy, S; Yi, J; Yin, Z; Yokoyama, H; Yoo, I K; Yu, W; Yuan, X; Yushmanov, I; Zabrodin, E; Zach, C; Zampolli, C; Zaporozhets, S; Zarochentsev, A; Zavada, P; Zaviyalov, N; Zbroszczyk, H; Zelnicek, P; Zenin, A; Zgura, I; Zhalov, M; Zhang, X; Zhou, D; Zichichi, A; Zinovjev, G; Zoccarato, Y; Zynovyev, M

2010-01-01

The first measurement of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at a centre-of-mass energy per nucleon pair sqrt(sNN) = 2.76 TeV is presented. For an event sample corresponding to the most central 5% of the hadronic cross section the pseudo-rapidity density of primary charged particles at mid-rapidity is 1584 +- 4 (stat) +- 76 (sys.), which corresponds to 8.3 +- 0.4 (sys.) per participating nucleon pair. This represents an increase of about a factor 1.9 relative to pp collisions at similar collision energies, and about a factor 2.2 to central Au-Au collisions at sqrt(sNN) = 0.2 TeV. This measurement provides the first experimental constraint for models of nucleus-nucleus collisions at LHC energies.

Modelling the structure of complex networks

DEFF Research Database (Denmark)

Herlau, Tue

networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

Spatial Epidemic Modelling in Social Networks

Science.gov (United States)

Simoes, Joana Margarida

2005-06-01

The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

Modeling of fluctuating reaction networks

International Nuclear Information System (INIS)

Lipshtat, A.; Biham, O.

2004-01-01

Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

Role of rho exchange in isobar contributions to the NN interaction

International Nuclear Information System (INIS)

Bagnoud, X.; Holinde, K.; Machleidt, R.

1984-01-01

The fourth-order noniterative diagrams of πrho exchange involving nucleon-isobar intermediate states are evaluated in momentum space in the framework of noncovariant perturbation theory. It is shown that the sum of all time orderings (iterative plus noniterative) can be reasonably well approximated by twice the isoscalar piece of the iterative ones (which are much simpler to evaluate). The same approximation is used in order to describe the sum of all time orderings for the corresponding diagrams involving double-isobar intermediate states. The role of these contributions is studied in NN scattering. Especially, it is investigated whether such contributions can quantitatively replace part of the ω-exchange contribution used in one-boson-exchange models

Deterministic ripple-spreading model for complex networks.

Science.gov (United States)

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

Network model of security system

Directory of Open Access Journals (Sweden)

Adamczyk Piotr

2016-01-01

Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

Neural network modeling of emotion

Science.gov (United States)

Levine, Daniel S.

2007-03-01

This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

The NN and NantiN interactions

International Nuclear Information System (INIS)

Vinh Mau, R.

1981-01-01

The present status of the low and medium energy NN interaction and of the low energy NantiN interaction is reviewed. Careful confrontation of theoretical predictions with the most recent results on experimental observables is emphasized. The question of the dibaryon resonances is discussed. For the NantiN interaction, in view of the problem of the existence of baryonium states as bound states or resonant states of the NantiN system and of their properties, tests of different types of annihilation potentials against the existing experimental data are examined. Implications for the future experimental program at LEAR are discussed

An acoustical model based monitoring network

NARCIS (Netherlands)

Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

2010-01-01

In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

How to model wireless mesh networks topology

International Nuclear Information System (INIS)

Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

2013-01-01

The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Directory of Open Access Journals (Sweden)

M. Picchiani

2011-12-01

Full Text Available Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis.

In this work we have developed a fast and accurate Neural Network (NN approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS data in the Thermal InfraRed (TIR spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases.

The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part.

The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km⁻² has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement.

Precise strength of the πNN coupling constant

International Nuclear Information System (INIS)

Ericson, T.E.O.; Loiseau, B.; Rahm, J.; Blomgren, J.; Olsson, N.; Thomas, A. W.

1999-01-01

We report here a preliminary value for the πNN coupling constant deduced from the Goldberger-Miyazawa-Oehme sum rule for forward πN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives g 2 c =13.99(24)

Modeling the interdependent network based on two-mode networks

Science.gov (United States)

An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

2017-10-01

Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

Entropy Characterization of Random Network Models

Directory of Open Access Journals (Sweden)

Pedro J. Zufiria

2017-06-01

Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

The model of social crypto-network

Directory of Open Access Journals (Sweden)

Марк Миколайович Орел

2015-06-01

Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

Towards reproducible descriptions of neuronal network models.

Directory of Open Access Journals (Sweden)

Eilen Nordlie

2009-08-01

Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

Designing Network-based Business Model Ontology

DEFF Research Database (Denmark)

Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

2015-01-01

Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

An Improved Car-Following Model in Vehicle Networking Based on Network Control

Directory of Open Access Journals (Sweden)

D. Y. Kong

2014-01-01

Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.

Multiplicative Attribute Graph Model of Real-World Networks

Energy Technology Data Exchange (ETDEWEB)

Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)

2010-10-20

Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.

Systematics of gamma decay through low-lying vibrational levels of even--even nuclei excited by (p,p') and (n,n') reactions

International Nuclear Information System (INIS)

Koopman, R.P.

1977-01-01

A series of experiments was performed in which gamma-ray spectra were measured, using a Ge(Li) detector, for incident 7 to 26-MeV protons on the even-even vibrational nuclei 56 Fe, 62 Ni, 64 Zn, 108 Pd, 110 Cd, 114 Cd, 116 Cd, 116 Sn, 120 Sn, and 206 Pb, and for incident 14-MeV neutrons on natural Fe, Ni, Zn, Cd, Sn, and Pb. These measurements yielded gamma-ray cross sections from which it was inferred that almost all of the gamma cascades from (p,p') and (n,n') reactions passed down through the first 2 + levels. Consequently, the strength of the 2 + → 0 + gamma transitions were found to be an indirect measure of the (p,p') or (n,n') cross sections. Several types of nuclear model calculations were performed and compared with experimental results. These calculations included coupled-channel calculations to reproduce the direct, collective excitation of the low-lying levels, and statistical plus pre-equilibrium model calculations to reproduce the (p,p') and the (n,n') cross sections for comparison with the 2 + → 0 + gamma measurements. The agreement between calculation and experiment was generally good except at high energies, where pre-equilibrium processes dominate (i.e. around 26-MeV). Here discrepancies between calculations from the two different pre-equilibrium models and between the data and the calculations were found. Significant isospin mixing of T/sub greater than/ into T/sub less than/ states was necessary in order to have the calculations match the data for the (p,p') reactions, up to about 18-MeV

Developing Personal Network Business Models

DEFF Research Database (Denmark)

Saugstrup, Dan; Henten, Anders

2006-01-01

The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...

Unambiguous amplitude analysis of NN {yields} {Delta}N transition from asymmetry measurements

Energy Technology Data Exchange (ETDEWEB)

Auger, J.P. [Universite d`Orleans (France). Lab. de Physique Theorique; Lazard, C. [Paris-11 Univ., 91 - Orsay (France). Div. de Physique Theorique

1997-12-31

For particular {Delta}-production angles, an unambiguous determination of the NN {yields} {Delta}N transition amplitudes is performed, from NN {yields} (N{pi})N experiments, in which the polarization states are measured in the entrance channel, only. A three-step method is developed, which determines, firstly, the magnitudes of the amplitudes, secondly, independent relative phases, and thirdly, some dependent relative phases for resolving the remaining discrete ambiguities. A rule of ambiguity elimination is applied, which is based on the closure of a chain of consecutive independent relative phases, by means of the ad-hoc dependent one. A generalization of this rule is given, for the case of a non-diagonal matrix connecting observables and bilinear combinations of amplitudes. (author) 18 refs.

A novel Direct Small World network model

Directory of Open Access Journals (Sweden)

LIN Tao

2016-10-01

Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.

Evidence of tensor correlations in the nuclear many-body system using a modern NN potential

International Nuclear Information System (INIS)

Fiase, J.O.; Nkoma, J.S.; Sharmaand, L.K.; Hosaka, A.

2003-01-01

In this paper we show evidence of the importance of tensor correlations in the nuclear many-body system by calculating the effective two-body nuclear matrix elements in the frame work of the Lowest-Order Constrained Variational (LOCV) technique with two-body correlation functions using the Reid93 potential. We have achieved this by switching on and off the strength of the tensor correlations, α k . We have found that in order to obtain reasonable agreement with earlier calculations based on the G-matrix theory, we must turn on the strength of the tensor correlations especially in the triplet even (TE) and tensor even (TNE) channels to take the value of approximately, 0.05. As an application, we have estimated the value of the Landau - Migdal parameter, g' NN which we found to be g' NN = 0.65. This compares favorably with the G-matrix calculated value of g' NN = 0.54. (author)

NN wave function at small distances and hard bremsstrahlung in the process pp→ppγ

International Nuclear Information System (INIS)

Neudatchin, V. G.; Khokhlov, N. A.; Shirokov, A. M.; Knyr, V. A.

1997-01-01

Various possibilities of studying the NN wave function at small distances--and in particular, quark degrees of freedom in the NN system--are discussed. It is shown that there is such a possibility at moderate energies--namely, hard bremsstrahlung in the process pp→ppγ at proton-beam energies in the range 350-450 MeV permits distinguishing between the pp wave function with nodes in S and P waves that corresponds to the Moscow potential of NN interaction from functions obtained with repulsive-core mesonic potentials. In the regions where photon energies in the c.m.s. are maximal (forward and backward photon emission angles in the laboratory frame), the pp→ppγ cross section calculated with the Moscow potential has maxima at which it is approximately five times larger than the analogous cross section calculated with repulsive-core mesonic potentials. The coordinate-representation formalism of the theory of bremsstrahlung is expounded

STUDY OF ESTIMATE CONCENTRATION OF WATER CONSTITUENTS AT BADUNG STRAIT BALI USING INVERSE MODEL

Directory of Open Access Journals (Sweden)

I Ketut Swardika

2012-11-01

Full Text Available An algorithm was employed to retrieve the concentrations of three water constituents, chlorophyll-a,suspended matter and colored dissolved organic matter (CDOM from MODIS (Moderate-ResolutionImaging Spectrometer in wide range covering from oligotrophic case-1 to turbid case-2 waters at theBadung Strait Bali. The algorithm is a neural network (NN which is used to parameterize the inverse of aradiative transfer model. It’s used in this study as a multiple nonlinear regression technique. The NN is a feedforward back propagation model with two hidden layers. The NN was trained with computed radiancecovering the range of chlorophyll-a from 0.001 to 64.0 ?g/l, inorganic suspended matter from 0.01 to 50.0mg/l, and CDOM absorption at 440nm from 0.001 to 5.0 m-1. Inputs to the NN are the radiance of the fivespectral channels which were under discussion for MODIS. The outputs are the three water constituentconcentrations. The NN algorithm was tested using in-situ data set on May, September, November 2005 atthe Badung Strait Bali and the north sea of Sumbawa Island and applied to MODIS. The coefficient ofdetermination (R2 between chlorophyll-a concentrations derived from simulation and in-situ data is 0.327,for suspended matter R2 is 0.408. No in-situ measurements of CDOM available for validation. Also, in-situdata were compared with the corresponding distribution obtained by the NASA standard OC4 (OC3M forMODIS chlorophyll-a algorithm and giving R2 0.188. This study gives better accuracy compare withstandard algorithm. How ever both studies are giving over estimate chlorophyll-a concentration. Since thereare no standard MODIS products available for suspended matter and CDOM, the result of the retrieval by theNN for these two variables could only be assessed by a general knowledge of their concentrations anddistribution patterns

Non-consensus Opinion Models on Complex Networks

Science.gov (United States)

Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo

2013-04-01

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not

Measurements of directed, elliptic, and triangular flow in Cu + Au collisions at √{sNN}=200 GeV

Science.gov (United States)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Alexander, J.; Alfred, M.; Aoki, K.; Apadula, N.; Asano, H.; Atomssa, E. T.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Bai, X.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Bathe, S.; Baublis, V.; Baumann, C.; Baumgart, S.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belmont, R.; Berdnikov, A.; Berdnikov, Y.; Black, D.; Blau, D. S.; Bok, J. S.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Buesching, H.; Bumazhnov, V.; Butsyk, S.; Campbell, S.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choi, S.; Christiansen, P.; Chujo, T.; Cianciolo, V.; Citron, Z.; Cole, B. A.; Cronin, N.; Crossette, N.; Csanád, M.; Csörgő, T.; Danley, T. W.; Datta, A.; Daugherity, M. S.; David, G.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Ding, L.; Dion, A.; Diss, P. B.; Do, J. H.; D'Orazio, L.; Drapier, O.; Drees, A.; Drees, K. A.; Durham, J. M.; Durum, A.; Engelmore, T.; Enokizono, A.; Esumi, S.; Eyser, K. O.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fukao, Y.; Fusayasu, T.; Gainey, K.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, A.; Garishvili, I.; Ge, H.; Giordano, F.; Glenn, A.; Gong, X.; Gonin, M.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guragain, H.; Hachiya, T.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamilton, H. F.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Hayano, R.; He, X.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hollis, R. S.; Homma, K.; Hong, B.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Ichihara, T.; Ikeda, Y.; Imai, K.; Imazu, Y.; Inaba, M.; Iordanova, A.; Isenhower, D.; Isinhue, A.; Ivanishchev, D.; Jacak, B. V.; Jeon, S. J.; Jezghani, M.; Jia, J.; Jiang, X.; Johnson, B. M.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kamin, J.; Kanda, S.; Kang, B. H.; Kang, J. H.; Kang, J. S.; Kapustinsky, J.; Kawall, D.; Kazantsev, A. V.; Key, J. A.; Khachatryan, V.; Khandai, P. K.; Khanzadeev, A.; Kijima, K. M.; Kim, C.; Kim, D. J.; Kim, E.-J.; Kim, G. W.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kimelman, B.; Kistenev, E.; Kitamura, R.; Klatsky, J.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kofarago, M.; Komkov, B.; Koster, J.; Kotchetkov, D.; Kotov, D.; Krizek, F.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, G. H.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, S.; Lee, S. H.; Leitch, M. J.; Leitgab, M.; Lewis, B.; Li, X.; Lim, S. H.; Liu, M. X.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Manion, A.; Manko, V. I.; Mannel, E.; Maruyama, T.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Mohapatra, S.; Montuenga, P.; Moon, T.; Morrison, D. P.; Moskowitz, M.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagamiya, S.; Nagashima, K.; Nagle, J. L.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nattrass, C.; Netrakanti, P. K.; Nihashi, M.; Niida, T.; Nishimura, S.; Nouicer, R.; Novák, T.; Novitzky, N.; Nyanin, A. S.; O'Brien, E.; Ogilvie, C. A.; Oide, H.; Okada, K.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, J. S.; Park, S.; Park, S. K.; Pate, S. F.; Patel, L.; Patel, M.; Peng, J.-C.; Perepelitsa, D. V.; Perera, G. D. N.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Purschke, M. L.; Qu, H.; Rak, J.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Rinn, T.; Riveli, N.; Roach, D.; Rolnick, S. D.; Rosati, M.; Rowan, Z.; Rubin, J. G.; Ryu, M. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sako, H.; Samsonov, V.; Sarsour, M.; Sato, S.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sekiguchi, Y.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shaver, A.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Singh, B. K.; Singh, C. P.; Singh, V.; Skolnik, M.; Slunečka, M.; Snowball, M.; Solano, S.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Stankus, P. W.; Steinberg, P.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Stone, M. R.; Sugitate, T.; Sukhanov, A.; Sumita, T.; Sun, J.; Sziklai, J.; Takahara, A.; Taketani, A.; Tanaka, Y.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tennant, E.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Tomášek, M.; Torii, H.; Towell, C. L.; Towell, R.; Towell, R. S.; Tserruya, I.; van Hecke, H. W.; Vargyas, M.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Whitaker, S.; White, A. S.; Wolin, S.; Woody, C. L.; Wysocki, M.; Xia, B.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yanovich, A.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zhou, S.; Zou, L.; Phenix Collaboration

2016-11-01

Measurements of anisotropic flow Fourier coefficients (vn) for inclusive charged particles and identified hadrons π±, K±, p , and p ¯ produced at midrapidity in Cu +Au collisions at √{s NN}=200 GeV are presented. The data were collected in 2012 by the PHENIX experiment at the Relativistic Heavy-Ion Collider (RHIC). The particle azimuthal distributions with respect to different-order symmetry planes Ψn, for n =1 , 2, and 3 are studied as a function of transverse momentum pT over a broad range of collision centralities. Mass ordering, as expected from hydrodynamic flow, is observed for all three harmonics. The charged-particle results are compared with hydrodynamical and transport model calculations. We also compare these Cu +Au results with those in Cu +Cu and Au +Au collisions at the same √{sNN} and find that the v2 and v3, as a function of transverse momentum, follow a common scaling with 1 /(ɛnNpart1 /3) .

Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements

Science.gov (United States)

Kosmopoulos, Panagiotis G.; Kazadzis, Stelios; Taylor, Michael; Raptis, Panagiotis I.; Keramitsoglou, Iphigenia; Kiranoudis, Chris; Bais, Alkiviadis F.

2018-02-01

This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05°, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range -15 to 15 % for the NN that produces spectral irradiances (NNS), 5-6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from -100 to 40 and -20 to 20 W m-2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.

Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

Science.gov (United States)

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

2016-02-01

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

Science.gov (United States)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Direct determinations of the πNN coupling constants

International Nuclear Information System (INIS)

Ericson, T.E.O.; ); Loiseau, B.

1998-01-01

A novel extrapolation method has been used to deduce directly the charged πNN coupling constant from backward np differential scattering cross sections. The extracted value, g c 2 = 14.52(026)is higher than the indirectly deduced values obtained in nucleon-nucleon energy-dependent partial-wave analyses. Our preliminary direct value from a reanalysis of the GMO sum-rule points to an intermediate value of g c 2 about 13.97(30). (author)

Open Charm Yields in d+Au Collisions at √sNN = 200 GeV

International Nuclear Information System (INIS)

Adams, J.; Aggarwal, M.M.; Ahammed, Z.; Amonett, J.; Anderson, B.D.; Arkhipkin, D.; Averichev, G.S.; Badyal, S.K.; Bai, Y.; Balewski, J.; Barannikova, O.; Barnby, L.S.; Baudot, J.; Bekele, S.; Belaga, V.V.; Bellwied, R.; Berger, J.; Bezverkhny, B.I.; Bharadwaj, S.; Bhasin, A.; Bhati, A.K.; Bhatia, V.S.; Bichsel, H.; Billmeier, A.; Bland, L.C.; Blyth, C.O.; Bonner, B.E.; Botje, M.; Boucham, A.; Brandin, A.; Bravar, A.; Bystersky, M.; Cadman, R.V.; Cai, X.Z.; Caines, H.; Calderon de la Barca Sanchez, M.; Castillo, J.; Cebra, D.; Chajecki, Z.; Chaloupka, P.; Chattopadhyay, S.; Chen, H.F.; Chen, Y.; Cheng, J.; Cherney, M.; Chikanian, A.; Christie, W.; Coffin, J.P.; Cormier, T.M.; Cramer, J.G.; Crawford, H.J.; Das, D.; Das, S.; De Moura, M.M.; Derevschikov, A.A.; Didenko, L.; Dietel, T.; Dogra, S.M.; Dong, W.J.; Dong, X.; Draper, J.E.; Du, F.; Dubey, A.K.; Dunin, V.B.; Dunlop, J.C.; Dutta Mazumda, M.R.; Eckardt, V.; Edwards, W.R.; Efimov, L.G.; Emelianov, V.; Engelage, J.; Eppley, G.; Erazmus, B.; Estienne, M.; Fachini, P.; Faivre, J.; Fatemi, R.; Fedorisin, J.; Filimonov, K.; Filip, P.; Finch, E.; Fine, V.; Fisyak, Y.; Fomenko, K.; Fu, J.; Gagliardi, C.A.; Gaillard, L.; Gans, J.; Ganti, M.S.; Gaudichet, L.; Geurts, F.; Ghazikhanian, V.; Ghosh, P.; Gonzalez, J.F.; Grachov, O.; Grebenyuk, O.; Grosnick, D.; Guertin, S.M.; Guo, Y.; Gupta, A.; Gutierrez, T.D.; Hallman, T.J.; Hamed, A.; Hardtke, D.; Harris, J.W.; Heinz, M.; Henry, T.W.; Heppelmann, S.; Hippolyte, B.; Hirsch, A.; Hjort, E.; Hoffmann, G.W.; Huang, H.Z.; Huang, S.L.; Hughes, E.W.; Humanic, T.J.; Igo, G.; Ishihara, A.; Ivanshin, Yu.I.; Jacobs, P.; Jacobs, W.W.; Janik, M.; Jiang, H.; Jones, P.G.; Judd, E.G.; Kabana, S.; Kang, K.; Kaplan, M.; Keane, D.; Khodyrev, V.Yu.; Kiryluk, J.; Kisiel, A.; Kislov, E.M.; Klay, J.; Klein, S.R.; Koetke, D.D.; Kollegger, T.; Kopytine, S.M.; Kotchenda, L.; Kramer, M.; Kravtsov, P.; Kravtsov, V.I.; Krueger, K.; Kuhn, C.; Kulikov, A.I.; Kumar, A.

2005-01-01

Mid-rapidity open charm spectra from direct reconstruction of D 0 ((bar D) 0 ) → K ± π ± in d+Au collisions and indirect electron/positron measurements via charm semileptonic decays in p+p and d+Au collisions at √s NN = 200 GeV are reported. The D 0 ((bar D) 0 ) spectrum covers a transverse momentum (p T ) range of 0.1 T T cbar c NN /dy = 0.30 ± 0.04 (stat.) ± 0.09(syst.) mb. The results are compared to theoretical calculations. Implications for charmonium results in A+A collisions are discussed

Network structure exploration via Bayesian nonparametric models

International Nuclear Information System (INIS)

Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

2015-01-01

Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier

Directory of Open Access Journals (Sweden)

Elize A. Shirdel

2011-01-01

Full Text Available Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher’s exact test probability P<0.00023 [2 tailed], Matthews’ correlation coefficient +0.83. Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment.

Neural network-based control of an intelligent solar Stirling pump

International Nuclear Information System (INIS)

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

2016-01-01

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

Homophyly/Kinship Model: Naturally Evolving Networks

Science.gov (United States)

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-10-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

Modeling, robust and distributed model predictive control for freeway networks

NARCIS (Netherlands)

Liu, S.

2016-01-01

In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

Neural network analysis in pharmacogenetics of mood disorders

Directory of Open Access Journals (Sweden)

Serretti Alessandro

2004-12-01

Full Text Available Abstract Background The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods. Methods In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR and in the Tryptophan Hydroxylase (TPH gene were analysed simultaneously. Results A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731 – empirical p value = 0.0082. Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005. Conclusions Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.

Iterated interactions method. Realistic NN potential

International Nuclear Information System (INIS)

Gorbatov, A.M.; Skopich, V.L.; Kolganova, E.A.

1991-01-01

The method of iterated potential is tested in the case of realistic fermionic systems. As a base for comparison calculations of the 16 O system (using various versions of realistic NN potentials) by means of the angular potential-function method as well as operators of pairing correlation were used. The convergence of genealogical series is studied for the central Malfliet-Tjon potential. In addition the mathematical technique of microscopical calculations is improved: new equations for correlators in odd states are suggested and the technique of leading terms was applied for the first time to calculations of heavy p-shell nuclei in the basis of angular potential functions

A new cascade NN based method to short-term load forecast in deregulated electricity market

International Nuclear Information System (INIS)

Kouhi, Sajjad; Keynia, Farshid

2013-01-01

Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method

Chiral 2π exchange at fourth order and peripheral NN scattering

International Nuclear Information System (INIS)

Entem, D.R.; Machleidt, R.

2002-01-01

We calculate the impact of the complete set of two-pion exchange contributions at chiral fourth order (also known as next-to-next-to-next-to-leading order) on peripheral partial waves of nucleon-nucleon scattering. Our calculations are based upon the analytical studies by Kaiser. It turns out that the contribution of fourth order is substantially smaller than the one of third order, indicating convergence of the chiral expansion. We compare the prediction from chiral pion exchange with the corresponding one from conventional meson theory as represented by the Bonn full model and find, in general, good agreement. Our calculations provide a sound basis for investigating the issue whether the low-energy constants determined from πN lead to reasonable predictions for NN

Cyber threat model for tactical radio networks

Science.gov (United States)

Kurdziel, Michael T.

2014-05-01

The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

Tool wear modeling using abductive networks

Science.gov (United States)

Masory, Oren

1992-09-01

A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

Directory of Open Access Journals (Sweden)

Maja eStikic

2014-11-01

Full Text Available The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make deadly force decisions in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects’ performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback, and applied across a variety of training environments.

Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Directory of Open Access Journals (Sweden)

Caihong Zhang

2014-01-01

Full Text Available This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results.

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

2015-01-01

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

Exact Cross-Validation for kNN and applications to passive and active learning in classification

OpenAIRE

Célisse, Alain; Mary-Huard, Tristan

2011-01-01

In the binary classification framework, a closed form expression of the cross-validation Leave-p-Out (LpO) risk estimator for the k Nearest Neighbor algorithm (kNN) is derived. It is first used to study the LpO risk minimization strategy for choosing k in the passive learning setting. The impact of p on the choice of k and the LpO estimation of the risk are inferred. In the active learning setting, a procedure is proposed that selects new examples using a LpO committee of kNN classifiers. The...

Synergistic effects in threshold models on networks

Science.gov (United States)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.