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Sample records for network nn techniques

  1. FDI and Accommodation Using NN Based Techniques

    Science.gov (United States)

    Garcia, Ramon Ferreiro; de Miguel Catoira, Alberto; Sanz, Beatriz Ferreiro

    Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.

  2. Neural networks (NN applied to the commercial properties valuation

    Directory of Open Access Journals (Sweden)

    J. M. Núñez Tabales

    2017-03-01

    Full Text Available Several agents, such as buyers and sellers, or local or tax authorities need to estimate the value of properties. There are different approaches to obtain the market price of a dwelling. Many papers have been produced in the academic literature for such purposes, but, these are, almost always, oriented to estimate hedonic prices of residential properties, such as houses or apartments. Here these methodologies are used in the field of estimate market price of commercial premises, using AI techniques. A case study is developed in Cordova —city in the South of Spain—. Neural Networks are an attractive alternative to the traditional hedonic modelling approaches, as they are better adapted to non-linearities of causal relationships and they also produce smaller valuation errors. It is also possible, from the NN model, to obtain implicit prices associated to the main attributes that can explain the variability of the market price of commercial properties.

  3. PyNN: A Common Interface for Neuronal Network Simulators

    Science.gov (United States)

    Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

    2008-01-01

    Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

  4. PyNN: a common interface for neuronal network simulators

    Directory of Open Access Journals (Sweden)

    Andrew P Davison

    2009-01-01

    Full Text Available Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware. PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization, and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

  5. Hidden Neural Networks: A Framework for HMM/NN Hybrids

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric; Krogh, Anders Stærmose

    1997-01-01

    This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...

  6. A high performance k-NN approach using binary neural networks

    OpenAIRE

    Hodge, V J; Lees, K J; Austin, J L

    2004-01-01

    This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches. We compare various configurations of the binary approach to a ...

  7. The European Narcolepsy Network (EU-NN) database.

    Science.gov (United States)

    Khatami, Ramin; Luca, Gianina; Baumann, Christian R; Bassetti, Claudio L; Bruni, Oliviero; Canellas, Francesca; Dauvilliers, Yves; Del Rio-Villegas, Rafael; Feketeova, Eva; Ferri, Raffaele; Geisler, Peter; Högl, Birgit; Jennum, Poul; Kornum, Birgitte R; Lecendreux, Michel; Martins-da-Silva, Antonio; Mathis, Johannes; Mayer, Geert; Paiva, Teresa; Partinen, Markku; Peraita-Adrados, Rosa; Plazzi, Guiseppe; Santamaria, Joan; Sonka, Karel; Riha, Renata; Tafti, Mehdi; Wierzbicka, Aleksandra; Young, Peter; Lammers, Gert Jan; Overeem, Sebastiaan

    2016-06-01

    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 few European countries have registered narcolepsy cases in databases of the International Classification of Diseases or in registries of the European health authorities. A promising approach to identify disease-specific adverse health effects and needs in healthcare delivery in the field of rare diseases is to establish a distributed expert network. A first and important step is to create a database that allows collection, storage and dissemination of data on narcolepsy in a comprehensive and systematic way. Here, the first prospective web-based European narcolepsy database hosted by the European 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 promising to provide a better insight into many unsolved aspects of narcolepsy and related disorders, including clear phenotype characterization of subtypes of narcolepsy, more precise epidemiological data and knowledge on the natural history of narcolepsy, expectations about treatment effects, identification of post-marketing medication side-effects, and will contribute to improve clinical trial designs and provide facilities to further develop phase III trials. © 2016 European Sleep Research Society.

  8. 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 few European countries have registered narcolepsy cases in databases of the International Classification of Diseases or in registries of the European health authorities. A promising approach to identify disease-specific adverse health effects and needs in healthcare delivery in the field of rare...... diseases is to establish a distributed expert network. A first and important step is to create a database that allows collection, storage and dissemination of data on narcolepsy in a comprehensive and systematic way. Here, the first prospective web-based European narcolepsy database hosted by the European...

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

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    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...... 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/Net...

  10. Network acceleration techniques

    Science.gov (United States)

    Crowley, Patricia (Inventor); Awrach, James Michael (Inventor); Maccabe, Arthur Barney (Inventor)

    2012-01-01

    Splintered offloading techniques with receive batch processing are described for network acceleration. Such techniques offload specific functionality to a NIC while maintaining the bulk of the protocol processing in the host operating system ("OS"). The resulting protocol implementation allows the application to bypass the protocol processing of the received data. Such can be accomplished this by moving data from the NIC directly to the application through direct memory access ("DMA") and batch processing the receive headers in the host OS when the host OS is interrupted to perform other work. Batch processing receive headers allows the data path to be separated from the control path. Unlike operating system bypass, however, the operating system still fully manages the network resource and has relevant feedback about traffic and flows. Embodiments of the present disclosure can therefore address the challenges of networks with extreme bandwidth delay products (BWDP).

  11. Neural Network (NN) retrievals of Stratocumulus cloud properties using multi-angle polarimetric observations during ORACLES

    Science.gov (United States)

    Segal-Rosenhaimer, M.; Knobelspiesse, K. D.; Redemann, J.; Cairns, B.; Alexandrov, M. D.

    2016-12-01

    The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign is taking place in the South-East Atlantic during the Austral Spring for three consecutive years from 2016-2018. The study area encompasses one of the Earth's three semi-permanent subtropical Stratocumulus (Sc) cloud decks, and experiences very large aerosol optical depths, mainly biomass burning, originating from Africa. Over time, cloud optical depth (COD), lifetime and cloud microphysics (number concentration, effective radii Reff and precipitation) are expected to be influenced by indirect aerosol effects. These changes play a key role in the energetic balance of the region, and are part of the core investigation objectives of the ORACLES campaign, which acquires measurements of clean and polluted scenes of above cloud aerosols (ACA). Simultaneous retrievals of aerosol and cloud optical properties are being developed (e.g. MODIS, OMI), but still challenging, especially for passive, single viewing angle instruments. By comparison, multiangle polarimetric instruments like RSP (Research Scanning Polarimeter) show promise for detection and quantification of ACA, however, there are no operational retrieval algorithms available yet. Here we describe a new algorithm to retrieve cloud and aerosol optical properties from observations by RSP flown on the ER-2 and P-3 during the 2016 ORACLES campaign. The algorithm is based on training a NN, and is intended to retrieve aerosol and cloud properties simultaneously. However, the first step was to establish the retrieval scheme for low level Sc cloud optical properties. The NN training was based on simulated RSP total and polarized radiances for a range of COD, Reff, and effective variances, spanning 7 wavelength bands and 152 viewing zenith angles. Random and correlated noise were added to the simulations to achieve a more realistic representation of the signals. Before introducing the input variables to the network, the signals are projected

  12. Techniques for Modelling Network Security

    OpenAIRE

    Lech Gulbinovič

    2012-01-01

    The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...

  13. Underwater Acoustic Networking Techniques

    CERN Document Server

    Otnes, Roald; Casari, Paolo; Goetz, Michael; Husøy, Thor; Nissen, Ivor; Rimstad, Knut; van Walree, Paul; Zorzi, Michele

    2012-01-01

    This literature study presents an overview of underwater acoustic networking. It provides a background and describes the state of the art of all networking facets that are relevant for underwater applications. This report serves both as an introduction to the subject and as a summary of existing protocols, providing support and inspiration for the development of network architectures.

  14. Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

    Directory of Open Access Journals (Sweden)

    Vladimir Krasnopolsky

    2016-01-01

    Full Text Available A neural network (NN technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013, and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN’s generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

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

    Directory of Open Access Journals (Sweden)

    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.

  16. Application of LMS-Based NN Structure for Power Quality Enhancement in a Distribution Network Under Abnormal Conditions.

    Science.gov (United States)

    Agarwal, Rahul Kumar; Hussain, Ikhlaq; Singh, Bhim

    2017-03-16

    This paper proposes an application of a least mean-square (LMS)-based neural network (NN) structure for the power quality improvement of a three-phase power distribution network under abnormal conditions. It uses a single-layer neuron structure for the control in a distribution static compensator (DSTATCOM) to attenuate the harmonics such as noise, bias, notches, dc offset, and distortion, injected in the grid current due to connection of several nonlinear loads. This admittance LMS-based NN structure has a simple architecture which reduces the computational complexity and burden which makes it easy to implement. A DSTATCOM is a custom power device which performs various functionalities such as harmonics attenuation, reactive power compensation, load balancing, zero voltage regulation, and power factor correction. Other main contribution of this paper involves operation of the system under abnormal conditions of distribution network which means noise and distortion in voltage and imbalance in three-phase voltages at the point of interconnection. For substantiating and demonstrating the performance of proposed control approach, simulations are carried on MATLAB/Simulink software and corresponding experimental tests are conducted on a developed prototype in the laboratory.

  17. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  18. A Soft Technique for Measuring Friction Force Using Neural Network

    Directory of Open Access Journals (Sweden)

    Sunan HUANG

    2011-10-01

    Full Text Available There are two approaches to measure a friction force: force sensor, software estimation algorithm. This paper will focus on software approach to measure friction. The proposed approach uses a neural network (NN to approximate the friction force in a mechanical system. Since the friction force considered is a speed-dependent function, a learning algorithm is adopted to update the NN weights so as to follow unknown friction behaviors. The advantage of the proposed friction estimation method is that it is based on the built NN model, and it does not require the force sensor measurement. Simulation test is given to verify the effectiveness of the proposed approach.

  19. WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

    Science.gov (United States)

    Padgett, Mary L. (Editor)

    1993-01-01

    The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

  20. Emerging wireless networks concepts, techniques and applications

    CERN Document Server

    Makaya, Christian

    2011-01-01

    An authoritative collection of research papers and surveys, Emerging Wireless Networks: Concepts, Techniques, and Applications explores recent developments in next-generation wireless networks (NGWNs) and mobile broadband networks technologies, including 4G (LTE, WiMAX), 3G (UMTS, HSPA), WiFi, mobile ad hoc networks, mesh networks, and wireless sensor networks. Focusing on improving the performance of wireless networks and provisioning better quality of service and quality of experience for users, it reports on the standards of different emerging wireless networks, applications, and service fr

  1. Cognitive optical networks: architectures and techniques

    Science.gov (United States)

    Grebeshkov, Alexander Y.

    2017-04-01

    This article analyzes architectures and techniques of the optical networks with taking into account the cognitive methodology based on continuous cycle "Observe-Orient-Plan-Decide-Act-Learn" and the ability of the cognitive systems adjust itself through an adaptive process by responding to new changes in the environment. Cognitive optical network architecture includes cognitive control layer with knowledge base for control of software-configurable devices as reconfigurable optical add-drop multiplexers, flexible optical transceivers, software-defined receivers. Some techniques for cognitive optical networks as flexible-grid technology, broker-oriented technique, machine learning are examined. Software defined optical network and integration of wireless and optical networks with radio over fiber technique and fiber-wireless technique in the context of cognitive technologies are discussed.

  2. Blockmodeling techniques for complex networks

    Science.gov (United States)

    Ball, Brian Joseph

    The class of network models known as stochastic blockmodels has recently been gaining popularity. In this dissertation, we present new work that uses blockmodels to answer questions about networks. We create a blockmodel based on the idea of link communities, which naturally gives rise to overlapping vertex communities. We derive a fast and accurate algorithm to fit the model to networks. This model can be related to another blockmodel, which allows the method to efficiently find nonoverlapping communities as well. We then create a heuristic based on the link community model whose use is to find the correct number of communities in a network. The heuristic is based on intuitive corrections to likelihood ratio tests. It does a good job finding the correct number of communities in both real networks and synthetic networks generated from the link communities model. Two commonly studied types of networks are citation networks, where research papers cite other papers, and coauthorship networks, where authors are connected if they've written a paper together. We study a multi-modal network from a large dataset of Physics publications that is the combination of the two, allowing for directed links between papers as citations, and an undirected edge between a scientist and a paper if they helped to write it. This allows for new insights on the relation between social interaction and scientific production. We also have the publication dates of papers, which lets us track our measures over time. Finally, we create a stochastic model for ranking vertices in a semi-directed network. The probability of connection between two vertices depends on the difference of their ranks. When this model is fit to high school friendship networks, the ranks appear to correspond with a measure of social status. Students have reciprocated and some unreciprocated edges with other students of closely similar rank that correspond to true friendship, and claim an aspirational friendship with a much

  3. Performance Monitoring Techniques Supporting Cognitive Optical Networking

    DEFF Research Database (Denmark)

    Caballero Jambrina, Antonio; Borkowski, Robert; Zibar, Darko

    2013-01-01

    to solve this issue by realizing a network that can observe, act, learn and optimize its performance, taking into account end-to-end goals. In this letter we present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive...... Heterogeneous Reconfigurable Optical Network. We focus on the approaches developed in the project for optical performance monitoring, which enable the feedback from the physical layer to the cognitive decision system by providing accurate description of the performance of the established lightpaths.......High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can help...

  4. Composite learning from adaptive backstepping neural network control.

    Science.gov (United States)

    Pan, Yongping; Sun, Tairen; Liu, Yiqi; Yu, Haoyong

    2017-11-01

    In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. 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/.

  6. Wireless Sensor Networks Formation: Approaches and Techniques

    Directory of Open Access Journals (Sweden)

    Miriam Carlos-Mancilla

    2016-01-01

    Full Text Available Nowadays, wireless sensor networks (WSNs emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs. In this paper, the reviewed works are classified into distributed and centralized techniques. The analysis is focused on whether a single or multiple sinks are employed, nodes are static or mobile, the formation is event detection based or not, and network backbone is formed or not. We focus on recent works and present a discussion of their advantages and drawbacks. Finally, the paper overviews a series of open issues which drive further research in the area.

  7. The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

    Science.gov (United States)

    Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad

    2017-03-01

    Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

  8. Noise Reduction Technique for Images using Radial Basis Function Neural Networks

    Directory of Open Access Journals (Sweden)

    Sander Ali Khowaja

    2014-07-01

    Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.

  9. Voltage sags and transient detection and classification using half/one-cycle windowing techniques based on continuous s-transform with neural network

    Science.gov (United States)

    Daud, Kamarulazhar; Abidin, Ahmad Farid; Ismail, Ahmad Puad

    2017-08-01

    This paper was conducted to detect and classify the different power quality disturbance (PQD) using Half and One-Cycle Windowing Technique (WT) based on Continuous S-Transform (CST) and Neural Network (NN). The system using 14 bus bars based on IEEE standard had been designing using MATLAB©/Simulink to provide PQD data. The datum of PQD is analyzed by using WT based on CST to extract features and it characteristics. Besides, the study focused an important issue concerning the identification of PQD selection and detection, the feature and characteristics of two types of signals such as voltage sag and transient signal are obtained. After the feature extraction, the classified process had been done using NN to show the percentage of classification PQD either voltage sags or transients. The analysis show which selection of cycle for windowing technique can provide the smooth detection of PQD and the suitable characteristic to provide the highest percentage of classification of PQD.

  10. Design of a Seismic Reflection Multi-Attribute Workflow for Delineating Karst Pore Systems Using Neural Networks and Statistical Dimensionality Reduction Techniques

    Science.gov (United States)

    Ebuna, D. R.; Kluesner, J.; Cunningham, K. J.; Edwards, J. H.

    2016-12-01

    An effective method for determining the approximate spatial extent of karst pore systems is critical for hydrological modeling in such environments. When using geophysical techniques, karst features are especially challenging to constrain due to their inherent heterogeneity and complex seismic signatures. We present a method for mapping these systems using three-dimensional seismic reflection data by combining applications of machine learning and modern data science. Supervised neural networks (NN) have been successfully implemented in seismic reflection studies to produce multi-attributes (or meta-attributes) for delineating faults, chimneys, salt domes, and slumps. Using a seismic reflection dataset from southeast Florida, we develop an objective multi-attribute workflow for mapping karst in which potential interpreter bias is minimized by applying linear and non-linear data transformations for dimensionality reduction. This statistical approach yields a reduced set of input seismic attributes to the NN by eliminating irrelevant and overly correlated variables, while still preserving the vast majority of the observed data variance. By initiating the supervised NN from an eigenspace that maximizes the separation between classes, the convergence time and accuracy of the computations are improved since the NN only needs to recognize small perturbations to the provided decision boundaries. We contend that this 3D seismic reflection, data-driven method for defining the spatial bounds of karst pore systems provides great value as a standardized preliminary step for hydrological characterization and modeling in these complex geological environments.

  11. Techniques Used in String Matching for Network Security

    OpenAIRE

    Jamuna Bhandari

    2014-01-01

    String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

  12. Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

    OpenAIRE

    Malathy, E. M.; Vijayalakshmi Muthuswamy

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the...

  13. Traffic volume estimation using network interpolation techniques.

    Science.gov (United States)

    2013-12-01

    Kriging method is a frequently used interpolation methodology in geography, which enables estimations of unknown values at : certain places with the considerations of distances among locations. When it is used in transportation field, network distanc...

  14. Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

    Science.gov (United States)

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay. PMID:26237221

  15. Anomaly Detection Techniques for Ad Hoc Networks

    Science.gov (United States)

    Cai, Chaoli

    2009-01-01

    Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

  16. Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

    Directory of Open Access Journals (Sweden)

    Ahmed El-habashi

    2016-05-01

    Full Text Available We describe the application of a Neural Network (NN previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs that plague the coasts of the West Florida Shelf (WFS using Visible Infrared Imaging Radiometer Suite (VIIRS satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678 needed for both the normalized fluorescence height (nFLH and Red Band Difference algorithms (RBD currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (aph443 using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The aph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved aph443 images are filtered by applying limiting constraints, defined by (i low backscatter at Rrs 551 nm and (ii a minimum aph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs.

  17. Localization in wireless sensor networks: Classification and evaluation of techniques

    National Research Council Canada - National Science Library

    Ewa Niewiadomska-Szynkiewicz

    2012-01-01

      Localization in wireless sensor networks: Classification and evaluation of techniques Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices...

  18. Power Minimization techniques for Networked Data Centers.

    Energy Technology Data Exchange (ETDEWEB)

    Low, Steven; Tang, Kevin

    2011-09-28

    Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

  19. Outlier Detection Techniques For Wireless Sensor Networks: A Survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are

  20. Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

    2011-01-01

    We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...

  1. Survey of Green Radio Communications Networks: Techniques and Recent Advances

    Directory of Open Access Journals (Sweden)

    Mohammed H. Alsharif

    2013-01-01

    Full Text Available Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of each of the techniques and thereby offer clear insights to researchers about how to choose the best ways to reduce energy consumption in future green radio networks.

  2. Criminal Network Investigation: Processes, Tools, and Techniques

    DEFF Research Database (Denmark)

    Petersen, Rasmus Rosenqvist

    intelligence products that can be disseminated to their customers. Investigators deal with an increasing amount of information from a variety of sources, especially the Internet, all of which are important to their analysis and decision making process. But information abundance is far from the only or most...... a target-centric process model (acquisition, synthesis, sense-making, dissemination, cooperation) encouraging and supporting an iterative and incremental evolution of the criminal network across all five investigation processes. The first priority of the process model is to address the problems of linear...

  3. Real-time network traffic classification technique for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

  4. Nucleon-nucleon final state interactions in NN NN

    Energy Technology Data Exchange (ETDEWEB)

    Dubach, J.; Kloet, W.M.; Silbar, R.R.

    1986-01-01

    Peaks in cross sections for the NN NN reaction at low relative momentum for the final nucleon-nucleon pair are successfully explained using S0 and TS1 final state interactions. Both singlet and triplet final state interactions are important and can interfere dramatically in certain spin observables.

  5. Techniques for labeling of optical signals in bust switched networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Koonen, A. M. J.; Zhang, Jianfeng

    2003-01-01

    We present a review of significant issues related to labeled optical burst switched (LOBS) networks and technologies enabling future optical internet networks. Labeled optical burst switching provides a quick and efficient forwarding mechanism of IP packets/bursts over wavelength division...... multiplexed (WDM) networks due to its single forwarding algorithm, thus yielding low latency, and it enables scaling to terabit rates. Moreover, LOBS is compatible with the general multiprotocol label switching (GMPLS) framework for a unified control plane. We present a review on techniques for labeling...... of optical signals for LOBS networks, including experimental results, we discuss as well issues for further research....

  6. Around the laboratories: Rutherford: Successful tests on bubble chamber target technique; Stanford (SLAC): New storage rings proposal; Berkeley: The HAPPE project to examine cosmic rays with superconducting magnets; The 60th birthday of Professor N.N. Bogolyubov; Argonne: Performance of the automatic film measuring system POLLY II

    CERN Multimedia

    1969-01-01

    Around the laboratories: Rutherford: Successful tests on bubble chamber target technique; Stanford (SLAC): New storage rings proposal; Berkeley: The HAPPE project to examine cosmic rays with superconducting magnets; The 60th birthday of Professor N.N. Bogolyubov; Argonne: Performance of the automatic film measuring system POLLY II

  7. Neural Networks for the Extraction of the ΛC Signal in p-Pb collisions at 
 √sNN = 5.02 TeV

    CERN Document Server

    Giampaolo, Alberto

    2016-01-01

    The charmed baryon ΛC is of interest for the characterization of the quark-gluon plasma (QGP) created in Pb-Pb collisions, due to its sensitivity to c-quark thermalization and to the hadronization mechanisms. The measurement in pp an p-Pb collisions is of interest both as a reference for the Pb- Pb result and in the context of recent observations suggesting the possible creation of a QGP in small colliding systems. This project is focused on the study of the extraction of the ΛC signal in p-Pb collisions with the ALICE detector, through the usage of deep learning, a machine learning technique. In a few weeks we were able to reproduce the results of the existing BDT analysis with a simple shallow networks. In the 6 to 8 pT bin, deep networks using low-level variables get close to the performance of the topological variable analysis, but with the architectures tested in this project they do not seem to be able to outperform it.

  8. Cooperative Technique Based on Sensor Selection in Wireless Sensor Network

    OpenAIRE

    ISLAM, M. R.; KIM, J.

    2009-01-01

    An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs) are used to form a Multiple Input Single Output (MISO) structure wirelessly connected with a Network Capable Application Processor (NCAP). Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for...

  9. High Dimensional Modulation and MIMO Techniques for Access Networks

    DEFF Research Database (Denmark)

    Binti Othman, Maisara

    Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless amplitudep...... wired-wireless access networks....... the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially...... to be utilized for multiple service allocation to different users. MIMO multiplexing techniques with OFDM provides the scalability in increasing spectral efficiency and bit rates for RoF systems. High dimensional CAP and MIMO multiplexing techniques are two promising solutions for supporting wired and hybrid...

  10. Large-scale Multi-label Text Classification - Revisiting Neural Networks

    OpenAIRE

    Nam, Jinseok; Kim, Jungi; Mencía, Eneldo Loza; Gurevych, Iryna; Fürnkranz, Johannes

    2013-01-01

    Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking lo...

  11. Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks

    Directory of Open Access Journals (Sweden)

    Fazli Subhan

    2013-01-01

    Full Text Available This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN of the target node, while in the second stage it uses trilateration approach to estimate the coordinate without the use of radio propagation model. The distance between calculated NN and detective access points (AP is estimated using Euclidian distance formula. Thus, distance between NN and APs provides radii for trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter is used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting approaches, respectively.

  12. Advanced network programming principles and techniques : network application programming with Java

    CERN Document Server

    Ciubotaru, Bogdan

    2013-01-01

    Answering the need for an accessible overview of the field, this text/reference presents a manageable introduction to both the theoretical and practical aspects of computer networks and network programming. Clearly structured and easy to follow, the book describes cutting-edge developments in network architectures, communication protocols, and programming techniques and models, supported by code examples for hands-on practice with creating network-based applications. Features: presents detailed coverage of network architectures; gently introduces the reader to the basic ideas underpinning comp

  13. A technique for choosing an option for SDH network upgrade

    Directory of Open Access Journals (Sweden)

    V. A. Bulanov

    2014-01-01

    Full Text Available Rapidly developing data transmission technologies result in making the network equipment modernization inevitable. There are various options to upgrade the SDH networks, for example, by increasing the capacity of network overloaded sites, the entire network capacity by replacement of the equipment or by creation of a parallel network, by changing the network structure with the organization of multilevel hierarchy of a network, etc. All options vary in a diversity of parameters starting with the solution cost and ending with the labor intensiveness of their realization. Thus, there are no certain standard approaches to the rules to choose an option for the network development. The article offers the technique for choosing the SHD network upgrade based on method of expert evaluations using as a tool the software complex that allows us to have quickly the quantitative characteristics of proposed network option. The technique is as follows:1. Forming a perspective matrix of services inclination to the SDH networks.2. Developing the several possible options for a network modernization.3. Formation of the list of criteria and a definition of indicators to characterize them by two groups, namely costs of the option implementation and arising losses; positive effect from the option introduction.4. Criteria weight coefficients purpose.5. Indicators value assessment within each criterion for each option by each expert. Rationing of the obtained values of indicators in relation to the maximum value of an indicator among all options.6. Calculating the integrated indicators of for each option by criteria groups.7. Creating a set of Pareto by drawing two criteria groups of points, which correspond to all options in the system of coordinates on the plane. Option choice.In implementation of point 2 the indicators derivation owing to software complex plays a key role. This complex should produce a structure of the network equipment, types of multiplexer sections

  14. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    FUMET: A fuzzy network module extraction technique for gene expression data. Priyakshi Mahanta Hasin Afzal Ahmed ... Bhattacharyya1 Ashish Ghosh2. Department of Computer Science and Engineering, Tezpur University, Napaam 784 028, India; Machine Intelligent Unit, Indian Statistical Institute, Kolkata 700 108, India ...

  15. Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

    Directory of Open Access Journals (Sweden)

    DongHo Kang

    2014-01-01

    Full Text Available Internet of Things (IoT consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.

  16. Stochastic Optimal Regulation of Nonlinear Networked Control Systems by Using Event-Driven Adaptive Dynamic Programming.

    Science.gov (United States)

    Sahoo, Avimanyu; Jagannathan, Sarangapani

    2017-02-01

    In this paper, an event-driven stochastic adaptive dynamic programming (ADP)-based technique is introduced for nonlinear systems with a communication network within its feedback loop. A near optimal control policy is designed using an actor-critic framework and ADP with event sampled state vector. First, the system dynamics are approximated by using a novel neural network (NN) identifier with event sampled state vector. The optimal control policy is generated via an actor NN by using the NN identifier and value function approximated by a critic NN through ADP. The stochastic NN identifier, actor, and critic NN weights are tuned at the event sampled instants leading to aperiodic weight tuning laws. Above all, an adaptive event sampling condition based on estimated NN weights is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals along with the approximation accuracy. The net result is event-driven stochastic ADP technique that can significantly reduce the computation and network transmissions. Finally, the analytical design is substantiated with simulation results.

  17. Eigentime identities for random walks on a family of treelike networks and polymer networks

    Science.gov (United States)

    Dai, Meifeng; Wang, Xiaoqian; Sun, Yanqiu; Sun, Yu; Su, Weiyi

    2017-10-01

    In this paper, we investigate the eigentime identities quantifying as the sum of reciprocals of all nonzero normalized Laplacian eigenvalues on a family of treelike networks and the polymer networks. Firstly, for a family of treelike networks, it is shown that all their eigenvalues can be obtained by computing the roots of several small-degree polynomials defined recursively. We obtain the scalings of the eigentime identity on a family of treelike with network size Nn is Nn lnNn. Then, for the polymer networks, we apply the spectral decimation approach to determine analytically all the eigenvalues and their corresponding multiplicities. Using the relationship between the generation and the next generation of eigenvalues we obtain the scalings of the eigentime identity on the polymer networks with network size Nn is Nn lnNn. By comparing the eigentime identities on these two kinds of networks, their scalings with network size Nn are all Nn lnNn.

  18. Remote Sensing Techniques Applying Neural Networks for Effective Retrieval of Harmful Algal Blooms in the West Florida Shelf from VIIRS Satellite Observations, without the need for a Fluorescence Channel, and their comparisons with other Techniques.

    Science.gov (United States)

    El-habashi, A.; Ahmed, S.; Lovko, V. J.

    2016-02-01

    Remote sensing approaches using neural networks (NN), are described that make use of the Ocean Color Remote Sensing Reflectances (OC Rrs) available from Visible Infrared Imaging Radiometer Suite (VIIRS) satellite bands at 486, 551 and 671nm to detect and retrieve Karenia brevis (KB) Harmful Algal Blooms (HABs) that plague West Florida Shelf (WFS) coasts impacting the environment and tourism. This approach is necessitated because VIIRS, unfortunately, unlike the Moderate Resolution Imaging Spectroradiometer (MODIS), does not have a 678 nm chlorophyll-a fluorescence channel that is normally effectively used with the normalized fluorescence height (nFLH) algorithm for detecting KB HABs in the WFS. We describe here the application of neural networks (NNs) previously reported by us for Chesapeake Bay chlorophyll retrievals, for the retrieval of phytoplankton absorption at 443 nm (aph443) in VIIRS images of the WFS using the existing VIIRS 486, 551 and 671nm bands. These NN retrieved aph443 values, are then converted to equivalent [Chla] values using known empirical relationships between them for the WFS. Now waters compatible with KB HABS in the WFS are known to be characterized by a minimum permissible [Chla] and a maximum permissible particulate backscatter bbp at 551nm (and therefore a maximum permissible VIIRS Rrs 551 nm). These two limiting criteria are then used to create exclusion masks which are then consecutively applied as filters to retrieved VIIRS Rrs551nm images and then to VIIRS [Chla] images (obtained from equivalent NN retrieved aph443 images). The residual [Chla] image after application of the filters then shows values compatible with KB HABS in the WFS having satisfied both maximum Rrs551nm and minimum [Chla] criteria. The residual images of KB compatible [Chla] values are then used to identify, delineate and quantify the existing KB HABS. Comparisons with in-situ measurements and other techniques, including nFLH with MODIS, are presented, and confirm

  19. Interaction techniques for selecting and manipulating subgraphs in network visualizations.

    Science.gov (United States)

    McGuffin, Michael J; Jurisica, Igor

    2009-01-01

    We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands for translation, rotation, scaling, or modifying graphical properties (such as opacity) and layout patterns can be performed by using a hotbox (a transiently popped-up, semi-transparent set of widgets) that has been extended in novel ways to integrate specification of commands with 1D or 2D arguments. Our techniques require only one mouse button and one keyboard key, and are designed for fast, gestural, in-place interaction. We present the design and integration of these interaction techniques, and illustrate their use in interactive graph visualization. Our techniques are implemented in NAViGaTOR, a software package for visualizing and analyzing biological networks. An initial usability study is also reported.

  20. Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

    Science.gov (United States)

    Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

    2017-05-01

    Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

  1. Mathematical analysis techniques for modeling the space network activities

    Science.gov (United States)

    Foster, Lisa M.

    1992-01-01

    The objective of the present work was to explore and identify mathematical analysis techniques, and in particular, the use of linear programming. This topic was then applied to the Tracking and Data Relay Satellite System (TDRSS) in order to understand the space network better. Finally, a small scale version of the system was modeled, variables were identified, data was gathered, and comparisons were made between actual and theoretical data.

  2. Power Optimization Techniques for Next Generation Wireless Networks

    OpenAIRE

    Ratheesh R; Vetrivelan P

    2016-01-01

    The massive data traffic and the need for high speed wireless communication is increasing day by day corresponds to an exponential increase in the consumption of power by Information and Communication Technology (ICT) sector. Reducing consumption of power in wireless network is a challenging topic and has attracted the attention of researches around the globe. Many techniques like multiple-input multiple-output (MIMO), cognitive radio, cooperative heterogeneous communications and new netwo...

  3. Node Augmentation Technique in Bayesian Network Evidence Analysis and Marshaling

    Energy Technology Data Exchange (ETDEWEB)

    Keselman, Dmitry [Los Alamos National Laboratory; Tompkins, George H [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory

    2010-01-01

    Given a Bayesian network, sensitivity analysis is an important activity. This paper begins by describing a network augmentation technique which can simplifY the analysis. Next, we present two techniques which allow the user to determination the probability distribution of a hypothesis node under conditions of uncertain evidence; i.e. the state of an evidence node or nodes is described by a user specified probability distribution. Finally, we conclude with a discussion of three criteria for ranking evidence nodes based on their influence on a hypothesis node. All of these techniques have been used in conjunction with a commercial software package. A Bayesian network based on a directed acyclic graph (DAG) G is a graphical representation of a system of random variables that satisfies the following Markov property: any node (random variable) is independent of its non-descendants given the state of all its parents (Neapolitan, 2004). For simplicities sake, we consider only discrete variables with a finite number of states, though most of the conclusions may be generalized.

  4. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Ziaul Huque

    2007-08-31

    This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

  5. Review of feed forward neural network classification preprocessing techniques

    Science.gov (United States)

    Asadi, Roya; Kareem, Sameem Abdul

    2014-06-01

    The best feature of artificial intelligent Feed Forward Neural Network (FFNN) classification models is learning of input data through their weights. Data preprocessing and pre-training are the contributing factors in developing efficient techniques for low training time and high accuracy of classification. In this study, we investigate and review the powerful preprocessing functions of the FFNN models. Currently initialization of the weights is at random which is the main source of problems. Multilayer auto-encoder networks as the latest technique like other related techniques is unable to solve the problems. Weight Linear Analysis (WLA) is a combination of data pre-processing and pre-training to generate real weights through the use of normalized input values. The FFNN model by using the WLA increases classification accuracy and improve training time in a single epoch without any training cycle, the gradient of the mean square error function, updating the weights. The results of comparison and evaluation show that the WLA is a powerful technique in the FFNN classification area yet.

  6. Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer.

    Science.gov (United States)

    Mehdy, M M; Ng, P Y; Shair, E F; Saleh, N I Md; Gomes, C

    2017-01-01

    Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

  7. Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

    Directory of Open Access Journals (Sweden)

    M. M. Mehdy

    2017-01-01

    Full Text Available Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

  8. Impact of sensor installation techniques on seismic network performance

    Science.gov (United States)

    Bainbridge, Geoffrey; Laporte, Michael; Baturan, Dario; Greig, Wesley

    2015-04-01

    The magnitude of completeness (Mc) of a seismic network is determined by a number of factors including station density, self-noise and passband of the sensor used, ambient noise environment and sensor installation method and depth. Sensor installation techniques related to depth are of particular importance due to their impact on overall monitoring network deployment costs. We present a case study which evaluates performance of Trillium Compact Posthole seismometers installed using different methods as well as depths, and evaluate its impact on seismic network operation in terms of the target area of interest average magnitude of completeness in various monitoring applications. We evaluate three sensor installation methods: direct burial in soil at 0.5 m depth, 5 m screwpile and 15 m cemented casing borehole at sites chosen to represent high, medium and low ambient noise environments. In all cases, noise performance improves with depth with noise suppression generally more prominent at higher frequencies but with significant variations from site to site. When extended to overall network performance, the observed noise suppression results in improved (decreased) target area average Mc. However, the extent of the improvement with depth varies significantly, and can be negligible. The increased cost associated with installation at depth uses funds that could be applied to the deployment of additional stations. Using network modelling tools, we compare the improvement in magnitude of completeness and location accuracy associated with increasing installation depth to those associated with increased number of stations. The appropriate strategy is applied on a case-by-case and driven by network-specific performance requirements, deployment constraints and site noise conditions.

  9. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.

    2010-11-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine the location of the source using the direct and the relayed signal at the destination. We derive the Cramer-Rao lower bound (CRLB) expressions separately for x and y coordinates of the location estimate. We analyze the effects of cognitive behaviour of the relay on the performance of the proposed method. We also discuss and quantify the reliability of the location estimate using the proposed technique if the source is not stationary. The overall performance of the proposed method is presented through simulations. ©2010 IEEE.

  10. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  11. Display techniques for dynamic network data in transportation GIS

    Energy Technology Data Exchange (ETDEWEB)

    Ganter, J.H.; Cashwell, J.W.

    1994-05-01

    Interest in the characteristics of urban street networks is increasing at the same time new monitoring technologies are delivering detailed traffic data. These emerging streams of data may lead to the dilemma that airborne remote sensing has faced: how to select and access the data, and what meaning is hidden in them? computer-assisted visualization techniques are needed to portray these dynamic data. Of equal importance are controls that let the user filter, symbolize, and replay the data to reveal patterns and trends over varying time spans. We discuss a prototype software system that addresses these requirements.

  12. Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks.

    Science.gov (United States)

    Howsmon, Daniel P; Hahn, Juergen

    2016-09-01

    Models of biochemical reaction networks commonly contain a large number of parameters while at the same time there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data, however, the small amount of available data and the large number of parameters commonly found in these types of models, require the use of regularization techniques to avoid over fitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross validation rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.

  13. Cooperative cognitive radio networking system model, enabling techniques, and performance

    CERN Document Server

    Cao, Bin; Mark, Jon W

    2016-01-01

    This SpringerBrief examines the active cooperation between users of Cooperative Cognitive Radio Networking (CCRN), exploring the system model, enabling techniques, and performance. The brief provides a systematic study on active cooperation between primary users and secondary users, i.e., (CCRN), followed by the discussions on research issues and challenges in designing spectrum-energy efficient CCRN. As an effort to shed light on the design of spectrum-energy efficient CCRN, they model the CCRN based on orthogonal modulation and orthogonally dual-polarized antenna (ODPA). The resource allocation issues are detailed with respect to both models, in terms of problem formulation, solution approach, and numerical results. Finally, the optimal communication strategies for both primary and secondary users to achieve spectrum-energy efficient CCRN are analyzed.

  14. Calibration Technique of the Irradiated Thermocouple using Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Jin Tae; Joung, Chang Young; Ahn, Sung Ho; Yang, Tae Ho; Heo, Sung Ho; Jang, Seo Yoon [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    To correct the signals, the degradation rate of sensors needs to be analyzed, and re-calibration of sensors should be followed periodically. In particular, because thermocouples instrumented in the nuclear fuel rod are degraded owing to the high neutron fluence generated from the nuclear fuel, the periodic re-calibration process is necessary. However, despite the re-calibration of the thermocouple, the measurement error will be increased until next re-calibration. In this study, based on the periodically calibrated temperature - voltage data, an interpolation technique using the artificial neural network will be introduced to minimize the calibration error of the C-type thermocouple under the irradiation test. The test result shows that the calculated voltages derived from the interpolation function have good agreement with the experimental sampling data, and they also accurately interpolate the voltages at arbitrary temperature and neutron fluence. That is, once the reference data is obtained by experiments, it is possible to accurately calibrate the voltage signal at a certain neutron fluence and temperature using an artificial neural network.

  15. An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

    Science.gov (United States)

    Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo

    2017-05-01

    Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.

  16. Hybrid Model GSTAR-SUR-NN For Precipitation Data

    OpenAIRE

    Agus Dwi Sulistyono; Waego Hadi Nugroho; Rahma Fitriani; Atiek Iriani

    2016-01-01

    Spatio-temporal model that have been developed such as Space-Time Autoregressive (STAR) model, Generalized Space-Time Autoregressive (GSTAR), GSTAR-OLS and GSTAR-SUR. Besides spatio-temporal phenomena, in daily life, we often find nonlinear phenomena, uncommon patterns and unidentified characteristics of the data. One of current developed nonlinear model is a neural network. This study is conducted to form a hybrid model GSTAR-SUR-NN to develop spatio-temporal model that has better prediction...

  17. Evolving neural networks using a genetic algorithm for heartbeat classification.

    Science.gov (United States)

    Sekkal, Mansouria; Chikh, Mohamed Amine; Settouti, Nesma

    2011-07-01

    This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared. Copyright © 2011 Informa UK, Ltd.

  18. Toward a Practical Technique to Halt Multiple Virus Outbreaks on Computer Networks

    OpenAIRE

    Hole, Kjell Jørgen

    2012-01-01

    The author analyzes a technique to prevent multiple simultaneous virus epidemics on any vulnerable computer network with inhomogeneous topology. The technique immunizes a small fraction of the computers and utilizes diverse software platforms to halt the virus outbreaks. The halting technique is of practical interest since a network's detailed topology need not be known.

  19. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    Nabil Ali Alrajeh; Lloret, J.

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  20. FUMET: A fuzzy network module extraction technique for gene ...

    Indian Academy of Sciences (India)

    Supplementary figure 1. (A): Visualization of one of the network modules by GeneMania for dataset 4 (B): Visualization of one of the network modules by GeneMania for dataset 1 (C): Visualization of one of the network modules by GeneMania for dataset 3.

  1. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

    Directory of Open Access Journals (Sweden)

    Declan T. Delaney

    2016-12-01

    Full Text Available No single network solution for Internet of Things (IoT networks can provide the required level of Quality of Service (QoS for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  2. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection †

    Science.gov (United States)

    Delaney, Declan T.; O’Hare, Gregory M. P.

    2016-01-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks. PMID:27916929

  3. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

    Science.gov (United States)

    Delaney, Declan T; O'Hare, Gregory M P

    2016-12-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  4. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  5. Advanced techniques for multicast service provision in core transport networks

    OpenAIRE

    Fernández del Carpio, Gonzalo

    2012-01-01

    Although the network-based multicast service is the optimal way to support of a large variety of popular applications such as high-definition television (HDTV), videoon- demand (VoD), virtual private LAN service (VPLS), grid computing, optical storage area networks (O-SAN), video conferencing, e-learning, massive multiplayer online role-playing games (MMORPG), networked virtual reality, etc., there are a number of technological and operational reasons that prevents a wider deployment. This Ph...

  6. Methodologies and techniques for analysis of network flow data

    Energy Technology Data Exchange (ETDEWEB)

    Bobyshev, A.; Grigoriev, M.; /Fermilab

    2004-12-01

    Network flow data gathered at the border routers and core switches is used at Fermilab for statistical analysis of traffic patterns, passive network monitoring, and estimation of network performance characteristics. Flow data is also a critical tool in the investigation of computer security incidents. Development and enhancement of flow based tools is an on-going effort. This paper describes the most recent developments in flow analysis at Fermilab.

  7. Measuring the influence of networks on transaction costs using a non-parametric regression technique

    DEFF Research Database (Denmark)

    Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.

    . We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...

  8. A Survey of Neural Network Techniques for Feature Extraction from Text

    OpenAIRE

    John, Vineet

    2017-01-01

    This paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks.

  9. Wireless multimedia sensor networks on reconfigurable hardware information reduction techniques

    CERN Document Server

    Ang, Li-minn; Chew, Li Wern; Yeong, Lee Seng; Chia, Wai Chong

    2013-01-01

    Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs).

  10. Outlier detection techniques for wireless sensor networks: A survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2010-01-01

    In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection

  11. Hybrid Model GSTAR-SUR-NN For Precipitation Data

    Directory of Open Access Journals (Sweden)

    Agus Dwi Sulistyono

    2016-05-01

    Full Text Available Spatio-temporal model that have been developed such as Space-Time Autoregressive (STAR model, Generalized Space-Time Autoregressive (GSTAR, GSTAR-OLS and GSTAR-SUR. Besides spatio-temporal phenomena, in daily life, we often find nonlinear phenomena, uncommon patterns and unidentified characteristics of the data. One of current developed nonlinear model is a neural network. This study is conducted to form a hybrid model GSTAR-SUR-NN to develop spatio-temporal model that has better prediction. This research is conducted on ten-daily rainfall data at 2005 - 2015 for Blimbing, Singosari, Karangploso, Dau, and Wagir region. Based on the results of this research, indicated that the accuracy of GSTAR ((1, 1,2,3,12,36-SUR model used cross-covariance weight has relatively similar to GSTAR ((1, 1,2,3 , 12.36-SUR-NN (25-14-5 for  Blimbing and Singosari region with 5% error level. While Karangploso, Dau, and Wagir, GSTAR ((1, 1,2,3,12,36-SUR-NN (25-14-5 model has better accuracy in predicting the precipitation at three locations with the value of R2prediction for each location is 0.992, 0.580, and 0.474.

  12. A Comparison of Techniques for Reducing Unicast Traffic in HSR Networks

    Directory of Open Access Journals (Sweden)

    Nguyen Xuan Tien

    2015-10-01

    Full Text Available This paper investigates several existing techniques for reducing high-availability seamless redundancy (HSR unicast traffic in HSR networks for substation automation systems (SAS. HSR is a redundancy protocol for Ethernet networks that provides duplicate frames for separate physical paths with zero recovery time. This feature of HSR makes it very suited for real-time and mission-critical applications such as SAS systems. HSR is one of the redundancy protocols selected for SAS systems. However, the standard HSR protocol generates too much unnecessary redundant unicast traffic in connected-ring networks. This drawback degrades network performance and may cause congestion and delay. Several techniques have been proposed to reduce the redundant unicast traffic, resulting in the improvement of network performance in HSR networks. These HSR traffic reduction techniques are broadly classified into two categories based on their traffic reduction manner, including traffic filtering-based techniques and predefined path-based techniques. In this paper, we provide an overview and comparison of these HSR traffic reduction techniques found in the literature. The concepts, operational principles, network performance, advantages, and disadvantages of these techniques are investigated, summarized. We also provide a comparison of the traffic performance of these HSR traffic reduction techniques.

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Memory Compression Techniques for Network Address Management in MPI

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanfei; Archer, Charles J.; Blocksome, Michael; Parker, Scott; Bland, Wesley; Raffenetti, Ken; Balaji, Pavan

    2017-05-29

    MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.

  15. Sybil Defense Techniques in Online Social Networks: A Survey

    National Research Council Canada - National Science Library

    Al-Qurishi, Muhammad; Al-Rakhami, Mabrook; Alamri, Atif; Alrubaian, Majed; Rahman, Sk Md Mizanur; Hossain, M. Shamim

    2017-01-01

    The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online...

  16. Adverse Outcome Pathway Network Analyses: Techniques and benchmarking the AOPwiki

    Science.gov (United States)

    Abstract: As the community of toxicological researchers, risk assessors, and risk managers adopt the adverse outcome pathway (AOP) paradigm for organizing toxicological knowledge, the number and diversity of adverse outcome pathways and AOP networks are continuing to grow. This ...

  17. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    OpenAIRE

    Sadik Kamel Gharghan; Rosdiadee Nordin; Mahamod Ismail

    2016-01-01

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the...

  18. Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues

    OpenAIRE

    Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios

    2014-01-01

    In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...

  19. 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).

  20. Harmonic Mitigation Techniques Applied to Power Distribution Networks

    Directory of Open Access Journals (Sweden)

    Hussein A. Kazem

    2013-01-01

    Full Text Available A growing number of harmonic mitigation techniques are now available including active and passive methods, and the selection of the best-suited technique for a particular case can be a complicated decision-making process. The performance of some of these techniques is largely dependent on system conditions, while others require extensive system analysis to prevent resonance problems and capacitor failure. A classification of the various available harmonic mitigation techniques is presented in this paper aimed at presenting a review of harmonic mitigation methods to researchers, designers, and engineers dealing with power distribution systems.

  1. Model dependence of unitary isobar model treatments of NN. -->. NN. pi. at intermediate energies

    Energy Technology Data Exchange (ETDEWEB)

    Dubach, J.; Kloet, W.M.; Silbar, R.R.; Tjon, J.A.; van Faassen, E.

    1986-09-01

    We compare two ''conventional'' relativistic unitary isobar models for the NN..-->..NN..pi.. reaction. One model, which preserves two- and three-body unitarity, contains only pion exchange, while the other, based on a two-body coupled-channels approach, includes heavier meson exchanges. Thus, we attempt to identify the model dependence of predictions for the NN..-->..NN..pi.. process over a large part of final state phase space. Differential cross sections and a variety of spin observables are examined. Implications for the identification of exotic dibaryon resonances are briefly discussed.

  2. Developing Visualization Techniques for Semantics-based Information Networks

    Science.gov (United States)

    Keller, Richard M.; Hall, David R.

    2003-01-01

    Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

  3. Data mining techniques in sensor networks summarization, interpolation and surveillance

    CERN Document Server

    Appice, Annalisa; Fumarola, Fabio; Malerba, Donato

    2013-01-01

    Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data.

  4. Simulation of developing human neuronal cell networks.

    Science.gov (United States)

    Lenk, Kerstin; Priwitzer, Barbara; Ylä-Outinen, Laura; Tietz, Lukas H B; Narkilahti, Susanna; Hyttinen, Jari A K

    2016-08-30

    Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain. In this paper, we propose an in silico model of maturating hESC-NNs based on a phenomenological model called INEX. We focus on simulations of the development of bursts in hESC-NNs, which are the main feature of neuronal activation patterns. The model was developed with data from developing hESC-NN recordings on MEAs which showed increase in the neuronal activity during the investigated six measurement time points in the experimental and simulated data. Our simulations suggest that the maturation process of hESC-NN, resulting in the formation of bursts, can be explained by the development of synapses. Moreover, spike and burst rate both decreased at the last measurement time point suggesting a pruning of synapses as the weak ones are removed. To conclude, our model reflects the assumption that the interaction between excitatory and inhibitory neurons during the maturation of a neuronal network and the spontaneous emergence of bursts are due to increased connectivity caused by the forming of new synapses.

  5. Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

    Directory of Open Access Journals (Sweden)

    E M Malathy

    Full Text Available In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution yields remarkably better results in terms of the network performance measures such as throughput and delay.

  6. Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

    Science.gov (United States)

    Malathy, E M; Muthuswamy, Vijayalakshmi

    2015-01-01

    In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.

  7. Assessment of Software Modeling Techniques for Wireless Sensor Networks: A Survey

    Directory of Open Access Journals (Sweden)

    John Khalil Jacoub

    2012-03-01

    Full Text Available Wireless Sensor Networks (WSNs monitor environment phenomena and in some cases react in response to the observed phenomena. The distributed nature of WSNs and the interaction between software and hardware components makes it difficult to correctly design and develop WSN systems. One solution to the WSN design challenges is system modeling. In this paper we present a survey of 9 WSN modeling techniques and show how each technique models different parts of the system such as sensor behavior, sensor data and hardware. Furthermore, we consider how each modeling technique represents the network behavior and network topology. We also consider the available supporting tools for each of the modeling techniques. Based on the survey, we classify the modeling techniques and derive examples of the surveyed modeling techniques by using SensIV system.

  8. QoS Provisioning Techniques for Future Fiber-Wireless (FiWi Access Networks

    Directory of Open Access Journals (Sweden)

    Martin Maier

    2010-04-01

    Full Text Available A plethora of enabling optical and wireless access-metro network technologies have been emerging that can be used to build future-proof bimodal fiber-wireless (FiWi networks. Hybrid FiWi networks aim at providing wired and wireless quad-play services over the same infrastructure simultaneously and hold great promise to mitigate the digital divide and change the way we live and work by replacing commuting with teleworking. After overviewing enabling optical and wireless network technologies and their QoS provisioning techniques, we elaborate on enabling radio-over-fiber (RoF and radio-and-fiber (R&F technologies. We describe and investigate new QoS provisioning techniques for future FiWi networks, ranging from traffic class mapping, scheduling, and resource management to advanced aggregation techniques, congestion control, and layer-2 path selection algorithms.

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

    Science.gov (United States)

    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....

  10. Evaluation of Techniques to Detect Significant Network Performance Problems using End-to-End Active Network Measurements

    Energy Technology Data Exchange (ETDEWEB)

    Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark

    2006-01-25

    End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our

  11. Review Of Prevention Techniques For Denial Of Service DOS Attacks In Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Poonam Rolla

    2015-08-01

    Full Text Available Wireless Sensor Networks comprised of several tiny sensor nodes which are densely deployed over the region to monitor the environmental conditions. These sensor nodes have certain design issues out of which security is the main predominant factor as it effects the whole lifetime of network. DDoS Distributed denial of service attack floods unnecessary packets in the sensor network. A review on DDoS attacks and their prevention techniques have been done in this paper.

  12. Review Of Prevention Techniques For Denial Of Service DOS Attacks In Wireless Sensor Network

    OpenAIRE

    Poonam Rolla; Manpreet Kaur

    2015-01-01

    Wireless Sensor Networks comprised of several tiny sensor nodes which are densely deployed over the region to monitor the environmental conditions. These sensor nodes have certain design issues out of which security is the main predominant factor as it effects the whole lifetime of network. DDoS Distributed denial of service attack floods unnecessary packets in the sensor network. A review on DDoS attacks and their prevention techniques have been done in this paper.

  13. A new application of neural network technique to sensorless speed identification of induction motor

    OpenAIRE

    Mostefai, Mohamed; Miloud, Yahia; Abdullah MILOUDI

    2016-01-01

    A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasib...

  14. A new application of neural network technique to sensorless speed identification of induction motor

    Directory of Open Access Journals (Sweden)

    Mohamed MOSTEFAI

    2016-12-01

    Full Text Available A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasibility of the method.

  15. Auditing information structures in organizations: A review of data collection techniques for network analysis

    NARCIS (Netherlands)

    Koning, K.H.; de Jong, Menno D.T.

    2005-01-01

    Network analysis is one of the current techniques for investigating organizational communication. Despite the amount of how-to literature about using network analysis to assess information flows and relationships in organizations, little is known about the methodological strengths and weaknesses of

  16. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  17. Novel anti-jamming technique for OCDMA network through FWM in SOA based wavelength converter

    Science.gov (United States)

    Jyoti, Vishav; Kaler, R. S.

    2013-06-01

    In this paper, we propose a novel anti-jamming technique for optical code division multiple access (OCDMA) network through four wave mixing (FWM) in semiconductor optical amplifier (SOA) based wavelength converter. OCDMA signal can be easily jammed with high power jamming signal. It is shown that wavelength conversion through four wave mixing in SOA has improved capability of jamming resistance. It is observed that jammer has no effect on OCDMA network even at high jamming powers by using the proposed technique.

  18. A Bloom Filter-Powered Technique Supporting Scalable Semantic Discovery in Data Service Networks

    Science.gov (United States)

    Zhang, J.; Shi, R.; Bao, Q.; Lee, T. J.; Ramachandran, R.

    2016-12-01

    More and more Earth data analytics software products are published onto the Internet as a service, in the format of either heavyweight WSDL service or lightweight RESTful API. Such reusable data analytics services form a data service network, which allows Earth scientists to compose (mashup) services into value-added ones. Therefore, it is important to have a technique that is capable of helping Earth scientists quickly identify appropriate candidate datasets and services in the global data service network. Most existing services discovery techniques, however, mainly rely on syntax or semantics-based service matchmaking between service requests and available services. Since the scale of the data service network is increasing rapidly, the run-time computational cost will soon become a bottleneck. To address this issue, this project presents a way of applying network routing mechanism to facilitate data service discovery in a service network, featuring scalability and performance. Earth data services are automatically annotated in Web Ontology Language for Services (OWL-S) based on their metadata, semantic information, and usage history. Deterministic Annealing (DA) technique is applied to dynamically organize annotated data services into a hierarchical network, where virtual routers are created to represent semantic local network featuring leading terms. Afterwards Bloom Filters are generated over virtual routers. A data service search request is transformed into a network routing problem in order to quickly locate candidate services through network hierarchy. A neural network-powered technique is applied to assure network address encoding and routing performance. A series of empirical study has been conducted to evaluate the applicability and effectiveness of the proposed approach.

  19. Cognition-Enabling Techniques in Heterogeneous and Flexgrid Optical Communication Networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Caballero Jambrina, Antonio; Saldaña Cercos, Silvia

    2012-01-01

    High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can hel...

  20. 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.

  1. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  2. Computing distance-based topological descriptors of complex chemical networks: New theoretical techniques

    Science.gov (United States)

    Hayat, Sakander

    2017-11-01

    Structure-based topological descriptors/indices of complex chemical networks enable prediction of physico-chemical properties and the bioactivities of these compounds through QSAR/QSPR methods. In this paper, we have developed a rigorous computational and theoretical technique to compute various distance-based topological indices of complex chemical networks. A fullerene is called the IPR (Isolated-Pentagon-Rule) fullerene, if every pentagon in it is surrounded by hexagons only. To ensure the applicability of our technique, we compute certain distance-based indices of an infinite family of IPR fullerenes. Our results show that the proposed technique is more diverse and bears less algorithmic and combinatorial complexity.

  3. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  4. Reliability assessment of restructured power systems using reliability network equivalent and pseudo-sequential simulation techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Yi; Wang, Peng; Goel, Lalit [Nanyang Technological University, School of Electrical and Electronics Engineering, Block S1, Nanyang Avenue, Singapore 639798 (Singapore); Billinton, Roy; Karki, Rajesh [Department of Electrical Engineering, University of Saskatchewan, Saskatoon (Canada)

    2007-10-15

    This paper presents a technique to evaluate reliability of a restructured power system with a bilateral market. The proposed technique is based on the combination of the reliability network equivalent and pseudo-sequential simulation approaches. The reliability network equivalent techniques have been implemented in the Monte Carlo simulation procedure to reduce the computational burden of the analysis. Pseudo-sequential simulation has been used to increase the computational efficiency of the non-sequential simulation method and to model the chronological aspects of market trading and system operation. Multi-state Markov models for generation and transmission systems are proposed and implemented in the simulation. A new load shedding scheme is proposed during generation inadequacy and network congestion to minimize the load curtailment. The IEEE reliability test system (RTS) is used to illustrate the technique. (author)

  5. Broadcast Expenses Controlling Techniques in Mobile Ad-hoc Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Naeem Ahmad

    2016-07-01

    Full Text Available The blind flooding of query packets in route discovery more often characterizes the broadcast storm problem, exponentially increases energy consumption of intermediate nodes and congests the entire network. In such a congested network, the task of establishing the path between resources may become very complex and unwieldy. An extensive research work has been done in this area to improve the route discovery phase of routing protocols by reducing broadcast expenses. The purpose of this study is to provide a comparative analysis of existing broadcasting techniques for the route discovery phase, in order to bring about an efficient broadcasting technique for determining the route with minimum conveying nodes in ad-hoc networks. The study is designed to highlight the collective merits and demerits of such broadcasting techniques along with certain conclusions that would contribute to the choice of broadcasting techniques.

  6. Total alkalinity estimation using MLR and neural network techniques

    Science.gov (United States)

    Velo, A.; Pérez, F. F.; Tanhua, T.; Gilcoto, M.; Ríos, A. F.; Key, R. M.

    2013-02-01

    During the last decade, two important collections of carbon relevant hydrochemical data have become available: GLODAP and CARINA. These collections comprise a synthesis of bottle data for all ocean depths from many cruises collected over several decades. For a majority of the cruises at least two carbon parameters were measured. However, for a large number of stations, samples or even cruises, the carbonate system is under-determined (i.e., only one or no carbonate parameter was measured) resulting in data gaps for the carbonate system in these collections. A method for filling these gaps would be very useful, as it would help with estimations of the anthropogenic carbon (Cant) content or quantification of oceanic acidification. The aim of this work is to apply and describe, a 3D moving window multilinear regression algorithm (MLR) to fill gaps in total alkalinity (AT) of the CARINA and GLODAP data collections for the Atlantic. In addition to filling data gaps, the estimated AT values derived from the MLR are useful in quality control of the measurements of the carbonate system, as they can aid in the identification of outliers. For comparison, a neural network algorithm able to perform non-linear predictions was also designed. The goal here was to design an alternative approach to accomplish the same task of filling AT gaps. Both methods return internally consistent results, thereby giving confidence in our approach.

  7. Energy saving techniques applied over a nation-wide mobile network

    DEFF Research Database (Denmark)

    Perez, Eva; Frank, Philipp; Micallef, Gilbert

    2014-01-01

    Traffic carried over wireless networks has grown significantly in recent years and actual forecasts show that this trend is expected to continue. However, the rapid mobile data explosion and the need for higher data rates comes at a cost of increased complexity and energy consumption of the mobile...... on the energy consumption based on a nation-wide network of a leading European operator. By means of an extensive analysis, we show that with the proposed techniques significant energy savings can be realized....

  8. NEW BURST ASSEMBLY AND SCHEDULING TECHNIQUE FOR OPTICAL BURST SWITCHING NETWORKS

    OpenAIRE

    Kavitha, V.; V.Palanisamy

    2013-01-01

    The Optical Burst Switching is a new switching technology that efficiently utilizes the bandwidth in the optical layer. The key areas to be concentrated in Optical Burst Switching (OBS) networks are the burst assembly and burst scheduling i.e., assignment of wavelengths to the incoming bursts. This study presents a New Burst Assembly and Scheduling (NBAS) technique in a simultaneous multipath transmission for burst loss recovery in OBS networks. A Redundant Burst Segmentation (RBS) is used fo...

  9. IMPLEMENTATION OF IMPROVED NETWORK LIFETIME TECHNIQUE FOR WSN USING CLUSTER HEAD ROTATION AND SIMULTANEOUS RECEPTION

    Directory of Open Access Journals (Sweden)

    Arun Vasanaperumal

    2015-11-01

    Full Text Available There are number of potential applications of Wireless Sensor Networks (WSNs like wild habitat monitoring, forest fire detection, military surveillance etc. All these applications are constrained for power from a stand along battery power source. So it becomes of paramount importance to conserve the energy utilized from this power source. A lot of efforts have gone into this area recently and it remains as one of the hot research areas. In order to improve network lifetime and reduce average power consumption, this study proposes a novel cluster head selection algorithm. Clustering is the preferred architecture when the numbers of nodes are larger because it results in considerable power savings for large networks as compared to other ones like tree or star. Since majority of the applications generally involve more than 30 nodes, clustering has gained widespread importance and is most used network architecture. The optimum number of clusters is first selected based on the number of nodes in the network. When the network is in operation the cluster heads in a cluster are rotated periodically based on the proposed cluster head selection algorithm to increase the network lifetime. Throughout the network single-hop communication methodology is assumed. This work will serve as an encouragement for further advances in the low power techniques for implementing Wireless Sensor Networks (WSNs.

  10. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

    Science.gov (United States)

    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  11. NN->NN. pi. at intermediate energies: predictions of a unitary model

    Energy Technology Data Exchange (ETDEWEB)

    Dubach, J.; Kloet, W.M.; Silbar, R.R.

    1987-05-01

    We explore the NN->NN..pi.. reaction in detail within the context of a unified model which satisfies two- and three-body unitary. Predictions for differential cross sections and spin observables are examined and compared with experimental data, where available, for a variety of exclusive (pp->p(n)..pi../sup +/, pp->pp(..pi../sup 0/), and np->p(p)..pi../sup -/) and inclusive (pp->p(X) and pp->n(x)) reactions. Based on predictions of the model, further experiments are suggested to shed additional light on the NN->NN..pi.. process. Due to dynamical symmetries the np->p(p)..pi../sup -/ observables are strikingly different from the more familiar pp->p(n)..pi../sup +/ observables, and we urge that this reaction be investigated experimentally. Experimental tests for the existence of dibaryons are also briefly discussed.

  12. NNNN π at intermediate energies: Predictions of a unitary model

    Science.gov (United States)

    Dubach, J.; Kloet, W. M.; Silbar, Richard R.

    1987-05-01

    We explore the NNNN π reaction in detail within the context of a unified model which satisfies two- and three-body unitary. Predictions for differential cross sections and spin observables are examined and compared with experimental data, where available, for a variety of exclusive [pp → p(n) π+, pp → pp( π0), and np → p(p) π-] and inclusive [pp → p(X) and pp → n(X)] reactions. Based on predictions of the model, further experiments are suggested to shed additional light on the NNNN π process. Due to dynamical symmetries the np → p(p) π- observables are strikingly different from the more familiar pp → p(n) π+ observables, and we urge that this reaction be investigated experimentally. Experimental tests for the existence of dibaryons are also briefly discussed.

  13. ENERGY EFFICIENCY ANALYSIS OF ERROR CORRECTION TECHNIQUES IN UNDERWATER WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    M. NORDIN B. ZAKARIA

    2011-02-01

    Full Text Available Research in underwater acoustic networks has been developed rapidly to support large variety of applications such as mining equipment and environmental monitoring. As in terrestrial sensor networks; reliable data transport is demanded in underwater sensor networks. The energy efficiency of error correction technique should be considered because of the severe energy constraints of underwater wireless sensor networks. Forward error correction (FEC andautomatic repeat request (ARQ are the two main error correction techniques in underwater networks. In this paper, a mathematical energy efficiency analysis for FEC and ARQ techniques in underwater environment has been done based on communication distance and packet size. The effects of wind speed, and shipping factor are studied. A comparison between FEC and ARQ in terms of energy efficiency is performed; it is found that energy efficiency of both techniquesincreases with increasing packet size in short distances, but decreases in longer distances. There is also a cut-off distance below which ARQ is more energy efficient than FEC, and after which FEC is more energy efficient than ARQ. This cut-off distance decreases by increasing wind speed. Wind speed has great effecton energy efficiency where as shipping factor has unnoticeable effect on energy efficiency for both techniques.

  14. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

    Science.gov (United States)

    Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

    2005-01-01

    Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

  15. Application of artificial neural networks with backpropagation technique in the financial data

    Science.gov (United States)

    Jaiswal, Jitendra Kumar; Das, Raja

    2017-11-01

    The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.

  16. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

    Directory of Open Access Journals (Sweden)

    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.

  17. Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

    Directory of Open Access Journals (Sweden)

    Vladimir M. Krasnopolsky

    2013-01-01

    Full Text Available A novel approach based on the neural network (NN ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR Community Atmospheric Model (CAM. It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

  18. 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.

  19. Multipath Routing and Wavelength Assignment Technique in Optical WDM Mesh Networks

    Science.gov (United States)

    Kavitha, T.; Shiyamala, S.; Rajamani, V.

    2017-12-01

    A routing and wavelength assignment (RWA) technique for supporting multipath traffic in optical wavelength-division multiplexing (WDM) mesh network is proposed in this paper. The network can be preceded by accomplishing two processes: one is establishing connection node and the second one is identifying the multipath and assigning wavelength. The connection node is selected based on the load and current traffic-carrying capacity of that node. During wavelength allocation mechanism, cost function is considered as the major criterion. Based on the cost involved in every path, the wavelengths are selected such that wavelength with the minimum cost is allocated to that particular path. This technique efficiently allocates the wavelength to the selected multiple paths and the traffic is routed to the destination using multiple paths with wavelength allocation. For simulation, NS2 simulator is used by applying the optical WDM network simulator patch. The proposed multipath RWA technique is compared with the existing RWA technique. We achieved a throughput of 12,625 packets for ten numbers of wavelengths. But the existing approach achieved a throughput of 10,189 packets only for the same numbers of wavelengths. Channel utilization is more, and delay is less compared with the existing technique. Hence, the proposed method is very efficient, since the router effectively routes the traffic within the network.

  20. Bias correction in SMAP soil moisture assimilation using a neural network approach

    Science.gov (United States)

    Kolassa, J.; Reichle, R. H.; Gentine, P.; Alemohammad, S. H.; Prigent, C.; Aires, F.; Draper, C. S.; Liu, Q.

    2016-12-01

    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, they can be used to implement an alternative bias correction to the local cumulative distribution function matching typically used in soil moisture data assimilation (DA) systems. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture (which is also 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. We assess the skill of the NN retrieval and the assimilation estimates through a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites. The NN method compares well against the official RTM based approach and is able to extract information from the SMAP observations that is complementary to the model. Additionally, we compare the NN method to more traditional bias correction approaches and analyze the potential of using spatially variable error estimates to improve the relative impact of observations in the assimilation.

  1. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  2. A STATISTICAL CORRELATION TECHNIQUE AND A NEURAL-NETWORK FOR THE MOTION CORRESPONDENCE PROBLEM

    NARCIS (Netherlands)

    VANDEEMTER, JH; MASTEBROEK, HAK

    A statistical correlation technique (SCT) and two variants of a neural network are presented to solve the motion correspondence problem. Solutions of the motion correspondence problem aim to maintain the identities of individuated elements as they move. In a preprocessing stage, two snapshots of a

  3. A control technique for integration of DG units to the electrical networks

    DEFF Research Database (Denmark)

    Pouresmaeil, Edris; Miguel-Espinar, Carlos; Massot-Campos, Miquel

    2013-01-01

    This paper deals with a multiobjective control technique for integration of distributed generation (DG) resources to the electrical power network. The proposed strategy provides compensation for active, reactive, and harmonic load current components during connection of DG link to the grid. The d...

  4. Overview of the neural network based technique for monitoring of road condition via reconstructed road profiles

    CSIR Research Space (South Africa)

    Ngwangwa, HM

    2008-07-01

    Full Text Available on the road and driver to assess the integrity of road and vehicle infrastructure. In this paper, vehicle vibration data are applied to an artificial neural network to reconstruct the corresponding road surface profiles. The results show that the technique...

  5. Performance Evaluation and Parameter Optimization of Wavelength Division Multiplexing Networks with Importance Sampling Techniques

    NARCIS (Netherlands)

    Remondo Bueno, D.; Srinivasan, R.; Nicola, V.F.; van Etten, Wim; Tattje, H.E.P.

    1998-01-01

    In this paper new adaptive importance sampling techniques are applied to the performance evaluation and parameter optimization of wavelength division multiplexing (WDM) network impaired by crosstalk in an optical cross-connect. Worst-case analysis is carried out including all the beat noise terms

  6. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  7. Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

    Directory of Open Access Journals (Sweden)

    Ali T. Hasan

    2012-01-01

    Full Text Available This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.

  8. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  9. High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

    Science.gov (United States)

    Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

    2013-12-01

    Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure-Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

  10. Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    2002-01-01

    Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

  11. Smart techniques in the dynamic spectrum alocation for cognitive wireless networks

    Directory of Open Access Journals (Sweden)

    Camila Salgado

    2016-09-01

    Full Text Available Objective: The objective of this work is to study the applications of different techniques of artificial intelligence and autonomous learning in the dynamic allocation of spectrum for cognitive wireless networks, especially the distributed ones. Method: The development of this work was done through the study and analysis of some of the most relevant publications in current literature through the search in indexed international journals in ISI and Scopus. Results: the most relevant techniques of artificial intelligence and autonomous learning were determined. Also, the ones with more applicability in the allocation of spectrum for cognitive wireless networks were determined, too. . Conclusions: The implementation of a technique, or set of them, depends on the needs in signal processing, compensation in response times, sample availability, storage capacity, learning ability and robustness, among others.

  12. Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yun-Yong Kim

    2014-01-01

    Full Text Available Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm, analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

  13. Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    A. M. Aibinu

    2010-01-01

    Full Text Available A new approach for determining the coefficients of a complex-valued autoregressive (CAR and complex-valued autoregressive moving average (CARMA model coefficients using complex-valued neural network (CVNN technique is discussed in this paper. The CAR and complex-valued moving average (CMA coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.

  14. Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

    KAUST Repository

    Ghazzai, Hakim

    2016-09-16

    Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user\\'s quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments. © 2016 IEEE.

  15. An Interference-Aware Distributed Transmission Technique for Dense Small Cell Networks

    DEFF Research Database (Denmark)

    Mahmood, Nurul Huda; Berardinelli, Gilberto; Pedersen, Klaus I.

    2015-01-01

    transmission technique that can efficiently manage the interference in an uncoordinated dense small cell network is investigated in this work. The proposed interference aware scheme only requires instantaneous channel state information at the transmitter end towards the desired receiver. Motivated by penalty...... methods in optimization studies, an interference dependent weighting factor is introduced to control the number of parallel transmission streams. The proposed scheme can outperform a more complex benchmark transmission scheme in terms of the sum network throughput in certain scenarios and with realistic...

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

    Directory of Open Access Journals (Sweden)

    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.

  17. Comparison of Available Bandwidth Estimation Techniques in Packet-Switched Mobile Networks

    DEFF Research Database (Denmark)

    López Villa, Dimas; Ubeda Castellanos, Carlos; Teyeb, Oumer Mohammed

    2006-01-01

    of information regarding the available bandwidth in the transport network, as it could end up being the bottleneck rather than the air interface. This paper provides a comparative study of three well known available bandwidth estimation techniques, i.e. TOPP, SLoPS and pathChirp, taking into account......The relative contribution of the transport network towards the per-user capacity in mobile telecommunication systems is becoming very important due to the ever increasing air-interface data rates. Thus, resource management procedures such as admission, load and handover control can make use...... the statistical conditions of the available bandwidth and assessing the variability of their estimations. Simulation-based studies on a mobile transport network show that pathChirp outperforms TOPP and SLoPS, both in terms of accuracy and efficiency....

  18. Efficiency of Software Testing Techniques: A Controlled Experiment Replication and Network Meta-analysis

    Directory of Open Access Journals (Sweden)

    Omar S. Gómez

    2017-07-01

    Full Text Available Background: Common approaches to software verification include static testing techniques, such as code reading, and dynamic testing techniques, such as black-box and white-box testing. Objective: With the aim of gaining a~better understanding of software testing techniques, a~controlled experiment replication and the synthesis of previous experiments which examine the efficiency of code reading, black-box and white-box testing techniques were conducted. Method: The replication reported here is composed of four experiments in which instrumented programs were used. Participants randomly applied one of the techniques to one of the instrumented programs. The outcomes were synthesized with seven experiments using the method of network meta-analysis (NMA. Results: No significant differences in the efficiency of the techniques were observed. However, it was discovered the instrumented programs had a~significant effect on the efficiency. The NMA results suggest that the black-box and white-box techniques behave alike; and the efficiency of code reading seems to be sensitive to other factors. Conclusion: Taking into account these findings, the Authors suggest that prior to carrying out software verification activities, software engineers should have a~clear understanding of the software product to be verified; they can apply either black-box or white-box testing techniques as they yield similar defect detection rates.

  19. Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

    Science.gov (United States)

    Muhammad, Umar B.; Ezugwu, Absalom E.; Ofem, Paulinus O.; Rajamäki, Jyri; Aderemi, Adewumi O.

    2017-06-01

    Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network.

  20. A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Marian Sorin Nistor

    2016-12-01

    Full Text Available The decision takers of the public transportation system, as part of urban critical infrastructures, need to increase the system resilience. For doing so, we identified analysis tools for biological networks as an adequate basis for visual analytics in that domain. In the paper at hand we therefore translate such methods for transportation systems and show the benefits by applying them on the Munich subway network. Here, visual analytics is used to identify vulnerable stations from different perspectives. The applied technique is presented step by step. Furthermore, the key challenges in applying this technique on transportation systems are identified. Finally, we propose the implementation of the presented features in a management cockpit to integrate the visual analytics mantra for an adequate decision support on transportation systems.

  1. A Novel Architecture for Adaptive Traffic Control in Network on Chip using Code Division Multiple Access Technique

    OpenAIRE

    Fatemeh. Dehghani; Shahram. Darooei

    2016-01-01

    Network on chip has emerged as a long-term and effective method in Multiprocessor System-on-Chip communications in order to overcome the bottleneck in bus based communication architectures. Efficiency and performance of network on chip is so dependent on the architecture and structure of the network. In this paper a new structure and architecture for adaptive traffic control in network on chip using Code Division Multiple Access technique is presented. To solve the problem of synchronous acce...

  2. Network module detection: Affinity search technique with the multi-node topological overlap measure

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2009-07-01

    Full Text Available Abstract Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST, which is a generalized version of the Cluster Affinity Search Technique (CAST. MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes. We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

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

    Energy Technology Data Exchange (ETDEWEB)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah [University of Medea, Medea (Algeria)

    2015-11-15

    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.

  4. Performance evaluation of an importance sampling technique in a Jackson network

    Science.gov (United States)

    brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

    2014-03-01

    Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.

  5. 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.

  6. Eta Meson Production in NN Collisions

    Science.gov (United States)

    Haidenbauer, J.; Baru, V.; Gasparyan, A. M.; Hanhart, C.; Speth, J.

    A model calculation for near-threshold h meson production in nucleon-nucleon collisions is presented. The h meson production is described by elementary rescattering processes via MN ® hN, with M = p, r, h and s. Corresponding amplitudes are taken from a multi-channel meson-exchange model of the pN system developed by the Jgroup. Effects of the NN interaction in the final as well as in the initial state are taken into account microscopically.

  7. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  8. WRHT: A Hybrid Technique for Detection of Wormhole Attack in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2016-01-01

    Full Text Available Wormhole attack is a challenging security threat to wireless sensor networks which results in disrupting most of the routing protocols as this attack can be triggered in different modes. In this paper, WRHT, a wormhole resistant hybrid technique, is proposed, which can detect the presence of wormhole attack in a more optimistic manner than earlier techniques. WRHT is based on the concept of watchdog and Delphi schemes and ensures that the wormhole will not be left untreated in the sensor network. WRHT makes use of the dual wormhole detection mechanism of calculating probability factor time delay probability and packet loss probability of the established path in order to find the value of wormhole presence probability. The nodes in the path are given different ranking and subsequently colors according to their behavior. The most striking feature of WRHT consists of its capacity to defend against almost all categories of wormhole attacks without depending on any required additional hardware such as global positioning system, timing information or synchronized clocks, and traditional cryptographic schemes demanding high computational needs. The experimental results clearly indicate that the proposed technique has significant improvement over the existing wormhole attack detection techniques.

  9. Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

    Directory of Open Access Journals (Sweden)

    Syed Ali Abbas Kazmi

    2017-02-01

    Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

  10. Adaptive Output Neural Network Control for a Class of Stochastic Nonlinear Systems With Dead-Zone Nonlinearities.

    Science.gov (United States)

    Wu, Li-Bing; Yang, Guang-Hong

    2017-03-01

    This paper investigates the problem of adaptive output neural network (NN) control for a class of stochastic nonaffine and nonlinear systems with actuator dead-zone inputs. First, based on the intermediate value theorem, a novel design scheme that converts the nonaffine system into the corresponding affine system is developed. In particular, the priori knowledge of the bound of the derivative of the nonaffine and nonlinear functions is removed; then, by employing NNs to approximate the appropriate nonlinear functions, the corresponding adaptive NN tracking controller with the adjustable parameter updated laws is designed through a backstepping technique. Furthermore, it is shown that all the closed-loop signals are bounded in probability, and the system output tracking error can converge to a small neighborhood in the sense of a mean quartic value. Finally, experimental simulations are provided to demonstrate the efficiency of the proposed adaptive NN tracking control method.

  11. Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

    Science.gov (United States)

    Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

    2015-07-28

    In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

  12. Reconstruction of chalk pore networks from 2D backscatter electron micrographs using a simulated annealing technique

    Energy Technology Data Exchange (ETDEWEB)

    Talukdar, M.S.; Torsaeter, O. [Department of Petroleum Engineering and Applied Geophysics, Norwegian University of Science and Technology, Trondheim (Norway)

    2002-05-01

    We report the stochastic reconstruction of chalk pore networks from limited morphological information that may be readily extracted from 2D backscatter electron (BSE) images of the pore space. The reconstruction technique employs a simulated annealing (SA) algorithm, which can be constrained by an arbitrary number of morphological descriptors. Backscatter electron images of a high-porosity North Sea chalk sample are analyzed and the morphological descriptors of the pore space are determined. The morphological descriptors considered are the void-phase two-point probability function and lineal path function computed with or without the application of periodic boundary conditions (PBC). 2D and 3D samples have been reconstructed with different combinations of the descriptors and the reconstructed pore networks have been analyzed quantitatively to evaluate the quality of reconstructions. The results demonstrate that simulated annealing technique may be used to reconstruct chalk pore networks with reasonable accuracy using the void-phase two-point probability function and/or void-phase lineal path function. Void-phase two-point probability function produces slightly better reconstruction than the void-phase lineal path function. Imposing void-phase lineal path function results in slight improvement over what is achieved by using the void-phase two-point probability function as the only constraint. Application of periodic boundary conditions appears to be not critically important when reasonably large samples are reconstructed.

  13. Interference-Aware Spectrum Sharing Techniques for Next Generation Wireless Networks

    KAUST Repository

    Qaraqe, Marwa Khalid

    2011-11-20

    Background: Reliable high-speed data communication that supports multimedia application for both indoor and outdoor mobile users is a fundamental requirement for next generation wireless networks and requires a dense deployment of physically coexisting network architectures. Due to the limited spectrum availability, a novel interference-aware spectrum-sharing concept is introduced where networks that suffer from congested spectrums (secondary-networks) are allowed to share the spectrum with other networks with available spectrum (primary-networks) under the condition that limited interference occurs to primary networks. Objective: Multiple-antenna and adaptive rate can be utilized as a power-efficient technique for improving the data rate of the secondary link while satisfying the interference constraint of the primary link by allowing the secondary user to adapt its transmitting antenna, power, and rate according to the channel state information. Methods: Two adaptive schemes are proposed using multiple-antenna transmit diversity and adaptive modulation in order to increase the spectral-efficiency of the secondary link while maintaining minimum interference with the primary. Both the switching efficient scheme (SES) and bandwidth efficient scheme (BES) use the scan-and-wait combining antenna technique (SWC) where there is a secondary transmission only when a branch with an acceptable performance is found; else the data is buffered. Results: In both these schemes the constellation size and selected transmit branch are determined to minimized the average number of switches and achieve the highest spectral efficiency given a minimum bit-error-rate (BER), fading conditions, and peak interference constraint. For delayed sensitive applications, two schemes using power control are used: SES-PC and BES-PC. In these schemes the secondary transmitter sends data using a nominal power level, which is optimized to minimize the average delay. Several numerical examples show

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

    Directory of Open Access Journals (Sweden)

    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.

  15. Separable potential analyses of coupled NN-NΔ scattering

    Science.gov (United States)

    Kloet, W. M.; Tjon, J. A.

    1983-01-01

    The apparent differences between two analyses of the 1D2 and 3F3 nucleon-nucleon amplitudes by VerWest and the present authors are resolved. In both analyses dynamical poles are present in the scattering amplitude. These poles are associated with fixed left hand singularities. NUCLEAR REACTIONS Intermediate energy NN. Scattering theory. NN-NΔ coupled separable potentials.

  16. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-08-01

    Full Text Available In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs. The two techniques, Neural Fuzzy Inference System (ANFIS and Artificial Neural Network (ANN, focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO, Gravitational Search Algorithm (GSA, and Backtracking Search Algorithm (BSA. The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

  17. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

    Science.gov (United States)

    Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

    2016-08-06

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

  18. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Victor Garcia-Font

    2016-06-01

    Full Text Available In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  19. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  20. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks.

    Science.gov (United States)

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-06-13

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens' quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  1. Achieving energy efficiency in LTE with joint D2D communications and green networking techniques

    KAUST Repository

    Yaacoub, Elias E.

    2013-07-01

    In this paper, the joint operation of cooperative device-to-device (D2D) communications and green cellular communications is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. Furthermore, an energy-efficient technique for putting BSs in sleep mode in an LTE cellular network is presented. Finally, both methods are combined in order to ensure green communications for both the users\\' MTs and the operator\\'s BSs. The studied methods are investigated in the framework of OFDMA-based state-of-the-art LTE cellular networks, while taking into account intercell interference and resource allocation. © 2013 IEEE.

  2. ZnO nanowall network grown by chemical vapor deposition technique

    Science.gov (United States)

    Mukherjee, Amrita; Dhar, Subhabrata

    2015-06-01

    Network of wedge shaped ZnO nanowalls are grown on c-sapphire by Chemical Vapor Deposition (CVD) technique. Structural studies using x-ray diffraction show much better crystallinity in the nanowall sample as compared to the continuous film. Moreover, the defect related broad green luminescence is found to be suppressed in the nanowall sample. The low temperature photoluminescence study also suggests the quantum confinement of carriers in nanowall sample. Electrical studies performed on the nanowalls show higher conductivity, which has been explained in terms of the reduction of scattering cross-section as a result of 1D quantum confinement of carriers on the tip of the nanowalls.

  3. ZnO nanowall network grown by chemical vapor deposition technique

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, Amrita, E-mail: but.then.perhaps@gmail.com; Dhar, Subhabrata [Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai-400076 (India)

    2015-06-24

    Network of wedge shaped ZnO nanowalls are grown on c-sapphire by Chemical Vapor Deposition (CVD) technique. Structural studies using x-ray diffraction show much better crystallinity in the nanowall sample as compared to the continuous film. Moreover, the defect related broad green luminescence is found to be suppressed in the nanowall sample. The low temperature photoluminescence study also suggests the quantum confinement of carriers in nanowall sample. Electrical studies performed on the nanowalls show higher conductivity, which has been explained in terms of the reduction of scattering cross-section as a result of 1D quantum confinement of carriers on the tip of the nanowalls.

  4. A Comparison of Techniques for Camera Selection and Hand-Off in a Video Network

    Science.gov (United States)

    Li, Yiming; Bhanu, Bir

    Video networks are becoming increasingly important for solving many real-world problems. Multiple video sensors require collaboration when performing various tasks. One of the most basic tasks is the tracking of objects, which requires mechanisms to select a camera for a certain object and hand-off this object from one camera to another so as to accomplish seamless tracking. In this chapter, we provide a comprehensive comparison of current and emerging camera selection and hand-off techniques. We consider geometry-, statistics-, and game theory-based approaches and provide both theoretical and experimental comparison using centralized and distributed computational models. We provide simulation and experimental results using real data for various scenarios of a large number of cameras and objects for in-depth understanding of strengths and weaknesses of these techniques.

  5. Location Estimation in Wireless Sensor Networks Using Spring-Relaxation Technique

    Directory of Open Access Journals (Sweden)

    Qing Zhang

    2010-05-01

    Full Text Available Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN. Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  6. Location estimation in wireless sensor networks using spring-relaxation technique.

    Science.gov (United States)

    Zhang, Qing; Foh, Chuan Heng; Seet, Boon-Chong; Fong, A C M

    2010-01-01

    Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  7. Elliptic flow of identified hadrons in Pb-Pb collisions at root(NN)-N-s=2.76 Tev

    NARCIS (Netherlands)

    Abelev, B.; Adam, J.; Adamova, D.; Aggarwal, M. M.; Agnello, M.; Agostinelli, A.; Agrawal, N.; Ahammed, Z.; Ahmad, N.; Ahmed, I.; Ahn, S. U.; Ahn, S. A.; Aimo, I.; Aiola, S.; Ajaz, M.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Armesto, N.; Arnaldi, R.; Aronsson, T.; Arsene, I. C.; Arslandok, M.; Augustinus, A.; Averbeck, R.; Awes, T. C.; Azmi, M. D.; Bach, M.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Pedrosa, F. Baltasar Dos Santos; Baral, R. C.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartke, J.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Baumann, C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bellwied, R.; Belmont-Moreno, E.; Belmont, R.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Berger, M. E.; Bertens, R. A.|info:eu-repo/dai/nl/371577810; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.|info:eu-repo/dai/nl/371578248; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Bjelogrlic, S.|info:eu-repo/dai/nl/355079615; Blanco, F.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Bogolyubsky, M.; Boehmer, F. V.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Bossu, F.; Botje, M.|info:eu-repo/dai/nl/070139032; Botta, E.; Boettger, S.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.|info:eu-repo/dai/nl/411885812; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Castellanos, J. Castillo; Casula, E. A. R.; Catanescu, V.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Chang, B.; Chapeland, S.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Chochula, P.; Chojnacki, M.|info:eu-repo/dai/nl/411888056; 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.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortese, P.; Cortes Maldonado, I.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dainese, A.; Dang, R.; Danu, A.; Das, D.; Das, I.; Das, K.; Das, S.; Dash, A.; Dash, S.; De, S.; Delagrange, H.; Deloff, A.; Denes, E.; D'Erasmo, G.; De Caro, A.; de Cataldo, G.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; de Rooij, R.|info:eu-repo/dai/nl/315888644; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divia, R.; Di Bari, D.; Di Liberto, S.; Di Mauro, A.; Di Nezza, P.; Djuvsland, O.; Dobrin, A.|info:eu-repo/dai/nl/372618715; Dobrowolski, T.; Domenicis Gimenez, D.; Doenigus, B.; Dordic, O.; Dorheim, S.; Dubey, A. K.; Dubla, A.|info:eu-repo/dai/nl/355502488; Ducroux, L.; Dupieux, P.; Majumdar, A. K. Dutta; Hilden, T. E.; Ehlers, R. J.; Elia, D.; Engel, H.; Erazmus, B.; Erdal, H. A.; Eschweiler, D.; Espagnon, B.; Esposito, M.; Estienne, M.; Esumi, S.; Evans, D.; Evdokimov, S.; Fabris, D.; Faivre, J.; Falchieri, D.; Fantoni, A.; Fasel, M.; Fehlker, D.; Feldkamp, L.; Felea, D.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floratos, E.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Garishvili, I.; Gerhard, J.; Germain, M.; Gheata, A.; Gheata, M.; Ghidini, B.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Gladysz-Dziadus, E.; Glaessel, P.; Ramirez, A. Gomez; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Graczykowski, L. K.; Grelli, A.|info:eu-repo/dai/nl/326052577; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-Oetringhaus, J. F.; Grossiord, J. -Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Guilbaud, M.; Gulbrandsen, K.; Gulkanyan, H.; Gumbo, M.; Gunji, T.; Gupta, A.; Gupta, R.; Khan, K. H.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hanratty, L. D.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hippolyte, B.; Hladky, J.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Innocenti, G. M.; Ionita, C.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Jacholkowski, A.; Jacobs, P. M.; Jahnke, C.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kadyshevskiy, V.; Kalcher, S.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.|info:eu-repo/dai/nl/370530780; Svn, M. Keil; Khan, M. M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, J.; Klein-Boesing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Koehler, M. K.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Konevskikh, A.; Kovalenko, V.; Kowalski, M.; Kox, S.; Meethaleveedu, G. Koyithatta; Kral, J.; Kralik, I.; Kramer, F.; Kravcakova, A.; Krelina, M.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.|info:eu-repo/dai/nl/362845670; Kucera, V.; Kucheriaev, Y.; Kugathasan, T.; Kuhn, C.; Kuijer, P. G.|info:eu-repo/dai/nl/074064975; Kulakov, I.; Kumar, J.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; La Pointe, S. L.|info:eu-repo/dai/nl/355080192; La Rocca, P.; Lea, R.; Leardini, L.; Lee, G. R.; Legrand, I.; Lehnert, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Leoncino, M.; Leon Monzon, I.; Levai, P.; Li, S.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loggins, V. R.; Loginov, V.; Lohner, D.; Loizides, C.; Lopez, X.; Lopez Torres, E.; Lu, X. -G.; Luettig, P.; Lunardon, M.; Luparello, G.|info:eu-repo/dai/nl/355080400; Ma, R.; Maevskaya, A.; Mager, M.; Mahapatra, D. P.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manceau, L.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Marin, A.; Markert, C.; Marquard, M.; Martashvili, I.; Martin, N. A.; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Blanco, J. Martin; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Meddi, F.; Menchaca-Rocha, A.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.|info:eu-repo/dai/nl/325781435; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mlynarz, J.; Mohammadi, N.|info:eu-repo/dai/nl/369405870; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; Morando, M.; Moreira De Godoy, D. A.; Moretto, S.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mueller, H.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nicassio, M.; Niculescu, M.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Nilsen, B. S.; Noferini, F.; Nomokonov, P.; Nooren, G.|info:eu-repo/dai/nl/07051349X; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Okatan, A.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.|info:eu-repo/dai/nl/323375618; Onderwaater, J.; Oppedisano, C.; Velasquez, A. Ortiz; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Sahoo, P.; Pachmayer, Y.; Pachr, M.; Pagano, P.; Paic, G.; Painke, F.; Pajares, C.; Pal, S. K.; Palmeri, A.; Pant, D.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Patalakha, D. I.; Paticchio, V.; Paul, B.; Pawlak, T.; Peitzmann, T.|info:eu-repo/dai/nl/304833959; Da Costa, H. Pereira; Pereira De Oliveira Filho, E.; Peresunko, D.; Lara, C. E. Perez; Pesci, A.; Peskov, V.; Pestov, Y.; Petracek, V.; Petran, M.; Petris, M.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Pohjoisaho, E. H. O.; Polichtchouk, B.; Poljak, N.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Potukuchi, B.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Raniwala, R.; Raniwala, S.; Rasanen, S. S.; Rascanu, B. T.; Rathee, D.; Rauf, A. W.; Razazi, V.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reicher, M.|info:eu-repo/dai/nl/32823219X; Reidt, F.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J. -P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Rivetti, A.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohni, S.; Rohr, D.; Rohrich, D.; Romita, R.; 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.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, R.; Sahu, P. K.; Saini, J.; Sakai, S.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Castro, X. Sanchez; Sanchez Rodriguez, F. J.; Sandor, L.; Sandoval, A.; Sano, M.; Santagati, G.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Segato, G.; Seger, J. E.; Sekiguchi, Y.; Selyuzhenkov, I.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabetai, A.; Shabratova, G.; Shahoyan, R.; Shangaraev, A.; Sharma, N.; Sharma, S.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Skjerdal, K.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.|info:eu-repo/dai/nl/165585781; Sogaard, C.; Soltz, R.; Song, J.; Song, M.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Stolpovskiy, M.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Sumbera, M.; Susa, T.; Symons, T. J. M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Takahashi, J.; Tangaro, M. A.; Takaki, J. D. Tapia; Peloni, A. Tarantola; Martinez, A. Tarazona; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terrevoli, C.; Thaeder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Vajzer, M.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Vyvre, P. Vande; Van der Maarel, J.|info:eu-repo/dai/nl/412860996; Van Hoorne, J. W.; van Leeuwen, M.|info:eu-repo/dai/nl/250599171; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vechernin, V.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wagner, V.; Wang, M.; Wang, Y.; Watanabe, D.; Weber, M.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yamaguchi, Y.; Yang, H.; Yang, P.; Yang, S.; Yano, S.; Yasnopolskiy, S.; Yi, J.; Yin, Z.; Yoo, I. -K.; Yushmanov, I.; Zaccolo, V.; Zach, C.; Zaman, A.; Zampolli, C.; Zaporozhets, S.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, F.; Zhou, Y.; Zhou Zhuo, [No Value; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zoccarato, Y.; Zyzak, M.

    2015-01-01

    The elliptic flow coefficient (v(2)) of identified particles in Pb-Pb collisions at root s(NN) = 2.76 TeV was measured with the ALICE detector at the Large Hadron Collider (LHC). The results were obtained with the Scalar Product method, a two-particle correlation technique, using a pseudo-rapidity

  8. Spin amplitudes for NN → N Δ

    Science.gov (United States)

    Silbar, Richard R.; Lombard, R. J.; Kloet, W. M.

    1982-06-01

    The scattering amplitude for NN → N Δ is decomposed into sixteen independent spin-space operators Oi, where the corresponding coefficients Ai only depend on scattering angle, incoming energy, and the Δ -mass. In addition to the commonly used vector spin-transition matrix, it is necessary to also employ a tensor spin-transition matrix. The Oi can be chosen in two ways, either to facilitate discussion of underlying dynamical mechanisms or to simplify the antisymmetrization with respect to the initial nucleons. Relations between the Ai(θ) and the ( LSJ) partial-wave amplitudes are given. We evaluate the Ai using partial-wave amplitudes calculated elsewhere in a unitary three-body model with one-pion-exchange driving terms. Many amplitudes are of competing importance after antisymmetrization, and they are strongly dependent upon incident energy and Δ invariant mass.

  9. Spin amplitudes for NN. -->. N. delta

    Energy Technology Data Exchange (ETDEWEB)

    Silbar, R.R. (Paris-11 Univ., 91 - Orsay (France). Div. de Physique Theorique; Los Alamos National Lab., NM (USA). Theoretical Div.); Lombard, R.J. (Paris-11 Univ., 91 - Orsay (France). Div. de Physique Theorique); Kloet, W.M. (Rutgers - the State Univ., New Brunswick, NJ (USA). Dept. of Physics and Astronomy)

    1982-06-21

    The scattering amplitude for NN..-->..N..delta.. is decomposed into sixteen independent spin-space operators Osub(i), where the corresponding coefficients Asub(i) only depend on scattering angle, incoming energy, and the ..delta..-mass. In addition to the commonly used vector spin-transition matrix, it is necessary to also employ a tensor spin-transition matrix. The Osub(i) can be chosen in two ways, either to facilitate discussion of underlying dynamical mechanisms or to simplify the antisymmetrization with respect to the initial nucleons. Relations between the Asub(i) (THETA) and the (LSJ) partial-wave amplitudes are given. We evaluate the Asub(i) using partial-wave amplitudes calculated elsewhere in a unitary three-body model with one-pion-exchange driving terms. Many amplitudes are of competing importance after antisymmetrization, and they are strongly dependent upon incident energy and ..delta.. invariant mass.

  10. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2018-01-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  12. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2017-08-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  13. Threshold selection in gene co-expression networks using spectral graph theory techniques.

    Science.gov (United States)

    Perkins, Andy D; Langston, Michael A

    2009-10-08

    Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff. We apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae. This method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations.

  14. Designing and Implementation of Solar Street Lighting Management System Using Wide Area Network Technique

    Directory of Open Access Journals (Sweden)

    Ala'a Imran Al-Mutairi

    2016-03-01

    Full Text Available In this work, an innovate system is designed and implemented to manage solar street lighting units which are distributed in urban. The designed system overcomes wireless sensor network's essential drawback used to automate these units. This drawback is represented by multi-hops networking technique. The system constructs from several clusters, each one is represented by single-hop topology of Wide Area Network (WAN. Each cluster has three type of nodes namely: lamppost stations, local station, and optionally master station. All designed stations are attached to SX1272 modem from Semtech company in order to establish the WAN. This module provides ultra-long transmission range up to 1.4 Km with high interference immunity. The system tested practically in real urban environment. It gives an excellent results with respect to maximum communication range and monitoring of vital operation parameters related to solar panel and other unit equipment's. In addition, the system performed on/off command to controlling lamp. During the test period, the designed system was very efficient in a manner it enabled operator to identify the possible system malfunctions and increase solar lighting units lifespan, reduce maintenance costs consequently it ensures the continues the service from these units.

  15. Impedance measurement techniques for one-port and two-port networks.

    Science.gov (United States)

    Bai, Mingsian R; Lo, Yi-Yang; Chen, You Siang

    2015-10-01

    A microphone array impedance matrix measurement technique is presented for linear and passive acoustic two-port networks. Two impedance tubes fitted with three non-uniformly spaced microphones are required in the measurement. The non-uniform spacing is intended to avoid ill-posedness problems in calculating two plane-wave components traveling in opposite directions. Based on the one-port measurement, acoustic two-port networks modeled with the source and the load connected are examined. Three experimental procedures, the two-load measurement method (TLMM), the reciprocal-constrained method (RCM), and the reciprocity-symmetry-constrained method (RSCM), are developed to measure the acoustic impedance matrix. Experiments are conducted for several acoustic two-port systems to verify the proposed techniques. The results demonstrate the efficacy of the three experimental procedures when applied to symmetrical and reciprocal systems. For asymmetrical systems, the TLMM and RCM are preferred over the RSCM for measuring the impedance matrix. On top of that, the non-uniform array in conjunction with TLMM is extended to a general electroacoustic two-port system, which can be regarded as a unique contribution of the present work.

  16. Cross-Layer Techniques for Adaptive Video Streaming over Wireless Networks

    Directory of Open Access Journals (Sweden)

    Yufeng Shan

    2005-02-01

    Full Text Available Real-time streaming media over wireless networks is a challenging proposition due to the characteristics of video data and wireless channels. In this paper, we propose a set of cross-layer techniques for adaptive real-time video streaming over wireless networks. The adaptation is done with respect to both channel and data. The proposed novel packetization scheme constructs the application layer packet in such a way that it is decomposed exactly into an integer number of equal-sized radio link protocol (RLP packets. FEC codes are applied within an application packet at the RLP packet level rather than across different application packets and thus reduce delay at the receiver. A priority-based ARQ, together with a scheduling algorithm, is applied at the application layer to retransmit only the corrupted RLP packets within an application layer packet. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket-level simulations are presented to verify the effectiveness of our approach.

  17. Cross-Layer Techniques for Adaptive Video Streaming over Wireless Networks

    Science.gov (United States)

    Shan, Yufeng

    2005-12-01

    Real-time streaming media over wireless networks is a challenging proposition due to the characteristics of video data and wireless channels. In this paper, we propose a set of cross-layer techniques for adaptive real-time video streaming over wireless networks. The adaptation is done with respect to both channel and data. The proposed novel packetization scheme constructs the application layer packet in such a way that it is decomposed exactly into an integer number of equal-sized radio link protocol (RLP) packets. FEC codes are applied within an application packet at the RLP packet level rather than across different application packets and thus reduce delay at the receiver. A priority-based ARQ, together with a scheduling algorithm, is applied at the application layer to retransmit only the corrupted RLP packets within an application layer packet. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket-level simulations are presented to verify the effectiveness of our approach.

  18. Vehicle Assisted Data Delievery Technique To Control Data Dissemination In Vehicular AD - HOC Networks Vanets

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar

    2015-08-01

    Full Text Available Abstract Multi-hop data delivery through vehicular ad hoc networks is complicated by the fact that vehicular networks are highly mobile and frequently disconnected. To address this issue the idea of helper node is opted where a moving vehicles carries the packet until a new vehicle moves into its vicinity and forwards the packet. Different from existing helper node solution use of the predicable vehicle mobility is made which is limited by the traffic pattern and the road layout. Based on the existing traffic pattern a vehicle can find the next road to forward packet a vehicle can find the next road to forward the packet to reduce the delay. Several vehicle-assisted date delievery VADD protocol is proposed to forward the packet to the best road with the road with the lowest data delivery delay. Experiment results are used to evaluate the proposed solutions. Results show that the proposed VADD protocol outperform existing solution in terms of packet delivery ratio data packet delay and protocol overhead. Among the proposed VADD protocols the Hybrid probe HVADD protocol has much better performance. In this Solution the helper node technique is provider with which the helper node will contain destination node path and the path in routine table continuously changes with the help of helper node technique.

  19. Optical burst add-drop multiplexing technique for sub-wavelength granularity in wavelength multiplexed ring networks.

    Science.gov (United States)

    Cho, Jeong Sik; Seo, Young Kwang; Yoo, Hark; Park, Paul K; Rhee, June-Koo; Won, Yong Hyub; Kang, Min Ho

    2007-10-01

    We demonstrate optical burst add-drop multiplexing as a practical application of the optical burst switching technology in a wavelength-division-multiplexed ring network. To control optical bursts in the network, a burst identifier (BI) for delivering control information, and a BI processor for handling the BI, were designed. Optical bursts of 10- to 100-mus in length were optically multiplexed or demultiplexed in an intermediate node of the ring network. The demonstration shows that the optical burst add-drop multiplexing technique provides sub-wavelength granularity to a ring network.

  20. Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Joa-Hyoung Lee

    2010-03-01

    Full Text Available Congestion in a wireless sensor network causes an increase in the amount of data loss and delays in data transmission. In this paper, we propose a new congestion control technique (ACT, Adaptive Compression-based congestion control Technique based on an adaptive compression scheme for packet reduction in case of congestion. The compression techniques used in the ACT are Discrete Wavelet Transform (DWT, Adaptive Differential Pulse Code Modulation (ADPCM, and Run-Length Coding (RLC. The ACT first transforms the data from the time domain to the frequency domain, reduces the range of data by using ADPCM, and then reduces the number of packets with the help of RLC before transferring the data to the source node. It introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. The ACT assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the amount of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrate that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods. Moreover, it exhibits a very high ratio of the available data in the sink.

  1. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  2. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  3. Fundamental study of interpretation technique for 3-D magnetotelluric data using neural networks; Neural network wo mochiita sanjigen MT ho data kaishaku gijutsu no kisoteki kenkyu

    Energy Technology Data Exchange (ETDEWEB)

    Kobayashi, T.; Fukuoka, K.; Shima, H. [Oyo Corp., Tokyo (Japan); Mogi, T. [Kyushu University, Fukuoka (Japan). Faculty of Engineering; Spichak, V.

    1997-05-27

    The research and development have been conducted to apply neural networks to interpretation technique for 3-D MT data. In this study, a data base of various data was made from the numerical modeling of 3-D fault model, and the data base management system was constructed. In addition, an unsupervised neural network for treating noise and a supervised neural network for estimating fault parameters such as dip, strike and specific resistance were made, and a basic neural network system was constructed. As a result of the application to the various data, basically sufficient performance for estimating the fault parameters was confirmed. Thus, the optimum MT data for this system were selected. In future, it is necessary to investigate the optimum model and the number of models for learning these neural networks. 3 refs., 5 figs., 2 tabs.

  4. 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.

  5. BIA Wingate High School WWTF, Fort Wingate, NM: NN0020958

    Science.gov (United States)

    NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0020958 to Bureau of Indian Affairs (BIA) Wingate High School Wastewater Treatment Lagoon, Fort Wingate, NM.

  6. An Alternative to Optimize the Indonesian’s Airport Network Design: An Application of Minimum Spanning Tree (MST Technique

    Directory of Open Access Journals (Sweden)

    Luluk Lusiantoro

    2012-09-01

    Full Text Available Using minimum spanning tree technique (MST, this exploratory research was done to optimize the interrelation and hierarchical network design of Indonesian’s airports. This research also identifies the position of the Indonesian’s airports regionally based on the ASEAN Open Sky Policy 2015. The secondary data containing distance between airports (both in Indonesia and in ASEAN, flight frequency, and correlation of Gross Domestic Regional Product (GDRP for each region in Indonesia are used as inputs to form MST networks. The result analysis is done by comparing the MST networks with the existing network in Indonesia. This research found that the existing airport network in Indonesia does not depict the optimal network connecting all airports with the shortest distance and maximizing the correlation of regional economic potential in the country. This research then suggests the optimal networks and identifies the airports and regions as hubs and spokes formed by the networks. Lastly, this research indicates that the Indonesian airports have no strategic position in the ASEAN Open Sky network, but they have an opportunity to get strategic positions if 33 airports in 33 regions in Indonesia are included in the network.

  7. Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

    Directory of Open Access Journals (Sweden)

    Anup Kumar Paul

    2017-10-01

    Full Text Available Localization is an important aspect in the field of wireless sensor networks (WSNs that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

  8. Dynamic Beamforming for Three-Dimensional MIMO Technique in LTE-Advanced Networks

    Directory of Open Access Journals (Sweden)

    Yan Li

    2013-01-01

    Full Text Available MIMO system with large number of antennas, referred to as large MIMO or massive MIMO, has drawn increased attention as they enable significant throughput and coverage improvement in LTE-Advanced networks. However, deploying huge number of antennas in both transmitters and receivers was a great challenge in the past few years. Three-dimensional MIMO (3D MIMO is introduced as a promising technique in massive MIMO networks to enhance the cellular performance by deploying antenna elements in both horizontal and vertical dimensions. Radio propagation of user equipments (UE is considered only in horizontal domain by applying 2D beamforming. In this paper, a dynamic beamforming algorithm is proposed where vertical domain of antenna is fully considered and beamforming vector can be obtained according to UEs’ horizontal and vertical directions. Compared with the conventional 2D beamforming algorithm, throughput of cell edge UEs and cell center UEs can be improved by the proposed algorithm. System level simulation is performed to evaluate the proposed algorithm. In addition, the impacts of downtilt and intersite distance (ISD on spectral efficiency and cell coverage are explored.

  9. A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

    Science.gov (United States)

    Gholami, Mohammad; Brennan, Robert W

    2016-01-06

    In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

  10. Efficiency Optimization for Disassembly Tools via Using NN-GA Approach

    Directory of Open Access Journals (Sweden)

    Guangdong Tian

    2013-01-01

    Full Text Available Disassembly issues have been widely attracted in today’s sustainable development context. One of them is the selection of disassembly tools and their efficiency comparison. To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process. Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN and genetic algorithm (GA. Their efficiency comparison is discussed. Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.

  11. Application of MIMO Techniques in sky-surface wave hybrid networking sea-state radar system

    Science.gov (United States)

    Zhang, L.; Wu, X.; Yue, X.; Liu, J.; Li, C.

    2016-12-01

    The sky-surface wave hybrid networking sea-state radar system contains of the sky wave transmission stations at different sites and several surface wave radar stations. The subject comes from the national 863 High-tech Project of China. The hybrid sky-surface wave system and the HF surface wave system work simultaneously and the HF surface wave radar (HFSWR) can work in multi-static and surface-wave networking mode. Compared with the single mode radar system, this system has advantages of better detection performance at the far ranges in ocean dynamics parameters inversion. We have applied multiple-input multiple-output(MIMO) techniques in this sea-state radar system. Based on the multiple channel and non-causal transmit beam-forming techniques, the MIMO radar architecture can reduce the size of the receiving antennas and simplify antenna installation. Besides, by efficiently utilizing the system's available degrees of freedom, it can provide a feasible approach for mitigating multipath effect and Doppler-spread clutter in Over-the-horizon Radar. In this radar, slow-time phase-coded MIMO method is used. The transmitting waveforms are phase-coded in slow-time so as to be orthogonal after Doppler processing at the receiver. So the MIMO method can be easily implemented without the need to modify the receiver hardware. After the radar system design, the MIMO experiments of this system have been completed by Wuhan University during 2015 and 2016. The experiment used Wuhan multi-channel ionospheric sounding system(WMISS) as sky-wave transmitting source and three dual-frequency HFSWR developed by the Oceanography Laboratory of Wuhan University. The transmitter system located at Chongyang with five element linear equi-spaced antenna array and Wuhan with one log-periodic antenna. The RF signals are generated by synchronized, but independent digital waveform generators - providing complete flexibility in element phase and amplitude control, and waveform type and parameters

  12. 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

  13. An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

    Science.gov (United States)

    Pahlavani, P.; Gholami, A.; Azimi, S.

    2017-09-01

    This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

  14. The European Narcolepsy Network (EU-NN) database

    NARCIS (Netherlands)

    Khatami, R.; Luca, G. De; Baumann, C.R.; Bassetti, C.L.; Bruni, O.; Canellas, F.; Dauvilliers, Y.; Rio-Villegas, R. Del; Feketeova, E.; Ferri, R.; Geisler, P.; Hogl, B.; Jennum, P.; Kornum, B.R.; Lecendreux, M.; Martins-da-Silva, A.; Mathis, J.; Mayer, G.; Paiva, T.; Partinen, M.; Peraita-Adrados, R.; Plazzi, G.; Santamaria, J.; Sonka, K.; Riha, R.; Tafti, M.; Wierzbicka, A.; Young, P.; Lammers, G.J.; Overeem, S.

    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 few

  15. Event-Sampled Direct Adaptive NN Output- and State-Feedback Control of Uncertain Strict-Feedback System.

    Science.gov (United States)

    Szanto, Nathan; Narayanan, Vignesh; Jagannathan, Sarangapani

    2017-04-12

    In this paper, a novel event-triggered implementation of a tracking controller for an uncertain strict-feedback system is presented. Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an NN observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated, and subsequently, an event-execution control law is derived, such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers.

  16. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  17. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  18. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

  19. ImpNet: Programming Software-Defied Networks Using Imperative Techniques

    OpenAIRE

    El-Zawawy, Mohamed A.; AlSalem, Adel I.

    2014-01-01

    Software and hardware components are basic parts of modern networks. However the software compo- nent is typical sealed and function-oriented. Therefore it is very difficult to modify these components. This badly affected networking innovations. Moreover, this resulted in network policies having complex interfaces that are not user-friendly and hence resulted in huge and complicated flow tables on physical switches of networks. This greatly degrades the network performance in many cases. Soft...

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

    Directory of Open Access Journals (Sweden)

    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.

  1. Neural network retrievals of phytoplankton absorption and Karenia brevis harmful algal blooms in the West Florida Shelf

    Science.gov (United States)

    Ahmed, Sam; El-Habashi, Ahmed; Lovko, Vincent

    2017-05-01

    Preliminary results of previous work had shown a Neural Network (NN) technique developed by us as effective in detecting Karenia brevis Harmful Algal Blooms (KB HABs) plaguing West Florida Shelf (WFS) from VIIRS satellite observations. We extend comparisons of NN retrievals against a data set of near simultaneous in-situ measurements in the WFS spanning the 2012-2016 period for which there was available VIIRS data. Specifically we looked for match ups where the overlap time windows between satellite observations and in-situ measurements were 15 minutes and 100 minutes. We then compare the accuracy of the NN retrievals against the in-situ measurements, with the accuracies achieved with similar of retrievals using OC3, GIOP, QAA and RGCI algorithms. The NN technique exhibited the best retrieval accuracy statistics. The retrievals for all the algorithms very clearly showed the impact of temporal variations of the KB HABS on retrieval accuracies. Thus, retrievals using a 15 minutes overlap window between satellite observations and in-situ measurements yielded much higher accuracies than those with the 100 minutes overlap window. Temporal variabilities were also studied, using consecutive overlapping VIIRS images. These variabilities, as well as the patchiness of KB blooms were also confirmed by a set of in-situ measurements near Sarasota, FL.

  2. Developing a secured social networking site using information security awareness techniques

    Directory of Open Access Journals (Sweden)

    Julius O. Okesola

    2014-03-01

    Full Text Available Background: Ever since social network sites (SNS became a global phenomenon in almost every industry, security has become a major concern to many SNS stakeholders. Several security techniques have been invented towards addressing SNS security, but information security awareness (ISA remains a critical point. Whilst very few users have used social circles and applications because of a lack of users’ awareness, the majority have found it difficult to determine the basis of categorising friends in a meaningful way for privacy and security policies settings. This has confirmed that technical control is just part of the security solutions and not necessarily a total solution. Changing human behaviour on SNSs is essential; hence the need for a privately enhanced ISA SNS.Objective: This article presented sOcialistOnline – a newly developed SNS, duly secured and platform independent with various ISA techniques fully implemented.Method: Following a detailed literature review of the related works, the SNS was developed on the basis of Object Oriented Programming (OOP approach, using PhP as the coding language with the MySQL database engine at the back end.Result: This study addressed the SNS requirements of privacy, security and services, and attributed them as the basis of architectural design for sOcialistOnline. SNS users are more aware of potential risk and the possible consequences of unsecured behaviours.Conclusion: ISA is focussed on the users who are often the greatest security risk on SNSs, regardless of technical securities implemented. Therefore SNSs are required to incorporate effective ISA into their platform and ensure users are motivated to embrace it.

  3. Developing a secured social networking site using information security awareness techniques

    Directory of Open Access Journals (Sweden)

    Julius O. Okesola

    2014-11-01

    Full Text Available Background: Ever since social network sites (SNS became a global phenomenon in almost every industry, security has become a major concern to many SNS stakeholders. Several security techniques have been invented towards addressing SNS security, but information security awareness (ISA remains a critical point. Whilst very few users have used social circles and applications because of a lack of users’ awareness, the majority have found it difficult to determine the basis of categorising friends in a meaningful way for privacy and security policies settings. This has confirmed that technical control is just part of the security solutions and not necessarily a total solution. Changing human behaviour on SNSs is essential; hence the need for a privately enhanced ISA SNS. Objective: This article presented sOcialistOnline – a newly developed SNS, duly secured and platform independent with various ISA techniques fully implemented. Method: Following a detailed literature review of the related works, the SNS was developed on the basis of Object Oriented Programming (OOP approach, using PhP as the coding language with the MySQL database engine at the back end. Result: This study addressed the SNS requirements of privacy, security and services, and attributed them as the basis of architectural design for sOcialistOnline. SNS users are more aware of potential risk and the possible consequences of unsecured behaviours. Conclusion: ISA is focussed on the users who are often the greatest security risk on SNSs, regardless of technical securities implemented. Therefore SNSs are required to incorporate effective ISA into their platform and ensure users are motivated to embrace it.

  4. Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential

    Science.gov (United States)

    Li, Wenwen; Ando, Yasunobu; Watanabe, Satoshi

    2017-10-01

    Understanding atomistic details of diffusion processes in amorphous structures is becoming increasingly important due to the recent advances in various information and energy devices. Atomistic simulations based on the density functional theory (DFT) represent a powerful approach; however, the development of a method characterized by both high reliability and computational efficiency remains a challenge. In this study, a simple neural network (NN) interatomic potential is constructed from the results of DFT simulations to investigate the diffusion of a single Cu atom in amorphous Ta2O5. The proposed technique is as accurate as the DFT in predicting hopping paths and the corresponding barrier energies in a given amorphous structure. Although the developed NN-based approach exhibited some limitations since it was constructed specifically for Cu, the obtained results showed that the NN potential was able to satisfactorily describe the Cu diffusion behavior. Thus, the Cu diffusion activation energy calculated at low temperatures (between 500 and 800 K) using kinetic Monte Carlo simulations and the NN potential matched the experimental data reasonably well.

  5. Adaptive Predistortions Based on Neural Networks Associated with Levenberg-Marquardt Algorithm for Satellite Down Links

    Directory of Open Access Journals (Sweden)

    Roviras Daniel

    2008-01-01

    Full Text Available Abstract This paper presents adaptive predistortion techniques based on a feed-forward neural network (NN to linearize power amplifiers such as those used in satellite communications. Indeed, it presents the suitable NN structures which give the best performances for three satellite down links. The first link is a stationary memoryless travelling wave tube amplifier (TWTA, the second one is a nonstationary memoryless TWT amplifier while the third is an amplifier with memory modeled by a memoryless amplifier followed by a linear filter. Equally important, it puts forward the studies concerning the application of different NN training algorithms in order to determine the most prefermant for adaptive predistortions. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, with a Saleh's TWT amplifier, is based on some quality measure (mean square error, the required training time to reach a particular quality level, and computation complexity. The chosen adaptive predistortions (NN structures associated with an adaptive algorithm have a low complexity, fast convergence, and best performance.

  6. Adaptive Predistortions Based on Neural Networks Associated with Levenberg-Marquardt Algorithm for Satellite Down Links

    Directory of Open Access Journals (Sweden)

    Daniel Roviras

    2008-08-01

    Full Text Available This paper presents adaptive predistortion techniques based on a feed-forward neural network (NN to linearize power amplifiers such as those used in satellite communications. Indeed, it presents the suitable NN structures which give the best performances for three satellite down links. The first link is a stationary memoryless travelling wave tube amplifier (TWTA, the second one is a nonstationary memoryless TWT amplifier while the third is an amplifier with memory modeled by a memoryless amplifier followed by a linear filter. Equally important, it puts forward the studies concerning the application of different NN training algorithms in order to determine the most prefermant for adaptive predistortions. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, with a Saleh's TWT amplifier, is based on some quality measure (mean square error, the required training time to reach a particular quality level, and computation complexity. The chosen adaptive predistortions (NN structures associated with an adaptive algorithm have a low complexity, fast convergence, and best performance.

  7. Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

    Science.gov (United States)

    Vyas, Bhargav Y; Das, Biswarup; Maheshwari, Rudra Prakash

    2016-08-01

    This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.

  8. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2017-08-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  9. Compression and Combining Based on Channel Shortening and Rank Reduction Technique for Cooperative Wireless Sensor Networks

    KAUST Repository

    Ahmed, Qasim Zeeshan

    2013-12-18

    This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected while the remaining (L?U) signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.

  10. Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis

    Science.gov (United States)

    Lo, Joseph Y.; Land, Walker H., Jr.; Morrison, Clayton T.

    2000-06-01

    An evolutionary programming (EP) technique was investigated to reduce the complexity of artificial neural network (ANN) models that predict the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the ANN predicted whether the lesion was benign or malignant, which may aide in reducing the number of unnecessary benign biopsies and thus the cost of mammography screening of breast cancer. The EP has the ability to optimize the ANN both structurally and parametrically. An EP was partially optimized using a data set of 882 biopsy-proven cases from Duke University Medical Center. Although many different architectures were evolved, the best were often perceptrons with no hidden nodes. A rank ordering of the inputs was performed using twenty independent EP runs. This confirmed the predictive value of the mass margin and patient age variables, and revealed the unexpected usefulness of the history of previous breast cancer. Further work is required to improve the performance of the EP over all cases in general and calcification cases in particular.

  11. A novel neural network-based technique for smart gas sensors operating in a dynamic environment.

    Science.gov (United States)

    Baha, Hakim; Dibi, Zohir

    2009-01-01

    Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor's response characteristics and eliminate its dependency on the environmental parameters. The corrector's responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor's responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.

  12. Low Latency Network-on-Chip Router Microarchitecture Using Request Masking Technique

    Directory of Open Access Journals (Sweden)

    Alireza Monemi

    2015-01-01

    Full Text Available Network-on-Chip (NoC is fast emerging as an on-chip communication alternative for many-core System-on-Chips (SoCs. However, designing a high performance low latency NoC with low area overhead has remained a challenge. In this paper, we present a two-clock-cycle latency NoC microarchitecture. An efficient request masking technique is proposed to combine virtual channel (VC allocation with switch allocation nonspeculatively. Our proposed NoC architecture is optimized in terms of area overhead, operating frequency, and quality-of-service (QoS. We evaluate our NoC against CONNECT, an open source low latency NoC design targeted for field-programmable gate array (FPGA. The experimental results on several FPGA devices show that our NoC router outperforms CONNECT with 50% reduction of logic cells (LCs utilization, while it works with 100% and 35%~20% higher operating frequency compared to the one- and two-clock-cycle latency CONNECT NoC routers, respectively. Moreover, the proposed NoC router achieves 2.3 times better performance compared to CONNECT.

  13. Projecting impacts of climate change on water availability using artificial neural network techniques

    Science.gov (United States)

    Swain, Eric D.; Gomez-Fragoso, Julieta; Torres-Gonzalez, Sigfredo

    2017-01-01

    Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.

  14. Vibration control of a class of semiactive suspension system using neural network and backstepping techniques

    Science.gov (United States)

    Zapateiro, M.; Luo, N.; Karimi, H. R.; Vehí, J.

    2009-08-01

    In this paper, we address the problem of designing the semiactive controller for a class of vehicle suspension system that employs a magnetorheological (MR) damper as the actuator. As the first step, an adequate model of the MR damper must be developed. Most of the models found in literature are based on the mechanical behavior of the device, with the Bingham and Bouc-Wen models being the most popular ones. These models can estimate the damping force of the device taking the control voltage and velocity inputs as variables. However, the inverse model, i.e., the model that computes the control variable (generally the voltage) is even more difficult to find due to the numerical complexity that implies the inverse of the nonlinear forward model. In our case, we develop a neural network being able to estimate the control voltage input to the MR damper, which is necessary for producing the optimal force predicted by the controller so as to reduce the vibrations. The controller is designed following the standard backstepping technique. The performance of the control system is evaluated by means of simulations in MATLAB/Simulink.

  15. A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Zohir Dibi

    2009-11-01

    Full Text Available Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…. We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor’s response characteristics and eliminate its dependency on the environmental parameters. The corrector’s responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration. The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering. The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.

  16. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

    Directory of Open Access Journals (Sweden)

    H.H. Soliman

    2012-11-01

    Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

  17. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  18. General Dentists’ Use of Isolation Techniques During Root Canal Treatment: from the National Dental Practice-Based Research Network

    Science.gov (United States)

    Lawson, Nathaniel C.; Gilbert, Gregg H.; Funkhouser, Ellen; Eleazer, Paul D.; Benjamin, Paul L.; Worley, Donald C.

    2015-01-01

    Introduction A preliminary study done by a National Dental Practice-Based Research Network precursor observed that 44% of general dentists (GDs) reported always using a rubber dam (RD) during root canal treatment (RCT). This full-scale study quantified use of all isolation techniques, including RD use. Methods Network practitioners completed a questionnaire about isolation techniques used during RCT. Network Enrollment Questionnaire data provided practitioner characteristics. Results 1,490 of 1,716 eligible GDs participated (87%); 697 (47%) reported always using a RD. This percentage varied by tooth type. These GDs were more likely to always use a RD: do not own a private practice; perform less than 10 RCT/month; have postgraduate training. Conclusions Most GDs do not use a RD all the time. Ironically, RDs are used more frequently by GDs who do not perform molar RCT. RD use varies with tooth type and certain dentist, practice, and patient characteristics. PMID:26015159

  19. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

    Full Text Available . The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect...

  20. Bandwidth re-distribution techniques for extended epon based multi-wavelength networks

    NARCIS (Netherlands)

    Roy, R.; Manhoudt, Gert; van Etten, Wim

    2007-01-01

    The broadband photonics project (BBP) under the Freehand consortium of projects looks into the design of an extended access network. The network is a photonic network which can be dynamically reconfigured to distribute bandwidth in an optimised manner. This paper presents linear programming based

  1. Portable Rule Extraction Method for Neural Network Decisions Reasoning

    Directory of Open Access Journals (Sweden)

    Darius PLIKYNAS

    2005-08-01

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

  2. Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

    Directory of Open Access Journals (Sweden)

    Nattawat Khamphakdee

    2015-07-01

    Full Text Available The intrusion detection system (IDS is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

  3. DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.

    Science.gov (United States)

    Singh, A; Quek, C; Cho, S Y

    2008-04-01

    Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds

  4. Low-energy NN scattering with a Brazilian chiral potential

    Energy Technology Data Exchange (ETDEWEB)

    Batista, E.F. [Universidade Estadual do Sudoeste da Bahia (UESB), Vitoria da Conquista, BA (Brazil); Rocha, C.A. [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), SP (Brazil); Szpigel, S. [Universidade Presbiteriana Mackenzie (UPM), SP (Brazil); Timoteo, V.S. [Universidade Estadual de Campinas (UNICAMP), SP (Brazil)

    2012-07-01

    Full text: We apply the subtracted kernel method (SKM), a renormalization approach based on recursive multiple subtractions performed in the kernel of the scattering equation, to a Brazilian chiral nucleon-nucleon (NN) interactions up to next-to-next-to-next-to-leading-order (N3LO). We evaluate the phase shifts in the 1S0 and 3P0 channels and explicitly demonstrate that the SKM procedure is renormalization group invariant under the change of the subtraction scale through a non-relativistic Callan-Symanzik flow equation for the evolution of the renormalized NN interactions. (author)

  5. Error Control Techniques for Efficient Multicast Streaming in UMTS Networks: Proposals andPerformance Evaluation

    Directory of Open Access Journals (Sweden)

    Michele Rossi

    2004-06-01

    Full Text Available In this paper we introduce techniques for efficient multicast video streaming in UMTS networks where a video content has to be conveyed to multiple users in the same cell. Efficient multicast data delivery in UMTS is still an open issue. In particular, suitable solutions have to be found to cope with wireless channel errors, while maintaining both an acceptable channel utilization and a controlled delivery delay over the wireless link between the serving base station and the mobile terminals. Here, we first highlight that standard solutions such as unequal error protection (UEP of the video flow are ineffective in the UMTS systems due to its inherent large feedback delay at the link layer (Radio Link Control, RLC. Subsequently, we propose a local approach to solve errors directly at the UMTS link layer while keeping a reasonably high channel efficiency and saving, as much as possible, system resources. The solution that we propose in this paper is based on the usage of the common channel to serve all the interested users in a cell. In this way, we can save resources with respect to the case where multiple dedicated channels are allocated for every user. In addition to that, we present a hybrid ARQ (HARQ proactive protocol that, at the cost of some redundancy (added to the link layer flow, is able to consistently improve the channel efficiency with respect to the plain ARQ case, by therefore making the use of a single common channel for multicast data delivery feasible. In the last part of the paper we give some hints for future research, by envisioning the usage of the aforementioned error control protocols with suitably encoded video streams.

  6. Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

    Science.gov (United States)

    Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

    2014-07-01

    Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

  7. Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source.

    Science.gov (United States)

    Dimopoulos, Ioannis F; Tsiros, Ioannis X; Serelis, Konstantinos; Chronopoulou, Aikaterini

    2004-12-01

    Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

  8. Impression Techniques Used for Single-Unit Crowns: Findings from the National Dental Practice-Based Research Network.

    Science.gov (United States)

    McCracken, Michael S; Louis, David R; Litaker, Mark S; Minyé, Helena M; Oates, Thomas; Gordan, Valeria V; Marshall, Don G; Meyerowitz, Cyril; Gilbert, Gregg H

    2017-01-11

    To: (1) determine which impression and gingival displacement techniques practitioners use for single-unit crowns on natural teeth; and (2) test whether certain dentist and practice characteristics are significantly associated with the use of these techniques. Dentists participating in the National Dental Practice-Based Research Network were eligible for this survey study. The study used a questionnaire developed by clinicians, statisticians, laboratory technicians, and survey experts. The questionnaire was pretested via cognitive interviewing with a regionally diverse group of practitioners. The survey included questions regarding gingival displacement and impression techniques. Survey responses were compared by dentist and practice characteristics using ANOVA. The response rate was 1777 of 2132 eligible dentists (83%). Regarding gingival displacement, most clinicians reported using either a single cord (35%) or dual cord (35%) technique. About 16% of respondents preferred an injectable retraction technique. For making impressions, the most frequently used techniques and materials are: poly(vinyl siloxane), 77%; polyether, 12%; optical/digital, 9%. A dental auxiliary or assistant made the final impression 2% of the time. Regarding dual-arch impression trays, 23% of practitioners report they typically use a metal frame tray, 60% use a plastic frame, and 16% do not use a dual-arch tray. Clinicians using optical impression techniques were more likely to be private practice owners or associates. This study documents current techniques for gingival displacement and making impressions for crowns. Certain dentist and practice characteristics are significantly associated with these techniques. © 2017 by the American College of Prosthodontists.

  9. Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process.

    Science.gov (United States)

    Ma, Yongwen; Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Wang, Yan; Zhang, Huiping

    2011-01-01

    In this paper, a hybrid artificial neural network (ANN) - genetic algorithm (GA) numerical technique was successfully developed to deal with complicated problems that cannot be solved by conventional solutions. ANNs and Gas were used to model and simulate the process of removing chemical oxygen demand (COD) in an anoxic/oxic system. The minimization of the error function with respect to the network parameters (weights and biases) has been considered as training of the network. Real-coded genetic algorithm was used to train the network in an unsupervised manner. Meanwhile the important process parameters, such as the influent COD (COD(in)), reflux ratio (R(r)), carbon-nitrogen ratio (C/N) and the effluent COD (COD(out)) were considered. The result shows that compared with the performance of ANN model, the performance of the GA-ANN (genetic algorithm - artificial neural network) network was found to be more impressive. Using ANN, the mean absolute percentage error (MAPE), mean squared error (MSE) and correlation coefficient (R) were 9.33×10(-4), 2.82 and 0.98596, respectively; while for the GA-ANN, they were converged to be 4.18×10(-4), 1.12 and 0.99476, respectively.

  10. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 – 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  11. Accuracy assessment of the scalar network analyzer using sliding termination techniques

    DEFF Research Database (Denmark)

    Knudsen, Bent; Engen, Glenn F.; Guldbrandsen, Birthe

    1989-01-01

    In the absence of phase response the major, if not the primary, sources of error in the scalar network analyzer are the imperfect directivity, etc., associated with its internal and frequently inaccessible test set or measurement network. An explicit expression is obtained for this error in terms...

  12. Practical network design techniques a complete guide for WANs and LANs

    CERN Document Server

    Held, Gilbert

    2004-01-01

    This new edition has two parts. The first part focuses on wide area networks; the second, which is entirely new, focuses on local area networks. Because Ethernet emerged victorious in the LAN war, the second section pays particular attention to Ethernet design and performance characteristics.

  13. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at

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

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

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

  15. A study on Optical Labelling Techniques for All-Optical Networks

    DEFF Research Database (Denmark)

    Holm-Nielsen, Pablo Villanueva

    2005-01-01

    Optical switching has been proposed as an effective solution to overcoming the potential electronic bottleneck in all-optical network nodes carrying IP over WDM. The solution builds on the use of optical labelling as a mean to route packets or bursts of packets through the network. In addition to...

  16. A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-01-01

    Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

  17. ENBFS+kNN: Hybrid ensemble classifier using entropy-based naïve Bayes with feature selection and k-nearest neighbor

    Science.gov (United States)

    Sainin, Mohd Shamrie; Alfred, Rayner; Ahmad, Faudziah

    2016-08-01

    A hybrid ensemble classifier which combines the entropy based naive Bayes (ENB) classifier strategy and k-nearest neighbor (k-NN) is examined. The classifiers are joined in light of the fact that naive Bayes gives prior estimations taking into account entropy while k-NN gives neighborhood estimate to model for a deferred characterization. While original NB utilizes the probabilities, this study utilizes the entropy as priors for class estimations. The result of the hybrid ensemble classifier demonstrates that by consolidating the classifiers, the proposed technique accomplishes promising execution on several benchmark datasets.

  18. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

    DEFF Research Database (Denmark)

    Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

    2015-01-01

    mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug...... networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew's coefficient of 0...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....

  19. A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks.

    Science.gov (United States)

    Al-Shammary, Dhiah; Khalil, Ibrahim

    2010-01-01

    Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.

  20. A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Muhammad Asif Zahoor Raja

    2012-01-01

    Full Text Available A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.

  1. [Leo Bormans. Õnn on] / Krista Kivisalu

    Index Scriptorium Estoniae

    Kivisalu, Krista, 1968-

    2015-01-01

    Tutvustus: Õnn on : kogu maailma õnneraamat : teadmisi ja tarkusi enam kui sajalt õnneasjatundjalt kogu maailmast / toimetanud Leo Bormans ; [inglise keelest tõlkinud Triin Aimla-Laid ; toimetanud Violetta Riidas ; originaali kujundus: Kris Demey] Ilmunud [Tallinn] : Pegasus, c2015

  2. Nucleon-nucleon final state interactions in NN-->NNπ

    Science.gov (United States)

    Dubach, J.; Kloet, W. M.; Silbar, R. R.

    1986-01-01

    Peaks in cross sections for the NN-->NNπ reaction at low relative momentum for the final nucleon-nucleon pair are successfully explained using 1S0 and 3S1 final state interactions. Both singlet and triplet final state interactions are important and can interfere dramatically in certain spin observables.

  3. Stress Assignment in N+N Combinations in Arabic

    Directory of Open Access Journals (Sweden)

    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.

  4. Tandem synthesis of N,N -alkylidenebisamides promoted by nano ...

    Indian Academy of Sciences (India)

    alkylidenebisamides in the presence of nano-SnCl4.SiO2 as a catalysis described. N,N -alkylidenebisamides have been prepared via condensation reaction of various aldehydes and amides. All of the reactions proceeded in high yields and in moderately ...

  5. Renormalization of NN Interaction with Relativistic Chiral Two Pion Exchange

    Energy Technology Data Exchange (ETDEWEB)

    Higa, R; Valderrama, M Pavon; Arriola, E Ruiz

    2007-06-14

    The renormalization of the NN interaction with the Chiral Two Pion Exchange Potential computed using relativistic baryon chiral perturbation theory is considered. The short distance singularity reduces the number of counter-terms to about a half as those in the heavy-baryon expansion. Phase shifts and deuteron properties are evaluated and a general overall agreement is observed.

  6. Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at √sNN=2.76TeV

    NARCIS (Netherlands)

    Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aimo, I.; Aiola, S.; Ajaz, M.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Armesto, N.; Arnaldi, R.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Bach, M.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baltasar Dos Santos Pedrosa, F.; 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.; 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, R.; Belmont-moreno, 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.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blanco, F.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Böttger, S.; Braun-munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Chochula, P.; Choi, K.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chunhui, Z.; 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.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; De Cataldo, G.; De Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; D'erasmo, G.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrowolski, T.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Eschweiler, D.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Felea, D.; 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.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-solis, E.; Gargiulo, C.; Gasik, P.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; González-zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-oetringhaus, J. F.; Grossiord, J.-y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gulkanyan, H.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hippolyte, B.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jadlovska, S.; Jahnke, C.; Jang, H. 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.; Kamin, J.; 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.; Khan, K. H.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, J. S.; Kim, M.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-bösing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; 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.; Koyithatta Meethaleveedu, G.; Kral, J.; Králik, I.; Kravčáková, A.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Kubera, A. M.; Kučera, V.; Kugathasan, T.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Legrand, I.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Leoncino, M.; Lévai, P.; 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.; Luz, P. H. F. N. D.; 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.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Masui, H.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Morando, M.; Moreira De Godoy, D. A.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, 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.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pajares, C.; Pal, S. K.; Pan, J.; Pant, D.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Peitzmann, T.; Pereira Da Costa, H.; Pereira De Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poonsawat, W.; Pop, A.; 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.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-p.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rivetti, A.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; 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.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Soltz, R.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Šumbera, M.; Symons, T. J. M.; Szabo, A.; Szanto De Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tangaro, M. A.; Tapia Takaki, J. D.; Tarantola Peloni, A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; 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.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; Von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yano, S.; Yokoyama, H.; Yoo, I.-k.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; 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.; Zhao, C.; Zhigareva, N.; Zhou, Z.; Zhu, H.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

    2016-01-01

    We report on results obtained with the event-shape engineering technique applied to Pb-Pb collisions at √sNN=2.76 TeV. By selecting events in the same centrality interval, but with very different average flow, different initial-state conditions can be studied. We find the effect of the event-shape

  7. MIMO wireless networks channels, techniques and standards for multi-antenna, multi-user and multi-cell systems

    CERN Document Server

    Clerckx, Bruno

    2013-01-01

    This book is unique in presenting channels, techniques and standards for the next generation of MIMO wireless networks. Through a unified framework, it emphasizes how propagation mechanisms impact the system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time signal processing, the book progressively derives innovative designs for space-time coding and precoding as well as multi-user and multi-cell techniques, taking into consideration that MIMO channels are often far from ideal. Reflecting developments

  8. Use of Dynamic Time Warping and Network Based Techniques for Mapping Trends and Patterns in Vegetation Using MODIS Data.

    Science.gov (United States)

    Singh, N.; Lucey, R.; Lunga, D.

    2016-12-01

    Normalized Difference Vegetation Index (NDVI) can be used as an indicator of healthy, green vegetation. NDVI values can be used to categorize the characteristics of land features. Similarly, a time-series analysis of NDVI can be used to monitor trends of vegetation patterns on the land surface. However vegetation has a natural phenology cycle, which varies from location to location based on various parameters like rainfall, temperature, soil etc. which makes it difficult to identify changes in land cover changes using conventional change detection techniques. Changes in vegetation pattern can be detected by using high temporal resolution satellite data but it can be computationally challenging to apply change detection over large areas and normal classification techniques don't perform well. In this study, we use annual stacks of NDVI time-series data at 8-day temporal resolution obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to classify and monitor land cover change. We show how dynamic time warping (DTW) and deep recurrent network based techniques perform better in classifying land cover and detecting changes using noisy high temporal resolution satellite data which is not possible by conventional remote sensing classification techniques. We present the application and results of the DTW and network based techniques and compare the results with standard supervised classification methods.

  9. 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

  10. Mitigating Handoff Call Dropping in Wireless Cellular Networks: A Call Admission Control Technique

    Science.gov (United States)

    Ekpenyong, Moses Effiong; Udoh, Victoria Idia; Bassey, Udoma James

    2016-06-01

    Handoff management has been an important but challenging issue in the field of wireless communication. It seeks to maintain seamless connectivity of mobile users changing their points of attachment from one base station to another. This paper derives a call admission control model and establishes an optimal step-size coefficient (k) that regulates the admission probability of handoff calls. An operational CDMA network carrier was investigated through the analysis of empirical data collected over a period of 1 month, to verify the performance of the network. Our findings revealed that approximately 23 % of calls in the existing system were lost, while 40 % of the calls (on the average) were successfully admitted. A simulation of the proposed model was then carried out under ideal network conditions to study the relationship between the various network parameters and validate our claim. Simulation results showed that increasing the step-size coefficient degrades the network performance. Even at optimum step-size (k), the network could still be compromised in the presence of severe network crises, but our model was able to recover from these problems and still functions normally.

  11. Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

    Science.gov (United States)

    Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

    2001-03-01

    This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

  12. Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...

  13. [Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

    Science.gov (United States)

    Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

    2006-08-01

    In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

  14. Improving Air Force Active Network Defense Systems through an Analysis of Intrusion Detection Techniques

    National Research Council Canada - National Science Library

    Dunklee, David R

    2007-01-01

    .... The research then presents four recommendations to improve DCC operations. These include: Transition or improve the current signature-based IDS systems to include the capability to query and visualize network flows to detect malicious traffic...

  15. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  16. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  17. Monitoring soil moisture patterns in alpine meadows using ground sensor networks and remote sensing techniques

    Science.gov (United States)

    Bertoldi, Giacomo; Brenner, Johannes; Notarnicola, Claudia; Greifeneder, Felix; Nicolini, Irene; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike

    2015-04-01

    Soil moisture content (SMC) is a key factor for numerous processes, including runoff generation, groundwater recharge, evapotranspiration, soil respiration, and biological productivity. Understanding the controls on the spatial and temporal variability of SMC in mountain catchments is an essential step towards improving quantitative predictions of catchment hydrological processes and related ecosystem services. The interacting influences of precipitation, soil properties, vegetation, and topography on SMC and the influence of SMC patterns on runoff generation processes have been extensively investigated (Vereecken et al., 2014). However, in mountain areas, obtaining reliable SMC estimations is still challenging, because of the high variability in topography, soil and vegetation properties. In the last few years, there has been an increasing interest in the estimation of surface SMC at local scales. On the one hand, low cost wireless sensor networks provide high-resolution SMC time series. On the other hand, active remote sensing microwave techniques, such as Synthetic Aperture Radars (SARs), show promising results (Bertoldi et al. 2014). As these data provide continuous coverage of large spatial extents with high spatial resolution (10-20 m), they are particularly in demand for mountain areas. However, there are still limitations related to the fact that the SAR signal can penetrate only a few centimeters in the soil. Moreover, the signal is strongly influenced by vegetation, surface roughness and topography. In this contribution, we analyse the spatial and temporal dynamics of surface and root-zone SMC (2.5 - 5 - 25 cm depth) of alpine meadows and pastures in the Long Term Ecological Research (LTER) Area Mazia Valley (South Tyrol - Italy) with different techniques: (I) a network of 18 stations; (II) field campaigns with mobile ground sensors; (III) 20-m resolution RADARSAT2 SAR images; (IV) numerical simulations using the GEOtop hydrological model (Rigon et al

  18. Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

    Science.gov (United States)

    Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.

    2017-09-01

    To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.

  19. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

    Science.gov (United States)

    Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

    2010-05-01

    Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

  20. Applying Fuzzy Logic and Data Mining Techniques in Wireless Sensor Network for Determination Residential Fire Confidence

    Directory of Open Access Journals (Sweden)

    Mirjana Maksimović

    2014-09-01

    Full Text Available The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques… is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical d

  1. Efficient Pricing Technique for Resource Allocation Problem in Downlink OFDM Cognitive Radio Networks

    Science.gov (United States)

    Abdulghafoor, O. B.; Shaat, M. M. R.; Ismail, M.; Nordin, R.; Yuwono, T.; Alwahedy, O. N. A.

    2017-05-01

    In this paper, the problem of resource allocation in OFDM-based downlink cognitive radio (CR) networks has been proposed. The purpose of this research is to decrease the computational complexity of the resource allocation algorithm for downlink CR network while concerning the interference constraint of primary network. The objective has been secured by adopting pricing scheme to develop power allocation algorithm with the following concerns: (i) reducing the complexity of the proposed algorithm and (ii) providing firm power control to the interference introduced to primary users (PUs). The performance of the proposed algorithm is tested for OFDM- CRNs. The simulation results show that the performance of the proposed algorithm approached the performance of the optimal algorithm at a lower computational complexity, i.e., O(NlogN), which makes the proposed algorithm suitable for more practical applications.

  2. Communication: Fitting potential energy surfaces with fundamental invariant neural network

    Science.gov (United States)

    Shao, Kejie; Chen, Jun; Zhao, Zhiqiang; Zhang, Dong H.

    2016-08-01

    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 OH3 and CH4 were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

  3. 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

  4. Evaluation of normalization methods for cDNA microarray data by k-NN classification

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J

    2004-12-17

    Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double

  5. Deriving Frequency-Dependent Spatial Patterns in MEG-Derived Resting State Sensorimotor Network: A Novel Multiband ICA Technique

    Science.gov (United States)

    Nugent, Allison C.; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E.; Coppola, Richard; Zarate, Carlos A.

    2016-01-01

    Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. PMID:27770478

  6. A simulator to assess energy-saving techniques in content distribution networks

    NARCIS (Netherlands)

    Bostoen, T.; Napper, J.; Mullender, Sape J.; Berbers, Y.

    2013-01-01

    The scalable and bandwidth-efficient delivery of IPTV services to an increasingly diverse set of screens requires the deployment of telco content distribution networks (CDNs). These CDNs are composed of cache servers located in the telco's data centers close to the end user. The additional cache

  7. Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.

    All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to n...

  8. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    Science.gov (United States)

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  9. Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research

  10. Ultra low power and interference robust transceiver techniques for wireless sensor networks

    NARCIS (Netherlands)

    Dutta, R.

    2016-01-01

    Wireless sensor networks (WSNs) have the potential to build breakthrough technologies for a variety of applications to improve human life. Some of the important applications are prevention, prediction and rescue of disasters, medical study and cure, improve the energy efficiency of homes and

  11. Forwarding Techniques for IP Fragmented Packets in a Real 6LoWPAN Network

    Directory of Open Access Journals (Sweden)

    Jordi Casademont

    2011-01-01

    Full Text Available Wireless Sensor Networks (WSNs are attracting more and more interest since they offer a low-cost solution to the problem of providing a means to deploy large sensor networks in a number of application domains. We believe that a crucial aspect to facilitate WSN diffusion is to make them interoperable with external IP networks. This can be achieved by using the 6LoWPAN protocol stack. 6LoWPAN enables the transmission of IPv6 packets over WSNs based on the IEEE 802.15.4 standard. IPv6 packet size is considerably larger than that of IEEE 802.15.4 data frame. To overcome this problem, 6LoWPAN introduces an adaptation layer between the network and data link layers, allowing IPv6 packets to be adapted to the lower layer constraints. This adaptation layer provides fragmentation and header compression of IP packets. Furthermore, it also can be involved in routing decisions. Depending on which layer is responsible for routing decisions, 6LoWPAN divides routing in two categories: mesh under if the layer concerned is the adaptation layer and route over if it is the network layer. In this paper we analyze different routing solutions (route over, mesh under and enhanced route over focusing on how they forward fragments. We evaluate their performance in terms of latency and energy consumption when transmitting IP fragmented packets. All the tests have been performed in a real 6LoWPAN implementation. After consideration of the main problems in forwarding of mesh frames in WSN, we propose and analyze a new alternative scheme based on mesh under, which we call controlled mesh under.

  12. "Soft-pion" production in the reaction NN-->NNπ

    Science.gov (United States)

    Dubach, J.; Silbar, R. R.; Kloet, W. M.

    1980-12-01

    We examine the reaction NN-->NNπ for "soft-pion" kinematics (p-->π=0 in the center of mass). An earlier calculation based on the Adler-Dothan theorem severely underestimated the p-->π=0 cross section for pp-->npπ+ at 740 MeV. We attempt to understand this discrepancy in the context of a relativistic, unitary, three-body isobar model. The postemission Δ-pole contributions are shown to be very important, even for these "soft-pion" kinematics. Relatively high partial waves in the NN-->NΔ isobar amplitude are significant. However, after summing all the postemission and preemission diagrams, the discrepancy with experiment is only partially removed.

  13. Estimation of the volumetric oxygen tranfer coefficient (KLa from the gas balance and using a neural network technique

    Directory of Open Access Journals (Sweden)

    CRUZ A. J. G.

    1999-01-01

    Full Text Available This paper reports on the use of the gas balance and dynamic methods to obtain an estimate of the volumetric oxygen transfer coefficient (kLa in a conventional reactor during the growth phase of the microorganism Cephalosporium acremonium. A new way of calculating kLa by the dynamic method employing an electrode with a slow response, is proposed. The calculated values of kLa were used in the training of a feedforward neural network, for which the inputs were the parameter measurements of the related variables. The neural network technique proved effective, predicting values of kLa accurately from input data not used during the training phase. In contrast, the gas balance method was shown to be less useful. This could be attributed to the poor data obtained with the apparatus used to measure the oxygen in the exhaust gas, explained by the low rate of oxygen consumption by the microorganism.

  14. 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).

  15. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

    National Research Council Canada - National Science Library

    Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

    2016-01-01

    ...) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes...

  16. Recent advances on artificial intelligence and learning techniques in cognitive radio networks

    National Research Council Canada - National Science Library

    Abbas, Nadine; Nasser, Youssef; Ahmad, Karim El

    2015-01-01

    ... of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved...

  17. Information Exchange Between Resilient and High-Threat Networks: Techniques for Threat Mitigation

    Science.gov (United States)

    2004-11-01

    details; e-mail arriving from the Internet carrying viruses that infect a business ’ Intranet; or more recently, the emergence of ‘phishing’ where...unavoidable business requirement to share information. RTO-MP-IST-041 16 - 11 Information Exchange between Resilient and High-Threat Networks...December 1999 on a Community framework for electronic signatures. Official Journal L 013, 19/01/2000 p. 0012 – 0020. http://europa.eu.int/ISPO/ ecommerce

  18. A Partnership Training Program in Breast Cancer Diagnosis: Concept Development of the Next Generation Diagnostic Breast Imaging Using Digital Image Library and Networking Techniques

    National Research Council Canada - National Science Library

    Chouikha, Mohamed F

    2004-01-01

    ...); and Georgetown University (Image Science and Information Systems, ISIS). In this partnership training program, we will train faculty and students in breast cancer imaging, digital image database library techniques and network communication strategy...

  19. WDM Optical Access Network for Full-Duplex and Reconfigurable Capacity Assignment Based on PolMUX Technique

    Directory of Open Access Journals (Sweden)

    Jose Mora

    2014-12-01

    Full Text Available We present a novel bidirectional WDM-based optical access network featuring reconfigurable capacity assignment. The architecture relies on the PolMUX technique allowing a compact, flexible, and bandwidth-efficient router in addition to source-free ONUs and color-less ONUs for cost/complexity minimization. Moreover, the centralized architecture contemplates remote management and control of polarization. High-quality transmission of digital signals is demonstrated through different routing scenarios where all channels are dynamically assigned in both downlink and uplink directions.

  20. Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

    Directory of Open Access Journals (Sweden)

    Wei Kong

    2014-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. Quantitative comparison of performance analysis techniques for modular and generic network-on-chip

    Directory of Open Access Journals (Sweden)

    M. C. Neuenhahn

    2009-05-01

    Full Text Available NoC-specific parameters feature a huge impact on performance and implementation costs of NoC. Hence, performance and cost evaluation of these parameter-dependent NoC is crucial in different design-stages but the requirements on performance analysis differ from stage to stage. In an early design-stage an analysis technique featuring reduced complexity and limited accuracy can be applied, whereas in subsequent design-stages more accurate techniques are required.

    In this work several performance analysis techniques at different levels of abstraction are presented and quantitatively compared. These techniques include a static performance analysis using timing-models, a Colored Petri Net-based approach, VHDL- and SystemC-based simulators and an FPGA-based emulator. Conducting NoC-experiments with NoC-sizes from 9 to 36 functional units and various traffic patterns, characteristics of these experiments concerning accuracy, complexity and effort are derived.

    The performance analysis techniques discussed here are quantitatively evaluated and finally assigned to the appropriate design-stages in an automated NoC-design-flow.

  3. Mobility based key management technique for multicast security in mobile ad hoc networks.

    Science.gov (United States)

    Madhusudhanan, B; Chitra, S; Rajan, C

    2015-01-01

    In MANET multicasting, forward and backward secrecy result in increased packet drop rate owing to mobility. Frequent rekeying causes large message overhead which increases energy consumption and end-to-end delay. Particularly, the prevailing group key management techniques cause frequent mobility and disconnections. So there is a need to design a multicast key management technique to overcome these problems. In this paper, we propose the mobility based key management technique for multicast security in MANET. Initially, the nodes are categorized according to their stability index which is estimated based on the link availability and mobility. A multicast tree is constructed such that for every weak node, there is a strong parent node. A session key-based encryption technique is utilized to transmit a multicast data. The rekeying process is performed periodically by the initiator node. The rekeying interval is fixed depending on the node category so that this technique greatly minimizes the rekeying overhead. By simulation results, we show that our proposed approach reduces the packet drop rate and improves the data confidentiality.

  4. Acupuncture-Related Techniques for Psoriasis: A Systematic Review with Pairwise and Network Meta-Analyses of Randomized Controlled Trials.

    Science.gov (United States)

    Yeh, Mei-Ling; Ko, Shu-Hua; Wang, Mei-Hua; Chi, Ching-Chi; Chung, Yu-Chu

    2017-12-01

    There has be a large body of evidence on the pharmacological treatments for psoriasis, but whether nonpharmacological interventions are effective in managing psoriasis remains largely unclear. This systematic review conducted pairwise and network meta-analyses to determine the effects of acupuncture-related techniques on acupoint stimulation for the treatment of psoriasis and to determine the order of effectiveness of these remedies. This study searched the following databases from inception to March 15, 2016: Medline, PubMed, Cochrane Central Register of Controlled Trials, EBSCO (including Academic Search Premier, American Doctoral Dissertations, and CINAHL), Airiti Library, and China National Knowledge Infrastructure. Randomized controlled trials (RCTs) on the effects of acupuncture-related techniques on acupoint stimulation as intervention for psoriasis were independently reviewed by two researchers. A total of 13 RCTs with 1,060 participants were included. The methodological quality of included studies was not rigorous. Acupoint stimulation, compared with nonacupoint stimulation, had a significant treatment for psoriasis. However, the most common adverse events were thirst and dry mouth. Subgroup analysis was further done to confirm that the short-term treatment effect was superior to that of the long-term effect in treating psoriasis. Network meta-analysis identified acupressure or acupoint catgut embedding, compared with medication, and had a significant effect for improving psoriasis. It was noted that acupressure was the most effective treatment. Acupuncture-related techniques could be considered as an alternative or adjuvant therapy for psoriasis in short term, especially of acupressure and acupoint catgut embedding. This study recommends further well-designed, methodologically rigorous, and more head-to-head randomized trials to explore the effects of acupuncture-related techniques for treating psoriasis.

  5. Modeling and Optimization Technique of a Chilled Water AHU Using Artificial Neural Network Methods

    Science.gov (United States)

    Talib, Rand Issa

    Heating, ventilation, and air conditioning (HVAC) systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The chilled water system is one Heating, ventilation, and air conditioning systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The design of these systems constitutes a large impact on the energy usage and operating cost of buildings they serve. The ability to accurately predict the performance of these systems is integral to designing more energy efficient and sustainable building systems. In this thesis the modeling of a chilled water air handling units using Artificial Neural Networks model is proposed. The Artificial neural network model was built using four inputs (1) Chilled water temperature (CHWT), (2) Chilled water valve position (CWVLV), (3) Mixed air temperature (MAT), and (4) Supply air flow (SAF). The output of the model is to predict supply air temperature. Moreover, another model was constructed to predict the fan power as a function of the fan air flow and fan speed. The data that were collected from a real building in a span of three months were processed. The ANN model was trained using the measured data and different model structure were then tested with various time delay, feedback time, and number of neurons to determine the best structure. In addition, an optimization method is developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The Coefficient of variances which was used to determine the error value was recorded to be as low as 1.22 for the best model structure. The obtained results validate the Artificial neural network model created as an accurate tool for predicting the performance of a chilled water air handling unit.

  6. Efficient Techniques to Detect the Various Attacks in Ad-Hoc Network

    OpenAIRE

    Rakesh Kumar Sahu; Dr. Narendra S. Chaudhari

    2012-01-01

    This paper is mainly focused on Denial of Service (DoS) attack, where a server or a node cannot give service to the other nodes as it is under an attack. There are various attacks in the Ad hoc network but our paper is mainly focused on two types of DoS attacks viz SYN-Flooding and Worm-Hole attacks. How we can detect any one of attacks is addressed in this paper. We have discussed the CPU and memory utilization during the attack. We have given two separate algorithms for each attack and a...

  7. Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks

    Directory of Open Access Journals (Sweden)

    Abubakar Muhammad Umaru

    2014-01-01

    Full Text Available The optical burst switching (OBS paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT burst assembly algorithm via simulation. Simulation results show that the proposed algorithm outperforms the hybrid and the FAT algorithms in terms of burst end-to-end delay, packet end-to-end delay, and packet loss ratio.

  8. A practical application and implementation of adaptive techniques using neural networks in autoreclose protection and system control

    Energy Technology Data Exchange (ETDEWEB)

    Gardiner, I.P.

    1997-12-31

    Reyrolle Protection have carried out research in conjunction with Bath University into applying adaptive techniques to autoreclose schemes and have produced an algorithm based on an artificial neural network which can recognise when it is ``safe to reclose`` and when it is ``unsafe to reclose``. This algorithm is based on examination of the induced voltage on the faulted phase and by applying pattern recognition techniques determines when the secondary arc extinguishes. Significant operational advantages can now be realised using this technology resulting in changes to existing operational philosophy. Conventional autoreclose relays applied to the system have followed the philosophy of ``reclose to restore the system``, but a progression from this philosophy to ``reclose only if safe to do so`` can now be made using this adaptive approach. With this adaptive technique the main requirement remains to protect the investment i.e. the system, by reducing damaging shocks and voltage dips and maintaining continuity of supply. The adaptive technique can be incorporated into a variety of schemes which will act to further this goal in comparison with conventional autoreclose. (Author)

  9. Pithy Review on Routing Protocols in Wireless Sensor Networks and Least Routing Time Opportunistic Technique in WSN

    Science.gov (United States)

    Salman Arafath, Mohammed; Rahman Khan, Khaleel Ur; Sunitha, K. V. N.

    2018-01-01

    Nowadays due to most of the telecommunication standard development organizations focusing on using device-to-device communication so that they can provide proximity-based services and add-on services on top of the available cellular infrastructure. An Oppnets and wireless sensor network play a prominent role here. Routing in these networks plays a significant role in fields such as traffic management, packet delivery etc. Routing is a prodigious research area with diverse unresolved issues. This paper firstly focuses on the importance of Opportunistic routing and its concept then focus is shifted to prime aspect i.e. on packet reception ratio which is one of the highest QoS Awareness parameters. This paper discusses the two important functions of routing in wireless sensor networks (WSN) namely route selection using least routing time algorithm (LRTA) and data forwarding using clustering technique. Finally, the simulation result reveals that LRTA performs relatively better than the existing system in terms of average packet reception ratio and connectivity.

  10. Selecting Strategies to Reduce High-Risk Unsafe Work Behaviors Using the Safety Behavior Sampling Technique and Bayesian Network Analysis.

    Science.gov (United States)

    Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas; Mohammadfam, Iraj

    2017-03-04

    High-risk unsafe behaviors (HRUBs) have been known as the main cause of occupational accidents. Considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. The aim of the present study was to find strategies for decreasing the rate of HRUBs using an integrated approach of safety behavior sampling technique and Bayesian networks analysis. A cross-sectional study. The Bayesian network was constructed using a focus group approach. The required data was collected using the safety behavior sampling, and the parameters of the network were estimated using Expectation-Maximization algorithm. Using sensitivity analysis and belief updating, it was determined that which factors had the highest influences on unsafe behavior. Based on BN analyses, safety training was the most important factor influencing employees' behavior at the workplace. High quality safety training courses can reduce the rate of HRUBs about 10%. Moreover, the rate of HRUBs increased by decreasing the age of employees. The rate of HRUBs was higher in the afternoon and last days of a week. Among the investigated variables, training was the most important factor affecting safety behavior of employees. By holding high quality safety training courses, companies would be able to reduce the rate of HRUBs significantly.

  11. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  12. Neural Network-Based Segmentation of Textures Using Gabor Features

    OpenAIRE

    Ramakrishnan, AG; Raja, Kumar S; Ram, Ragu HV

    2002-01-01

    The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptr...

  13. A statistical approach for site error correction in lightning location networks with DF/TOA technique and its application results

    Science.gov (United States)

    Lu, Tao; Chen, Mingli; Du, Yaping; Qiu, Zongxu

    2017-02-01

    Lightning location network (LLN) with DF/TOA (direction-finder/time-of-arrival) combined technique has been widely used in the world. However, the accuracy of the lightning data from such LLNs has still been restricted by "site error", especially for those detected only by two DF/TOA sensors. In this paper we practice a statistical approach for evaluation and correction of "site error" for DF/TOA type LLN based on its lightning data. By comparing lightning locations recorded by at least 4 sensors between DF and TOA techniques, the spatial characteristics of "site error" for each sensor in the network can be obtained. The obtained "site error" then can be used to improve the accuracy of lightning locations especially those recorded by only 2 sensors. With this approach, the "site error" patterns for 23 sensors in Yunnan LLN are obtained. The features of these site error patterns are in good consistency with those in literature. Significant differences in lightning locations before and after "site error" corrections indicate that the proposed approach works effectively.

  14. Modeling and adaptive control of a camless engine using neural networks and estimation techniques

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-08-09

    A system to control the cylinder air charge (CAC) in a camless internal combustion (IC) engine was recently developed. The performance of an IC engine connected to an adaptive artificial neural network (ANN) based feedback controller was then investigated. A control oriented model for the engine intake process was created based on thermodynamics laws and was validated against engine experimental data. Input-output data at a speed of 1500 RPM was generated and used to train an ANN model for the engine. The inputs were the intake valve lift (IVL) and closing timing (IVC). The output was the CAC. The controller consisted of a feedforward controller, CAC estimator, and on-line ANN parameter estimator. The feedforward controller provided IVL and IVC that satisfied the driver's torque demand and was the inverse of the engine ANN model. The on-line ANN used the error between the CAC measurement from the CAC estimator and its predicted value from the ANN to update the network's parameters. The feedforward controller was therefore adapted since its operation depended on the ANN model. The adaptation scheme improved the ANN prediction accuracy when the engine parts degraded, the speed changed or when modeling errors occurred. The engine controller exhibited good CAC tracking performance. Computer simulation demonstrated the capability of the camless engine controller. 17 refs., 5 figs.

  15. Using Web 2.0 Techniques in NASA's Ares Engineering Operations Network (AEON) Environment - First Impressions

    Science.gov (United States)

    Scott, David W.

    2010-01-01

    The Mission Operations Laboratory (MOL) at Marshall Space Flight Center (MSFC) is responsible for Engineering Support capability for NASA s Ares rocket development and operations. In pursuit of this, MOL is building the Ares Engineering and Operations Network (AEON), a web-based portal to support and simplify two critical activities: Access and analyze Ares manufacturing, test, and flight performance data, with access to Shuttle data for comparison Establish and maintain collaborative communities within the Ares teams/subteams and with other projects, e.g., Space Shuttle, International Space Station (ISS). AEON seeks to provide a seamless interface to a) locally developed engineering applications and b) a Commercial-Off-The-Shelf (COTS) collaborative environment that includes Web 2.0 capabilities, e.g., blogging, wikis, and social networking. This paper discusses how Web 2.0 might be applied to the typically conservative engineering support arena, based on feedback from Integration, Verification, and Validation (IV&V) testing and on searching for their use in similar environments.

  16. Tensor network techniques for the computation of dynamical observables in one-dimensional quantum spin systems

    Science.gov (United States)

    Müller-Hermes, Alexander; Cirac, J. Ignacio; Bañuls, Mari Carmen

    2012-07-01

    We analyze the recently developed folding algorithm (Bañuls et al 2009 Phys. Rev. Lett. 102 240603) for simulating the dynamics of infinite quantum spin chains and we relate its performance to the kind of entanglement produced under the evolution of product states. We benchmark the accomplishments of this technique with respect to alternative strategies using Ising Hamiltonians with transverse and parallel fields, as well as XY models. Also, we evaluate its capability of finding ground and thermal equilibrium states.

  17. A new jamming technique for secrecy in multi-antenna wireless networks

    KAUST Repository

    Bakr, Omar

    2010-06-01

    We consider the problem of secure wireless communication in the presence of an eavesdropper when the transmitter has multiple antennas, using a variation of the recently proposed artificial noise technique. Under this technique, the transmitter sends a pseudo-noise jamming signal to selectively degrade the link to the eavesdropper without affecting the desired receiver. The previous work in the literature focuses on ideal Gaussian signaling for both the desired signal and the noise signal. The main contribution of this paper is to show that the Gaussian signaling model has important limitations and propose an alternative "induced fading" jamming technique that takes some of these limitations into account. Specifically we show that under the Gaussian noise scheme, the eavesdropper is able to recover the desired signal with very low bit error rates when the transmitter is constrained to use constant envelope signaling. Furthermore, we show that an eavesdropper with multiple antennas is able to use simple, blind constant-envelope algorithms to completely remove the Gaussian artificial noise signal and thus defeat the secrecy scheme. We propose an alternative scheme that induces artificial fading in the channel to the eavesdropper, and show that it outperforms the Gaussian noise scheme in the sense of causing higher bit error rates at the eavesdropper and is also more resistant to constant modulus-type algorithms. © 2010 IEEE.

  18. 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.

  19. Inductive coupling between overhead power lines and nearby metallic pipelines. A neural network approach

    Directory of Open Access Journals (Sweden)

    Levente Czumbil

    2015-12-01

    Full Text Available The current paper presents an artificial intelligence based technique applied in the investigation of electromagnetic interference problems between high voltage power lines (HVPL and nearby underground metallic pipelines (MP. An artificial neural network (NN solution has been implemented by the authors to evaluate the inductive coupling between HVPL and MP for different constructive geometries of an electromagnetic interference problem considering a multi-layer soil structure. Obtained results are compared to solutions provided by a finite element method (FEM based analysis and considered as reference. The advantage of the proposed method yields in a simplified computation model compared to FEM, and implicitly a lower computational time.

  20. Discrete-time adaptive backstepping nonlinear control via high-order neural networks.

    Science.gov (United States)

    Alanis, Alma Y; Sanchez, Edgar N; Loukianov, Alexander G

    2007-07-01

    This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

  1. Molecular Cloning and Expression Analysis of a Catalase Gene (NnCAT) from Nelumbo nucifera.

    Science.gov (United States)

    Dong, Chen; Zheng, Xingfei; Diao, Ying; Wang, Youwei; Zhou, Mingquan; Hu, Zhongli

    2015-11-01

    Rapid amplification cDNA end (RACE) assay was established to achieve the complete cDNA sequence of a catalase gene (NnCAT) from Nelumbo nucifera. The obtained full-length cDNA was 1666 bp in size and contained a 1476-bp open reading frame. The 3D structural model of NnCAT was constructed by homology modeling. The putative NnCAT possessed all the main characteristic amino acid residues and motifs of catalase (CAT) protein family, and the phylogenetic analysis revealed that NnCAT grouped together with high plants. Moreover, recombinant NnCAT showed the CAT activity (758 U/mg) at room temperature, holding high activity during temperature range of 20-50 °C, then the optimal pH of recombinant protein was assessed from pH 4 to pH 11. Additionally, real-time PCR assay demonstrated that NnCAT mRNA was expressed in various tissues of N. nucifera, with the highest expression in young leaf and lowest level in the root, and mRNA level of NnCAT was significantly augmented in response to short-time mechanical wounding. Different expression pattern of NnCAT gene suggested that NnCAT probably played a defensive role in the initial stages of oxidative stress, regulating the level of reactive oxygen species (ROS) by extracellular stimuli such as short-time mechanical wounding.

  2. A diversity compression and combining technique based on channel shortening for cooperative networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2012-02-01

    The cooperative relaying process with multiple relays needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems where the nodes are equipped with very basic communication hardware. We consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination captures the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening principles. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. © 2012 IEEE.

  3. Whole-Building Airflow Network Characterization by a Many-Pressure-States (MPS) Technique

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Peter R.; Hadley, Donald L.; Stenner, Robert D.; Janus, Michael C.

    2001-06-01

    Applications of multi-zone airflow and contaminant dispersion models to specific buildings include air quality diagnosis, weatherization, smoke control, and pressure balancing for lab-hood safety. Research applications may involve energy and air quality impacts of infiltration, ventilation, and development of associated standards. The benefits of these applications are not being fully realized because of uncertainties in model inputs. An economical test method that is as accurate but less intrusive and faster than incremental or component-by-component blower door testing is needed. Existing methods of measuring inter-zone flows by tracers and flow-pressure network parameters by pressurization tests are reviewed. A method based on simultaneous measurement of all zone pressures within a given control volume and flow measurements on a few selected inter-zonal paths is presented. Flow rates at a subset of flow measuring locations are controlled during testing to take on two or more values. Constrained, nonlinear least squares analysis produces the power law parameters (flow coefficient and exponent) for each set of two-port aggregate flow paths within the topology. Testing cost and effort are reduced by relying mainly on zone pressure measurements and a rich set of HVAC- or blower-door-induced flow-pressure excitation states with relatively few flow measuring stationsements. Results of applying the method to a two-story test building are reported. Potential application to fault detection, acceptance testing, continuous commissioning, smoke dispersion and other problems of building performance and operation are outlined.

  4. 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.

  5. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  6. Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

    Directory of Open Access Journals (Sweden)

    Azadeh eRahmani-Badi

    2015-04-01

    Full Text Available Many bacterial pathogens use quorum-sensing (QS signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF, is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized as cis-2-decenoic acid (C10: Δ2, CDA that induces biofilm dispersal by multiple types of bacteria. Furthermore, CDA has been shown to be involved in inter-kingdom signaling that modulates fungal behavior. Therefore, an understanding of its signaling mechanism could suggest strategies for interference, with consequences for disease control. To identify the components of CDA signaling pathway in this pathogen, a comparative transcritpome analysis was conducted, in the presence and absence of CDA. A protein-protein interaction (PPI network for differentially expressed (DE genes with known function was then constructed by STRING and Cytoscape. In addition, the effects of CDA in combination with antimicrobial agents on the biofilm surface area and bacteria viability were evaluated using fluorescence microscopy and digital image analysis. Microarray analysis identified 666 differentially expressed genes in the presence of CDA and gene ontology (GO analysis revealed that in P. aeruginosa, CDA mediates dispersion of biofilms through signaling pathways, including enhanced motility, virulence as well as persistence at different temperatures. PPI data suggested that a cluster of five genes (PA4978, PA4979, PA4980, PA4982, PA4983 is involved in the CDA synthesis and perception. Combined treatments using both CDA and antimicrobial agents showed that following exposure of the biofilms to CDA, remaining cells on the surface were easily removed and killed by antimicrobials.

  7. Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

    Science.gov (United States)

    Cruz, Febus Reidj G.; Padilla, Dionis A.; Hortinela, Carlos C.; Bucog, Krissel C.; Sarto, Mildred C.; Sia, Nirlu Sebastian A.; Chung, Wen-Yaw

    2017-02-01

    This study is about the determination of moisture content of milled rice using image processing technique and perceptron neural network algorithm. The algorithm involves several inputs that produces an output which is the moisture content of the milled rice. Several types of milled rice are used in this study, namely: Jasmine, Kokuyu, 5-Star, Ifugao, Malagkit, and NFA rice. The captured images are processed using MATLAB R2013a software. There is a USB dongle connected to the router which provided internet connection for online web access. The GizDuino IOT-644 is used for handling the temperature and humidity sensor, and for sending and receiving of data from computer to the cloud storage. The result is compared to the actual moisture content range using a moisture tester for milled rice. Based on results, this study provided accurate data in determining the moisture content of the milled rice.

  8. Energy-efficient optical network units for OFDM PON based on time-domain interleaved OFDM technique.

    Science.gov (United States)

    Hu, Xiaofeng; Cao, Pan; Zhang, Liang; Jiang, Lipeng; Su, Yikai

    2014-06-02

    We propose and experimentally demonstrate a new scheme to reduce the energy consumption of optical network units (ONUs) in orthogonal frequency division multiplexing passive optical networks (OFDM PONs) by using time-domain interleaved OFDM (TI-OFDM) technique. In a conventional OFDM PON, each ONU has to process the complete downstream broadcast OFDM signal with a high sampling rate and a large FFT size to retrieve its required data, even if it employs a portion of OFDM subcarriers. However, in our scheme, the ONU only needs to sample and process one data group from the downlink TI-OFDM signal, effectively reducing the sampling rate and the FFT size of the ONU. Thus, the energy efficiency of ONUs in OFDM PONs can be greatly improved. A proof-of-concept experiment is conducted to verify the feasibility of the proposed scheme. Compared to the conventional OFDM PON, our proposal can save 17.1% and 26.7% energy consumption of ONUs by halving and quartering the sampling rate and the FFT size of ONUs with the use of the TI-OFDM technology.

  9. Hybrid evolutionary techniques in feed forward neural network with distributed error for classification of handwritten Hindi `SWARS'

    Science.gov (United States)

    Kumar, Somesh; Pratap Singh, Manu; Goel, Rajkumar; Lavania, Rajesh

    2013-12-01

    In this work, the performance of feedforward neural network with a descent gradient of distributed error and the genetic algorithm (GA) is evaluated for the recognition of handwritten 'SWARS' of Hindi curve script. The performance index for the feedforward multilayer neural networks is considered here with distributed instantaneous unknown error i.e. different error for different layers. The objective of the GA is to make the search process more efficient to determine the optimal weight vectors from the population. The GA is applied with the distributed error. The fitness function of the GA is considered as the mean of square distributed error that is different for each layer. Hence the convergence is obtained only when the minimum of different errors is determined. It has been analysed that the proposed method of a descent gradient of distributed error with the GA known as hybrid distributed evolutionary technique for the multilayer feed forward neural performs better in terms of accuracy, epochs and the number of optimal solutions for the given training and test pattern sets of the pattern recognition problem.

  10. Detecting Falls with Wearable Sensors Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Ahmet Turan Özdemir

    2014-06-01

    Full Text Available Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects’ body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass. Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs, resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers: the k-nearest neighbor (k-NN classifier, least squares method (LSM, support vector machines (SVM, Bayesian decision making (BDM, dynamic time warping (DTW, and artificial neural networks (ANNs. We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

  11. 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...

  12. MPEG-4 ASP SoC receiver with novel image enhancement techniques for DAB networks

    Science.gov (United States)

    Barreto, D.; Quintana, A.; García, L.; Callicó, G. M.; Núñez, A.

    2007-05-01

    This paper presents a system for real-time video reception in low-power mobile devices using Digital Audio Broadcast (DAB) technology for transmission. A demo receiver terminal is designed into a FPGA platform using the Advanced Simple Profile (ASP) MPEG-4 standard for video decoding. In order to keep the demanding DAB requirements, the bandwidth of the encoded sequence must be drastically reduced. In this sense, prior to the MPEG-4 coding stage, a pre-processing stage is performed. It is firstly composed by a segmentation phase according to motion and texture based on the Principal Component Analysis (PCA) of the input video sequence, and secondly by a down-sampling phase, which depends on the segmentation results. As a result of the segmentation task, a set of texture and motion maps are obtained. These motion and texture maps are also included into the bit-stream as user data side-information and are therefore known to the receiver. For all bit-rates, the whole encoder/decoder system proposed in this paper exhibits higher image visual quality than the alternative encoding/decoding method, assuming equal image sizes. A complete analysis of both techniques has also been performed to provide the optimum motion and texture maps for the global system, which has been finally validated for a variety of video sequences. Additionally, an optimal HW/SW partition for the MPEG-4 decoder has been studied and implemented over a Programmable Logic Device with an embedded ARM9 processor. Simulation results show that a throughput of 15 QCIF frames per second can be achieved with low area and low power implementation.

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

    NARCIS (Netherlands)

    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

  14. Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

    Directory of Open Access Journals (Sweden)

    Geraldo A. R. Ramos

    2017-06-01

    Full Text Available In this work, a neuro-fuzzy (NF simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL and the learning capability of neural network (NN to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

  15. Three-body {lambda}NN force due to {lambda}-{sigma} coupling

    Energy Technology Data Exchange (ETDEWEB)

    Myint, Khin Swe [Mandalay Univ., Mandalay (Myanmar); Akaishi, Yoshinori [High Energy Accelerator Research Organization, Inst. of Particle and Nuclear Studies, Tsukuba, Ibaraki (Japan)

    2003-03-01

    The {lambda}NN three - body force due to coherent {lambda} - {sigma} Coupling effect was derived from realistic Nijmegen model D potential. Repulsive and attractive three - body {lambda}NN forces were reconcilably accounted. For {sup 5}He, within one - channel description, {lambda}NN 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 {lambda}NN force effect due to coherent {lambda} - {sigma} coupling effect is predicted in neutron - rich nuclei. The attractive coherent {lambda} - {sigma} coupling effect is largely enhanced at high density neutron matter. The attractive three - body {lambda}NN force effect is essential dynamics of {lambda} - {sigma} coupling while the repulsive Nogami three - body effect arises from Pauli forbidden diagrams. (Y. Kazumata)

  16. 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...

  17. New Neural Network Methods for Forecasting Regional Employment

    NARCIS (Netherlands)

    Patuelli, R.; Reggiani, A; Nijkamp, P.; Blien, U.

    2006-01-01

    In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying

  18. Prediction horizon effects on stochastic modelling hints for neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Drossu, R.; Obradovic, Z. [Washington State Univ., Pullman, WA (United States)

    1995-12-31

    The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.

  19. Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

    Directory of Open Access Journals (Sweden)

    Sepehr Sadighi

    2015-07-01

    Full Text Available In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU is presented. This model is also capable of predicting research octane number (RON and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days, fifty-eight points are selected for building and training the DANN (60%, nineteen data points for testing (20%, and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating data with AAD% (average absolute deviation of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively. © 2015 BCREC UNDIP. All rights reserved.Received: 27th July 2014; Revised: 31st May 2015; Accepted: 31th May 2015 How to Cite: Sadighi, S., Mohaddecy, R.S., Norouzian, A. (2015. Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique. Bulletin of Chemical Reaction Engineering & Catalysis, 10(2: 210-220. (doi:10.9767/bcrec.10.2.7171.210-220 Permalink/DOI: http://dx.doi.org/10.9767/bcrec.10.2.7171.210-220  

  20. Artificial intelligence techniques for clutter identification with polarimetric radar signatures

    Science.gov (United States)

    Islam, Tanvir; Rico-Ramirez, Miguel A.; Han, Dawei; Srivastava, Prashant K.

    2012-06-01

    The use of different artificial intelligence (AI) techniques for clutter signals identification in the context of radar based precipitation estimation is presented. The clutter signals considered are because of ground clutter, sea clutter and anomalous propagation whereas the explored AI techniques include the support vector machine (SVM), the artificial neural network (ANN), the decision tree (DT), and the nearest neighbour (NN) systems. Eight different radar measurement combinations comprising of various polarimetric spectral signatures — the reflectivity (ZH), differential reflectivity (ZDR), differential propagation phase (ΦDP), cross-correlation coefficient (ρHV), velocity (V) and spectral width (W) from a C-band polarimetric radar are taken into account as input vectors to the AI systems. The results reveal that all four AI classifiers can identify the clutter echoes with around 98-99% accuracy when all radar input signatures are used. As standalone input vectors, the polarimetric textures of the ΦDP and the ZDR have also demonstrated excellent skills distinguishing clutter echoes with an accuracy of 97-98% approximately. If no polarimetric signature is available, a combination of the texture of ZH, V and W representing typical measurements from a single-polarization Doppler radar may be used for clutter identification, but with a lower accuracy when compared to the use of polarimetric radar measurements. In contrast, the use of ZH or W alone is found less reliable for clutter classification. Among the AI techniques, the SVM has a slightly better score in terms of various clutter identification indicators as compared to the others. Conversely, the NN algorithm has shown a lower performance in identifying the clutter echoes correctly considering the standalone radar signatures as inputs. Despite this, the performance among the different AI techniques is comparable indicating the suitability of the developed systems, and this is further supported when

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

    Directory of Open Access Journals (Sweden)

    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.

  2. The {delta}N->NN transition in finite nuclei

    Energy Technology Data Exchange (ETDEWEB)

    Chumillas, C. [Departament ECM, Facultat de Fisica, Universitat de Barcelona, E-08028, Barcelona (Spain)]. E-mail chumi@ecm.ub.es; Parreno, A. [Departament ECM, Facultat de Fisica, Universitat de Barcelona, E-08028, Barcelona (Spain); Ramos, A. [Departament ECM, Facultat de Fisica, Universitat de Barcelona, E-08028, Barcelona (Spain)

    2007-07-15

    We perform a direct finite nucleus calculation of the partial width of a bound {delta} isobar decaying through the non-mesonic decay mode, {delta}N->NN. This transition is modeled by the exchange of the long ranged {pi} meson and the shorter ranged {rho} meson. The contribution of this decay channel is found to be approximately 60% of the decay width of the {delta} particle in free space. Considering the additional pionic decay mode, we conclude that the total decay width of a bound {delta} resonance in nuclei is of the order of 100 MeV and, consequently, no narrow {delta} nuclear states exist, contrary to recent claims in the literature. Our results are in complete agreement with microscopic many-body calculations and phenomenological approaches performed in nuclear matter.

  3. The GMO Sumrule and the πNN Coupling Constant

    Science.gov (United States)

    Ericson, T. E. O.; Loiseau, B.; Thomas, A. W.

    The isovector GMO sumrule for forward πN scattering is critically evaluated using the precise π-p and π-d scattering lengths obtained recently from pionic atom measurements. The charged πNN coupling constant is then deduced with careful analysis of systematic and statistical sources of uncertainties. This determination gives directly from data gc2(GMO)/4π = 14.17±0.09 (statistic) ±0.17 (systematic) or fc2/ 4π=0.078(11). This value is half-way between that of indirect methods (phase-shift analyses) and the direct evaluation from from backward np differential scattering cross sections (extrapolation to pion pole). From the π-p and π-d scattering lengths our analysis leads also to accurate values for (1/2)(aπ-p+aπ-n) and (1/2) (aπ-p-aπ-n).

  4. Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Sunil Khuntia

    2014-09-01

    Full Text Available This study presents the application of artificial neural networks (ANN and least square support vector machine (LS-SVM for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

  5. Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.

    Science.gov (United States)

    Hearty, Aine P; Gibney, Michael J

    2008-12-01

    At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques. The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system. Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals. This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively). ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.

  6. A Formal Method for Verification and Validation of Neural Network High Assurance Systems Project

    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...

  7. Node Attribute-enhanced Community Detection in Complex Networks.

    Science.gov (United States)

    Jia, Caiyan; Li, Yafang; Carson, Matthew B; Wang, Xiaoyang; Yu, Jian

    2017-05-25

    Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.

  8. Capacitive MEMS accelerometer wide range modeling using artificial neural network

    OpenAIRE

    A. Baharodimehr; A. Abolfazl Suratgar; H. Sadeghi

    2009-01-01

    This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA). System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN) uses the Levenberg‐Marquardt (LM) method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation result...

  9. Capacitive MEMS accelerometer wide range modeling using artificial neural network

    Directory of Open Access Journals (Sweden)

    A. Baharodimehr

    2009-08-01

    Full Text Available This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA. System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN uses the Levenberg‐Marquardt (LM method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation results are very promising.

  10. Power-Management Techniques for Wireless Sensor Networks and Similar Low-Power Communication Devices Based on Nonrechargeable Batteries

    Directory of Open Access Journals (Sweden)

    Agnelo Silva

    2012-01-01

    Full Text Available Despite the well-known advantages of communication solutions based on energy harvesting, there are scenarios where the absence of batteries (supercapacitor only or the use of rechargeable batteries is not a realistic option. Therefore, the alternative is to extend as much as possible the lifetime of primary cells (nonrechargeable batteries. By assuming low duty-cycle applications, three power-management techniques are combined in a novel way to provide an efficient energy solution for wireless sensor networks nodes or similar communication devices powered by primary cells. Accordingly, a customized node is designed and long-term experiments in laboratory and outdoors are realized. Simulated and empirical results show that the battery lifetime can be drastically enhanced. However, two trade-offs are identified: a significant increase of both data latency and hardware/software complexity. Unattended nodes deployed in outdoors under extreme temperatures, buried sensors (underground communication, and nodes embedded in the structure of buildings, bridges, and roads are some of the target scenarios for this work. Part of the provided guidelines can be used to extend the battery lifetime of communication devices in general.

  11. Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques

    Directory of Open Access Journals (Sweden)

    Pei-Yu Chen

    2013-01-01

    Full Text Available With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.

  12. Prediction of coal grindability based on petrography, proximate and ultimate analysis using neural networks and particle swarm optimization technique

    Energy Technology Data Exchange (ETDEWEB)

    Modarres, Hamid Reza; Kor, Mohammad; Abkhoshk, Emad; Alfi, Alireza; Lower, James C.

    2009-06-15

    In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks - particle swarm optimization method gave the most accurate HGI prediction.

  13. 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.

  14. Central Institutional Review Board Review for an Academic Trial Network

    Science.gov (United States)

    Kaufmann, Petra; O’Rourke, P. Pearl

    2016-01-01

    Problem Translating discoveries into therapeutics is often delayed by lengthy start-up periods for multicenter clinical trials. One cause of delay can be multiple institutional review board (IRB) reviews of the same protocol. Approach When developing the Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT; hereafter, NN), the National Institute of Neurological Disorders and Stroke (NINDS) established a central IRB (CIRB) based at Massachusetts General Hospital, the academic medical center that received the NN clinical coordinating center grant. The 25 NN sites, located at U.S. academic institutions, agreed to required CIRB use for NN trials. Outcomes To delineate roles and establish legal relationships between the NN sites and the CIRB, the CIRB executed reliance agreements with the sites and their affiliates that hold federalwide assurance for the protection of human subjects (FWA); this took, on average, 84 days. The first NN protocol reviewed by the CIRB achieved full approval to allow participant enrollment within 56 days and went from grant award to the first patient visit in less than four months. The authors describe anticipated challenges related to institutional oversight responsibilities versus regulatory CIRB review as well as unanticipated challenges related to working with complex organizations that include multiple FWA-holding affiliates. Next Steps The authors anticipate that CIRB use will decrease NN trial start-up time and thus promote efficient trial implementation. They plan to collect data on timelines and costs associated with CIRB use. The NINDS plans to promote CIRB use in future initiatives. PMID:25406606

  15. Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks

    Science.gov (United States)

    Shang, Ronghua; Liu, Huan; Jiao, Licheng

    2017-11-01

    This paper proposes a relatively all-purpose network clustering technique based on the framework of multi-objective evolutionary algorithms, which can effectively dispose the issue of community detection in unsigned social networks, as well as in signed social networks. Firstly, we formulate a generalized similarity function to construct a similarity matrix, and then a pre-partitioning strategy is projected according to the similarity matrix. The pre-partitioning strategy merely considers nodes with high similarity values, which avoids the interference of noise nodes during the label update phase. In this way, at the initial phase of the algorithm, nodes with strong connections are fleetly gathered into sub-communities. Secondly, we elaborately devise a crossover operator, called cross-merging operator, to merge sub-communities generated by the pre-partitioning technique. Moreover, a special mutation operator, based on the similarity matrix of nodes, is implemented to adjust boundary nodes connecting different communities. Finally, to handle different types of networks, we, therefore, have presented the novel multi-objective optimization models for this issue. Through a bulk of rigorous experiments on both unsigned and signed social networks, the preeminent clustering performance illustrate that the proposed algorithm is capable of mining communities effectively.

  16. Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

    Directory of Open Access Journals (Sweden)

    Mohammad Dreidy

    2017-01-01

    Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.

  17. Gaussian source OBEP and NN, YN and YY interactions

    Energy Technology Data Exchange (ETDEWEB)

    Wada, Masanobu [Nihon Univ., College of Science and Technology, Tokyo (Japan); Arisaka, Isamu; Nakagawa, Kimiko; Shinmura, Shoji

    2003-01-01

    Meson fields is a smooth function which has no singular point on the origin when fundamental particles with Gaussian distribution are assumed as the field source. One boson exchange potential (OBEP) based on this source (GSOBEP) has soft potential function form with no singular point on the origin. The potential has soft core of Gaussian like shape in the inside region while it behaves like normal Yukawa function at the outside region. Among the sixteen SU(3) coupling parameters of scalar, pseudo scalar and vector mesons, six are fixed due to analyses or theoretical requirements of quark models. It is assumed that the source has the spreading corresponding to the baryon and the spreading parameter corresponds to the strangeness dependence of the baryon mass, which gives rise to two parameters since nucleon and hyperon are discriminated. In total twelve free parameters are calculated here. Based on this very small number of free parameters of the GSOBEP good parameter sets are obtained, which reproduce NN, YN and YY experimental data satisfactorily. The following outlines of the result are shown graphically. Concerning NN data, calculation reproduces {sup 1}E, {sup 1}O, {sup 3}E and {sup 3}O states well but {sup 1}P{sub 1} and {sup 3}D{sub 3} states poorly. Data of D and its low energy parameters are reproduced nearly well. For YN interactions, calculation reproduces the scattering cross sections {sigma}'s and differential cross sections d {sigma}/d{omega} within error bars. Though direct experimental data do not exist on YY interactions, GSOBEP can be determined to reproduce the {sup 1}S{sub 0} phase difference of {lambda} {lambda} scattering from the analysis of Nagara events by Tamagaki et. al. This potential gives reasonable {sup 1}S{sub 0} phase difference of the {sigma}{sigma}{sup -} and {xi}{xi}{sup -} scattering. These results will be discussed in the research of heavy particle dark matter. From the spreading parameter {lambda}{sub B} of about 1800Me

  18. LITERATURE SURVEY ON EXISTING POWER SAVING ROUTING METHODS AND TECHNIQUES FOR INCREASING NETWORK LIFE TIME IN MANET

    Directory of Open Access Journals (Sweden)

    K Mariyappan

    2017-06-01

    Full Text Available Mobile ad hoc network (MANET is a special type of wireless network in which a collection of wireless mobile devices (called also nodes dynamically forming a temporary network without the need of any pre-existing network infrastructure or centralized administration. Currently, Mobile ad hoc networks (MANETs play a significant role in university campus, advertisement, emergency response, disaster recovery, military use in battle fields, disaster management scenarios, in sensor network, and so on. However, wireless network devices, especially in ad hoc networks, are typically battery-powered. Thus, energy efficiency is a critical issue for battery-powered mobile devices in ad hoc networks. This is due to the fact that failure of node or link allows re-routing and establishing a new path from source to destination which creates extra energy consumption of nodes and sparse network connectivity, leading to a more likelihood occurrences of network partition. Routing based on energy related parameters is one of the important solutions to extend the lifetime of the node and reduce energy consumption of the network. In this paper detail literature survey on existing energy efficient routing method are studied and compared for their performance under different condition. The result has shown that both the broadcast schemes and energy aware metrics have great potential in overcoming the broadcast storm problem associated with flooding. However, the performances of these approaches rely on either the appropriate selection of the broadcast decision parameter or an energy efficient path. In the earlier proposed broadcast methods, the forwarding probability is selected based on fixed probability or number of neighbors regardless of nodes battery capacity whereas in energy aware schemes energy inefficient node could be part of an established path. Therefore, in an attempt to remedy the paucity of research and to address the gaps identified in this area, a study

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

    Directory of Open Access Journals (Sweden)

    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.

  20. An analytic Study of the Key Factors In uencing the Design and Routing Techniques of a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Yogita Bahuguna

    2017-03-01

    Full Text Available A wireless sensor network contains various nodes having certain sensing, processing and communication capabilities. Actually they are multifunctional battery operated nodes called motes. These motes are small in size and battery constrained. They are operated by a power source. A wireless sensor network consists of a huge number of tiny sensor nodes which are deployed either randomly or according to some predefined distribution. The sensors nodes in a sensor network are cooperative among themselves having self-organizing ability. This ensures that a wireless network serves a wide variety of applications. Few of them are weather monitoring, health, security and military etc. As their applications are wide, this requires that sensors in a sensor network must play their role very efficiently. But, as discussed above, the sensor nodes have energy limitation. This limitation leads failure of nodes after certain round of communication. So, a sensor network suffers with sensors having energy limitations. Beside this, sensor nodes in a sensor network must fulfill connectivity and coverage requirements. In this paper, we have discussed various issues affecting the design of a wireless sensor network. This provides the readers various research issues in designing a wireless sensor network.

  1. Development of Ensemble Neural Network Convection Parameterizations for Climate Models

    Energy Technology Data Exchange (ETDEWEB)

    Fox-Rabinovitz, M. S.; Krasnopolsky, V. M.

    2012-05-02

    The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an

  2. Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

    Directory of Open Access Journals (Sweden)

    Chaudhari Narendra S

    2005-01-01

    Full Text Available We present an artificial neural-network- (NN- based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS operating in a wide temperature range of 0 to . Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS error of only over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU- based implementation scheme is also provided.

  3. Clinical, polysomnographic and genome-wide association analyses of narcolepsy with cataplexy: a European Narcolepsy Network study

    National Research Council Canada - National Science Library

    Luca, G. De; Haba-Rubio, J; Dauvilliers, Y; Lammers, G.J; Overeem, S; Donjacour, C.E; Mayer, G; Javidi, S; Iranzo, A; Santamaria, J; Peraita-Aados, R; Hor, H; Kutalik, Z; Plazzi, G; Poli, F; Pizza, F; Arnulf, I; Leceneux, M; Bassetti, C; Mathis, J; Heinzer, R; Jennum, P; Knudsen, S; Geisler, P; Wierzbicka, A; Feketeova, E; Pfister, C; Khatami, R; Baumann, C; Tafti, M

    2013-01-01

    The aim of this study was to describe the clinical and PSG characteristics of narcolepsy with cataplexy and their genetic predisposition by using the retrospective patient database of the European Narcolepsy Network (EU-NN...

  4. An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

    Science.gov (United States)

    Hippert, Henrique S; Taylor, James W

    2010-04-01

    Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

  5. Experimental study of the stress level at the workplace using an smart testbed of wireless sensor networks and ambient intelligence techniques

    OpenAIRE

    Silva, Fábio; Olivares, Teresa; Royo, Fernando; Vergara, M. A.; Analide, César

    2013-01-01

    "Natural and artificial computation in engineering and medical applications : 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, June 10-14, 2013. Proceedings, Part II", ISBN 978-364238621-3 This paper combines techniques of ambient intelligence and wireless sensor networks with the objective of obtain important conclusions to increase the quality of life of people. In particular, we oriented our study to the stress ...

  6. A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection.

    Science.gov (United States)

    Iliyasu, Abdullah M; Fatichah, Chastine

    2017-12-19

    A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles) that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

  7. 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.

  8. A Novel Approach for Transmission of 56 Gbit/s NRZ Signal in Access Network Using Spectrum Slicing Technique

    DEFF Research Database (Denmark)

    Spolitis, S.; Vegas Olmos, Juan José; Bobrovs, V.

    2013-01-01

    We present the spectrum slicing and stitching concept for high-capacity low optics complexity optical access networks. Spectrum slicing and stitching of a 56 Gbit/s NRZ electrical signal is experimentally demonstrated for the first time....

  9. Artificial neural networks in forecasting tourists’ flow, an intelligent technique to help the economic development of tourism in Albania.

    Directory of Open Access Journals (Sweden)

    Dezdemona Gjylapi

    2014-07-01

    The aim of this paper is to present the neural network usage in the tourists’ number forecasting and to determine the trends of the future tourist inflow, thus helping tourism management agencies in making scientific based financial decisions.

  10. Validation Techniques of network harmonic models based on switching of a series linear component and measuring resultant harmonic increments

    DEFF Research Database (Denmark)

    Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth

    2007-01-01

    In this paper two methods of validation of transmission network harmonic models are introduced. The methods were developed as a result of the work presented in [1]. The first method allows calculating the transfer harmonic impedance between two nodes of a network. Switching a linear, series network...... are used for calculation of the transfer harmonic impedance between the nodes. The determined transfer harmonic impedance can be used to validate a computer model of the network. The second method is an extension of the fist one. It allows switching a series element that contains a shunt branch...... component that links the nodes causes a variation of the background harmonic voltage distortion at the nodes. Harmonic current in the series element is measured together with the harmonic voltages at both nodes, before and after the switching. Obtained incremental values of harmonic voltages and the current...

  11. Using Expectation Maximization and Resource Overlap Techniques to Classify Species According to Their Niche Similarities in Mutualistic Networks

    Directory of Open Access Journals (Sweden)

    Hugo Fort

    2015-11-01

    Full Text Available Mutualistic networks in nature are widespread and play a key role in generating the diversity of life on Earth. They constitute an interdisciplinary field where physicists, biologists and computer scientists work together. Plant-pollinator mutualisms in particular form complex networks of interdependence between often hundreds of species. Understanding the architecture of these networks is of paramount importance for assessing the robustness of the corresponding communities to global change and management strategies. Advances in this problem are currently limited mainly due to the lack of methodological tools to deal with the intrinsic complexity of mutualisms, as well as the scarcity and incompleteness of available empirical data. One way to uncover the structure underlying complex networks is to employ information theoretical statistical inference methods, such as the expectation maximization (EM algorithm. In particular, such an approach can be used to cluster the nodes of a network based on the similarity of their node neighborhoods. Here, we show how to connect network theory with the classical ecological niche theory for mutualistic plant-pollinator webs by using the EM algorithm. We apply EM to classify the nodes of an extensive collection of mutualistic plant-pollinator networks according to their connection similarity. We find that EM recovers largely the same clustering of the species as an alternative recently proposed method based on resource overlap, where one considers each party as a consuming resource for the other party (plants providing food to animals, while animals assist the reproduction of plants. Furthermore, using the EM algorithm, we can obtain a sequence of successfully-refined classifications that enables us to identify the fine-structure of the ecological network and understand better the niche distribution both for plants and animals. This is an example of how information theoretical methods help to systematize and

  12. Entry Abort Determination Using Non-Adaptive Neural Networks for Mars Precision Landers

    Science.gov (United States)

    Graybeal, Sarah R.; Kranzusch, Kara M.

    2005-01-01

    The 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very

  13. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  14. Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000F2 ionospheric characteristics

    Directory of Open Access Journals (Sweden)

    T. D. Xenos

    2002-01-01

    Full Text Available In this work, Neural-Network-based single-station hourly daily foF2 and M(3000F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964- 1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000F2. The first foF2 model employs the E-layer local noon calculated daily critical frequency (foE12 and the local noon F2- layer critical frequency of the previous day. The second foF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12, and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000F2 model employs the E-layer local noon calculated daily critical frequency (foE12, its ± 3 h deviations and the local noon cosine of the solar zenith angle (cos c12. The second model, which introduces a new M(3000F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12, and the previous day F2-layer critical frequency measured at Juliusruh at noon.

  15. Alkene and Alkyne Reactivity Towards a Bisruthenium (II) µ2-Dinitrogen Complex Containing the 'Pincer' ligand 2,6-Bis[(dimethylamino)methyl]pyridine(NN'N). The X-ray Crystal Structures of [Ru(=C=CHPh)Cl2(NN'N)] and [Ru(=C=CHPh)(OTf)(NN'N)(PPh3)][OTf] (OTf =Trifluoromethane Sulfonate)

    NARCIS (Netherlands)

    Koten, G. van; Rio, I. del; Gossage, R.A.; Hannu, M.S.; Lutz, M.H.; Spek, A.L.

    1999-01-01

    The binuclear Ru(II) 2-dinitrogen complex [{RuCl2(3-NN'N)}2(-N2)] (1, NN'N = 2,6-bis[(dimethylamino)methyl]pyridine) is an excellent starting material for the synthesis of mononuclear 2-alkene complexes: mer,trans-[RuCl2(3-NN'N)(2-H2C=CHR)] (R = H, CH=CH2, Ph, CH2Ph, CH2Br, CH2OH, CHO, CN, CO2Me;

  16. NONLINEAR SYSTEM MODELING USING SINGLE NEURON CASCADED NEURAL NETWORK FOR REAL-TIME APPLICATIONS

    Directory of Open Access Journals (Sweden)

    S. Himavathi

    2012-04-01

    Full Text Available Neural Networks (NN have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationally less complex. In this paper a parametric method of analysis is adopted to determine the compact and faster NN model among various neural network architectures. This work proves through analysis and examples that the Single Neuron Cascaded (SNC architecture is distinct in providing compact and simpler models requiring lower execution time. The unique structural growth of SNC architecture enables automation in design. The SNC Network is shown to combine the advantages of both single and multilayer neural network architectures. Extensive analysis on selected architectures and their models for four benchmark nonlinear theoretical plants and a practical application are tested. A performance comparison of the NN models is presented to demonstrate the superiority of the single neuron cascaded architecture for online real time applications.

  17. 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.

  18. Techniques and materials used by general dentists during endodontic treatment procedures: Findings from The National Dental Practice-Based Research Network.

    Science.gov (United States)

    Eleazer, Paul D; Gilbert, Gregg H; Funkhouser, Ellen; Reams, Gregg J; Law, Alan S; Benjamin, Paul L

    2016-01-01

    Little is known about which materials and techniques general dentists (GDs) use during endodontic procedures. The objectives were to quantify GDs' use of specific endodontic tools, quantify inappropriate use, and ascertain whether inappropriate use is associated with GDs' practice characteristics. GDs in The National Dental Practice-Based Research Network reported in a questionnaire materials and techniques they use during endodontic procedures. Among eligible GDs, 1,490 (87%) participated. Most (93%; n = 1,383) used sodium hypochlorite to irrigate. The most commonly used sealers were zinc oxide eugenol (43%) and resin (40%), followed by calcium hydroxide (26%). Most (62%; n = 920) used a compaction obturation technique; 36% (n = 534) used a carrier-based method. Most (96%; n = 1,423) used gutta-percha as a filler; 5% used paste fillers. Few used irrigants (n = 46), techniques (n = 49), or fillers (n = 10) that investigators classified as inappropriate. GDs use a broad range of endodontic techniques and materials, often adapting to newer technologies as they become available. Few GDs use tools that the investigators classified as inappropriate. GDs use many types of endodontic techniques and materials, but only a small percentage of them are inappropriate. Copyright © 2016 American Dental Association. Published by Elsevier Inc. All rights reserved.

  19. Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at √sNN =2.76 TeV

    OpenAIRE

    Adam, Jaroslav; Adamová, Dagmar; Aggarwal, Madan M.; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Nikita; Ahammed, Zubayer; Ahn, Sang Un; Aimo, Ilaria; Aiola, Salvatore; Alme, Johan; Helstrup, Håvard; Hetland, Kristin Fanebust; Kileng, Bjarte; Altinpinar, Sedat

    2016-01-01

    We report on results obtained with the event-shape engineering technique applied to Pb-Pb collisions at √ s NN = 2.76 TeV. By selecting events in the same centrality interval, but with very different average flow, different initial-state conditions can be studied. We find the effect of the event-shape selection on the elliptic flow coefficient v2 to be almost independent of transverse momentum pT, which is as expected if this effect is attributable to fluctuations in the initial geometry of t...

  20. Study of Event-based Sampling Techniques and Their Influence on Greenhouse Climate Control with Wireless Sensors Network

    OpenAIRE

    Pawlowski, Andrzej; Guzman, Jose L.; Rodriguez, Francisco; Berenguel, Manuel; Sanchez, Jose; Dormido, Sebastian

    2010-01-01

    This paper presents a study of event-based sampling techniques and their application to the greenhouse climate control problem. It was possible to obtain important information about data transmission and control performance for all techniques. As conclusion, it was deduced

  1. Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

    Directory of Open Access Journals (Sweden)

    Sadik Kamel Gharghan

    2016-01-01

    Full Text Available In most wireless sensor network (WSN applications, the sensor nodes (SNs are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN using the received signal strength indicator (RSSI, while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm by 78%.

  2. Three Dimensional SRG Evolution of the NN Interaction Using Picard Iteration

    Science.gov (United States)

    Hadizadeh, M. R.; Wendt, K. A.; Elster, Ch.

    2014-09-01

    We solve the similarity renormalization group (SRG) flow equations in a Three Dimensional (3D) helicity representation (without partial wave decomposition) for realistic nucleon-nucleon (NN) interactions. During the 3D SRG evolution, the flow equations become extremely stiff for far off diagonal matrix elements (e.g. | k | >> |k' |). We alleviate this by transforming the differential form of the SRG flow equation into an integral equation that is solved using Picard iteration. The evolved NN interactions are obtained from realistic potentials by solving a single integral equation for total spin 0 and four coupled integral equations for total spin 1. We demonstrate the efficiency and accuracy of the Picard integral approach for the Bonn-B and Chiral-N2LO NN potentials. The successful 3D implementation paves the path to consider a 3D evolution of three-nucleon forces.

  3. Anion induced conformational preference of CαNN 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.

  4. Morphology of silver bromide crystals produced at presence of N,N -dimethylformamide

    Energy Technology Data Exchange (ETDEWEB)

    Dyonizy, A.; Nowak, P. [Institute of Physical and Theoretical Chemistry, Wroclaw University of Technology (Poland)

    2010-08-15

    The study deals with examination of conditions that are necessary to obtain flat crystals of silver bromide that grow in a water and gelatine crystallization environment where N,N -dimethylformamide is used as the agent that is conducive to complexing of sparingly soluble silver bromide. The examination focused on the issue how changes in volumetric concentration of N,N -dimethylformamide as well as concentration of excessive ions of silver bromide in the dispersive solution affect morphology and size of newly created of silver bromide. The completed experiments enabled to determine boundary limits of both N,N -dimethylformamide and bromide ions concentration where suspensions of silver bromide crystals exhibit predominant content of triangular, transient and hexagonal flat forms with very high aspect ratio. (copyright 2010 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  5. Adapting risk management and computational intelligence network optimization techniques to improve traffic throughput and tail risk analysis.

    Science.gov (United States)

    2014-04-01

    Risk management techniques are used to analyze fluctuations in uncontrollable variables and keep those fluctuations from impeding : the core function of a system or business. Examples of this are making sure that volatility in copper and aluminum pri...

  6. 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.

  7. Model dependence of unitary isobar model treatments of NN-->NNπ at intermediate energies

    Science.gov (United States)

    Dubach, J.; Kloet, W. M.; Silbar, Richard R.; Tjon, J. A.; van Faassen, E.

    1986-09-01

    We compare two ``conventional'' relativistic unitary isobar models for the NN-->NNπ reaction. One model, which preserves two- and three-body unitarity, contains only pion exchange, while the other, based on a two-body coupled-channels approach, includes heavier meson exchanges. Thus, we attempt to identify the model dependence of predictions for the NN-->NNπ process over a large part of final state phase space. Differential cross sections and a variety of spin observables are examined. Implications for the identification of exotic dibaryon resonances are briefly discussed.

  8. Isännöintitoimiston asiakasuskollisuuden vahvistaminen asiakaskokemusta kehittämällä

    OpenAIRE

    Kujanpää, Vesa

    2017-01-01

    Opinnäytetyön tarkoituksena oli selvittää, kuinka isännöintitoimiston asiakasuskollisuutta voidaan lisätä asiakaskokemusta kehittämällä. Kehittämistyössä tutkittiin, mitä on asiakasuskollisuus ja asiakaskokemus sekä millaiset asiat asiakaskokemuksessa vaikuttavat asiakasuskollisuuteen eli sitouttavat asiakasta palveluyritykseen. Lisäksi selvitettiin Pirkanmaan Ammatti-isännöinnin henkilöstön näkemyksiä yhtiön asiakaspalvelun nykytilanteesta. Opinnäytetyön lähestymistapana on ollut toimint...

  9. 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

    NN2211 is a long-acting, metabolically stable glucagon-like peptide-1 (GLP-1) derivative designed for once daily administration in humans. NN2211 dose dependently reduced the glycemic levels in ob/ob mice, with antihyperglycemic activity still evident 24 h postdose. Apart from an initial reduction...

  10. Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

    Science.gov (United States)

    Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

    2014-06-01

    This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

  11. Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao

    2012-04-01

    Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

  12. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

    Science.gov (United States)

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-09-05

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

  13. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

    Science.gov (United States)

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-01-01

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

  14. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

    Directory of Open Access Journals (Sweden)

    Rodrigo Hernández-Alvarado

    2016-09-01

    Full Text Available For decades, PID (Proportional + Integral + Derivative-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles where parameters (weight, buoyancy, added mass, among others change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

  15. 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.

  16. Network Coding

    Indian Academy of Sciences (India)

    Network coding is a technique to increase the amount of information °ow in a network by mak- ing the key observation that information °ow is fundamentally different from commodity °ow. Whereas, under traditional methods of opera- tion of data networks, intermediate nodes are restricted to simply forwarding their incoming.

  17. All-optical virtual private network system in OFDM based long-reach PON using RSOA re-modulation technique

    Science.gov (United States)

    Kim, Chang-Hun; Jung, Sang-Min; Kang, Su-Min; Han, Sang-Kook

    2015-01-01

    We propose an all-optical virtual private network (VPN) system in an orthogonal frequency division multiplexing (OFDM) based long reach PON (LR-PON). In the optical access network field, technologies based on fundamental upstream (U/S) and downstream (D/S) have been actively researched to accommodate explosion of data capacity. However, data transmission among the end users which is arisen from cloud computing, file-sharing and interactive game takes a large weight inside of internet traffic. Moreover, this traffic is predicted to increase more if Internet of Things (IoT) services are activated. In a conventional PON, VPN data is transmitted through ONU-OLT-ONU via U/S and D/S carriers. It leads to waste of bandwidth and energy due to O-E-O conversion in the OLT and round-trip propagation between OLT and remote node (RN). Also, it causes inevitable load to the OLT for electrical buffer, scheduling and routing. The network inefficiency becomes more critical in a LR-PON which has been researched as an effort to reduce CAPEX and OPEX through metro-access consolidation. In the proposed system, the VPN data is separated from conventional U/S and re-modulated on the D/S carrier by using RSOA in the ONUs to avoid bandwidth consumption of U/S and D/S unlike in previously reported system. Moreover, the transmitted VPN data is re-directed to the ONUs by wavelength selective reflector device in the RN without passing through the OLT. Experimental demonstration for the VPN communication system in an OFDM based LR-PON has been verified.

  18. Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

    Science.gov (United States)

    Lemeshewsky, G.P.; Schowengerdt, R.A.; ,

    2001-01-01

    The enhanced thematic mapper (plus) (ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significant improvements. In common are six multispectral bands with a 30-m ground-projected instantaneous field of view (GIFOV). However, the thermaL-IR (TIR) band now has a 60-m GIFOV, instead of 120-m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data from the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7, 5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TF) for Landsat 5 to simulate sensor coarser resolution imagery from higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size NNs (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further

  19. Integration of gene chip and topological network techniques to screen a candidate biomarker gene (CBG) for predication of the source water carcinogenesis risks on mouse Mus musculus.

    Science.gov (United States)

    Sun, Jie; Cheng, Shupei; Li, Aimin; Zhang, Rui; Wu, Bing; Zhang, Yan; Zhang, Xuxiang

    2011-07-01

    Screening of a candidate biomarker gene (CBG) to predicate the carcinogenesis risks in the Yangtze River source of drinking water in Nanjing area (YZR-SDW-NJ) on mouse (Mus musculus) was conducted in this research. The effects of YZR-SDW-NJ on the genomic transcriptional expression levels were measured by the GeneChip(®) Mouse Genome and data treated by the GO database analysis. The 298 genes discovered as the differently expressed genes (DEGs) were down-regulated and their values were ≤-1.5-fold. Of the 298 DEGs, 25 were cancer-related genes selected as the seed genes to build a topological network map with Genes2Networks software, only 7 of them occurred at the constructed map. Smad2 gene was at the constructed map center and could be identified as a candidate biomarker gene (CBG) primarily which involves the genesis and development of colorectal, leukemia, lung and prostate cancers directly. Analysis of the gene signal pathway further approved that smad2 gene had the relationships closely to other 16 cancer-related genes and could be used as a CBG to indicate the carcinogenic risks in YZR-SDW-NJ. The data suggest that integration of gene chip and network techniques may be a way effectively to screen a CBG. And the parameter values for further judgment of the CBG through signal pathway relationship analysis also will be discussed.

  20. Triple-Play and 60-GHz Radio-over-Fiber Techniques for Next-Generation Optical Access Networks

    DEFF Research Database (Denmark)

    Llorente, R.; Walker, S.; Tafur Monroy, Idelfonso

    2011-01-01

    Radio-over-fiber techniques apply to fiber-to-thehome distributions to reach the customer premises with the services to be received with full-standard low-cost equipment. Bi-directional coarse wavelength division multiplexing (CWDM) radio-over-fiber transmission of triple-format full-standard ort...

  1. Consistent circuit technique for zero-sequence currents evaluation in interconnected single/three-phase power networks

    Directory of Open Access Journals (Sweden)

    Diego Bellan

    2016-06-01

    Full Text Available This paper deals with a rigorous and mathematically consistent technique for circuit analysis of modern electrical power systems consisting in the interconnection of three-phase components and single-phase active loads. Indeed, it is well known that the standard technique based on the symmetrical components transformation is commonly used in the analysis of symmetrical threephase systems. Nowadays, however, the evolution of power systems towards the custom power conditioning (e.g., active filtering and the smart grid model requires the inclusion into the analytical tool of single-phase active loads. Starting from the symmetrical components transformation in its rational form instead of its classical form, a rigorous circuit representation of the interconnection of a three-phase system with single-phase active loads is derived in the paper. The proposed circuit representation allows the analysis of complex power systems by means of basic circuit techniques. In particular, the paper focuses on the evaluation of the zero-sequence component of the currents in any branch of the power system. The application of the proposed circuit technique is demonstrated through an example consisting in the analysis of an active filter designed to force to zero the current in the fourth wire of the mains.

  2. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  3. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing.

    Science.gov (United States)

    Taghadomi-Saberi, Saeedeh; Omid, Mahmoud; Emam-Djomeh, Zahra; Ahmadi, Hojjat

    2014-01-15

    This paper presents a versatile way for estimating antioxidant activity and anthocyanin content at different ripening stages of sweet cherry by combining image processing and two artificial intelligence (AI) techniques. In comparison with common time-consuming laboratory methods for determining these important attributes, this new way is economical and much faster. The accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models was studied to estimate the outputs. Sensitivity analysis and principal component analysis were used with ANN and ANFIS respectively to specify the most effective attributes on outputs. Among the designed ANNs, two hidden layer networks with 11-14-9-1 and 11-6-20-1 architectures had the highest correlation coefficients and lowest error values for modeling antioxidant activity (R = 0.93) and anthocyanin content (R = 0.98) respectively. ANFIS models with triangular and two-term Gaussian membership functions gave the best results for antioxidant activity (R = 0.87) and anthocyanin content (R = 0.90) respectively. Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes. © 2013 Society of Chemical Industry.

  4. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

    Science.gov (United States)

    Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  5. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

    Science.gov (United States)

    Naveros, Francisco; Garrido, Jesus A.; Carrillo, Richard R.; Ros, Eduardo; Luque, Niceto R.

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  6. Mechanical bone growth stimulation by magnetic fibre networks obtained through a competent finite element technique.

    Science.gov (United States)

    Bosbach, Wolfram A

    2017-09-11

    Fibre networks combined with a matrix material in their void phase make the design of novel and smart composite materials possible. Their application is of great interest in the field of advanced paper or as bioactive tissue engineering scaffolds. In the present study, we analyse the mechanical interaction between metallic fibre networks under magnetic actuation and a matrix material. Experimentally validated FE models are combined for that purpose in one joint simulation. High performance computing facilities are used. The resulting strain in the composite's matrix is not uniform across the sample volume. Instead we show that boundary conditions and proximity to the fibre structure strongly influence the local strain magnitude. An analytical model of local strain magnitude is derived. The strain magnitude of 0.001 which is of particular interest for bone growth stimulation is achievable by this assembly. In light of these findings, the investigated composite structure is suitable for creating and for regulating contactless a stress field which is to be imposed on the matrix material. Topics for future research will be the advanced modelling of the biological components and the potential medical utilisation.

  7. Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

    Science.gov (United States)

    Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

    2016-01-01

    The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

  8. Effects of temperature and CO2 pressure on the emission of N,N ...

    Indian Academy of Sciences (India)

    Effects of temperature and CO2 pressure on the emission of. N,N -dialkylated perylene diimides in poly(alkyl methacrylate) films. Are guest-host alkyl group interactions important? KIZHMURI P DIVYAa,b, MICHAEL J BERTOCCHIa and RICHARD G WEISSa,∗. aDepartment of Chemistry, Georgetown University, Washington ...

  9. Rapidity Dependence of Net-protons at sqrt(s_NN) = 200 GeV

    OpenAIRE

    Christiansen, Peter; collaboration, for the BRAHMS

    2002-01-01

    The BRAHMS collaboration has measured inclusive proton and anti-proton spectra at several rapidities (0 < y < 3) for Au+Au collisions at sqrt(s_NN) = 200 GeV. Rapidity densities of protons, anti-protons, and net-protons [N(p)-N(p-bar)] are presented as a function of rapidity.

  10. Three Dimensional SRG Evolution of the NN Interactions Using Picard Iteration

    Directory of Open Access Journals (Sweden)

    Hadizadeh M. R.

    2016-01-01

    Full Text Available The Similarity Renormalization Group (SRG evolution of nucleon-nucleon (NN interactions is calculated directly as function of momentum vectors for realistic potentials. To overcome the stiffness of the SRG flow equations in differential form for far off diagonal matrix elements, the differential equation is transformed to an integral form without employing a partial wave decomposition.

  11. Measurement of the gπNN(t) Form Factor

    Energy Technology Data Exchange (ETDEWEB)

    Vansyoc, Kelley Gene [Old Dominion Univ., Norfolk, VA (United States)

    2001-08-01

    Cross sections were measured for the reaction 1H(e, e' π+ )n at the energy W = 1.95 GeV and momentum transfer Q 2 = 0.6 (GeV/c) 2 . At this W and Q 2 , the longitudinal cross section is dominated by t-channel production, giving a unique opportunity to examine the strong coupling form factor g πNN(t). The measured cross sections were separated using a method similar to a Rosenbluth separation. For the extraction of g πNN (t), the Actor and Korner model [42] and a parameterization of the MAID2000 model [3] were employed to fit the longitudinal cross section. Three parameterizations gπNN(π) were used in both models. These fits resulted in a strong coupling constant gπNN(m$2\\atop{2}$) that is consistent with theoretical predictions. However, this coupling constant leads to a cutoff parameter that is less than 1 GeV.

  12. Pharmacokinetics of nevirapine: once-daily versus twice-daily dosing in the 2NN study

    NARCIS (Netherlands)

    Kappelhoff, Bregt S.; Huitema, Alwin D. R.; van Leth, Frank; Robinson, Patrick A.; MacGregor, Thomas R.; Lange, Joep M. A.; Beijnen, Jos H.

    2005-01-01

    OBJECTIVE: As part of the large international, randomized 2NN trial, the pharmacokinetics of nevirapine in once-daily 400 mg and twice-daily 200 mg dosing regimens were investigated. METHOD: Treatment-naive HIV-1-infected patients were randomized to receive nevirapine 400 mg once daily or 200 mg

  13. 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.

  14. compounds with N=N, C≡C or conjugated double-bonded systems

    Indian Academy of Sciences (India)

    N=N, C≡C or conjugated double-bonded systems. K C KUMARA SWAMY,* E BALARAMAN, M PHANI PAVAN, N N BHUVAN KUMAR,. K PRAVEEN KUMAR and N SATISH KUMAR. School of Chemistry, University of Hyderabad, Hyderabad 500 046 e-mail: kckssc@uohyd.ernet.in. Abstract. The diversity of products in the ...

  15. Synthesis of N-acylurea derivatives from carboxylic acids and N,N ...

    Indian Academy of Sciences (India)

    Synthesis of N-acylurea derivatives from carboxylic acids and. N,N -dialkyl carbodiimides in water. ALI RAMAZANIa,∗, FATEMEH ZEINALI NASRABADIb, ARAM REZAEIa,. MORTEZA ROUHANIc, HAMIDEH AHANKARd, PEGAH AZIMZADEH ASIABIa,. SANG WOO JOOe,∗. , KATARZYNA SLEPOKURAf and TADEUSZ LISf.

  16. Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

    Science.gov (United States)

    Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

    2007-01-01

    In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

  17. Practical recommendations for fertility preservation in women by the FertiPROTEKT network. Part II: fertility preservation techniques.

    Science.gov (United States)

    von Wolff, Michael; Germeyer, A; Liebenthron, J; Korell, M; Nawroth, F

    2018-01-01

    In addition to guidelines focusing on scientific evidence, practical recommendations on fertility preservation are also needed. A selective literature search was performed based on the clinical and scientific experience of the authors. This article (Part II) focuses on fertility preservation techniques. Part I, also published in this journal, provides information on disease prognosis, disease-specific therapy, and risks for loss of fertility. Ovarian stimulation including double stimulation and freezing of oocytes is the best-established therapy providing live birth chances in women good ovarian, if spontaneous conception is favoured and if preservation in women provides realistic chances of becoming pregnant. The choice of technique needs to be based on the time required, the woman's age, its risks and efficacy, and the individual preference of the patient.

  18. Neural network-based motion control of an underactuated wheeled inverted pendulum model.

    Science.gov (United States)

    Yang, Chenguang; Li, Zhijun; Cui, Rongxin; Xu, Bugong

    2014-11-01

    In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second order subsystem Σa consisting of planar movement of vehicle forward and yaw angular motions, and a nonactuated first order subsystem Σb of pendulum motion. Due to the unknown dynamics of subsystem Σa and the universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa . The model reference approach has been used whereas the reference model is optimized by the finite time linear quadratic regulation technique. The pendulum motion in the passive subsystem Σb is indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa , such that satisfactory tracking of a set pendulum tilt angle can be guaranteed. Rigours theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.

  19. Survey of WBSNs for Pre-Hospital Assistance: Trends to Maximize the Network Lifetime and Video Transmission Techniques.

    Science.gov (United States)

    Gonzalez, Enrique; Peña, Raul; Vargas-Rosales, Cesar; Avila, Alfonso; de Cerio, David Perez-Diaz

    2015-05-22

    This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN.

  20. Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques.

    Science.gov (United States)

    Gil, Carmen; Dorronsoro, Isabel; Castro, Ana; Martinez, Ana

    2005-04-01

    The current drugs available against human cytomegalovirus (HCMV) suffer from a number of shortcomings such as toxic side effect, poor bioavailability and/or risk for emergence of drug-resistance virus strains. Due to these limitations, the development of new drugs against HCMV is of great interest. Taking into account the therapeutic potential of benzothiadiazines dioxides (BTD) derivatives, it is most important to know their oral bioavailability because all the current clinical drugs are poorly absorbed. In this work, the utility of CODES neural networks and biopartitioning micellar chromatography (BMC) in predicting pharmacokinetic properties has been used to estimate the oral absorption of BTD derivatives and their efficacy has been verified. The results indicate higher values for BTD derivatives than the currently licensed anti-viral agents.

  1. Mapping the optimal forest road network based on the multicriteria evaluation technique: the case study of Mediterranean Island of Thassos in Greece.

    Science.gov (United States)

    Tampekis, Stergios; Sakellariou, Stavros; Samara, Fani; Sfougaris, Athanassios; Jaeger, Dirk; Christopoulou, Olga

    2015-11-01

    The sustainable management of forest resources can only be achieved through a well-organized road network designed with the optimal spatial planning and the minimum environmental impacts. This paper describes the spatial layout mapping for the optimal forest road network and the environmental impacts evaluation that are caused to the natural environment based on the multicriteria evaluation (MCE) technique at the Mediterranean island of Thassos in Greece. Data analysis and its presentation are achieved through a spatial decision support system using the MCE method with the contribution of geographic information systems (GIS). With the use of the MCE technique, we evaluated the human impact intensity to the forest ecosystem as well as the ecosystem's absorption from the impacts that are caused from the forest roads' construction. For the human impact intensity evaluation, the criteria that were used are as follows: the forest's protection percentage, the forest road density, the applied skidding means (with either the use of tractors or the cable logging systems in timber skidding), the timber skidding direction, the visitors' number and truck load, the distance between forest roads and streams, the distance between forest roads and the forest boundaries, and the probability that the forest roads are located on sights with unstable soils. In addition, for the ecosystem's absorption evaluation, we used forestry, topographical, and social criteria. The recommended MCE technique which is described in this study provides a powerful, useful, and easy-to-use implement in order to combine the sustainable utilization of natural resources and the environmental protection in Mediterranean ecosystems.

  2. Architectural Techniques for Improving the Power Consumption of NoC-Based CMPs: A Case Study of Cache and Network Layer

    Directory of Open Access Journals (Sweden)

    Emmanuel Ofori-Attah

    2017-05-01

    Full Text Available The disparity between memory and CPU have been ameliorated by the introduction of Network-on-Chip-based Chip-Multiprocessors (NoC-based CMPS. However, power consumption continues to be an aggressive stumbling block halting the progress of technology. Miniaturized transistors invoke many-core integration at the cost of high power consumption caused by the components in NoC-based CMPs; particularly caches and routers. If NoC-based CMPs are to be standardised as the future of technology design, it is imperative that the power demands of its components are optimized. Much research effort has been put into finding techniques that can improve the power efficiency for both cache and router architectures. This work presents a survey of power-saving techniques for efficient NoC designs with a focus on the cache and router components, such as the buffer and crossbar. Nonetheless, the aim of this work is to compile a quick reference guide of power-saving techniques for engineers and researchers.

  3. Anti arthritic and anti inflammatory activity of a cytotoxic protein NN-32 from Indian spectacle cobra (Naja naja) venom in male albino rats.

    Science.gov (United States)

    Gomes, Antony; Datta, Poulami; Das, Tanaya; Biswas, Ajoy Kumar; Gomes, Aparna

    2014-11-01

    The anti arthritic and anti inflammatory activity of NN-32, a cytotoxic protein from Indian spectacle cobra snake (Naja naja) venom has been studied in Freund's complete adjuvant (FCA) induced arthritis and carrageenan induced anti inflammatory model. NN-32 treatment showed significant decrease in physical and urinary parameters, serum enzymes, serum cytokines levels as compared to arthritic control group of rats. NN-32 treatment recovered carrageenan induced inflammation as compared to control group of rats. The findings showed that the cytotoxic protein NN-32 shares anti arthritic and anti inflammatory activity and thus NN-32 may target complex pathophysiological processes like cancer- arthritis-inflammation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Innovative energy resourceful merged layer technique (MLT of node deployment to enhance the lifetime of wireless sensor networks

    Directory of Open Access Journals (Sweden)

    S.G. Susila

    2015-03-01

    Full Text Available A wireless sensor network (WSN is consisting of anthology of large number of small sensor nodes which are deployed in a defined area to observe the surroundings parameters. Since, energy consumption is significant challenge in WSN. As sensor nodes are equipped with battery which has limited energy. Energy efficient information processing is most importance for many routing protocols were proposed to increase the lifetime of WSN. In order to improve the lifetime of WSN, the proposed MLT routing protocol has implemented where the sensor nodes are randomly deployed in the field. The merged layer node deployment pattern of the sensor nodes system operation maximizes the working time of full coverage in a given WSN. MLT provides energy-balancing while selecting cluster head (CH for each round. The cluster head selection mechanism is essential and has same procedure like Low Energy Adaptive Clustering Hierarchy (LEACH in MLT protocol. The main idea of this paper is combine two layers of sensor nodes which are belonging to the same set but in different group to improve the lifetime of WSN. MATLAB simulations are performed to analyze and compare the performance of MLT with LEACH protocol. The obtained simulation output has enhanced results and superfluous lifetime compared to other protocols.

  5. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

    Science.gov (United States)

    Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M

    2014-05-07

    Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results

  6. Representing molecule-surface interactions with symmetry-adapted neural networks.

    Science.gov (United States)

    Behler, Jörg; Lorenz, Sönke; Reuter, Karsten

    2007-07-07

    The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energy surface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.

  7. Assessment of Climatological Trends of Sea Level over the Indian Coast Using Artificial Neural Network and Wavelet Techniques

    Science.gov (United States)

    Sudha Rani, N. N. V.; Satyanarayana, A. N. V.; Bhaskaran, Prasad Kumar

    2017-04-01

    In the present study, an attempt has been made to understand the variability of mean sea level (MSL) over east and west coast of India during 1973-2010. For this purpose, the monthly tide gauge data available over Kandla, Mumbai and Cochin along west coast and Diamond Harbour, Haldia, Visakhapatnam and Chennai along east coast obtained from PSMSL data archives has been considered. Sea level data from the tide gauge records show loss of data due to any disfunctioning of equipment or upgrade of the tide gauge resulting loss of data. It requires no gaps in the time series of MSL during the study period, and needs to be filled with better accuracy and hence artificial neural networks was implemented. To examine any periodicities of MSL variability, continuous wavelet analysis was conducted. The interrelationships between the stations in time-frequency space were examined, using cross and coherence wavelet analysis as well. The study reveals notable interannual variability of MSL. An observational analysis was done to understand the relation between inter-annual variability of MSL anomalies and ENSO. During positive (negative) SOI as associated with positive (negative) MSL anomaly was noticed significantly for the winter season over east (west) coast, where as during post-monsoon season this was observed for east coast and is less prevalent along the west coast. The observational analysis revealed that for the west (east) coast positive IOD showed significantly increased (decreased) MSL anomalies and negative IOD showed significantly decreased (increased) MSL anomalies. It is also found that the concurrent ENSO and IOD may have a different impact on MSL. The observations also reveal an increase of 1.353 mm/year on the east coast and observed a total 0.372 mm/year on the west coast.

  8. Detection of breast cancer using advanced techniques of data mining with neural networks; Deteccion de cancer de mama usando tecnicas avanzadas de mineria de datos con redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz M, J. A.; Celaya P, J. M.; Martinez B, M. R.; Solis S, L. O.; Castaneda M, R.; Garza V, I.; Martinez F, M.; Lopez H, Y.; Ortiz R, J. M. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico)

    2016-10-15

    The breast cancer is one of the biggest health problems worldwide, is the most diagnosed cancer in women and prevention seems impossible since its cause is unknown, due to this; the early detection has a key role in the patient prognosis. In developing countries such as Mexico, where access to specialized health services is minimal, the regular clinical review is infrequent and there are not enough radiologists; the most common form of detection of breast cancer is through self-exploration, but this is only detected in later stages, when is already palpable. For these reasons, the objective of the present work is the creation of a system of computer assisted diagnosis (CAD x) using information analysis techniques such as data mining and advanced techniques of artificial intelligence, seeking to offer a previous medical diagnosis or a second opinion, as if it was a second radiologist in order to reduce the rate of mortality from breast cancer. In this paper, advances in the design of computational algorithms using computer vision techniques for the extraction of features derived from mammograms are presented. Using data mining techniques of data mining is possible to identify patients with a high risk of breast cancer. With the information obtained from the mammography analysis, the objective in the next stage will be to establish a methodology for the generation of imaging bio-markers to establish a breast cancer risk index for Mexican patients. In this first stage we present results of the classification of patients with high and low risk of suffering from breast cancer using neural networks. (Author)

  9. Assessment of neural networks performance in modeling rainfall ...

    African Journals Online (AJOL)

    This paper presents the evaluation of performance of Neural Network (NN) model in predicting the behavioral pattern of rainfall depths of some locations in the North Central zones of Nigeria. The input to the model is the consecutive rainfall depths data obtained from the Nigerian Meteorological (NiMET) Agency. The neural ...

  10. 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 ...

  11. Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

    Science.gov (United States)

    Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

    2014-12-01

    This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

  12. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2017-06-01

    Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.

  13. Measurement of the azimuthal anisotropy of neutral pions in PbPb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

    CERN Document Server

    Chatrchyan, Serguei; Sirunyan, Albert M; Tumasyan, Armen; Adam, Wolfgang; Aguilo, Ernest; Bergauer, Thomas; Dragicevic, Marko; Erö, Janos; Fabjan, Christian; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Hammer, Josef; Hörmann, Natascha; Hrubec, Josef; Jeitler, Manfred; Kiesenhofer, Wolfgang; Knünz, Valentin; Krammer, Manfred; Krätschmer, Ilse; Liko, Dietrich; Mikulec, Ivan; Pernicka, Manfred; Rahbaran, Babak; Rohringer, Christine; Rohringer, Herbert; Schöfbeck, Robert; Strauss, Josef; Taurok, Anton; Waltenberger, Wolfgang; Walzel, Gerhard; Widl, Edmund; Wulz, Claudia-Elisabeth; Mossolov, Vladimir; Shumeiko, Nikolai; Suarez Gonzalez, Juan; Bansal, Sunil; Cornelis, Tom; De Wolf, Eddi A; Janssen, Xavier; Luyckx, Sten; Mucibello, Luca; Ochesanu, Silvia; Roland, Benoit; Rougny, Romain; Selvaggi, Michele; Staykova, Zlatka; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Van Spilbeeck, Alex; Blekman, Freya; Blyweert, Stijn; D'Hondt, Jorgen; Gonzalez Suarez, Rebeca; Kalogeropoulos, Alexis; Maes, Michael; Olbrechts, Annik; Van Doninck, Walter; Van Mulders, Petra; Van Onsem, Gerrit Patrick; Villella, Ilaria; Clerbaux, Barbara; De Lentdecker, Gilles; Dero, Vincent; Gay, Arnaud; Hreus, Tomas; Léonard, Alexandre; Marage, Pierre Edouard; Reis, Thomas; Thomas, Laurent; Vander Marcken, Gil; Vander Velde, Catherine; Vanlaer, Pascal; Wang, Jian; Adler, Volker; Beernaert, Kelly; Cimmino, Anna; Costantini, Silvia; Garcia, Guillaume; Grunewald, Martin; Klein, Benjamin; Lellouch, Jérémie; Marinov, Andrey; Mccartin, Joseph; Ocampo Rios, Alberto Andres; Ryckbosch, Dirk; Strobbe, Nadja; Thyssen, Filip; Tytgat, Michael; Verwilligen, Piet; Walsh, Sinead; Yazgan, Efe; Zaganidis, Nicolas; Basegmez, Suzan; Bruno, Giacomo; Castello, Roberto; Ceard, Ludivine; Delaere, Christophe; Du Pree, Tristan; Favart, Denis; Forthomme, Laurent; Giammanco, Andrea; Hollar, Jonathan; Lemaitre, Vincent; Liao, Junhui; Militaru, Otilia; Nuttens, Claude; Pagano, Davide; Pin, Arnaud; Piotrzkowski, Krzysztof; Schul, Nicolas; Vizan Garcia, Jesus Manuel; Beliy, Nikita; Caebergs, Thierry; Daubie, Evelyne; Hammad, Gregory Habib; Alves, Gilvan; Correa Martins Junior, Marcos; De Jesus Damiao, Dilson; Martins, Thiago; Pol, Maria Elena; Henrique Gomes E Souza, Moacyr; Aldá Júnior, Walter Luiz; Carvalho, Wagner; Custódio, Analu; Melo Da Costa, Eliza; De Oliveira Martins, Carley; Fonseca De Souza, Sandro; Matos Figueiredo, Diego; Mundim, Luiz; Nogima, Helio; Oguri, Vitor; Prado Da Silva, Wanda Lucia; Santoro, Alberto; Soares Jorge, Luana; Sznajder, Andre; Souza Dos Anjos, Tiago; Bernardes, Cesar Augusto; De Almeida Dias, Flavia; Tomei, Thiago; De Moraes Gregores, Eduardo; Lagana, Caio; Da Cunha Marinho, Franciole; Mercadante, Pedro G; Novaes, Sergio F; Padula, Sandra; Genchev, Vladimir; Iaydjiev, Plamen; Piperov, Stefan; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Tcholakov, Vanio; Trayanov, Rumen; Vutova, Mariana; Dimitrov, Anton; Hadjiiska, Roumyana; Kozhuharov, Venelin; Litov, Leander; Pavlov, Borislav; Petkov, Peicho; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Jiang, Chun-Hua; Liang, Dong; Liang, Song; Meng, Xiangwei; Tao, Junquan; Wang, Jian; Wang, Xianyou; Wang, Zheng; Xiao, Hong; Xu, Ming; Zang, Jingjing; Zhang, Zhen; Asawatangtrakuldee, Chayanit; Ban, Yong; Guo, Shuang; Guo, Yifei; Li, Wenbo; Liu, Shuai; Mao, Yajun; Qian, Si-Jin; Teng, Haiyun; Wang, Dayong; Zhang, Linlin; Zhu, Bo; Zou, Wei; Avila, Carlos; Gomez, Juan Pablo; Gomez Moreno, Bernardo; Osorio Oliveros, Andres Felipe; Sanabria, Juan Carlos; Godinovic, Nikola; Lelas, Damir; Plestina, Roko; Polic, Dunja; Puljak, Ivica; Antunovic, Zeljko; Kovac, Marko; Brigljevic, Vuko; Duric, Senka; Kadija, Kreso; Luetic, Jelena; Morovic, Srecko; Attikis, Alexandros; Galanti, Mario; Mavromanolakis, Georgios; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Finger, Miroslav; Finger Jr, Michael; Assran, Yasser; Elgammal, Sherif; Ellithi Kamel, Ali; Khalil, Shaaban; Mahmoud, Mohammed; Radi, Amr; Kadastik, Mario; Müntel, Mait; Raidal, Martti; Rebane, Liis; Tiko, Andres; Eerola, Paula; Fedi, Giacomo; Voutilainen, Mikko; Härkönen, Jaakko; Heikkinen, Mika Aatos; Karimäki, Veikko; Kinnunen, Ritva; Kortelainen, Matti J; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Mäenpää, Teppo; Peltola, Timo; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Ungaro, Donatella; Wendland, Lauri; Banzuzi, Kukka; Karjalainen, Ahti; Korpela, Arja; Tuuva, Tuure; Besancon, Marc; Choudhury, Somnath; Dejardin, Marc; Denegri, Daniel; Fabbro, Bernard; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Locci, Elizabeth; Malcles, Julie; Millischer, Laurent; Nayak, Aruna; Rander, John; Rosowsky, André; Shreyber, Irina; Titov, Maksym; Baffioni, Stephanie; Beaudette, Florian; Benhabib, Lamia; Bianchini, Lorenzo; Bluj, Michal; Broutin, Clementine; Busson, Philippe; Charlot, Claude; Daci, Nadir; Dahms, Torsten; Dobrzynski, Ludwik; Granier de Cassagnac, Raphael; Haguenauer, Maurice; Miné, Philippe; Mironov, Camelia; Naranjo, Ivo Nicolas; Nguyen, Matthew; Ochando, Christophe; Paganini, Pascal; Sabes, David; Salerno, Roberto; Sirois, Yves; Veelken, Christian; Zabi, Alexandre; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Bodin, David; Brom, Jean-Marie; Cardaci, Marco; Chabert, Eric Christian; Collard, Caroline; Conte, Eric; Drouhin, Frédéric; Ferro, Cristina; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Juillot, Pierre; Le Bihan, Anne-Catherine; Van Hove, Pierre; Fassi, Farida; Mercier, Damien; Beauceron, Stephanie; Beaupere, Nicolas; Bondu, Olivier; Boudoul, Gaelle; Chasserat, Julien; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Fay, Jean; Gascon, Susan; Gouzevitch, Maxime; Ille, Bernard; Kurca, Tibor; Lethuillier, Morgan; Mirabito, Laurent; Perries, Stephane; Sordini, Viola; Tschudi, Yohann; Verdier, Patrice; Viret, Sébastien; Tsamalaidze, Zviad; Anagnostou, Georgios; Beranek, Sarah; Edelhoff, Matthias; Feld, Lutz; Heracleous, Natalie; Hindrichs, Otto; Jussen, Ruediger; Klein, Katja; Merz, Jennifer; Ostapchuk, Andrey; Perieanu, Adrian; Raupach, Frank; Sammet, Jan; Schael, Stefan; Sprenger, Daniel; Weber, Hendrik; Wittmer, Bruno; Zhukov, Valery; Ata, Metin; Caudron, Julien; Dietz-Laursonn, Erik; Duchardt, Deborah; Erdmann, Martin; Fischer, Robert; Güth, Andreas; Hebbeker, Thomas; Heidemann, Carsten; Hoepfner, Kerstin; Klingebiel, Dennis; Kreuzer, Peter; Magass, Carsten; Merschmeyer, Markus; Meyer, Arnd; Olschewski, Mark; Papacz, Paul; Pieta, Holger; Reithler, Hans; Schmitz, Stefan Antonius; Sonnenschein, Lars; Steggemann, Jan; Teyssier, Daniel; Weber, Martin; Bontenackels, Michael; Cherepanov, Vladimir; Flügge, Günter; Geenen, Heiko; Geisler, Matthias; Haj Ahmad, Wael; Hoehle, Felix; Kargoll, Bastian; Kress, Thomas; Kuessel, Yvonne; Nowack, Andreas; Perchalla, Lars; Pooth, Oliver; Sauerland, Philip; Stahl, Achim; Aldaya Martin, Maria; Behr, Joerg; Behrenhoff, Wolf; Behrens, Ulf; Bergholz, Matthias; Bethani, Agni; Borras, Kerstin; Burgmeier, Armin; Cakir, Altan; Calligaris, Luigi; Campbell, Alan; Castro, Elena; Costanza, Francesco; Dammann, Dirk; Diez Pardos, Carmen; Eckerlin, Guenter; Eckstein, Doris; Flucke, Gero; Geiser, Achim; Glushkov, Ivan; Gunnellini, Paolo; Habib, Shiraz; Hauk, Johannes; Hellwig, Gregor; Jung, Hannes; Kasemann, Matthias; Katsas, Panagiotis; Kleinwort, Claus; Kluge, Hannelies; Knutsson, Albert; Krämer, Mira; Krücker, Dirk; Kuznetsova, Ekaterina; Lange, Wolfgang; Lohmann, Wolfgang; Lutz, Benjamin; Mankel, Rainer; Marfin, Ihar; Marienfeld, Markus; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mnich, Joachim; Mussgiller, Andreas; Naumann-Emme, Sebastian; Olzem, Jan; Perrey, Hanno; Petrukhin, Alexey; Pitzl, Daniel; Raspereza, Alexei; Ribeiro Cipriano, Pedro M; Riedl, Caroline; Ron, Elias; Rosin, Michele; Salfeld-Nebgen, Jakob; Schmidt, Ringo; Schoerner-Sadenius, Thomas; Sen, Niladri; Spiridonov, Alexander; Stein, Matthias; Walsh, Roberval; Wissing, Christoph; Autermann, Christian; Blobel, Volker; Draeger, Jula; Enderle, Holger; Erfle, Joachim; Gebbert, Ulla; Görner, Martin; Hermanns, Thomas; Höing, Rebekka Sophie; Kaschube, Kolja; Kaussen, Gordon; Kirschenmann, Henning; Klanner, Robert; Lange, Jörn; Mura, Benedikt; Nowak, Friederike; Peiffer, Thomas; Pietsch, Niklas; Rathjens, Denis; Sander, Christian; Schettler, Hannes; Schleper, Peter; Schlieckau, Eike; Schmidt, Alexander; Schröder, Matthias; Schum, Torben; Seidel, Markus; Sola, Valentina; Stadie, Hartmut; Steinbrück, Georg; Thomsen, Jan; Vanelderen, Lukas; Barth, Christian; Berger, Joram; Böser, Christian; Chwalek, Thorsten; De Boer, Wim; Descroix, Alexis; Dierlamm, Alexander; Feindt, Michael; Guthoff, Moritz; Hackstein, Christoph; Hartmann, Frank; Hauth, Thomas; Heinrich, Michael; Held, Hauke; Hoffmann, Karl-Heinz; Honc, Simon; Katkov, Igor; Komaragiri, Jyothsna Rani; Lobelle Pardo, Patricia; Martschei, Daniel; Mueller, Steffen; Müller, Thomas; Niegel, Martin; Nürnberg, Andreas; Oberst, Oliver; Oehler, Andreas; Ott, Jochen; Quast, Gunter; Rabbertz, Klaus; Ratnikov, Fedor; Ratnikova, Natalia; Röcker, Steffen; Scheurer, Armin; Schilling, Frank-Peter; Schott, Gregory; Simonis, Hans-Jürgen; Stober, Fred-Markus Helmut; Troendle, Daniel; Ulrich, Ralf; Wagner-Kuhr, Jeannine; Wayand, Stefan; Weiler, Thomas; Zeise, Manuel; Daskalakis, Georgios; Geralis, Theodoros; Kesisoglou, Stilianos; Kyriakis, Aristotelis; Loukas, Demetrios; Manolakos, Ioannis; Markou, Athanasios; Markou, Christos; Mavrommatis, Charalampos; Ntomari, Eleni; Gouskos, Loukas; Mertzimekis, Theodoros; Panagiotou, Apostolos; Saoulidou, Niki; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Patras, Vaios; Bencze, Gyorgy; Hajdu, Csaba; Hidas, Pàl; Horvath, Dezso; Sikler, Ferenc; Veszpremi, Viktor; Vesztergombi, Gyorgy; Beni, Noemi; Czellar, Sandor; Molnar, Jozsef; Palinkas, Jozsef; Szillasi, Zoltan; Karancsi, János; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Beri, Suman Bala; Bhatnagar, Vipin; Dhingra, Nitish; Gupta, Ruchi; Bansal, Monika; Kaur, Manjit; Mehta, Manuk Zubin; Nishu, Nishu; Saini, Lovedeep Kaur; Sharma, Archana; Singh, Jasbir; Kumar, Ashok; Kumar, Arun; Ahuja, Sudha; Bhardwaj, Ashutosh; Choudhary, Brajesh C; Malhotra, Shivali; Naimuddin, Md; Ranjan, Kirti; Sharma, Varun; Shivpuri, Ram Krishen; Banerjee, Sunanda; Bhattacharya, Satyaki; Dutta, Suchandra; Gomber, Bhawna; Jain, Sandhya; Jain, Shilpi; Khurana, Raman; Sarkar, Subir; Sharan, Manoj; Abdulsalam, Abdulla; Choudhury, Rajani Kant; Dutta, Dipanwita; Kailas, Swaminathan; Kumar, Vineet; Mehta, Pourus; Mohanty, Ajit Kumar; Pant, Lalit Mohan; Shukla, Prashant; Aziz, Tariq; Ganguly, Sanmay; Guchait, Monoranjan; Maity, Manas; Majumder, Gobinda; Mazumdar, Kajari; Mohanty, Gagan Bihari; Parida, Bibhuti; Sudhakar, Katta; Wickramage, Nadeesha; Banerjee, Sudeshna; Dugad, Shashikant; Arfaei, Hessamaddin; Bakhshiansohi, Hamed; Etesami, Seyed Mohsen; Fahim, Ali; Hashemi, Majid; Hesari, Hoda; Jafari, Abideh; Khakzad, Mohsen; Mohammadi Najafabadi, Mojtaba; Paktinat Mehdiabadi, Saeid; Safarzadeh, Batool; Zeinali, Maryam; Abbrescia, Marcello; Barbone, Lucia; Calabria, Cesare; Chhibra, Simranjit Singh; Colaleo, Anna; Creanza, Donato; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Lusito, Letizia; Maggi, Giorgio; Maggi, Marcello; Marangelli, Bartolomeo; My, Salvatore; Nuzzo, Salvatore; Pacifico, Nicola; Pompili, Alexis; Pugliese, Gabriella; Selvaggi, Giovanna; Silvestris, Lucia; Singh, Gurpreet; Venditti, Rosamaria; Zito, Giuseppe; Abbiendi, Giovanni; Benvenuti, Alberto; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Grandi, Claudio; Guiducci, Luigi; Marcellini, Stefano; Masetti, Gianni; Meneghelli, Marco; Montanari, Alessandro; Navarria, Francesco; Odorici, Fabrizio; Perrotta, Andrea; Primavera, Federica; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gian Piero; Travaglini, Riccardo; Albergo, Sebastiano; Cappello, Gigi; Chiorboli, Massimiliano; Costa, Salvatore; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Frosali, Simone; Gallo, Elisabetta; Gonzi, Sandro; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Tropiano, Antonio; Benussi, Luigi; Bianco, Stefano; Colafranceschi, Stefano; Fabbri, Franco; Piccolo, Davide; Fabbricatore, Pasquale; Musenich, Riccardo; Tosi, Silvano; Benaglia, Andrea; De Guio, Federico; Di Matteo, Leonardo; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Martelli, Arabella; Massironi, Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Ragazzi, Stefano; Redaelli, Nicola; Sala, Silvano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Carrillo Montoya, Camilo Andres; Cavallo, Nicola; De Cosa, Annapaola; Dogangun, Oktay; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lista, Luca; Meola, Sabino; Merola, Mario; Paolucci, Pierluigi; Azzi, Patrizia; Bacchetta, Nicola; Bellan, Paolo; Bisello, Dario; Branca, Antonio; Checchia, Paolo; Dorigo, Tommaso; Dosselli, Umberto; Gasparini, Fabrizio; Gozzelino, Andrea; Gulmini, Michele; Kanishchev, Konstantin; Lacaprara, Stefano; Lazzizzera, Ignazio; Margoni, Martino; Maron, Gaetano; Meneguzzo, Anna Teresa; Nespolo, Massimo; Pazzini, Jacopo; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Vanini, Sara; Zotto, Pierluigi; Zumerle, Gianni; Gabusi, Michele; Ratti, Sergio P; Riccardi, Cristina; Torre, Paola; Vitulo, Paolo; Biasini, Maurizio; Bilei, Gian Mario; Fanò, Livio; Lariccia, Paolo; Lucaroni, Andrea; Mantovani, Giancarlo; Menichelli, Mauro; Nappi, Aniello; Romeo, Francesco; Saha, Anirban; Santocchia, Attilio; Spiezia, Aniello; Taroni, Silvia; Azzurri, Paolo; Bagliesi, Giuseppe; Boccali, Tommaso; Broccolo, Giuseppe; Castaldi, Rino; D'Agnolo, Raffaele Tito; Dell'Orso, Roberto; Fiori, Francesco; Foà, Lorenzo; Giassi, Alessandro; Kraan, Aafke; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Rizzi, Andrea; Serban, Alin Titus; Spagnolo, Paolo; Squillacioti, Paola; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Del Re, Daniele; Diemoz, Marcella; Fanelli, Cristiano; Grassi, Marco; Longo, Egidio; Meridiani, Paolo; Micheli, Francesco; Nourbakhsh, Shervin; Organtini, Giovanni; Paramatti, Riccardo; Rahatlou, Shahram; Sigamani, Michael; Soffi, Livia; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Biino, Cristina; Cartiglia, Nicolo; Costa, Marco; Dattola, Domenico; Demaria, Natale; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Musich, Marco; Obertino, Maria Margherita; Pastrone, Nadia; Pelliccioni, Mario; Potenza, Alberto; Romero, Alessandra; Sacchi, Roberto; Solano, Ada; Staiano, Amedeo; Vilela Pereira, Antonio; Belforte, Stefano; Candelise, Vieri; Cossutti, Fabio; Della Ricca, Giuseppe; Gobbo, Benigno; Marone, Matteo; Montanino, Damiana; Penzo, Aldo; Schizzi, Andrea; Heo, Seong Gu; Kim, Tae Yeon; Nam, Soon-Kwon; Chang, Sunghyun; Kim, Dong Hee; Kim, Gui Nyun; Kong, Dae Jung; Park, Hyangkyu; Ro, Sang-Ryul; Son, Dong-Chul; Son, Taejin; Kim, Jae Yool; Kim, Zero Jaeho; Song, Sanghyeon; Choi, Suyong; Gyun, Dooyeon; Hong, Byung-Sik; Jo, Mihee; Kim, Hyunchul; Kim, Tae Jeong; Lee, Kyong Sei; Moon, Dong Ho; Park, Sung Keun; Choi, Minkyoo; Kim, Ji Hyun; Park, Chawon; Park, Inkyu; Park, Sangnam; Ryu, Geonmo; Cho, Yongjin; Choi, Young-Il; Choi, Young Kyu; Goh, Junghwan; Kim, Min Suk; Kwon, Eunhyang; Lee, Byounghoon; Lee, Jongseok; Lee, Sungeun; Seo, Hyunkwan; Yu, Intae; Bilinskas, Mykolas Jurgis; Grigelionis, Ignas; Janulis, Mindaugas; Juodagalvis, Andrius; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-de La Cruz, Ivan; Lopez-Fernandez, Ricardo; Magaña Villalba, Ricardo; Martínez-Ortega, Jorge; Sánchez-Hernández, Alberto; Villasenor-Cendejas, Luis Manuel; Carrillo Moreno, Salvador; Vazquez Valencia, Fabiola; Salazar Ibarguen, Humberto Antonio; Casimiro Linares, Edgar; Morelos Pineda, Antonio; Reyes-Santos, Marco A; Krofcheck, David; Bell, Alan James; Butler, Philip H; Doesburg, Robert; Reucroft, Steve; Silverwood, Hamish; Ahmad, Muhammad; Ansari, Muhammad Hamid; Asghar, Muhammad Irfan; Hoorani, Hafeez R; Khalid, Shoaib; Khan, Wajid Ali; Khurshid, Taimoor; Qazi, Shamona; Shah, Mehar Ali; Shoaib, Muhammad; Bialkowska, Helena; Boimska, Bozena; Frueboes, Tomasz; Gokieli, Ryszard; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Wrochna, Grzegorz; Zalewski, Piotr; Brona, Grzegorz; Bunkowski, Karol; Cwiok, Mikolaj; Dominik, Wojciech; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Almeida, Nuno; Bargassa, Pedrame; David Tinoco Mendes, Andre; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Seixas, Joao; Varela, Joao; Vischia, Pietro; Afanasiev, Serguei; Bunin, Pavel; Gavrilenko, Mikhail; Golutvin, Igor; Gorbunov, Ilya; Kamenev, Alexey; Karjavin, Vladimir; Kozlov, Guennady; Lanev, Alexander; Malakhov, Alexander; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Shmatov, Sergey; Smirnov, Vitaly; Volodko, Anton; Zarubin, Anatoli; Evstyukhin, Sergey; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Vorobyev, Andrey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Kirsanov, Mikhail; Krasnikov, Nikolai; Matveev, Viktor; Pashenkov, Anatoli; Tlisov, Danila; Toropin, Alexander; Epshteyn, Vladimir; Erofeeva, Maria; Gavrilov, Vladimir; Kossov, Mikhail; Lychkovskaya, Natalia; Popov, Vladimir; Safronov, Grigory; Semenov, Sergey; Stolin, Viatcheslav; Vlasov, Evgueni; Zhokin, Alexander; Belyaev, Andrey; Boos, Edouard; Ershov, Alexander; Gribushin, Andrey; Klyukhin, Vyacheslav; Kodolova, Olga; Korotkikh, Vladimir; Lokhtin, Igor; Markina, Anastasia; Obraztsov, Stepan; Perfilov, Maxim; Petrushanko, Sergey; Popov, Andrey; Sarycheva, Ludmila; Savrin, Viktor; Snigirev, Alexander; Vardanyan, Irina; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Mesyats, Gennady; Rusakov, Sergey V; Vinogradov, Alexey; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Grishin, Viatcheslav; Kachanov, Vassili; Konstantinov, Dmitri; Korablev, Andrey; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Tourtchanovitch, Leonid; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Djordjevic, Milos; Ekmedzic, Marko; Krpic, Dragomir; Milosevic, Jovan; Aguilar-Benitez, Manuel; Alcaraz Maestre, Juan; Arce, Pedro; Battilana, Carlo; Calvo, Enrique; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Domínguez Vázquez, Daniel; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Ferrando, Antonio; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Merino, Gonzalo; Puerta Pelayo, Jesus; Quintario Olmeda, Adrián; Redondo, Ignacio; Romero, Luciano; Santaolalla, Javier; Senghi Soares, Mara; Willmott, Carlos; Albajar, Carmen; Codispoti, Giuseppe; de Trocóniz, Jorge F; Brun, Hugues; Cuevas, Javier; Fernandez Menendez, Javier; Folgueras, Santiago; Gonzalez Caballero, Isidro; Lloret Iglesias, Lara; Piedra Gomez, Jonatan; Brochero Cifuentes, Javier Andres; Cabrillo, Iban Jose; Calderon, Alicia; Chuang, Shan-Huei; Duarte Campderros, Jordi; Felcini, Marta; Fernandez, Marcos; Gomez, Gervasio; Gonzalez Sanchez, Javier; Graziano, Alberto; Jorda, Clara; Lopez Virto, Amparo; Marco, Jesus; Marco, Rafael; Martinez Rivero, Celso; Matorras, Francisco; Munoz Sanchez, Francisca Javiela; Rodrigo, Teresa; Rodríguez-Marrero, Ana Yaiza; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Sobron Sanudo, Mar; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Baillon, Paul; Ball, Austin; Barney, David; Benitez, Jose F; Bernet, Colin; Bianchi, Giovanni; Bloch, Philippe; Bocci, Andrea; Bonato, Alessio; Botta, Cristina; Breuker, Horst; Camporesi, Tiziano; Cerminara, Gianluca; Christiansen, Tim; Coarasa Perez, Jose Antonio; D'Enterria, David; Dabrowski, Anne; De Roeck, Albert; Di Guida, Salvatore; Dobson, Marc; Dupont-Sagorin, Niels; Elliott-Peisert, Anna; Frisch, Benjamin; Funk, Wolfgang; Georgiou, Georgios; Giffels, Manuel; Gigi, Dominique; Gill, Karl; Giordano, Domenico; Giunta, Marina; Glege, Frank; Gomez-Reino Garrido, Robert; Govoni, Pietro; Gowdy, Stephen; Guida, Roberto; Hansen, Magnus; Harris, Philip; Hartl, Christian; Harvey, John; Hegner, Benedikt; Hinzmann, Andreas; Innocente, Vincenzo; Janot, Patrick; Kaadze, Ketino; Karavakis, Edward; Kousouris, Konstantinos; Lecoq, Paul; Lee, Yen-Jie; Lenzi, Piergiulio; Lourenco, Carlos; Maki, Tuula; Malberti, Martina; Malgeri, Luca; Mannelli, Marcello; Masetti, Lorenzo; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moser, Roland; Mozer, Matthias Ulrich; Mulders, Martijn; Musella, Pasquale; Nesvold, Erik; Orimoto, Toyoko; Orsini, Luciano; Palencia Cortezon, Enrique; Perez, Emmanuelle; Perrozzi, Luca; Petrilli, Achille; Pfeiffer, Andreas; Pierini, Maurizio; Pimiä, Martti; Piparo, Danilo; Polese, Giovanni; Quertenmont, Loic; Racz, Attila; Reece, William; Rodrigues Antunes, Joao; Rolandi, Gigi; Rommerskirchen, Tanja; Rovelli, Chiara; Rovere, Marco; Sakulin, Hannes; Santanastasio, Francesco; Schäfer, Christoph; Schwick, Christoph; Segoni, Ilaria; Sekmen, Sezen; Sharma, Archana; Siegrist, Patrice; Silva, Pedro; Simon, Michal; Sphicas, Paraskevas; Spiga, Daniele; Tsirou, Andromachi; Veres, Gabor Istvan; Vlimant, Jean-Roch; Wöhri, Hermine Katharina; Worm, Steven; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Gabathuler, Kurt; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; König, Stefan; Kotlinski, Danek; Langenegger, Urs; Meier, Frank; Renker, Dieter; Rohe, Tilman; Sibille, Jennifer; Bäni, Lukas; Bortignon, Pierluigi; Buchmann, Marco-Andrea; Casal, Bruno; Chanon, Nicolas; Deisher, Amanda; Dissertori, Günther; Dittmar, Michael; Donegà, Mauro; Dünser, Marc; Eugster, Jürg; Freudenreich, Klaus; Grab, Christoph; Hits, Dmitry; Lecomte, Pierre; Lustermann, Werner; Marini, Andrea Carlo; Martinez Ruiz del Arbol, Pablo; Mohr, Niklas; Moortgat, Filip; Nägeli, Christoph; Nef, Pascal; Nessi-Tedaldi, Francesca; Pandolfi, Francesco; Pape, Luc; Pauss, Felicitas; Peruzzi, Marco; Ronga, Frederic Jean; Rossini, Marco; Sala, Leonardo; Sanchez, Ann - Karin; Starodumov, Andrei; Stieger, Benjamin; Takahashi, Maiko; Tauscher, Ludwig; Thea, Alessandro; Theofilatos, Konstantinos; Treille, Daniel; Urscheler, Christina; Wallny, Rainer; Weber, Hannsjoerg Artur; Wehrli, Lukas; Amsler, Claude; Chiochia, Vincenzo; De Visscher, Simon; Favaro, Carlotta; Ivova Rikova, Mirena; Millan Mejias, Barbara; Otiougova, Polina; Robmann, Peter; Snoek, Hella; Tupputi, Salvatore; Verzetti, Mauro; Chang, Yuan-Hann; Chen, Kuan-Hsin; Kuo, Chia-Ming; Li, Syue-Wei; Lin, Willis; Liu, Zong-Kai; Lu, Yun-Ju; Mekterovic, Darko; Singh, Anil; Volpe, Roberta; Yu, Shin-Shan; Bartalini, Paolo; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Dietz, Charles; Grundler, Ulysses; Hou, George Wei-Shu; Hsiung, Yee; Kao, Kai-Yi; Lei, Yeong-Jyi; Lu, Rong-Shyang; Majumder, Devdatta; Petrakou, Eleni; Shi, Xin; Shiu, Jing-Ge; Tzeng, Yeng-Ming; Wan, Xia; Wang, Minzu; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Dozen, Candan; Dumanoglu, Isa; Eskut, Eda; Girgis, Semiray; Gokbulut, Gul; Gurpinar, Emine; Hos, Ilknur; Kangal, Evrim Ersin; Karaman, Turker; Karapinar, Guler; Kayis Topaksu, Aysel; Onengut, Gulsen; Ozdemir, Kadri; Ozturk, Sertac; Polatoz, Ayse; Sogut, Kenan; Sunar Cerci, Deniz; Tali, Bayram; Topakli, Huseyin; Vergili, Latife Nukhet; Vergili, Mehmet; Akin, Ilina Vasileva; Aliev, Takhmasib; Bilin, Bugra; Bilmis, Selcuk; Deniz, Muhammed; Gamsizkan, Halil; Guler, Ali Murat; Ocalan, Kadir; Ozpineci, Altug; Serin, Meltem; Sever, Ramazan; Surat, Ugur Emrah; Yalvac, Metin; Yildirim, Eda; Zeyrek, Mehmet; Gülmez, Erhan; Isildak, Bora; Kaya, Mithat; Kaya, Ozlem; Ozkorucuklu, Suat; Sonmez, Nasuf; Cankocak, Kerem; Levchuk, Leonid; Bostock, Francis; Brooke, James John; Clement, Emyr; Cussans, David; Flacher, Henning; Frazier, Robert; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Kreczko, Lukasz; Metson, Simon; Newbold, Dave M; Nirunpong, Kachanon; Poll, Anthony; Senkin, Sergey; Smith, Vincent J; Williams, Thomas; Basso, Lorenzo; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Jackson, James; Kennedy, Bruce W; Olaiya, Emmanuel; Petyt, David; Radburn-Smith, Benjamin Charles; Shepherd-Themistocleous, Claire; Tomalin, Ian R; Womersley, William John; Bainbridge, Robert; Ball, Gordon; Beuselinck, Raymond; Buchmuller, Oliver; Colling, David; Cripps, Nicholas; Cutajar, Michael; Dauncey, Paul; Davies, Gavin; Della Negra, Michel; Ferguson, William; Fulcher, Jonathan; Futyan, David; Gilbert, Andrew; Guneratne Bryer, Arlo; Hall, Geoffrey; Hatherell, Zoe; Hays, Jonathan; Iles, Gregory; Jarvis, Martyn; Karapostoli, Georgia; Lyons, Louis; Magnan, Anne-Marie; Marrouche, Jad; Mathias, Bryn; Nandi, Robin; Nash, Jordan; Nikitenko, Alexander; Papageorgiou, Anastasios; Pela, Joao; Pesaresi, Mark; Petridis, Konstantinos; Pioppi, Michele; Raymond, David Mark; Rogerson, Samuel; Rose, Andrew; Ryan, Matthew John; Seez, Christopher; Sharp, Peter; Sparrow, Alex; Stoye, Markus; Tapper, Alexander; Vazquez Acosta, Monica; Virdee, Tejinder; Wakefield, Stuart; Wardle, Nicholas; Whyntie, Tom; Chadwick, Matthew; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leggat, Duncan; Leslie, Dawn; Martin, William; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Hatakeyama, Kenichi; Liu, Hongxuan; Scarborough, Tara; Charaf, Otman; Henderson, Conor; Rumerio, Paolo; Avetisyan, Aram; Bose, Tulika; Fantasia, Cory; Heister, Arno; St John, Jason; Lawson, Philip; Lazic, Dragoslav; Rohlf, James; Sperka, David; Sulak, Lawrence; Alimena, Juliette; Bhattacharya, Saptaparna; Cutts, David; Ferapontov, Alexey; Heintz, Ulrich; Jabeen, Shabnam; Kukartsev, Gennadiy; Laird, Edward; Landsberg, Greg; Luk, Michael; Narain, Meenakshi; Nguyen, Duong; Segala, Michael; Sinthuprasith, Tutanon; Speer, Thomas; Tsang, Ka Vang; Breedon, Richard; Breto, Guillermo; Calderon De La Barca Sanchez, Manuel; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Dolen, James; Erbacher, Robin; Gardner, Michael; Houtz, Rachel; Ko, Winston; Kopecky, Alexandra; Lander, Richard; Miceli, Tia; Pellett, Dave; Ricci-tam, Francesca; Rutherford, Britney; Searle, Matthew; Smith, John; Squires, Michael; Tripathi, Mani; Vasquez Sierra, Ricardo; Andreev, Valeri; Cline, David; Cousins, Robert; Duris, Joseph; Erhan, Samim; Everaerts, Pieter; Farrell, Chris; Hauser, Jay; Ignatenko, Mikhail; Jarvis, Chad; Plager, Charles; Rakness, Gregory; Schlein, Peter; Traczyk, Piotr; Valuev, Vyacheslav; Weber, Matthias; Babb, John; Clare, Robert; Dinardo, Mauro Emanuele; Ellison, John Anthony; Gary, J William; Giordano, Ferdinando; Hanson, Gail; Jeng, Geng-Yuan; Liu, Hongliang; Long, Owen Rosser; Luthra, Arun; Nguyen, Harold; Paramesvaran, Sudarshan; Sturdy, Jared; Sumowidagdo, Suharyo; Wilken, Rachel; Wimpenny, Stephen; Andrews, Warren; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Evans, David; Golf, Frank; Holzner, André; Kelley, Ryan; Lebourgeois, Matthew; Letts, James; Macneill, Ian; Mangano, Boris; Padhi, Sanjay; Palmer, Christopher; Petrucciani, Giovanni; Pieri, Marco; Sani, Matteo; Sharma, Vivek; Simon, Sean; Sudano, Elizabeth; Tadel, Matevz; Tu, Yanjun; Vartak, Adish; Wasserbaech, Steven; Würthwein, Frank; Yagil, Avraham; Yoo, Jaehyeok; Barge, Derek; Bellan, Riccardo; Campagnari, Claudio; D'Alfonso, Mariarosaria; Danielson, Thomas; Flowers, Kristen; Geffert, Paul; Incandela, Joe; Justus, Christopher; Kalavase, Puneeth; Koay, Sue Ann; Kovalskyi, Dmytro; Krutelyov, Vyacheslav; Lowette, Steven; Mccoll, Nickolas; Pavlunin, Viktor; Rebassoo, Finn; Ribnik, Jacob; Richman, Jeffrey; Rossin, Roberto; Stuart, David; To, Wing; West, Christopher; Apresyan, Artur; Bornheim, Adolf; Chen, Yi; Di Marco, Emanuele; Duarte, Javier; Gataullin, Marat; Ma, Yousi; Mott, Alexander; Newman, Harvey B; Rogan, Christopher; Spiropulu, Maria; Timciuc, Vladlen; Veverka, Jan; Wilkinson, Richard; Yang, Yong; Zhu, Ren-Yuan; Akgun, Bora; Azzolini, Virginia; Carroll, Ryan; Ferguson, Thomas; Iiyama, Yutaro; Jang, Dong Wook; Liu, Yueh-Feng; Paulini, Manfred; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Drell, Brian Robert; Edelmaier, Christopher; Ford, William T; Gaz, Alessandro; Heyburn, Bernadette; Luiggi Lopez, Eduardo; Smith, James; Stenson, Kevin; Ulmer, Keith; Wagner, Stephen Robert; Alexander, James; Chatterjee, Avishek; Eggert, Nicholas; Gibbons, Lawrence Kent; Heltsley, Brian; Khukhunaishvili, Aleko; Kreis, Benjamin; Mirman, Nathan; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Ryd, Anders; Salvati, Emmanuele; Sun, Werner; Teo, Wee Don; Thom, Julia; Thompson, Joshua; Tucker, Jordan; Vaughan, Jennifer; Weng, Yao; Winstrom, Lucas; Wittich, Peter; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Anderson, Jacob; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Bloch, Ingo; Burkett, Kevin; Butler, Joel Nathan; Chetluru, Vasundhara; Cheung, Harry; Chlebana, Frank; Elvira, Victor Daniel; Fisk, Ian; Freeman, Jim; Gao, Yanyan; Green, Dan; Gutsche, Oliver; Hanlon, Jim; Harris, Robert M; Hirschauer, James; Hooberman, Benjamin; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Kilminster, Benjamin; Klima, Boaz; Kunori, Shuichi; Kwan, Simon; Leonidopoulos, Christos; Linacre, Jacob; Lincoln, Don; Lipton, Ron; Lykken, Joseph; Maeshima, Kaori; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Mishra, Kalanand; Mrenna, Stephen; Musienko, Yuri; Newman-Holmes, Catherine; O'Dell, Vivian; Prokofyev, Oleg; Sexton-Kennedy, Elizabeth; Sharma, Seema; Spalding, William J; Spiegel, Leonard; Tan, Ping; Taylor, Lucas; Tkaczyk, Slawek; Tran, Nhan Viet; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vidal, Richard; Whitmore, Juliana; Wu, Weimin; Yang, Fan; Yumiceva, Francisco; Yun, Jae Chul; Acosta, Darin; Avery, Paul; Bourilkov, Dimitri; Chen, Mingshui; Cheng, Tongguang; Das, Souvik; De Gruttola, Michele; Di Giovanni, Gian Piero; Dobur, Didar; Drozdetskiy, Alexey; Field, Richard D; Fisher, Matthew; Fu, Yu; Furic, Ivan-Kresimir; Gartner, Joseph; Hugon, Justin; Kim, Bockjoo; Konigsberg, Jacobo; Korytov, Andrey; Kropivnitskaya, Anna; Kypreos, Theodore; Low, Jia Fu; Matchev, Konstantin; Milenovic, Predrag; Mitselmakher, Guenakh; Muniz, Lana; Remington, Ronald; Rinkevicius, Aurelijus; Sellers, Paul; Skhirtladze, Nikoloz; Snowball, Matthew; Yelton, John; Zakaria, Mohammed; Gaultney, Vanessa; Hewamanage, Samantha; Lebolo, Luis Miguel; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Adams, Todd; Askew, Andrew; Bochenek, Joseph; Chen, Jie; Diamond, Brendan; Gleyzer, Sergei V; Haas, Jeff; Hagopian, Sharon; Hagopian, Vasken; Jenkins, Merrill; Johnson, Kurtis F; Prosper, Harrison; Veeraraghavan, Venkatesh; Weinberg, Marc; Baarmand, Marc M; Dorney, Brian; Hohlmann, Marcus; Kalakhety, Himali; Vodopiyanov, Igor; Adams, Mark Raymond; Anghel, Ioana Maria; Apanasevich, Leonard; Bai, Yuting; Bazterra, Victor Eduardo; Betts, Russell Richard; Bucinskaite, Inga; Callner, Jeremy; Cavanaugh, Richard; Dragoiu, Cosmin; Evdokimov, Olga; Gauthier, Lucie; Gerber, Cecilia Elena; Hofman, David Jonathan; Khalatyan, Samvel; Lacroix, Florent; Malek, Magdalena; O'Brien, Christine; Silkworth, Christopher; Strom, Derek; Varelas, Nikos; Akgun, Ugur; Albayrak, Elif Asli; Bilki, Burak; Clarida, Warren; Duru, Firdevs; Griffiths, Scott; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Newsom, Charles Ray; Norbeck, Edwin; Onel, Yasar; Ozok, Ferhat; Sen, Sercan; Tiras, Emrah; Wetzel, James; Yetkin, Taylan; Yi, Kai; Barnett, Bruce Arnold; Blumenfeld, Barry; Bolognesi, Sara; Fehling, David; Giurgiu, Gavril; Gritsan, Andrei; Guo, Zijin; Hu, Guofan; Maksimovic, Petar; Rappoccio, Salvatore; Swartz, Morris; Whitbeck, Andrew; Baringer, Philip; Bean, Alice; Benelli, Gabriele; Grachov, Oleg; Kenny Iii, Raymond Patrick; Murray, Michael; Noonan, Daniel; Sanders, Stephen; Stringer, Robert; Tinti, Gemma; Wood, Jeffrey Scott; Zhukova, Victoria; Barfuss, Anne-Fleur; Bolton, Tim; Chakaberia, Irakli; Ivanov, Andrew; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Shrestha, Shruti; Svintradze, Irakli; Gronberg, Jeffrey; Lange, David; Wright, Douglas; Baden, Drew; Boutemeur, Madjid; Calvert, Brian; Eno, Sarah Catherine; Gomez, Jaime; Hadley, Nicholas John; Kellogg, Richard G; Kirn, Malina; Kolberg, Ted; Lu, Ying; Marionneau, Matthieu; Mignerey, Alice; Pedro, Kevin; Peterman, Alison; Skuja, Andris; Temple, Jeffrey; Tonjes, Marguerite; Tonwar, Suresh C; Twedt, Elizabeth; Apyan, Aram; Bauer, Gerry; Bendavid, Joshua; Busza, Wit; Butz, Erik; Cali, Ivan Amos; Chan, Matthew; Dutta, Valentina; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Hahn, Kristan Allan; Kim, Yongsun; Klute, Markus; Krajczar, Krisztian; Li, Wei; Luckey, Paul David; Ma, Teng; Nahn, Steve; Paus, Christoph; Ralph, Duncan; Roland, Christof; Roland, Gunther; Rudolph, Matthew; Stephans, George; Stöckli, Fabian; Sumorok, Konstanty; Sung, Kevin; Velicanu, Dragos; Wenger, Edward Allen; Wolf, Roger; Wyslouch, Bolek; Xie, Si; Yang, Mingming; Yilmaz, Yetkin; Yoon, Sungho; Zanetti, Marco; Cooper, Seth; Dahmes, Bryan; De Benedetti, Abraham; Franzoni, Giovanni; Gude, Alexander; Kao, Shih-Chuan; Klapoetke, Kevin; Kubota, Yuichi; Mans, Jeremy; Pastika, Nathaniel; Rusack, Roger; Sasseville, Michael; Singovsky, Alexander; Tambe, Norbert; Turkewitz, Jared; Cremaldi, Lucien Marcus; Kroeger, Rob; Perera, Lalith; Rahmat, Rahmat; Sanders, David A; Avdeeva, Ekaterina; Bloom, Kenneth; Bose, Suvadeep; Butt, Jamila; Claes, Daniel R; Dominguez, Aaron; Eads, Michael; Keller, Jason; Kravchenko, Ilya; Lazo-Flores, Jose; Malbouisson, Helena; Malik, Sudhir; Snow, Gregory R; Baur, Ulrich; Godshalk, Andrew; Iashvili, Ia; Jain, Supriya; Kharchilava, Avto; Kumar, Ashish; Shipkowski, Simon Peter; Smith, Kenneth; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Chasco, Matthew; Haley, Joseph; Nash, David; Trocino, Daniele; Wood, Darien; Zhang, Jinzhong; Anastassov, Anton; Kubik, Andrew; Mucia, Nicholas; Odell, Nathaniel; Ofierzynski, Radoslaw Adrian; Pollack, Brian; Pozdnyakov, Andrey; Schmitt, Michael Henry; Stoynev, Stoyan; Velasco, Mayda; Won, Steven; Antonelli, Louis; Berry, Douglas; Brinkerhoff, Andrew; Hildreth, Michael; Jessop, Colin; Karmgard, Daniel John; Kolb, Jeff; Lannon, Kevin; Luo, Wuming; Lynch, Sean; Marinelli, Nancy; Morse, David Michael; Pearson, Tessa; Ruchti, Randy; Slaunwhite, Jason; Valls, Nil; Wayne, Mitchell; Wolf, Matthias; Bylsma, Ben; Durkin, Lloyd Stanley; Hill, Christopher; Hughes, Richard; Kotov, Khristian; Ling, Ta-Yung; Puigh, Darren; Rodenburg, Marissa; Vuosalo, Carl; Williams, Grayson; Winer, Brian L; Adam, Nadia; Berry, Edmund; Elmer, Peter; Gerbaudo, Davide; Halyo, Valerie; Hebda, Philip; Hegeman, Jeroen; Hunt, Adam; Jindal, Pratima; Lopes Pegna, David; Lujan, Paul; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Piroué, Pierre; Quan, Xiaohang; Raval, Amita; Safdi, Ben; Saka, Halil; Stickland, David; Tully, Christopher; Werner, Jeremy Scott; Zuranski, Andrzej; Acosta, Jhon Gabriel; Brownson, Eric; Huang, Xing Tao; Lopez, Angel; Mendez, Hector; Oliveros, Sandra; Ramirez Vargas, Juan Eduardo; Zatserklyaniy, Andriy; Alagoz, Enver; Barnes, Virgil E; Benedetti, Daniele; Bolla, Gino; Bortoletto, Daniela; De Mattia, Marco; Everett, Adam; Hu, Zhen; Jones, Matthew; Koybasi, Ozhan; Kress, Matthew; Laasanen, Alvin T; Leonardo, Nuno; Maroussov, Vassili; Merkel, Petra; Miller, David Harry; Neumeister, Norbert; Shipsey, Ian; Silvers, David; Svyatkovskiy, Alexey; Vidal Marono, Miguel; Yoo, Hwi Dong; Zablocki, Jakub; Zheng, Yu; Guragain, Samir; Parashar, Neeti; Adair, Antony; Boulahouache, Chaouki; Ecklund, Karl Matthew; Geurts, Frank JM; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Zabel, James; Betchart, Burton; Bodek, Arie; Chung, Yeon Sei; Covarelli, Roberto; de Barbaro, Pawel; Demina, Regina; Eshaq, Yossof; Garcia-Bellido, Aran; Goldenzweig, Pablo; Han, Jiyeon; Harel, Amnon; Miner, Daniel Carl; Vishnevskiy, Dmitry; Zielinski, Marek; Bhatti, Anwar; Ciesielski, Robert; Demortier, Luc; Goulianos, Konstantin; Lungu, Gheorghe; Malik, Sarah; Mesropian, Christina; Arora, Sanjay; Barker, Anthony; Chou, John Paul; Contreras-Campana, Christian; Contreras-Campana, Emmanuel; Duggan, Daniel; Ferencek, Dinko; Gershtein, Yuri; Gray, Richard; Halkiadakis, Eva; Hidas, Dean; Lath, Amitabh; Panwalkar, Shruti; Park, Michael; Patel, Rishi; Rekovic, Vladimir; Robles, Jorge; Rose, Keith; Salur, Sevil; Schnetzer, Steve; Seitz, Claudia; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Cerizza, Giordano; Hollingsworth, Matthew; Spanier, Stefan; Yang, Zong-Chang; York, Andrew; Eusebi, Ricardo; Flanagan, Will; Gilmore, Jason; Kamon, Teruki; Khotilovich, Vadim; Montalvo, Roy; Osipenkov, Ilya; Pakhotin, Yuriy; Perloff, Alexx; Roe, Jeffrey; Safonov, Alexei; Sakuma, Tai; Sengupta, Sinjini; Suarez, Indara; Tatarinov, Aysen; Toback, David; Akchurin, Nural; Damgov, Jordan; Dudero, Phillip Russell; Jeong, Chiyoung; Kovitanggoon, Kittikul; Lee, Sung Won; Libeiro, Terence; Roh, Youn; Volobouev, Igor; Appelt, Eric; Delannoy, Andrés G; Florez, Carlos; Greene, Senta; Gurrola, Alfredo; Johns, Willard; Johnston, Cody; Kurt, Pelin; Maguire, Charles; Melo, Andrew; Sharma, Monika; Sheldon, Paul; Snook, Benjamin; Tuo, Shengquan; Velkovska, Julia; Arenton, Michael Wayne; Balazs, Michael; Boutle, Sarah; Cox, Bradley; Francis, Brian; Goodell, Joseph; Hirosky, Robert; Ledovskoy, Alexander; Lin, Chuanzhe; Neu, Christopher; Wood, John; Yohay, Rachel; Gollapinni, Sowjanya; Harr, Robert; Karchin, Paul Edmund; Kottachchi Kankanamge Don, Chamath; Lamichhane, Pramod; Sakharov, Alexandre; Anderson, Michael; Bachtis, Michail; Belknap, Donald; Borrello, Laura; Carlsmith, Duncan; Cepeda, Maria; Dasu, Sridhara; Friis, Evan; Gray, Lindsey; Grogg, Kira Suzanne; Grothe, Monika; Hall-Wilton, Richard; Herndon, Matthew; Hervé, Alain; Klabbers, Pamela; Klukas, Jeffrey; Lanaro, Armando; Lazaridis, Christos; Leonard, Jessica; Loveless, Richard; Mohapatra, Ajit; Ojalvo, Isabel; Palmonari, Francesco; Pierro, Giuseppe Antonio; Ross, Ian; Savin, Alexander; Smith, Wesley H; Swanson, Joshua

    2013-01-22

    First measurements of the azimuthal anisotropy of neutral pions produced in PbPb collisions at a center-of-mass energy of $\\sqrt{s_{NN}}$ = 2.76 TeV are presented. The amplitudes of the second Fourier component ($v_2$) of the neutral pion azimuthal distributions are extracted using an event-plane technique. The values of $v_2$ are studied as a function of the neutral pion transverse momentum ($p_T$) for different classes of collision centrality in the kinematic range 1.6 < $p_T$ < 8.0 GeV, within the pseudorapidity interval |$\\eta$| < 0.8. The CMS measurements of $v_2(p_T)$ are similar to previously reported neutral pion azimuthal anisotropy results from $\\sqrt{s_{NN}}$ = 200 GeV AuAu collisions at RHIC, despite a factor of about 14 increase in the center-of-mass energy. In the momentum range 2.5 < $p_T$ < 5.0 GeV, the neutral pion anisotropies are found to be smaller than those observed by CMS for inclusive charged particles.

  14. Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

    Science.gov (United States)

    Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

    2012-04-01

    The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

  15. 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.

  16. Low Momentum NN-interactions and Nuclear Matter Calculations

    OpenAIRE

    Kohler, H. S.

    2005-01-01

    Separable nucleon-nucleon potentials are calculated using inverse scattering techniques as presented in previously published work. The dependence of the potentials on the momentum cut-off of the scattering phase-shifts is studied. Some comparison is made with the V-low-k potential. The effect of the cut-off on nuclear matter binding energy calculated by standard Brueckner theory is also presented. It is foumd that a cut-off larger than about 4 fm-1 will keep the error to within one Mev around...

  17. Application of gene network analysis techniques identifies AXIN1/PDIA2 and endoglin haplotypes associated with bicuspid aortic valve.

    Directory of Open Access Journals (Sweden)

    Eric C Wooten

    2010-01-01

    Full Text Available Bicuspid Aortic Valve (BAV is a highly heritable congenital heart defect. The low frequency of BAV (1% of general population limits our ability to perform genome-wide association studies. We present the application of four a priori SNP selection techniques, reducing the multiple-testing penalty by restricting analysis to SNPs relevant to BAV in a genome-wide SNP dataset from a cohort of 68 BAV probands and 830 control subjects. Two knowledge-based approaches, CANDID and STRING, were used to systematically identify BAV genes, and their SNPs, from the published literature, microarray expression studies and a genome scan. We additionally tested Functionally Interpolating SNPs (fitSNPs present on the array; the fourth consisted of SNPs selected by Random Forests, a machine learning approach. These approaches reduced the multiple testing penalty by lowering the fraction of the genome probed to 0.19% of the total, while increasing the likelihood of studying SNPs within relevant BAV genes and pathways. Three loci were identified by CANDID, STRING, and fitSNPS. A haplotype within the AXIN1-PDIA2 locus (p-value of 2.926x10(-06 and a haplotype within the Endoglin gene (p-value of 5.881x10(-04 were found to be strongly associated with BAV. The Random Forests approach identified a SNP on chromosome 3 in association with BAV (p-value 5.061x10(-06. The results presented here support an important role for genetic variants in BAV and provide support for additional studies in well-powered cohorts. Further, these studies demonstrate that leveraging existing expression and genomic data in the context of GWAS studies can identify biologically relevant genes and pathways associated with a congenital heart defect.

  18. Off-energy-shell behavior of scattering amplitude and the structure of NN potentials

    CERN Document Server

    Almaliev, A N; Shikhalev, M A

    2002-01-01

    For two rather distinct types of NN potentials - with forbidden states and common ones with short-range repulsion - an extensive investigation into off-shell behavior of the scattering amplitudes and associated pp -> pp gamma Bremsstrahlung observables are undertaken. The emphasis is on the action of the shape and deepness of the central part of the potentials on the mentioned quantities. The necessity of taking into consideration the meson-exchange sector of the NN interaction is also stressed. It is shown that, provided one uses potentials which give a good description of phase shifts as well as low off-shell momentum-transfer behavior of the scattering amplitudes, the reaction pp -> pp gamma cannot allow to make a choice between the potentials, at least up to the energy of protons about 400 MeV in laboratory frame

  19. 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.

  20. A symmetry perceiving adaptive neural network and facial image recognition.

    Science.gov (United States)

    Sinha, P

    1998-11-30

    The paper deals with the forensic problem of comparing nearly from view and facial images for personal identification. The human recognition process for such problems, is primarily based on both holistic as well as feature-wise symmetry perception aided by subjective analysis for detecting ill-defined features. It has been attempted to approach the modelling of such a process by designing a robust symmetry perceiving adaptive neural network. The pair of images to be compared should be presented to the proposed neural network (NN) as source (input) and target images. The NN learns about the symmetry between the pair of images by analysing examples of associated feature pairs belonging to the source and the target images. In order to prepare a paired example of associated features for training purpose, when we select one particular feature on the source image as a unique pixel, we must associate it with the corresponding feature on the target image also. But, in practice, it is not always possible to fix the latter feature also as a unique pixel due to pictorial ambiguity. The robust or fault tolerant NN takes care of such a situation and allows fixing the associated target feature as a rectangular array of pixels, rather than fixing it as a unique pixel, which is pretty difficult to be done with certainty. From such a pair of sets of associated features, the NN searches out proper locations of the target features from the sets of ambiguous target features by a fuzzy analysis during its learning. If any of target features, searched out by the NN, lies outside the prespecified zone, the training of the NN is unsuccessful. This amounts to non-existence of symmetry between the pair of images and confirms non-identity. In case of a successful training, the NN gets adapted with appropriate symmetry relation between the pair of images and when the source image is input to the trained NN, it responds by outputting a processed source image which is superimposable over the

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  2. Maan painuminen Pohjois-Pohjanmaan säännösteltyjen jokien tulvapelloilla

    OpenAIRE

    Ikkala, L. (Lauri)

    2017-01-01

    Tiivistelmä Maan painuminen on vesistöjen läheisyydessä viljelytoimintaa haittaava ongelma. Tässä diplomityössä tutkittiin säännöstelyn ja tulvapeltojen viljelyn välistä vuorovaikutussuhdetta Kala- ja Siikajoen tulvapelloilla sekä painumista ilmiönä, sen syitä ja seurauksia. Tulviminen, sinänsä luonnollinen ilmiö tasaisella Pohjanmaalla, koetaan herkästi sä...

  3. 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.

  4. Artificial Neural Network for Displacement Vectors Determination

    Directory of Open Access Journals (Sweden)

    P. Bohmann

    1997-09-01

    Full Text Available An artificial neural network (NN for displacement vectors (DV determination is presented in this paper. DV are computed in areas which are essential for image analysis and computer vision, in areas where are edges, lines, corners etc. These special features are found by edges operators with the following filtration. The filtration is performed by a threshold function. The next step is DV computation by 2D Hamming artificial neural network. A method of DV computation is based on the full search block matching algorithms. The pre-processing (edges finding is the reason why the correlation function is very simple, the process of DV determination needs less computation and the structure of the NN is simpler.

  5. Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed, M; Gu, F; Ball, A, E-mail: M.Ahmed@hud.ac.uk [Diagnostic Engineering Research Group, University of Huddersfield, HD1 3DH (United Kingdom)

    2011-07-19

    Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

  6. Ab initio quality neural-network potential for sodium

    Science.gov (United States)

    Eshet, Hagai; Khaliullin, Rustam Z.; Kühne, Thomas D.; Behler, Jörg; Parrinello, Michele

    2010-05-01

    An interatomic potential for high-pressure high-temperature (HPHT) crystalline and liquid phases of sodium is created using a neural-network (NN) representation of the ab initio potential-energy surface. It is demonstrated that the NN potential provides an ab initio quality description of multiple properties of liquid sodium and bcc, fcc, and cI16 crystal phases in the P-T region up to 120 GPa and 1200 K. The unique combination of computational efficiency of the NN potential and its ability to reproduce quantitatively experimental properties of sodium in the wide P-T range enables molecular-dynamics simulations of physicochemical processes in HPHT sodium of unprecedented quality.

  7. Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

    Directory of Open Access Journals (Sweden)

    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.

  8. A comparison of six metamodeling techniques applied to building performance simulations

    DEFF Research Database (Denmark)

    Østergård, Torben; Jensen, Rasmus Lund; Maagaard, Steffen Enersen

    2018-01-01

    Highlights •Linear regression (OLS), support vector regression (SVR), regression splines (MARS). •Random forest (RF), Gaussian processes (GPR), neural network (NN). •Accuracy, time, interpretability, ease-of-use, model selection, and robustness. •13 problems modelled for 9 training set sizes...... spanning from 32 to 8192 simulations. •Methodology for comparison using exhaustive grid searches and sensitivity analysis....

  9. PEMILIHAN STRATEGI PEMASARAN DI KAMPOENG KOPI BANARAN MENGGUNAKAN PENDEKATAN METODE ANALYTICAL NETWORK PROCESS (ANP DAN TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO AN IDEAL SOLUTION (TOPSIS

    Directory of Open Access Journals (Sweden)

    Aries Susanty

    2014-09-01

    Full Text Available Kampoeng Kopi Banaran belum dapat mencapai laba sesuai dengan target yang telah ditetapkan. Diduga hal ini terjadi karena semakin banyaknya pesaing dengan usaha sejenis, seperti Cimory, Kampoeng Rawa, Tlogo Plantation, Salib Putih, dan Umbul Sidomukti serta belum dimilikinya strategi pemasaran yang tepat oleh Kampoeng Kopi Banaran. Selama ini, Kampoeng Kopi Banaran baru memasarkan  produk-produk yang dimilikinya dengan menggunakan website, brosur,  dan promosi mulut ke mulut.  Berdasarkan hal tersebut, penelitian ini bertujuan untuk  mengidentifikasi kriteria dan subkriteria yang tepat bagi penyusunan strategi pemasaran dari Kampoeng Kopi Banaran, menentukan bobot dari setiap kriteria dan subkriteria tersebut, serta mengusulkan strategi pemasaran tertentu berdasarkan kriteria dan subkriteria tersebut. Dalam penelitian ini, terdapat delapan buah kriteria yang digunakan sebagai dasar untuk menyusun strategi pemasaran bagi Kampoeng Kopi Banaran, yaitu Managerial Capabilities (MC, Market Innovation Capabilities (MIC, Customer Linking Capabilities (CLC, Human Resource Assetes (HRA, Reputational Asset (RA, Competition (C, Economy (E, dan Social and cultural (SC. Selanjutnya kedelapan kriteria tersebut akan dijabarkan lagi menjadi sejumlah subkriteria. Metoda yang digunakan untuk menghitung bobot dari kriteria dan subkriteria adalah Analitycal Network Process (ANP; sedangkan metoda yang digunakan untuk penyusunan strategi pemasaran adalah  Technique for Others Reference by Similarity to Ideal Solution (TOPSIS . Data untuk penelitian ini diperoleh dengan melakukan penyebaran kuesioner kepada manager dan bagian marketing Kampoeng Kopi Banaran. Hasil pengolahan data menunjukkan kriteria yang memiliki bobot tertinggi untuk penyusunan strategi pemasaran di Kampoeng Kopi Banaran adalah Managerial Capabilities (MC (0,1897 dan sub kriteria yagn memiliki bobot tertinggi adalah subkriteria brand atau reputasi (0,1277. Selanjutnya, strategi yang terbaik

  10. The National Network of Libraries of Medicine's outreach to the public health workforce: 2001–2006

    Science.gov (United States)

    Cogdill, Keith W.; Ruffin, Angela B.; Stavri, P. Zoë

    2007-01-01

    Objective: The paper provides an overview of the National Network of Libraries of Medicine's (NN/ LM's) outreach to the public health workforce from 2001 to 2006. Description: NN/LM conducts outreach through the activities of the Regional Medical Library (RML) staff and RML-sponsored projects led by NN/LM members. Between 2001 and 2006, RML staff provided training on information resources and information management for public health personnel at national, state, and local levels. The RMLs also contributed significantly to the Partners in Information Access for the Public Health Workforce collaboration. Methods: Data were extracted from telephone interviews with directors of thirty-seven NN/LM-sponsored outreach projects directed at the public health sector. A review of project reports informed the interviews, which were transcribed and subsequently coded for emergent themes using qualitative analysis software. Results: Analysis of interview data led to the identification of four major themes: training, collaboration, evaluation of outcomes, and challenges. Sixteen subthemes represented specific lessons learned from NN/LM members' outreach to the public health sector. Conclusions: NN/LM conducted extensive information-oriented outreach to the public health workforce during the 2001-to-2006 contract period. Lessons learned from this experience, most notably the value of collaboration and the need for flexibility, continue to influence outreach efforts in the current contract period. PMID:17641766

  11. The National Network of Libraries of Medicine's outreach to the public health workforce: 2001-2006.

    Science.gov (United States)

    Cogdill, Keith W; Ruffin, Angela B; Stavri, P Zoë

    2007-07-01

    The paper provides an overview of the National Network of Libraries of Medicine's (NN/ LM's) outreach to the public health workforce from 2001 to 2006. NN/LM conducts outreach through the activities of the Regional Medical Library (RML) staff and RML-sponsored projects led by NN/LM members. Between 2001 and 2006, RML staff provided training on information resources and information management for public health personnel at national, state, and local levels. The RMLs also contributed significantly to the Partners in Information Access for the Public Health Workforce collaboration. Data were extracted from telephone interviews with directors of thirty-seven NN/LM-sponsored outreach projects directed at the public health sector. A review of project reports informed the interviews, which were transcribed and subsequently coded for emergent themes using qualitative analysis software. Analysis of interview data led to the identification of four major themes: training, collaboration, evaluation of outcomes, and challenges. Sixteen subthemes represented specific lessons learned from NN/LM members' outreach to the public health sector. NN/LM conducted extensive information-oriented outreach to the public health workforce during the 2001-to-2006 contract period. Lessons learned from this experience, most notably the value of collaboration and the need for flexibility, continue to influence outreach efforts in the current contract period.

  12. Elliptic flow of identified hadrons in Pb-Pb collisions at $\\sqrt{s_{\\rm{NN}}}$ = 2.76 TeV

    CERN Document Server

    Abelev, Betty Bezverkhny; Adamova, Dagmar; Aggarwal, Madan Mohan; Agnello, Michelangelo; Agostinelli, Andrea; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Nazeer; Ahmed, Ijaz; Ahn, Sang Un; Ahn, Sul-Ah; Aimo, Ilaria; Aiola, Salvatore; Ajaz, Muhammad; Akindinov, Alexander; Alam, Sk Noor; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Armesto Perez, Nestor; Arnaldi, Roberta; Aronsson, Tomas; Arsene, Ionut Cristian; Arslandok, Mesut; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Baltasar Dos Santos Pedrosa, Fernando; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont Moreno, Ernesto; Belmont Iii, Ronald John; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Berger, Martin Emanuel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Bjelogrlic, Sandro; Blanco, Fernando; Blau, Dmitry; Blume, Christoph; Bock, Friederike; 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    2015-06-29

    The elliptic flow coefficient ($v_{2}$) of identified particles in Pb--Pb collisions at $\\sqrt{s_\\mathrm{{NN}}} = 2.76$ TeV was measured with the ALICE detector at the LHC. The results were obtained with the Scalar Product method, a two-particle correlation technique, using a pseudo-rapidity gap of $|\\Delta\\eta| > 0.9$ between the identified hadron under study and the reference particles. The $v_2$ is reported for $\\pi^{\\pm}$, $\\mathrm{K}^{\\pm}$, $\\mathrm{K}^0_\\mathrm{S}$, p+$\\overline{\\mathrm{p}}$, $\\mathrm{\\phi}$, $\\Lambda$+$\\overline{\\mathrm{\\Lambda}}$, $\\Xi^-$+$\\overline{\\Xi}^+$ and $\\Omega^-$+$\\overline{\\Omega}^+$ in several collision centralities. In the low transverse momentum ($p_{\\mathrm{T}}$) region, $p_{\\mathrm{T}} 3 $GeV/$c$.

  13. Azimuthally anisotropic emission of low-momentum direct photons in Au$+$Au collisions at $\\sqrt{s_{_{NN}}}=200$ GeV

    CERN Document Server

    Adare, A; Aidala, C; Ajitanand, N N; Akiba, Y; Akimoto, R; Al-Bataineh, H; Alexander, J; Alfred, M; Al-Ta'ani, H; Angerami, A; Aoki, K; Apadula, N; Aramaki, Y; Asano, H; Aschenauer, E C; Atomssa, E T; Averbeck, R; Awes, T C; Azmoun, B; Babintsev, V; Bai, M; Baksay, G; Baksay, L; Bandara, N S; Bannier, B; Barish, K N; Bassalleck, B; Basye, A T; Bathe, S; Baublis, V; Baumann, C; Baumgart, S; Bazilevsky, A; Beaumier, M; Beckman, S; Belikov, S; Belmont, R; Bennett, R; Berdnikov, A; Berdnikov, Y; Bickley, A A; Blau, D S; Bok, J S; Boyle, K; Brooks, M L; Bryslawskyj, J; Buesching, H; Bumazhnov, V; Bunce, G; Butsyk, S; Camacho, C M; Campbell, S; Castera, P; Chen, C -H; Chi, C Y; Chiu, M; Choi, I J; Choi, J B; Choi, S; Choudhury, R K; Christiansen, P; Chujo, T; Chung, P; Chvala, O; Cianciolo, V; Citron, Z; Cole, B A; Connors, M; Constantin, P; Csanád, M; Csörgő, T; Dahms, T; Dairaku, S; Danchev, I; Danley, D; Das, K; Datta, A; Daugherity, M S; David, G; DeBlasio, K; Dehmelt, K; Denisov, A; 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Velkovska, J; Vértesi, R; Vinogradov, A A; Virius, M; Vossen, A; Vrba, V; Vznuzdaev, E; Wang, X R; Watanabe, D; Watanabe, K; Watanabe, Y; Watanabe, Y S; Wei, F; Wei, R; Wessels, J; White, A S; White, S N; Winter, D; Wolin, S; Wood, J P; Woody, C L; Wright, R M; Wysocki, M; Xia, B; Xie, W; Xue, L; Yalcin, S; Yamaguchi, Y L; Yamaura, K; Yang, R; Yanovich, A; Ying, J; Yokkaichi, S; Yoo, J H; Yoon, I; You, Z; Young, G R; Younus, I; Yu, H; Yushmanov, I E; Zajc, W A; Zelenski, A; Zhang, C; Zhou, S; Zolin, L; Zou, L

    2015-01-01

    The PHENIX experiment at the Relativistic Heavy Ion Collider has measured 2nd and 3rd order Fourier coefficients of the azimuthal distributions of direct photons emitted at midrapidity in Au$+$Au collisions at $\\sqrt{s_{_{NN}}}=200$ GeV for various collision centralities. Combining two different analysis techniques, results were obtained in the transverse momentum range of $0.4

  14. Production of identified particles in p–Pb collisions at √sNN = 5.02 TeV measured with ALICE

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    Transverse momentum distributions of identified particles have been measured in several multiplicity classes in p-Pb collisions at sqrt(s_NN) = 5.02 TeV. This measurement can shed light on the understanding of possible collective effects in high multiplicity events. Furthermore p-Pb collisions bridge the charged multiplicity gap between pp and low multiplicity Pb–Pb collisions. Studying the particle production in this region can improve the understanding of the underlying production mechanisms. Particles are reconstructed with the central barrel detectors over a wide transverse momentum range (from 0 up to 15 GeV/c), exploiting different identification techniques.   Primary charged particles (pions, kaons, protons, antiprotons, deuterons and anti-deuterons) are identified by their specific energy loss (dE/dx) and time-of-flight. Weakly decaying particles are identified by their characteristic decay topology. Particle-production yields, spectral shapes and particle ratios have been m...

  15. Event shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at $\\sqrt{s_\\rm{NN}}=2.76$ TeV

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Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastroserio, Annalisa; Masui, Hiroshi; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Minervini, Lazzaro Manlio; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Murray, Sean; Musa, Luciano; Musinsky, Jan; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Nattrass, Christine; Nayak, Kishora; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Paola; Paic, Guy; Pajares Vales, Carlos; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Pant, Divyash; Papcun, Peter; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; 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 Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rivetti, Angelo; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Romita, Rosa; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salgado Lopez, Carlos Alberto; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schuster, Tim Robin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Seo, Jeewon; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Shigaki, Kenta; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Soegaard, Carsten; Soltz, Ron Ariel; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stefanek, Grzegorz; Steinpreis, Matthew Donald; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Sultanov, Rishat; Sumbera, Michal; Symons, Timothy; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tangaro, Marco-Antonio; Tapia Takaki, Daniel Jesus; Tarantola Peloni, Attilio; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Wang, Yifei; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Wessels, Johannes Peter; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yaldo, Chris G; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym

    2016-03-31

    We report on results obtained with the Event Shape Engineering technique applied to Pb-Pb collisions at $\\sqrt{s_\\rm{NN}}=2.76$ TeV. By selecting events in the same centrality interval, but with very different average flow, different initial state conditions can be studied. We find the effect of the event-shape selection on the elliptic flow coefficient $v_2$ to be almost independent of transverse momentum $p_\\rm{T}$, as expected if this effect is due to fluctuations in the initial geometry of the system. Charged hadron, pion, kaon, and proton transverse momentum distributions are found to be harder in events with higher-than-average elliptic flow, indicating an interplay between radial and elliptic flow.

  16. Pseudorapidity density of charged particles and its centrality dependence in Pb–Pb collisions at √(s{sub NN})=2.76TeV

    Energy Technology Data Exchange (ETDEWEB)

    Guilbaud, Maxime

    2013-05-02

    We present the measurement of the charged-particle pseudorapidity (η) density distribution, (dN{sub ch})/(dη) , for a number of centrality bins in Pb–Pb collisions at √(s{sub NN})=2.76TeV over a wide pseudorapidity range. Using the innermost pixel layers of the ALICE tracking system and the ALICE forward detectors (VZERO and FMD), we cover the pseudorapidity range: −5<η<5.5. The analysis is performed using a dedicated technique utilizing the collisions with LHC ‘satellite’ bunches. These collisions have displaced vertices in the range −187.5

  17. 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

  18. 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.

  19. Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders

    KAUST Repository

    Marquet, Pierre

    2014-09-22

    Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

  20. Neural networks combined with region growing techniques for tumor detection in [18F]-fluorothymidine dynamic positron emission tomography breast cancer studies

    Science.gov (United States)

    Cseh, Zoltan; Kenny, Laura; Swingland, James; Bose, Subrata; Turheimer, Federico E.

    2013-03-01

    Early detection and precise localization of malignant tumors has been a primary challenge in medical imaging in recent years. Functional modalities play a continuously increasing role in these efforts. Image segmentation algorithms which enable automatic, accurate tumor visualization and quantification on noisy positron emission tomography (PET) images would significantly improve the quality of treatment planning processes and in turn, the success of treatments. In this work a novel multistep method has been applied in order to identify tumor regions in 4D dynamic [18F] fluorothymidine (FLT) PET studies of patients with locally advanced breast cancer. In order to eliminate the effect of inherently detectable high inhomogeneity inside tumors, specific voxel-kinetic classes were initially introduced by finding characteristic FLT-uptake curves with K-means algorithm on a set of voxels collected from each tumor. Image voxel sets were then split based on voxel time-activity curve (TAC) similarities, and models were generated separately on each voxel set. At first, artificial neural networks, in comparison with linear classification algorithms were applied to distinguish tumor and healthy regions relying on the characteristics of TACs of the individual voxels. The outputs of the best model with very high specificity were then used as input seeds for region shrinking and growing techniques, the application of which considerably enhanced the sensitivity and specificity (78.65% +/- 0.65% and 98.98% +/- 0.03%, respectively) of the final image segmentation model.

  1. Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

    Science.gov (United States)

    Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

    2016-12-01

    The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

  2. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

    Science.gov (United States)

    Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L; Nicolau, Alex; Veidenbaum, Alexander V

    2009-01-01

    Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, biologically realistic, large-scale SNN simulator that runs on a single GPU. The SNN model includes Izhikevich spiking neurons, detailed models of synaptic plasticity and variable axonal delay. We allow user-defined configuration of the GPU-SNN model by means of a high-level programming interface written in C++ but similar to the PyNN programming interface specification. PyNN is a common programming interface developed by the neuronal simulation community to allow a single script to run on various simulators. The GPU implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7 Hz. For simulation of 10 Million synaptic connections and 100K neurons, the GPU SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results was validated on CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. Our simulator is publicly available to the modeling community so that researchers will have easy access to large-scale SNN simulations.

  3. [Identification methods of crop and weeds based on Vis/NIR spectroscopy and RBF-NN model].

    Science.gov (United States)

    Zhu, Deng-Sheng; Pan, Jia-Zhi; He, Yong

    2008-05-01

    The automated recognition of crop and weed by using Vis/NIR spectral in field is one of hottest research branches of agriculture engineering. If the recognition is efficient and effective, then the variate operations of herbicide or fertilizer spraying in field could be realized. Many researches have pointed out that the reflectance rate of green plant leaves could be used to identify the varieties. As the colors and surface textures of crop and weed were change in different living phases, these changes may exert great influence on the reflectance spectral of plant leaves. Vis/NIR spectra of three weeds and one crop in two different terms were recorded by spectral meter ASD FieldSpec Pro FR. Its wave band is from 325 to 1 075 nm. The scan time was 270 ms. The scanning times of per sample was set to 30 times. Firstly, 23 days after the planting of soybean, some soybean leaves and weeds leave were picked from the field, and brought to lab to record spectral. The lighting condition was controlled by an artificial halogen bulb. Secondly, on the 45th day, the same experiment was done. The three weeds were goose grass, alligator alternanthera and emarginate amaranth. The crop was soybean seedling. Totally 378 samples were drawn for two terms. As one original reflectance spectrum contains 651 float numbers, the total data volume was huge. Using wavelet transform to compress data volume and extract characteristic spectral data was tried. The result was 114 float numbers per sample. Among them, 250 samples from two terms were used as input data to build artificial neural network model, and those left were used to check the validation. Radial basis function neural network model is widely used in pattern recognition problems. It is a nonlinear and self adaptive parallel. By assigning a 1 by 4 vector to each spectral samples, the source data could be used to build an RBF-NN model. All the samples were assigned these standard output data. Then, the left 128 samples were used to

  4. Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

    Science.gov (United States)

    CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

    2014-01-01

    Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

  5. Fuzzy logic, neural networks, and soft computing

    Science.gov (United States)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  6. A comparison of the spatial linear model to Nearest Neighbor (k-NN) methods for forestry applications.

    Science.gov (United States)

    Ver Hoef, Jay M; Temesgen, Hailemariam

    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, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages.

  7. Elliptic flow in Au+Au collisions at $\\sqrt{S_{NN}}$=130 GeV

    CERN Document Server

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