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Sample records for two-layer feed-forward neural

  1. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  2. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

  3. Prediction of metal corrosion using feed-forward neural networks

    International Nuclear Information System (INIS)

    Mahjani, M.G.; Jalili, S.; Jafarian, M.; Jaberi, A.

    2004-01-01

    The reliable prediction of corrosion behavior for the effective control of corrosion is a fundamental requirement. Since real world corrosion never seems to involve quite the same conditions that have previously been tested, using corrosion literature does not provide the necessary answers. In order to provide a methodology for predicting corrosion in real and complex situations, artificial neural networks can be utilized. Feed-forward artificial neural network (FFANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the human brain process information.The aim of the present work is to predict corrosion behavior in critical conditions, such as industrial applications, based on some laboratory experimental data. Electrochemical behavior of stainless steel in different conditions were studied, using polarization technique and Tafel curves. Back-propagation neural networks models were developed to predict the corrosion behavior. The trained networks result in predicted value in good comparison to the experimental data. They have generally been claimed to be successful in modeling the corrosion behavior. The results are presented in two tables. Table 1 gives corrosion behavior of stainless-steel as a function of pH and CuSO 4 concentration and table 2 gives corrosion behavior of stainless - steel as a function of electrode surface area and CuSO 4 concentration. (authors)

  4. Feed-Forward Neural Networks and Minimal Search Space Learning

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman

    2005-01-01

    Roč. 4, č. 12 (2005), s. 1867-1872 ISSN 1109-2750 R&D Projects: GA ČR GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : search space * feed-forward networks * genetic algorithm s Subject RIV: BA - General Mathematics

  5. Classification of Urinary Calculi using Feed-Forward Neural Networks

    African Journals Online (AJOL)

    NJD

    Genetic algorithms were used for optimization of neural networks and for selection of the ... Urinary calculi, infrared spectroscopy, classification, neural networks, variable ..... note that the best accuracy is obtained for whewellite, weddellite.

  6. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    Science.gov (United States)

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  7. Study on feed forward neural network convex optimization for LiFePO4 battery parameters

    Science.gov (United States)

    Liu, Xuepeng; Zhao, Dongmei

    2017-08-01

    Based on the modern facility agriculture automatic walking equipment LiFePO4 Battery, the parameter identification of LiFePO4 Battery is analyzed. An improved method for the process model of li battery is proposed, and the on-line estimation algorithm is presented. The parameters of the battery are identified using feed forward network neural convex optimization algorithm.

  8. Training feed-forward neural networks with gain constraints

    Science.gov (United States)

    Hartman

    2000-04-01

    Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

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

    Science.gov (United States)

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

    2013-09-01

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

  10. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    Science.gov (United States)

    Kmet', Tibor; Kmet'ová, Mária

    2009-09-01

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  11. Selection of hadronic W-decays in DELPHI with feed forward neural networks - An update

    CERN Document Server

    Becks, K H; Müller, U; Wahlen, H

    2003-01-01

    Since 1998 feed forward neural networks have been successfully applied to select candidates of hadronic W-decays measured at different center of mass-energies by the DELPHI collaboration at the Large Electron Positron collider at CERN. To prepare the final publication, the neural network was adapted to all center of mass- energies. Detailed studies were performed concerning the level of preselection, the choice of network parameters and especially of the network architecture. The number of hidden nodes was optimized by testing different pruning methods. All studies and results will be discussed.

  12. Selection of hadronic W-decays in DELPHI with feed forward neural networks - an update

    International Nuclear Information System (INIS)

    Becks, K.-H.; Drees, J.; Mueller, U.; Wahlen, H.

    2003-01-01

    Since 1998 feed forward neural networks have been successfully applied to select candidates of hadronic W-decays measured at different center of mass-energies by the DELPHI collaboration at the Large Electron Positron collider at CERN. To prepare the final publication, the neural network was adapted to all center of mass-energies. Detailed studies were performed concerning the level of preselection, the choice of network parameters and especially of the network architecture. The number of hidden nodes was optimized by testing different pruning methods. All studies and results will be discussed

  13. Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

    International Nuclear Information System (INIS)

    Makaveev, Dimitre; Dupre, Luc; De Wulf, Marc; Melkebeek, Jan

    2001-01-01

    A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling. [copyright] 2001 American Institute of Physics

  14. Modeling of an industrial process of pleuromutilin fermentation using feed-forward neural networks

    Directory of Open Access Journals (Sweden)

    L. Khaouane

    2013-03-01

    Full Text Available This work investigates the use of artificial neural networks in modeling an industrial fermentation process of Pleuromutilin produced by Pleurotus mutilus in a fed-batch mode. Three feed-forward neural network models characterized by a similar structure (five neurons in the input layer, one hidden layer and one neuron in the output layer are constructed and optimized with the aim to predict the evolution of three main bioprocess variables: biomass, substrate and product. Results show a good fit between the predicted and experimental values for each model (the root mean squared errors were 0.4624% - 0.1234 g/L and 0.0016 mg/g respectively. Furthermore, the comparison between the optimized models and the unstructured kinetic models in terms of simulation results shows that neural network models gave more significant results. These results encourage further studies to integrate the mathematical formulae extracted from these models into an industrial control loop of the process.

  15. LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING

    Directory of Open Access Journals (Sweden)

    OMER MAHMOUD

    2007-08-01

    Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.

  16. A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    M. Njah

    2017-06-01

    Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

  17. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  18. Development of Sorting System for Fishes by Feed-forward Neural Networks Using Rotation Invariant Features

    Science.gov (United States)

    Shiraishi, Yuhki; Takeda, Fumiaki

    In this research, we have developed a sorting system for fishes, which is comprised of a conveyance part, a capturing image part, and a sorting part. In the conveyance part, we have developed an independent conveyance system in order to separate one fish from an intertwined group of fishes. After the image of the separated fish is captured in the capturing part, a rotation invariant feature is extracted using two-dimensional fast Fourier transform, which is the mean value of the power spectrum with the same distance from the origin in the spectrum field. After that, the fishes are classified by three-layered feed-forward neural networks. The experimental results show that the developed system classifies three kinds of fishes captured in various angles with the classification ratio of 98.95% for 1044 captured images of five fishes. The other experimental results show the classification ratio of 90.7% for 300 fishes by 10-fold cross validation method.

  19. 3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

    Directory of Open Access Journals (Sweden)

    Emmanouil Piperakis

    2003-06-01

    Full Text Available In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly - without decompression - used for rendering. The neural compression - representation is viable to 3D transformations without the need of any anti-aliasing techniques - transformations do not disrupt the accuracy of the geometry. Our method does not su.er any scaling problem and was tested with objects of 300 to 107 polygons - such as the David of Michelangelo - achieving in all cases an order of O(b3 less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.

  20. A feed forward neural network for classification of bull's-eye myocardial perfusion images

    International Nuclear Information System (INIS)

    Hamilton, D.; Riley, P.J.; Miola, U.J.; Amro, A.A.

    1995-01-01

    Identification of hypoperfused areas in myocardial perfusion single-photon emission tomography studies can be aided by bull's-eye representation of raw counts, lesion extent and lesion severity, the latter two being produced by comparison of the raw bull's-eye data with a normal data base. An artificial intelligence technique which is presently becoming widely popular and which is particularly suitable for pattern recognition is that of artificial neural network. We have studied the ability of feed forward neural networks to extract patterns from bull's-eye data by assessing their capability to predict lesion presence without direct comparison with a normal data base. Studies were undertaken on both simulation data and on real stress-rest data obtained from 410 male patients undergoing routine thallium-201 myocardial perfusion scintigraphy. The ability of trained neural networks to predict lesion presence was quantified by calculating the areas under receiver operating characteristic curves. Figures as high as 0.96 for non-preclassified patient data were obtained, corresponding to an accuracy of 92%. The results demonstrate that neural networks can accurately classify patterns from bull's-eye myocardial perfusion images and detect the presence of hypoperfused areas without the need for comparison with a normal data base. Preliminary work suggests that this technique could be used to study perfusion patterns in the myocardium and their correlation with clinical parameters. (orig.)

  1. Interpretation of correlated neural variability from models of feed-forward and recurrent circuits

    Science.gov (United States)

    2018-01-01

    Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. PMID:29408930

  2. Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2018-02-01

    Full Text Available Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

  3. Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

    Directory of Open Access Journals (Sweden)

    Hossein Jafari Mansoorian

    2017-01-01

    Full Text Available Background & Aims of the Study: A feed forward artificial neural network (FFANN was developed to predict the efficiency of total petroleum hydrocarbon (TPH removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments.

  4. Selection of W-pair-production in DELPHI with feed-forward neural networks

    International Nuclear Information System (INIS)

    Becks, K.-H.; Buschmann, P.; Drees, J.; Mueller, U.; Wahlen, H.

    2001-01-01

    Since 1998 feed-forward networks have been applied for the separation of hadronic WW-decays from background processes measured by the DELPHI collaboration at different center-of-mass energies of the Large Electron Positron collider at CERN. Prior to the publication of the 189 GeV results intensive studies of systematic effects and uncertainties were performed. The methods and results will be discussed and compared to standard selection procedures

  5. Accurate estimation of CO2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN algorithm

    Directory of Open Access Journals (Sweden)

    Alireza Rostami

    2018-03-01

    Full Text Available Global warming due to greenhouse effect has been considered as a serious problem for many years around the world. Among the different gases which cause greenhouse gas effect, carbon dioxide is of great difficulty by entering into the surrounding atmosphere. So CO2 capturing and separation especially by adsorption is one of the most interesting approaches because of the low equipment cost, ease of operation, simplicity of design, and low energy consumption.In this study, experimental results are presented for the adsorption equilibria of carbon dioxide on activated carbon. The adsorption equilibrium data for carbon dioxide were predicted with two commonly used isotherm models in order to compare with multi-layer feed-forward neural network (MLFNN algorithm for a wide range of partial pressure. As a result, the ANN-based algorithm shows much better efficiency and accuracy than the Sips and Langmuir isotherms. In addition, the applicability of the Sips and Langmuir models are limited to isothermal conditions, even though the ANN-based algorithm is not restricted to the constant temperature condition. Consequently, it is proved that MLFNN algorithm is a promising model for calculation of CO2 adsorption density on activated carbon. Keywords: Global warming, CO2 adsorption, Activated carbon, Multi-layer feed-forward neural network algorithm, Statistical quality measures

  6. A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

    Science.gov (United States)

    Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

    2016-06-01

    There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

  7. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  8. Precision requirements for single-layer feed-forward neural networks

    NARCIS (Netherlands)

    Annema, Anne J.; Hoen, K.; Hoen, Klaas; Wallinga, Hans

    1994-01-01

    This paper presents a mathematical analysis of the effect of limited precision analog hardware for weight adaptation to be used in on-chip learning feedforward neural networks. Easy-to-read equations and simple worst-case estimations for the maximum tolerable imprecision are presented. As an

  9. Implementation of a feed-forward artificial neural network in VHDL on FPGA

    NARCIS (Netherlands)

    Dondon, P.; Carvalho, J.; Gardere, R.; Lahalle, P.; Tsenov, G.; Mladenov, V.M.; Reljin, B.; Stankovic, S.

    2014-01-01

    Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and

  10. Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller

    International Nuclear Information System (INIS)

    Nur Aira Abd Rahman

    2005-01-01

    Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)

  11. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  12. Design of a universal two-layered neural network derived from the PLI theory

    Science.gov (United States)

    Hu, Chia-Lun J.

    2004-05-01

    The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

  13. Two-Layer Feedback Neural Networks with Associative Memories

    International Nuclear Information System (INIS)

    Gui-Kun, Wu; Hong, Zhao

    2008-01-01

    We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)

  14. In vivo temporal property of GABAergic neural transmission in collateral feed-forward inhibition system of hippocampal-prefrontal pathway.

    Science.gov (United States)

    Takita, Masatoshi; Kuramochi, Masahito; Izaki, Yoshinori; Ohtomi, Michiko

    2007-05-30

    Anatomical evidence suggests that rat CA1 hippocampal afferents collaterally innervate excitatory projecting pyramidal neurons and inhibitory interneurons, creating a disynaptic, feed-forward inhibition microcircuit in the medial prefrontal cortex (mPFC). We investigated the temporal relationship between the frequency of paired synaptic transmission and gamma-aminobutyric acid (GABA)ergic receptor-mediated modulation of the microcircuit in vivo under urethane anesthesia. Local perfusions of a GABAa antagonist (-)-bicuculline into the mPFC via microdialysis resulted in a statistically significant disinhibitory effect on intrinsic GABA action, increasing the first and second mPFC responses following hippocampal paired stimulation at interstimulus intervals of 100-200 ms, but not those at 25-50 ms. This (-)-bicuculline-induced disinhibition was compensated by the GABAa agonist muscimol, which itself did not attenuate the intrinsic oscillation of the local field potentials. The perfusion of a sub-minimal concentration of GABAb agonist (R)-baclofen slightly enhanced the synaptic transmission, regardless of the interstimulus interval. In addition to the tonic control by spontaneous fast-spiking GABAergic neurons, it is clear the sequential transmission of the hippocampal-mPFC pathway can phasically drive the collateral feed-forward inhibition system through activation of a GABAa receptor, bringing an active signal filter to the various types of impulse trains that enter the mPFC from the hippocampus in vivo.

  15. Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    Jie-Sheng Wang

    2015-01-01

    Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, a feed-forward neural network (FNN based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO algorithm and gravitational search algorithm (GSA is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

  16. Prediction of the binary density of the ILs+ water using back-propagated feed forward artificial neural network

    Directory of Open Access Journals (Sweden)

    Shojaee Safar Ali

    2014-01-01

    Full Text Available In this study, feasibility of a back-propagated artificial neural network to correlate the binary density of ionic liquids (ILs mixtures containing water as the common solvent has been investigated. To verify the optimized parameters of the neural network, total of 1668 data were collected and divided into two different subsets. The first subsets consisted of more than two-third (1251 data points of data bank was used to find the optimum parameters including weights and biases, number of neurons (7 neurons, transfer functions in hidden and output layer which were tansig and purelin, respectively. In addition, the correlative capability of network was examined using testing subset (417 data points not considered during the training stage. The overall obtained results revealed that the proposed network is accurate enough to correlate the binary density of the ionic liquids mixtures with average absolute relative deviation (AARD % and average relative deviation (ARD % of 1.56% and -0.04 %, respectively. Finally, the correlative capability of the proposed ANN model was compared with one of the available correlations proposed by Rodríguez and Brennecke.

  17. Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

    Science.gov (United States)

    Abiriand Bhekisipho Twala, Olufunminiyi

    2017-08-01

    In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.

  18. Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks

    Directory of Open Access Journals (Sweden)

    Reinhold Decker

    2014-11-01

    Full Text Available In the recent past, the quantitative analysis of online product reviews (OPRs has become a popular manifestation of marketing intelligence activities focusing on products that are frequently subject to electronic word-of-mouth (eWOM. Typical elements of OPRs are overall star ratings, product at- tribute scores, recommendations, pros and cons, and free texts. The first three elements are of pa r- ticular interest because they provide an aggregate view of reviewers’ opinions about the products of interest. However, the significance of individual product attributes in the overall evaluation pro c- ess  can  vary  in  the  course  of  time.  Accordingly,  ad  hoc  analyses  of  OPRs  that  have  been downloaded at a certain point in time are of limited value for dynamic eWOM monitoring because of their snapshot character. On the other hand, opinion platforms can increase the meaningfulness of the OPRs posted there and, therewith, the usefulness of the platform as a whole, by directing eWOM activities to those product attributes that really matter at present. This paper therefore in- troduces a neural network-based approach that allows the dynamic tracking of the influence the posted scores of product attributes have on the overall star ratings of the concerning products. By using an elasticity measure, this approach supports the identification of those attributes that tend to lose or gain significance in the product evaluation process over time. The usability of this ap- proach is demonstrated using real OPR data on digital cameras and hotels.

  19. Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases

    Science.gov (United States)

    Malshe, M.; Raff, L. M.; Hagan, M.; Bukkapatnam, S.; Komanduri, R.

    2010-05-01

    The variation in the fitting accuracy of neural networks (NNs) when used to fit databases comprising potential energies obtained from ab initio electronic structure calculations is investigated as a function of the number and nature of the elements employed in the input vector to the NN. Ab initio databases for H2O2, HONO, Si5, and H2CCHBr were employed in the investigations. These systems were chosen so as to include four-, five-, and six-body systems containing first, second, third, and fourth row elements with a wide variety of chemical bonding and whose conformations cover a wide range of structures that occur under high-energy machining conditions and in chemical reactions involving cis-trans isomerizations, six different types of two-center bond ruptures, and two different three-center dissociation reactions. The ab initio databases for these systems were obtained using density functional theory/B3LYP, MP2, and MP4 methods with extended basis sets. A total of 31 input vectors were investigated. In each case, the elements of the input vector were chosen from interatomic distances, inverse powers of the interatomic distance, three-body angles, and dihedral angles. Both redundant and nonredundant input vectors were investigated. The results show that among all the input vectors investigated, the set employed in the Z-matrix specification of the molecular configurations in the electronic structure calculations gave the lowest NN fitting accuracy for both Si5 and vinyl bromide. The underlying reason for this result appears to be the discontinuity present in the dihedral angle for planar geometries. The use of trigometric functions of the angles as input elements produced significantly improved fitting accuracy as this choice eliminates the discontinuity. The most accurate fitting was obtained when the elements of the input vector were taken to have the form Rij-n, where the Rij are the interatomic distances. When the Levenberg-Marquardt procedure was modified

  20. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

  1. neural network based model o work based model of an industrial oil

    African Journals Online (AJOL)

    eobe

    technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

  2. Learning behavior and temporary minima of two-layer neural networks

    NARCIS (Netherlands)

    Annema, Anne J.; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

    1994-01-01

    This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of

  3. A two-layer recurrent neural network for nonsmooth convex optimization problems.

    Science.gov (United States)

    Qin, Sitian; Xue, Xiaoping

    2015-06-01

    In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

  4. Theoretical properties of the global optimizer of two layer neural network

    OpenAIRE

    Boob, Digvijay; Lan, Guanghui

    2017-01-01

    In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves "almost" all functions which are not piecewise linear), we have that first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. ...

  5. Natural Language Processing with Small Feed-Forward Networks

    OpenAIRE

    Botha, Jan A.; Pitler, Emily; Ma, Ji; Bakalov, Anton; Salcianu, Alex; Weiss, David; McDonald, Ryan; Petrov, Slav

    2017-01-01

    We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory...

  6. Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

    Science.gov (United States)

    Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan

    2001-07-01

    A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.

  7. Feed-forward general-purpose computer

    Energy Technology Data Exchange (ETDEWEB)

    Yamada, H; Yoshioka, Y; Nakamura, T; Shigei, Y

    1983-08-01

    The feed forward machine (FFM) proposed by the authors has a CPU composed of many fixed arithmetic units and registers. Many features of the FFM which are compatible with concurrent operating and reduce the instruction requirement for store are reported. In order to evaluate the FFM, the minimum execution time of instructions is discussed by using the Petri Net model. From this it is predicted that the execution time will be 0.46-0.6 times the real execution time. Furthermore, it is concluded that the program for the FFM will be reduced in size with respect to the program for the Von Neumann computers. 12 references.

  8. Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü = Forecasting of Turkey Inflation with Hybrid of Feed Forward and Recurrent Artifical Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Rezan USLU

    2010-01-01

    Full Text Available Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI. A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.

  9. The mechanism of synchronization in feed-forward neuronal networks

    International Nuclear Information System (INIS)

    Goedeke, S; Diesmann, M

    2008-01-01

    Synchronization in feed-forward subnetworks of the brain has been proposed to explain the precisely timed spike patterns observed in experiments. While the attractor dynamics of these networks is now well understood, the underlying single neuron mechanisms remain unexplained. Previous attempts have captured the effects of the highly fluctuating membrane potential by relating spike intensity f(U) to the instantaneous voltage U generated by the input. This article shows that f is high during the rise and low during the decay of U(t), demonstrating that the U-dot-dependence of f, not refractoriness, is essential for synchronization. Moreover, the bifurcation scenario is quantitatively described by a simple f(U,U-dot) relationship. These findings suggest f(U,U-dot) as the relevant model class for the investigation of neural synchronization phenomena in a noisy environment

  10. Feed forward control: An implementation at CIRFEL

    International Nuclear Information System (INIS)

    Krishnaswamy, J.; Lehrman, I.S.; Hartley, R.

    1995-01-01

    An integral part of the Compact InfraRed Free Electron LASER (CIRFEL) is control of the phase and amplitude stability in the RF power system. We have implemented such a Feed Forward system using the LabView software package, by National Instruments. We will discuss implementation and performance data of the Feed Forward control of the RF power system at CIRFEL. We will also briefly discuss some conditions under which the problem is ill-conditioned, and what idealizations can be made to remedy these ill-conditioned systems. Using an arbitrary function generator, we generate a driving signal for a voltage-controlled attenuator at the input side of the RF system, and we monitor the RF voltage in cell I of the photocathode gun using a digital storage oscilliscope in averaging mode. The system is stable enough to use data from one shot to modify the inputs for future shots. After downloading the averaged data to a personal computer via a GPIB (IEEE 488) bus, we use a simple linear transformation on the difference waveform between the current shot and the target to produce a correction signal. This signal is added to the driving signal in the arbitrary function generator, and the process is repeated until we get the flatness we need in the output signals from cell 1. The system for phase control is similar, with a voltage-controlled phase shifter replacing the attenuator, and monitoring of the RF phase in cell I replacing the monitoring of RF voltage. By repeatedly alternating between correcting the RF voltage (equivalent to correcting the RF power) and RF phase in cell 1, we are able to achieve simultaneous phase variations of <±1 degrees and amplitude variations of <±0.1% over a 3μsec pulse

  11. Feed-Forward Control in Resonant DC Link Inverter

    OpenAIRE

    Apinan Aurasopon; Worawat Sa-ngiavibool

    2008-01-01

    This paper proposes a feed-forward control in resonant dc link inverter. The feed-forward control configuration is based on synchronous sigma-delta modulation. The simulation results showing the proposed technique can reject non-ideal dc bus improving the total harmonic distortion.

  12. On Optimal Input Design for Feed-forward Control

    OpenAIRE

    Hägg, Per; Wahlberg, Bo

    2013-01-01

    This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in a system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are an...

  13. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  14. Regularization and Complexity Control in Feed-forward Networks

    OpenAIRE

    Bishop, C. M.

    1995-01-01

    In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

  15. Adaptive feed forward in the LANL RF control system

    International Nuclear Information System (INIS)

    Ziomek, C.D.

    1992-01-01

    This paper describes an adaptive feed forward system that corrects repetitive errors in the amplitude and phase of the RF field of a pulsed accelerator. High-frequency disturbances that are beyond the effective bandwidth of the RF-field feedback control system can be eliminated with a feed forward system. Many RF-field disturbances for a pulsed accelerator are repetitive, occurring at the same relative time in every pulse. This design employs digital signal processing hardware to adaptively determine and track the control signals required to eliminate the repetitive errors in the feedback control system. In order to provide the necessary high-frequency response, the adaptive feed forward hardware provides the calculated control signal prior to the repetitive disturbance that it corrects. This system has been demonstrated to reduce the transient disturbances caused by beam pulses. Furthermore, it has been shown to negate high-frequency phase and amplitude oscillations in a high-power klystron amplifier caused by PFN ripple on the high-voltage. The design and results of the adaptive feed forward system are presented. (Author) 3 figs., 2 refs

  16. Learning and Generalisation in Neural Networks with Local Preprocessing

    OpenAIRE

    Kutsia, Merab

    2007-01-01

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

  17. Feed-forward segmentation of figure-ground and assignment of border-ownership.

    Directory of Open Access Journals (Sweden)

    Hans Supèr

    Full Text Available Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment.

  18. Feed-forward segmentation of figure-ground and assignment of border-ownership.

    Science.gov (United States)

    Supèr, Hans; Romeo, August; Keil, Matthias

    2010-05-19

    Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment.

  19. Protecting nonlocality of multipartite states by feed-forward control

    Science.gov (United States)

    Li, Xiao-Gang; Zou, Jian; Shao, Bin

    2018-06-01

    Nonlocality is a useful resource in quantum communication and quantum information processing. In practical quantum communication, multipartite entangled states must be distributed between different users in different places through a channel. However, the channel is usually inevitably disturbed by the environment in quantum state distribution processing and then the nonlocality of states will be weakened and even lost. In this paper, we use a feed-forward control scheme to protect the nonlocality of the Bell and GHZ states against dissipation. We find that this protection scheme is very effective, specifically, for the Bell state, we can increase the noise threshold from 0.5 to 0.98, and for GHZ state from 0.29 to 0.96. And we also find that entanglement is relatively easier to be protected than nonlocality. For our scheme, protecting entanglement is equivalent to protecting the state in the case of Bell state, while protecting nonlocality is not.

  20. Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex.

    OpenAIRE

    Swadlow, Harvey A

    2002-01-01

    Intracortical inhibition plays a role in shaping sensory cortical receptive fields and is mediated by both feed-forward and feedback mechanisms. Feed-forward inhibition is the faster of the two processes, being generated by inhibitory interneurons driven by monosynaptic thalamocortical (TC) input. In principle, feed-forward inhibition can prevent targeted cortical neurons from ever reaching threshold when TC input is weak. To do so, however, inhibitory interneurons must respond to TC input at...

  1. Two layer powder pressing

    International Nuclear Information System (INIS)

    Schreiner, H.

    1979-01-01

    First, significance and advantages of sintered materials consisting of two layers are pointed out. By means of the two layer powder pressing technique metal powders are formed resulting in compacts with high accuracy of shape and mass. Attributes of basic powders, different filling methods and pressing techniques are discussed. The described technique is supposed to find further applications in the field of two layer compacts in the near future

  2. Noise-enhanced categorization in a recurrently reconnected neural network

    International Nuclear Information System (INIS)

    Monterola, Christopher; Zapotocky, Martin

    2005-01-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails

  3. Noise-enhanced categorization in a recurrently reconnected neural network

    Science.gov (United States)

    Monterola, Christopher; Zapotocky, Martin

    2005-03-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.

  4. Feed-Forward Control of Kite Power Systems

    International Nuclear Information System (INIS)

    Fechner, Uwe; Schmehl, Roland

    2014-01-01

    Kite power technology is a novel solution to harvest wind energy from altitudes that can not be reached by conventional wind turbines. The use of a lightweight but strong tether in place of an expensive tower provides an additional cost advantage, next to the higher capacity factor. This paper describes a method to estimate the wind velocity at the kite using measurement data at the kite and at the ground. Focussing on a kite power system, which is converting the traction power of a kite in a pumping mode of operation, a reel-out speed predictor is presented for use in feed-forward control of the tether reel-out speed of the winch. The results show, that the developed feedforward controller improves the force control accuracy by a factor of two compared to the previously used feedback controller. This allows to use a higher set force during the reel-out phase which in turn increases the average power output by more than 4%. Due to its straightforward implementation and low computational requirements feedforward control is considered a promising technique for the reliable and efficient operation of traction-based kite power systems

  5. Adaptive Feed-Forward Control of Low Frequency Interior Noise

    CERN Document Server

    Kletschkowski, Thomas

    2012-01-01

    This book presents a mechatronic approach to Active Noise Control (ANC). It describes the required elements of system theory, engineering acoustics, electroacoustics and adaptive signal processing in a comprehensive, consistent and systematic manner using a unified notation. Furthermore, it includes a design methodology for ANC-systems, explains its application and describes tools to be used for ANC-system design. From the research point of view, the book presents new approaches to sound source localization in weakly damped interiors. One is based on the inverse finite element method, the other is based on a sound intensity probe with an active free field. Furthermore, a prototype of an ANC-system able to reach the physical limits of local (feed-forward) ANC is described. This is one example for applied research in ANC-system design. Other examples are given for (i) local ANC in a semi-enclosed subspace of an aircraft cargo hold and (ii) for the combination of audio entertainment with ANC.

  6. A Feed-forward Geometrical Compensation and Adaptive Feedback Control Algorithm for Hydraulic Robot Manipulators

    DEFF Research Database (Denmark)

    Conrad, Finn; Zhou, Jianjun; Gabacik, Andrzej

    1998-01-01

    Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control.......Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control....

  7. MIMO feed-forward design in wafer scanners using a gradient approximation-based algorithm

    NARCIS (Netherlands)

    Heertjes, M.F.; Hennekens, D.W.T.; Steinbuch, M.

    2010-01-01

    An experimental demonstration is given of a data-based multi-input multi-output (MIMO) feed-forward control design applied to the motion systems of a wafer scanner. Atop a nominal single-input single-output (SISO) feed-forward controller, a MIMO controller is designed having a finite impulse

  8. A low-complexity feed-forward I/Q imbalance compensation algorithm

    NARCIS (Netherlands)

    Moseley, N.A.; Slump, Cornelis H.

    2006-01-01

    This paper presents a low-complexity adaptive feed- forward I/Q imbalance compensation algorithm. The feed-forward so- lution has guaranteed stability. Due to its blind nature the algorithm is easily incorporated into an existing receiver design. The algorithm uses three estimators to obtain the

  9. Feed-forward motor control of ultrafast, ballistic movements.

    Science.gov (United States)

    Kagaya, K; Patek, S N

    2016-02-01

    To circumvent the limits of muscle, ultrafast movements achieve high power through the use of springs and latches. The time scale of these movements is too short for control through typical neuromuscular mechanisms, thus ultrafast movements are either invariant or controlled prior to movement. We tested whether mantis shrimp (Stomatopoda: Neogonodactylus bredini) vary their ultrafast smashing strikes and, if so, how this control is achieved prior to movement. We collected high-speed images of strike mechanics and electromyograms of the extensor and flexor muscles that control spring compression and latch release. During spring compression, lateral extensor and flexor units were co-activated. The strike initiated several milliseconds after the flexor units ceased, suggesting that flexor activity prevents spring release and determines the timing of strike initiation. We used linear mixed models and Akaike's information criterion to serially evaluate multiple hypotheses for control mechanisms. We found that variation in spring compression and strike angular velocity were statistically explained by spike activity of the extensor muscle. The results show that mantis shrimp can generate kinematically variable strikes and that their kinematics can be changed through adjustments to motor activity prior to the movement, thus supporting an upstream, central-nervous-system-based control of ultrafast movement. Based on these and other findings, we present a shishiodoshi model that illustrates alternative models of control in biological ballistic systems. The discovery of feed-forward control in mantis shrimp sets the stage for the assessment of targets, strategic variation in kinematics and the role of learning in ultrafast animals. © 2016. Published by The Company of Biologists Ltd.

  10. Unlearning in feed-forward multi-nets

    NARCIS (Netherlands)

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

    2001-01-01

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

  11. Hippocampus-driven feed-forward inhibition of the prefrontal cortex mediates relapse of extinguished fear.

    Science.gov (United States)

    Marek, Roger; Jin, Jingji; Goode, Travis D; Giustino, Thomas F; Wang, Qian; Acca, Gillian M; Holehonnur, Roopashri; Ploski, Jonathan E; Fitzgerald, Paul J; Lynagh, Timothy; Lynch, Joseph W; Maren, Stephen; Sah, Pankaj

    2018-03-01

    The medial prefrontal cortex (mPFC) has been implicated in the extinction of emotional memories, including conditioned fear. We found that ventral hippocampal (vHPC) projections to the infralimbic (IL) cortex recruited parvalbumin-expressing interneurons to counter the expression of extinguished fear and promote fear relapse. Whole-cell recordings ex vivo revealed that optogenetic activation of vHPC input to amygdala-projecting pyramidal neurons in the IL was dominated by feed-forward inhibition. Selectively silencing parvalbumin-expressing, but not somatostatin-expressing, interneurons in the IL eliminated vHPC-mediated inhibition. In behaving rats, pharmacogenetic activation of vHPC→IL projections impaired extinction recall, whereas silencing IL projectors diminished fear renewal. Intra-IL infusion of GABA receptor agonists or antagonists, respectively, reproduced these effects. Together, our findings describe a previously unknown circuit mechanism for the contextual control of fear, and indicate that vHPC-mediated inhibition of IL is an essential neural substrate for fear relapse.

  12. Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

    Science.gov (United States)

    DeMarse, Thomas B; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J; Wheeler, Bruce C

    2016-01-01

    Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized

  13. Position Control of Servo Systems Using Feed-Forward Friction Compensation

    International Nuclear Information System (INIS)

    Park, Min Gyu; Kim, Han Me; Shin, Jong Min; Kim, Jong Shik

    2009-01-01

    Friction is an important factor for precise position tracking control of servo systems. Servo systems with highly nonlinear friction are sensitive to the variation of operating condition. To overcome this problem, we use the LuGre friction model which can consider dynamic characteristics of friction. The LuGre friction model is used as a feed-forward compensator to improve tracking performance of servo systems. The parameters of the LuGre friction model are identified through experiments. The experimental result shows that the tracking performance of servo systems with higherly nonlinear friction can be improved by using feed-forward friction compensation

  14. Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit.

    Directory of Open Access Journals (Sweden)

    Tiffany Kee

    2015-10-01

    Full Text Available Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs, provide feed-forward inhibition onto Kenyon cells (KCs to maintain their sparse firing--a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN, is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.

  15. A novel controller for bipedal locomotion integrating feed-forward and feedback mechanisms

    NARCIS (Netherlands)

    Xiong, Xiaofeng; Sartori, Massimo; Dosen, Strahinja; González-Vargas, José; Wörgötter, Florentin; Farina, Dario; Ibanez, J.; González-Vargas, J.; Azorin, J.M.; Akay, M.; Pons, J.L.

    2017-01-01

    It has been recognized that bipedal locomotion is controlled using feed-forward (e.g., patterned) and feedback (e.g., reflex) control schemes. However, most current controllers fail to integrate the two schemes to simplify speed control of bipedal locomotion. To solve this problem, we here propose a

  16. High-speed linear optics quantum computing using active feed-forward.

    Science.gov (United States)

    Prevedel, Robert; Walther, Philip; Tiefenbacher, Felix; Böhi, Pascal; Kaltenbaek, Rainer; Jennewein, Thomas; Zeilinger, Anton

    2007-01-04

    As information carriers in quantum computing, photonic qubits have the advantage of undergoing negligible decoherence. However, the absence of any significant photon-photon interaction is problematic for the realization of non-trivial two-qubit gates. One solution is to introduce an effective nonlinearity by measurements resulting in probabilistic gate operations. In one-way quantum computation, the random quantum measurement error can be overcome by applying a feed-forward technique, such that the future measurement basis depends on earlier measurement results. This technique is crucial for achieving deterministic quantum computation once a cluster state (the highly entangled multiparticle state on which one-way quantum computation is based) is prepared. Here we realize a concatenated scheme of measurement and active feed-forward in a one-way quantum computing experiment. We demonstrate that, for a perfect cluster state and no photon loss, our quantum computation scheme would operate with good fidelity and that our feed-forward components function with very high speed and low error for detected photons. With present technology, the individual computational step (in our case the individual feed-forward cycle) can be operated in less than 150 ns using electro-optical modulators. This is an important result for the future development of one-way quantum computers, whose large-scale implementation will depend on advances in the production and detection of the required highly entangled cluster states.

  17. Investigating transfer gate potential barrier by feed-forward effect measurement

    NARCIS (Netherlands)

    Xu, Y.; Ge, X.; Theuwissen, A.J.P.

    2015-01-01

    In a 4T pixel, the transfer gate (TG) “OFF” surface potential is one of the important parameters, which determines the pinned photodiode (PPD) full well capacity. The feed-forward effect measurement is a powerful tool to characterize the relationship of the PPD injection potential and the

  18. Feed-Forward Corrections for Tune and Chromaticity Injection Decay During 2015 LHC Operation

    CERN Document Server

    Solfaroli Camillocci, Matteo; Lamont, Mike; Schaumann, Michaela; Todesco, Ezio; Wenninger, Jorg

    2016-01-01

    After two years of shutdown, the Large Hadron Collider (LHC) has been operated in 2015 at 6.5 TeV, close to its designed energy. When the current is stable at low field, the harmonic components of the main circuits are subject to a dynamic variation induced by current redistribution on the superconducting cables. The Field Description of the LHC (FiDel) foresaw an increase of the decay at injection of tune (quadrupolar components) and chromaticity (sextupolar components) of about 50% with respect to LHC Run1 due to the higher operational current. This paper discusses the beam-based measurements of the decay during the injection plateau and the implementation and accuracy of the feed-forward corrections as present in 2015. Moreover, the observed tune shift proportional to the circulating beam intensity and it's foreseen feed-forward correction are covered.

  19. A mixed incoherent feed-forward loop contributes to the regulation of bacterial photosynthesis genes.

    Science.gov (United States)

    Mank, Nils N; Berghoff, Bork A; Klug, Gabriele

    2013-03-01

    Living cells use a variety of regulatory network motifs for accurate gene expression in response to changes in their environment or during differentiation processes. In Rhodobacter sphaeroides, a complex regulatory network controls expression of photosynthesis genes to guarantee optimal energy supply on one hand and to avoid photooxidative stress on the other hand. Recently, we identified a mixed incoherent feed-forward loop comprising the transcription factor PrrA, the sRNA PcrZ and photosynthesis target genes as part of this regulatory network. This point-of-view provides a comparison to other described feed-forward loops and discusses the physiological relevance of PcrZ in more detail.

  20. Aversive Learning and Appetitive Motivation Toggle Feed-Forward Inhibition in the Drosophila Mushroom Body.

    Science.gov (United States)

    Perisse, Emmanuel; Owald, David; Barnstedt, Oliver; Talbot, Clifford B; Huetteroth, Wolf; Waddell, Scott

    2016-06-01

    In Drosophila, negatively reinforcing dopaminergic neurons also provide the inhibitory control of satiety over appetitive memory expression. Here we show that aversive learning causes a persistent depression of the conditioned odor drive to two downstream feed-forward inhibitory GABAergic interneurons of the mushroom body, called MVP2, or mushroom body output neuron (MBON)-γ1pedc>α/β. However, MVP2 neuron output is only essential for expression of short-term aversive memory. Stimulating MVP2 neurons preferentially inhibits the odor-evoked activity of avoidance-directing MBONs and odor-driven avoidance behavior, whereas their inhibition enhances odor avoidance. In contrast, odor-evoked activity of MVP2 neurons is elevated in hungry flies, and their feed-forward inhibition is required for expression of appetitive memory at all times. Moreover, imposing MVP2 activity promotes inappropriate appetitive memory expression in food-satiated flies. Aversive learning and appetitive motivation therefore toggle alternate modes of a common feed-forward inhibitory MVP2 pathway to promote conditioned odor avoidance or approach. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  1. Feed-forward and feedback projections of midbrain reticular formation neurons in the cat

    Directory of Open Access Journals (Sweden)

    Eddie ePerkins

    2014-01-01

    Full Text Available Gaze changes involving the eyes and head are orchestrated by brainstem gaze centers found within the superior colliculus (SC, paramedian pontine reticular formation (PPRF, and medullary reticular formation (MdRF. The mesencephalic reticular formation (MRF also plays a role in gaze. It receives a major input from the ipsilateral SC and contains cells that fire in relation to gaze changes. Moreover, it provides a feedback projection to the SC and feed-forward projections to the PPRF and MdRF. We sought to determine whether these MRF feedback and feed-forward projections originate from the same or different neuronal populations by utilizing paired fluorescent retrograde tracers in cats. Specifically, we tested: 1. whether MRF neurons that control eye movements form a single population by injecting the SC and PPRF with different tracers, and 2. whether MRF neurons that control head movements form a single population by injecting the SC and MdRF with different tracers. In neither case were double labeled neurons observed, indicating that feedback and feed-forward projections originate from separate MRF populations. In both cases, the labeled reticulotectal and reticuloreticular neurons were distributed bilaterally in the MRF. However, neurons projecting to the MdRF were generally constrained to the medial half of the MRF, while those projecting to the PPRF, like MRF reticulotectal neurons, were spread throughout the mediolateral axis. Thus, the medial MRF may be specialized for control of head movements, with control of eye movements being more widespread in this structure.

  2. Cell cycle regulation by feed-forward loops coupling transcription and phosphorylation

    DEFF Research Database (Denmark)

    Csikász-Nagy, Attila; Kapuy, Orsolya; Tóth, Attila

    2009-01-01

    of these EPs. From genome-scale data sets of budding yeast, we identify 126 EPs that are regulated by Cdk1 both through direct phosphorylation of the EP and through phosphorylation of the transcription factors that control expression of the EP, so that each of these EPs is regulated by a feed-forward loop (FFL......) from Cdk1. By mathematical modelling, we show that such FFLs can activate EPs at different phases of the cell cycle depending of the effective signs (+ or -) of the regulatory steps of the FFL. We provide several case studies of EPs that are controlled by FFLs exactly as our models predict. The signal...

  3. Feed-forward control of a solid oxide fuel cell system with anode offgas recycle

    Science.gov (United States)

    Carré, Maxime; Brandenburger, Ralf; Friede, Wolfgang; Lapicque, François; Limbeck, Uwe; da Silva, Pedro

    2015-05-01

    In this work a combined heat and power unit (CHP unit) based on the solid oxide fuel cell (SOFC) technology is analysed. This unit has a special feature: the anode offgas is partially recycled to the anode inlet. Thus it is possible to increase the electrical efficiency and the system can be operated without external water feeding. A feed-forward control concept which allows secure operating conditions of the CHP unit as well as a maximization of its electrical efficiency is introduced and validated experimentally. The control algorithm requires a limited number of measurement values and few deterministic relations for its description.

  4. A high-speed tunable beam splitter for feed-forward photonic quantum information processing.

    Science.gov (United States)

    Ma, Xiao-Song; Zotter, Stefan; Tetik, Nuray; Qarry, Angie; Jennewein, Thomas; Zeilinger, Anton

    2011-11-07

    We realize quantum gates for path qubits with a high-speed, polarization-independent and tunable beam splitter. Two electro-optical modulators act in a Mach-Zehnder interferometer as high-speed phase shifters and rapidly tune its splitting ratio. We test its performance with heralded single photons, observing a polarization-independent interference contrast above 95%. The switching time is about 5.6 ns, and a maximal repetition rate is 2.5 MHz. We demonstrate tunable feed-forward operations of a single-qubit gate of path-encoded qubits and a two-qubit gate via measurement-induced interaction between two photons.

  5. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

    Directory of Open Access Journals (Sweden)

    René Felix Reinhart

    2017-02-01

    Full Text Available Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

  6. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

    Science.gov (United States)

    Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

    2017-02-08

    Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

  7. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

    Science.gov (United States)

    Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

    2017-01-01

    Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697

  8. Visual language recognition with a feed-forward network of spiking neurons

    Energy Technology Data Exchange (ETDEWEB)

    Rasmussen, Craig E [Los Alamos National Laboratory; Garrett, Kenyan [Los Alamos National Laboratory; Sottile, Matthew [GALOIS; Shreyas, Ns [INDIANA UNIV.

    2010-01-01

    An analogy is made and exploited between the recognition of visual objects and language parsing. A subset of regular languages is used to define a one-dimensional 'visual' language, in which the words are translational and scale invariant. This allows an exploration of the viewpoint invariant languages that can be solved by a network of concurrent, hierarchically connected processors. A language family is defined that is hierarchically tiling system recognizable (HREC). As inspired by nature, an algorithm is presented that constructs a cellular automaton that recognizes strings from a language in the HREC family. It is demonstrated how a language recognizer can be implemented from the cellular automaton using a feed-forward network of spiking neurons. This parser recognizes fixed-length strings from the language in parallel and as the computation is pipelined, a new string can be parsed in each new interval of time. The analogy with formal language theory allows inferences to be drawn regarding what class of objects can be recognized by visual cortex operating in purely feed-forward fashion and what class of objects requires a more complicated network architecture.

  9. Local excitation-inhibition ratio for synfire chain propagation in feed-forward neuronal networks

    Science.gov (United States)

    Guo, Xinmeng; Yu, Haitao; Wang, Jiang; Liu, Jing; Cao, Yibin; Deng, Bin

    2017-09-01

    A leading hypothesis holds that spiking activity propagates along neuronal sub-populations which are connected in a feed-forward manner, and the propagation efficiency would be affected by the dynamics of sub-populations. In this paper, how the interaction between local excitation and inhibition effects on synfire chain propagation in feed-forward network (FFN) is investigated. The simulation results show that there is an appropriate excitation-inhibition (EI) ratio maximizing the performance of synfire chain propagation. The optimal EI ratio can significantly enhance the selectivity of FFN to synchronous signals, which thereby increases the stability to background noise. Moreover, the effect of network topology on synfire chain propagation is also investigated. It is found that synfire chain propagation can be maximized by an optimal interlayer linking probability. We also find that external noise is detrimental to synchrony propagation by inducing spiking jitter. The results presented in this paper may provide insights into the effects of network dynamics on neuronal computations.

  10. A Feed-Forward Control Realizing Fast Response for Three-Branch Interleaved DC-DC Converter in DC Microgrid

    Directory of Open Access Journals (Sweden)

    Haojie Wang

    2016-07-01

    Full Text Available It is a common practice for storage batteries to be connected to DC microgrid buses through DC-DC converters for voltage support on islanded operation mode. A feed-forward control based dual-loop constant voltage PI control for three-branch interleaved DC-DC converters (TIDC is proposed for storage batteries in DC microgrids. The working principle of TIDC is analyzed, and the factors influencing the response rate based on the dual-loop constant voltage control for TIDC are discussed, and then the method of feed-forward control for TIDC is studied to improve the response rate for load changing. A prototype of the TIDC is developed and an experimental platform is built. The experiment results show that DC bus voltage sags or swells caused by load changing can be reduced and the time for voltage recovery can be decreased significantly with the proposed feed-forward control.

  11. A Feed-Forward Control Realizing Fast Response for Three-Branch Interleaved DC-DC Converter in DC Microgrid

    DEFF Research Database (Denmark)

    Wang, Haojie; Han, Minxiao; Yan, Wenli

    2016-01-01

    It is a common practice for storage batteries to be connected to DC microgrid buses through DC-DC converters for voltage support on islanded operation mode. A feed-forward control based dual-loop constant voltage PI control for three-branch interleaved DC-DC converters (TIDC) is proposed...... for storage batteries in DC microgrids. The working principle of TIDC is analyzed, and the factors influencing the response rate based on the dual-loop constant voltage control for TIDC are discussed, and then the method of feed-forward control for TIDC is studied to improve the response rate for load...... changing. A prototype of the TIDC is developed and an experimental platform is built. The experiment results show that DC bus voltage sags or swells caused by load changing can be reduced and the time for voltage recovery can be decreased significantly with the proposed feed-forward control....

  12. Entry, Descent and Landing Systems Analysis: Exploration Feed Forward Internal Peer Review Slide Package

    Science.gov (United States)

    Dwyer Cianciolo, Alicia M. (Editor)

    2011-01-01

    NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to successfully land large payloads at Mars for both robotic and human-scale missions. Year 1 of the study focused on technologies required for Exploration-class missions to land payloads of 10 to 50 mt. Inflatable decelerators, rigid aeroshell and supersonic retro-propulsion emerged as the top candidate technologies. In Year 2 of the study, low TRL technologies identified in Year 1, inflatables aeroshells and supersonic retropropulsion, were combined to create a demonstration precursor robotic mission. This part of the EDL-SA Year 2 effort, called Exploration Feed Forward (EFF), took much of the systems analysis simulation and component model development from Year 1 to the next level of detail.

  13. Hippocampus-driven feed-forward inhibition of the prefrontal cortex mediates relapse of extinguished fear

    DEFF Research Database (Denmark)

    Marek, Roger; Jin, Jingji; Goode, Travis D.

    2018-01-01

    The medial prefrontal cortex (mPFC) has been implicated in the extinction of emotional memories, including conditioned fear. We found that ventral hippocampal (vHPC) projections to the infralimbic (IL) cortex recruited parvalbumin-expressing interneurons to counter the expression of extinguished...... fear and promote fear relapse. Whole-cell recordings ex vivo revealed that optogenetic activation of vHPC input to amygdala-projecting pyramidal neurons in the IL was dominated by feed-forward inhibition. Selectively silencing parvalbumin-expressing, but not somatostatin-expressing, interneurons...... in the IL eliminated vHPC-mediated inhibition. In behaving rats, pharmacogenetic activation of vHPC→IL projections impaired extinction recall, whereas silencing IL projectors diminished fear renewal. Intra-IL infusion of GABA receptor agonists or antagonists, respectively, reproduced these effects. Together...

  14. A Novel Feed-Forward Modeling System Leads to Sustained Improvements in Attention and Academic Performance.

    Science.gov (United States)

    McDermott, Ashley F; Rose, Maya; Norris, Troy; Gordon, Eric

    2016-01-28

    This study tested a novel feed-forward modeling (FFM) system as a nonpharmacological intervention for the treatment of ADHD children and the training of cognitive skills that improve academic performance. This study implemented a randomized, controlled, parallel design comparing this FFM with a nonpharmacological community care intervention. Improvements were measured on parent- and clinician-rated scales of ADHD symptomatology and on academic performance tests completed by the participant. Participants were followed for 3 months after training. Participants in the FFM training group showed significant improvements in ADHD symptomatology and academic performance, while the control group did not. Improvements from FFM were sustained 3 months later. The FFM appeared to be an effective intervention for the treatment of ADHD and improving academic performance. This FFM training intervention shows promise as a first-line treatment for ADHD while improving academic performance. © The Author(s) 2016.

  15. Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks.

    Science.gov (United States)

    Gorochowski, Thomas E; Grierson, Claire S; di Bernardo, Mario

    2018-03-01

    Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli . Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution.

  16. Entry, Descent and Landing Systems Analysis Study: Phase 2 Report on Exploration Feed-Forward Systems

    Science.gov (United States)

    Dwyer Ciancolo, Alicia M.; Davis, Jody L.; Engelund, Walter C.; Komar, D. R.; Queen, Eric M.; Samareh, Jamshid A.; Way, David W.; Zang, Thomas A.; Murch, Jeff G.; Krizan, Shawn A.; hide

    2011-01-01

    NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to successfully land large payloads at Mars for both robotic and human-scale missions. Year 1 of the study focused on technologies required for Exploration-class missions to land payloads of 10 to 50 t. Inflatable decelerators, rigid aeroshell and supersonic retro-propulsion emerged as the top candidate technologies. In Year 2 of the study, low TRL technologies identified in Year 1, inflatables aeroshells and supersonic retropropulsion, were combined to create a demonstration precursor robotic mission. This part of the EDL-SA Year 2 effort, called Exploration Feed Forward (EFF), took much of the systems analysis simulation and component model development from Year 1 to the next level of detail.

  17. Mitigation of ground motion effects in linear accelerators via feed-forward control

    Directory of Open Access Journals (Sweden)

    J. Pfingstner

    2014-12-01

    Full Text Available Ground motion is a severe problem for many particle accelerators, since it excites beam oscillations, which decrease the beam quality and create beam-beam offset (at colliders. Orbit feedback systems can only compensate ground motion effects at frequencies significantly smaller than the beam repetition rate. In linear colliders, where the repetition rate is low, additional counter measures have to be put in place. For this reason, a ground motion mitigation method based on feed-forward control is presented in this paper. It has several advantages compared to other techniques (stabilization systems and intratrain feedback systems such as cost reduction and potential performance improvement. An analytical model is presented that allows the derivation of hardware specification and performance estimates for a specific accelerator and ground motion model. At the Accelerator Test Facility (ATF2, ground motion sensors have been installed to verify the feasibility of important parts of the mitigation strategy. In experimental studies, it has been shown that beam excitations due to ground motion can be predicted from ground motion measurements on a pulse-to-pulse basis. Correlations of up to 80% between the estimated and measured orbit jitter have been observed. Additionally, an orbit jitter source was identified and has been removed, which halved the orbit jitter power at ATF2 and shows that the feed-forward scheme is also very useful for the detection of installation issues. We believe that the presented mitigation method has the potential to reduce costs and improve the performance of linear colliders and potentially other linear accelerators.

  18. Table making system for feed forward control to improve transient emissions; Katoji haiki gas joka table no sakusei system

    Energy Technology Data Exchange (ETDEWEB)

    Sakamaki, T; Morita, S; Takada, Y [Osaka City University, Osaka (Japan)

    1997-10-01

    Intake manifold fuel injection type engines have a weak point that the emissions are not so good in transient operation, because the balance of fuel adhesion and evaporation goes out, so that three way catalyst does not work in good efficiency. For this countermeasure, it is effective to add feed forward control to conventional O2 feedback one. In order to achieve this feed forward control, the fuel table is necessary, and to make it needs enormous time and effort even for one target type engine. To overcome this difficulty, table making system was constructed, and expected result was obtained. 4 refs., 8 figs.

  19. Development of an Accurate Feed-Forward Temperature Control Tankless Water Heater

    Energy Technology Data Exchange (ETDEWEB)

    David Yuill

    2008-06-30

    The following document is the final report for DE-FC26-05NT42327: Development of an Accurate Feed-Forward Temperature Control Tankless Water Heater. This work was carried out under a cooperative agreement from the Department of Energy's National Energy Technology Laboratory, with additional funding from Keltech, Inc. The objective of the project was to improve the temperature control performance of an electric tankless water heater (TWH). The reason for doing this is to minimize or eliminate one of the barriers to wider adoption of the TWH. TWH use less energy than typical (storage) water heaters because of the elimination of standby losses, so wider adoption will lead to reduced energy consumption. The project was carried out by Building Solutions, Inc. (BSI), a small business based in Omaha, Nebraska. BSI partnered with Keltech, Inc., a manufacturer of electric tankless water heaters based in Delton, Michigan. Additional work was carried out by the University of Nebraska and Mike Coward. A background study revealed several advantages and disadvantages to TWH. Besides using less energy than storage heaters, TWH provide an endless supply of hot water, have a longer life, use less floor space, can be used at point-of-use, and are suitable as boosters to enable alternative water heating technologies, such as solar or heat-pump water heaters. Their disadvantages are their higher cost, large instantaneous power requirement, and poor temperature control. A test method was developed to quantify performance under a representative range of disturbances to flow rate and inlet temperature. A device capable of conducting this test was designed and built. Some heaters currently on the market were tested, and were found to perform quite poorly. A new controller was designed using model predictive control (MPC). This control method required an accurate dynamic model to be created and required significant tuning to the controller before good control was achieved. The MPC

  20. Demonstration of feed-forward control for linear optics quantum computation

    International Nuclear Information System (INIS)

    Pittman, T.B.; Jacobs, B.C.; Franson, J.D.

    2002-01-01

    One of the main requirements in linear optics quantum computing is the ability to perform single-qubit operations that are controlled by classical information fed forward from the output of single-photon detectors. These operations correspond to predetermined combinations of phase corrections and bit flips that are applied to the postselected output modes of nondeterministic quantum logic devices. Corrections of this kind are required in order to obtain the correct logical output for certain detection events, and their use can increase the overall success probability of the devices. In this paper, we report on the experimental demonstration of the use of this type of feed-forward system to increase the probability of success of a simple nondeterministic quantum logic operation from approximately (1/4) to (1/2). This logic operation involves the use of one target qubit and one ancilla qubit which, in this experiment, are derived from a parametric down-conversion photon pair. Classical information describing the detection of the ancilla photon is fed forward in real time and used to alter the quantum state of the output photon. A fiber-optic delay line is used to store the output photon until a polarization-dependent phase shift can be applied using a high-speed Pockels cell

  1. Limits to the development of feed-forward structures in large recurrent neuronal networks

    Directory of Open Access Journals (Sweden)

    Susanne Kunkel

    2011-02-01

    Full Text Available Spike-timing dependent plasticity (STDP has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above. In this paper we first review modeling choices that carry particularly high risks of producing non-generalizable results in the context of STDP in recurrent networks. We then develop a theory for the development of feed-forward structure in random networks and conclude that an unstable fixed point in the dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify candidate biologically motivated adaptations to the balanced random network model that might enable it.

  2. Noise processing by microRNA-mediated circuits: The Incoherent Feed-Forward Loop, revisited

    Directory of Open Access Journals (Sweden)

    Silvia Grigolon

    2016-04-01

    Full Text Available The intrinsic stochasticity of gene expression is usually mitigated in higher eukaryotes by post-transcriptional regulation channels that stabilise the output layer, most notably protein levels. The discovery of small non-coding RNAs (miRNAs in specific motifs of the genetic regulatory network has led to identifying noise buffering as the possible key function they exert in regulation. Recent in vitro and in silico studies have corroborated this hypothesis. It is however also known that miRNA-mediated noise reduction is hampered by transcriptional bursting in simple topologies. Here, using stochastic simulations validated by analytical calculations based on van Kampen's expansion, we revisit the noise-buffering capacity of the miRNA-mediated Incoherent Feed Forward Loop (IFFL, a small module that is widespread in the gene regulatory networks of higher eukaryotes, in order to account for the effects of intermittency in the transcriptional activity of the modulator gene. We show that bursting considerably alters the circuit's ability to control static protein noise. By comparing with other regulatory architectures, we find that direct transcriptional regulation significantly outperforms the IFFL in a broad range of kinetic parameters. This suggests that, under pulsatile inputs, static noise reduction may be less important than dynamical aspects of noise and information processing in characterising the performance of regulatory elements.

  3. Embedding the dynamics of a single delay system into a feed-forward ring.

    Science.gov (United States)

    Klinshov, Vladimir; Shchapin, Dmitry; Yanchuk, Serhiy; Wolfrum, Matthias; D'Huys, Otti; Nekorkin, Vladimir

    2017-10-01

    We investigate the relation between the dynamics of a single oscillator with delayed self-feedback and a feed-forward ring of such oscillators, where each unit is coupled to its next neighbor in the same way as in the self-feedback case. We show that periodic solutions of the delayed oscillator give rise to families of rotating waves with different wave numbers in the corresponding ring. In particular, if for the single oscillator the periodic solution is resonant to the delay, it can be embedded into a ring with instantaneous couplings. We discover several cases where the stability of a periodic solution for the single unit can be related to the stability of the corresponding rotating wave in the ring. As a specific example, we demonstrate how the complex bifurcation scenario of simultaneously emerging multijittering solutions can be transferred from a single oscillator with delayed pulse feedback to multijittering rotating waves in a sufficiently large ring of oscillators with instantaneous pulse coupling. Finally, we present an experimental realization of this dynamical phenomenon in a system of coupled electronic circuits of FitzHugh-Nagumo type.

  4. Predictive zero-dimensional combustion model for DI diesel engine feed-forward control

    International Nuclear Information System (INIS)

    Catania, Andrea Emilio; Finesso, Roberto; Spessa, Ezio

    2011-01-01

    Highlights: → Zero-dimensional low-throughput combustion model for real-time control in diesel engine applications. → Feed-forward control of MFB50, p max and IMEP in both conventional and PCCI combustion modes. → Capability of resolving the contribution to HRR of each injection pulse in multiple injection schedule. → Ignition delay and model parameters estimated through physically consistent and easy-to-tune correlations. - Abstract: An innovative zero-dimensional predictive combustion model has been developed for the estimation of HRR (heat release rate) and in-cylinder pressure traces. This model has been assessed and applied to conventional and PCCI (premixed charge compression ignition) DI diesel engines for model-based feed-forward control purposes. The injection rate profile is calculated on the basis of the injected fuel quantities and on the injection parameters, such as SOI (start of injection), ET (energizing time), and DT (dwell time), taking the injector NOD (nozzle opening delay) and NCD (nozzle closure delay) into account. The injection rate profile in turn allows the released chemical energy Q ch to be estimated. The approach starts from the assumption that, at each time instant, the HRR is proportional to the energy associated with the accumulated fuel mass in the combustion chamber. The main novelties of the proposed approach consist of the method that is adopted to estimate the fuel ignition delay and of injection rate splitting for HRR estimation. The procedure allows an accurate calculation to be made of the different combustion parameters that are important for engine calibration, such as SOC (start of combustion) and MFB50 (50% of fuel mass fraction burned angle). On the basis of an estimation of the fuel released chemical energy, of the heat globally exchanged from the charge with the walls and of the energy associated with the fuel evaporation, the charge net energy is calculated, for a subsequent evaluation of the in

  5. Predictive zero-dimensional combustion model for DI diesel engine feed-forward control

    Energy Technology Data Exchange (ETDEWEB)

    Catania, Andrea Emilio; Finesso, Roberto [IC Engines Advanced Laboratory, Politecnico di Torino, c.so Duca degli Abruzzi 24, 10129 Torino (Italy); Spessa, Ezio, E-mail: ezio.spessa@polito.it [IC Engines Advanced Laboratory, Politecnico di Torino, c.so Duca degli Abruzzi 24, 10129 Torino (Italy)

    2011-09-15

    Highlights: {yields} Zero-dimensional low-throughput combustion model for real-time control in diesel engine applications. {yields} Feed-forward control of MFB50, p{sub max} and IMEP in both conventional and PCCI combustion modes. {yields} Capability of resolving the contribution to HRR of each injection pulse in multiple injection schedule. {yields} Ignition delay and model parameters estimated through physically consistent and easy-to-tune correlations. - Abstract: An innovative zero-dimensional predictive combustion model has been developed for the estimation of HRR (heat release rate) and in-cylinder pressure traces. This model has been assessed and applied to conventional and PCCI (premixed charge compression ignition) DI diesel engines for model-based feed-forward control purposes. The injection rate profile is calculated on the basis of the injected fuel quantities and on the injection parameters, such as SOI (start of injection), ET (energizing time), and DT (dwell time), taking the injector NOD (nozzle opening delay) and NCD (nozzle closure delay) into account. The injection rate profile in turn allows the released chemical energy Q{sub ch} to be estimated. The approach starts from the assumption that, at each time instant, the HRR is proportional to the energy associated with the accumulated fuel mass in the combustion chamber. The main novelties of the proposed approach consist of the method that is adopted to estimate the fuel ignition delay and of injection rate splitting for HRR estimation. The procedure allows an accurate calculation to be made of the different combustion parameters that are important for engine calibration, such as SOC (start of combustion) and MFB50 (50% of fuel mass fraction burned angle). On the basis of an estimation of the fuel released chemical energy, of the heat globally exchanged from the charge with the walls and of the energy associated with the fuel evaporation, the charge net energy is calculated, for a subsequent

  6. Strategic Management Accounting and Feed-forward Management: with Reference to the Unified Management of Profit Opportunity and Risk

    OpenAIRE

    西村, 明

    2015-01-01

    This paper aims toreexamine strategic management accouning in relation to profit opportunity and risk from the viewpoint of feed-forward management , as few studies to date have discuued the relations between management accounting and the unified management of profit opportunity and risk.Many studies have been conducted that emphasize non-fine information and feedback organizational management in an attept to make traditional management accounting relevant to practical strategic needs. As lon...

  7. An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

    International Nuclear Information System (INIS)

    Avci, E.

    2007-01-01

    In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

  8. Neural network-based survey analysis of risk management practices in new product development

    DEFF Research Database (Denmark)

    Kampianakis, Andreas N.; Oehmen, Josef

    2017-01-01

    The current study investigates the applicability of Artificial Neural Networks (ANNs) to analyse survey data on the effectiveness of risk management practices in product development (PD) projects, and its ability to forecast project outcomes. Moreover, this study presents the relations between risk...... Neural Networks. Dataset used is a filtered survey of 291 product development programs. Answers of this survey are used as training input and target output, in pattern recognition two-layer feed forward networks, using various transfer functions. Using this method, relations among 6 project practices...... and 13 outcome metrics were revealed. Results of this analysis are compared with existent results made through statistical analysis in prior work of one of the authors. Future investigation is needed in order to tackle the lack of data and create an easy to use platform for industrial use....

  9. Proportional fuzzy feed-forward architecture control validation by wind tunnel tests of a morphing wing

    Directory of Open Access Journals (Sweden)

    Michel Joël Tchatchueng Kammegne

    2017-04-01

    wing for a specified flight condition. The feasibility and effectiveness of the developed control system by use of a proportional fuzzy feed-forward methodology are demonstrated experimentally through bench and wind tunnel tests of the morphing wing model.

  10. Simple, low-noise piezo driver with feed-forward for broad tuning of external cavity diode lasers.

    Science.gov (United States)

    Doret, S Charles

    2018-02-01

    We present an inexpensive, low-noise (piezo driver suitable for frequency tuning of external-cavity diode lasers. This simple driver improves upon many commercially available drivers by incorporating circuitry to produce a "feed-forward" signal appropriate for making simultaneous adjustments to the piezo voltage and laser current, enabling dramatic improvements in a mode-hop-free laser frequency tuning range. We present the theory behind our driver's operation, characterize its output noise, and demonstrate its use in absorption spectroscopy on the rubidium D 1 line.

  11. Efficacy of Feed Forward and Feedback Signaling for Inflations and Chest Compression Pressure During Cardiopulmonary Resuscitation in a Newborn Mannequin

    Science.gov (United States)

    Andriessen, Peter; Oetomo, Sidarto Bambang; Chen, Wei; Feijs, Loe MG

    2012-01-01

    Background The objective of the study was to evaluate a device that supports professionals during neonatal cardiopulmonary resuscitation (CPR). The device features a box that generates an audio-prompted rate guidance (feed forward) for inflations and compressions, and a transparent foil that is placed over the chest with marks for inter nipple line and sternum with LED’s incorporated in the foil indicating the exerted force (feedback). Methods Ten pairs (nurse/doctor) performed CPR on a newborn resuscitation mannequin. All pairs initially performed two sessions. Thereafter two sessions were performed in similar way, after randomization in 5 pairs that used the device and 5 pairs that performed CPR without the device (controls). A rhythm score was calculated based on the number of CPR cycles that were performed correctly. Results The rhythm score with the device improved from 85 ± 14 to 99 ± 2% (P CPR device compared to the controls. Conclusion Feed forward and feedback signaling leads to a more constant rhythm and chest compression pressure during CPR. PMID:22870175

  12. LIDAR Wind Speed Measurement Analysis and Feed-Forward Blade Pitch Control for Load Mitigation in Wind Turbines: January 2010--January 2011

    Energy Technology Data Exchange (ETDEWEB)

    Dunne, F.; Simley, E.; Pao, L.Y.

    2011-10-01

    This report examines the accuracy of measurements that rely on Doppler LIDAR systems to determine their applicability to wind turbine feed-forward control systems and discusses feed-forward control system designs that use preview wind measurements. Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feed-forward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. The first half of this report examines the accuracy of different measurement scenarios that rely on coherent continuous-wave or pulsed Doppler LIDAR systems to determine their applicability to feed-forward control. In particular, the impacts of measurement range and angular offset from the wind direction are studied for various wind conditions. A realistic case involving a scanning LIDAR unit mounted in the spinner of a wind turbine is studied in depth with emphasis on choices for scan radius and preview distance. The effects of turbulence parameters on measurement accuracy are studied as well. Continuous-wave and pulsed LIDAR models based on typical commercially available units were used in the studies present in this report. The second half of this report discusses feed-forward control system designs that use preview wind measurements. Combined feedback/feed-forward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent above-rated winds. The feed-forward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Three feed-forward designs are studied: non-causal series expansion, Preview Control, and optimized FIR filter. The input to the feed-forward controller is a measurement of

  13. Spatial frequency domain spectroscopy of two layer media

    Science.gov (United States)

    Yudovsky, Dmitry; Durkin, Anthony J.

    2011-10-01

    Monitoring of tissue blood volume and oxygen saturation using biomedical optics techniques has the potential to inform the assessment of tissue health, healing, and dysfunction. These quantities are typically estimated from the contribution of oxyhemoglobin and deoxyhemoglobin to the absorption spectrum of the dermis. However, estimation of blood related absorption in superficial tissue such as the skin can be confounded by the strong absorption of melanin in the epidermis. Furthermore, epidermal thickness and pigmentation varies with anatomic location, race, gender, and degree of disease progression. This study describes a technique for decoupling the effect of melanin absorption in the epidermis from blood absorption in the dermis for a large range of skin types and thicknesses. An artificial neural network was used to map input optical properties to spatial frequency domain diffuse reflectance of two layer media. Then, iterative fitting was used to determine the optical properties from simulated spatial frequency domain diffuse reflectance. Additionally, an artificial neural network was trained to directly map spatial frequency domain reflectance to sets of optical properties of a two layer medium, thus bypassing the need for iteration. In both cases, the optical thickness of the epidermis and absorption and reduced scattering coefficients of the dermis were determined independently. The accuracy and efficiency of the iterative fitting approach was compared with the direct neural network inversion.

  14. Feed-forward and generalized regression neural networks in modeling feeding behavior of pigs in the grow-finishing phase

    Science.gov (United States)

    Feeding patterns in group-housed grow-finishing pigs have been investigated for use in management decisions, identifying sick animals, and determining genetic differences within a herd. Development of models to predict swine feeding behaviour has been limited due the large number of potential enviro...

  15. Feed forward and feedback control for over-ground locomotion in anaesthetized cats

    Science.gov (United States)

    Mazurek, K. A.; Holinski, B. J.; Everaert, D. G.; Stein, R. B.; Etienne-Cummings, R.; Mushahwar, V. K.

    2012-04-01

    The biological central pattern generator (CPG) integrates open and closed loop control to produce over-ground walking. The goal of this study was to develop a physiologically based algorithm capable of mimicking the biological system to control multiple joints in the lower extremities for producing over-ground walking. The algorithm used state-based models of the step cycle each of which produced different stimulation patterns. Two configurations were implemented to restore over-ground walking in five adult anaesthetized cats using intramuscular stimulation (IMS) of the main hip, knee and ankle flexor and extensor muscles in the hind limbs. An open loop controller relied only on intrinsic timing while a hybrid-CPG controller added sensory feedback from force plates (representing limb loading), and accelerometers and gyroscopes (representing limb position). Stimulation applied to hind limb muscles caused extension or flexion in the hips, knees and ankles. A total of 113 walking trials were obtained across all experiments. Of these, 74 were successful in which the cats traversed 75% of the 3.5 m over-ground walkway. In these trials, the average peak step length decreased from 24.9 ± 8.4 to 21.8 ± 7.5 (normalized units) and the median number of steps per trial increased from 7 (Q1 = 6, Q3 = 9) to 9 (8, 11) with the hybrid-CPG controller. Moreover, within these trials, the hybrid-CPG controller produced more successful steps (step length ≤ 20 cm ground reaction force ≥ 12.5% body weight) than the open loop controller: 372 of 544 steps (68%) versus 65 of 134 steps (49%), respectively. This supports our previous preliminary findings, and affirms that physiologically based hybrid-CPG approaches produce more successful stepping than open loop controllers. The algorithm provides the foundation for a neural prosthetic controller and a framework to implement more detailed control of locomotion in the future.

  16. Stability analysis of a three-phase grid-connected DC power supply with small DC-link capacitor and voltage feed-forward compensation

    DEFF Research Database (Denmark)

    Török, Lajos; Mathe, L.

    2017-01-01

    The purpose of this work was to investigate effect of the DC-link voltage feed-forward compensation on the stability of the three-phase-grid connected DC power supply, used for electrolysis application, equipped with small DC link capacitor. In case of weak grid condition, the system...

  17. RA Acts in a Coherent Feed-Forward Mechanism with Tbx5 to Control Limb Bud Induction and Initiation

    Directory of Open Access Journals (Sweden)

    Satoko Nishimoto

    2015-08-01

    Full Text Available The retinoic acid (RA- and β-catenin-signaling pathways regulate limb bud induction and initiation; however, their mechanisms of action are not understood and have been disputed. We demonstrate that both pathways are essential and that RA and β-catenin/TCF/LEF signaling act cooperatively with Hox gene inputs to directly regulate Tbx5 expression. Furthermore, in contrast to previous models, we show that Tbx5 and Tbx4 expression in forelimb and hindlimb, respectively, are not sufficient for limb outgrowth and that input from RA is required. Collectively, our data indicate that RA signaling and Tbx genes act in a coherent feed-forward loop to regulate Fgf10 expression and, as a result, establish a positive feedback loop of FGF signaling between the limb mesenchyme and ectoderm. Our results incorporate RA-, β-catenin/TCF/LEF-, and FGF-signaling pathways into a regulatory network acting to recruit cells of the embryo flank to become limb precursors.

  18. Practical use of the amplitude and phase modulation of a high-power RF pulse via feed-forward control

    International Nuclear Information System (INIS)

    Kawase, Keigo; Kato, Ryukou; Irizawa, Akinori; Isoyama, Goro; Kashiwagi, Shigeru

    2013-01-01

    A new feed-forward control system to precisely control the amplitude and phase of the pulsed RF power in an electron linear accelerator (linac) is developed to make the accelerating field constant. Fast variations and ripples in the amplitude and phase in the RF pulses are compensated by modulating the amplitude and phase in the low-level system with a variable attenuator and phase shifter. The system is innovated the overdrive technique, which is commonly used in analog circuits, to speed up the slow response of the phase shifter, while the control signals are digitally processed; thus, the method is a hybrid of analog and digital techniques. By using the new control system, we find that the peak-to-peak variations in the amplitude and phase are reduced from 11.6% to 0.4% and from 6.1 degrees to 0.3 degrees, respectively, in 7.6-μs-long RF pulses for the L-band electron linac at Osaka University. (author)

  19. A current mode feed-forward gain control system for a 0.8 V CMOS hearing aid

    Energy Technology Data Exchange (ETDEWEB)

    Li Fanyang; Yang Haigang; Liu Fei; Yin Tao, E-mail: yanghg@mail.ie.ac.cn [Institute of Electronics, Chinese Academy of Sciences, Beijing 100080 (China)

    2011-06-15

    A current mode feed-forward gain control (CMFGC) technique is presented, which is applied in the front-end system of a hearing aid chip. Compared with conventional automatic gain control (AGC), CMFGC significantly improves the total harmonic distortion (THD) by digital gain control. To attain the digital gain control codes according to the extremely weak output signal from the microphone, a rectifier and a state controller implemented in current mode are proposed. A prototype chip has been designed based on a 0.13 {mu}m standard CMOS process. The measurement results show that the supply voltage can be as low as 0.6 V. And with the 0.8 V supply voltage, the THD is improved and below 0.06% (-64 dB) at the output level of 500 mV{sub p-p}, yet the power consumption is limited to 40 {mu}W. In addition, the input referred noise is only 4 {mu}V{sub rms} and the maximum gain is maintained at 33 dB. (semiconductor integrated circuits)

  20. Simultaneously precise frequency transfer and time synchronization using feed-forward compensation technique via 120 km fiber link.

    Science.gov (United States)

    Chen, Xing; Lu, Jinlong; Cui, Yifan; Zhang, Jian; Lu, Xing; Tian, Xusheng; Ci, Cheng; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

    2015-12-22

    Precision time synchronization between two remote sites is desired in many applications such as global positioning satellite systems, long-baseline interferometry, coherent radar detection and fundamental physics constant measurements. The recently developed frequency dissemination technologies based on optical fiber link have improved the transfer instability to the level of 10(-19)/day at remote location. Therefore it is possible to keep clock oscillation at remote locations continuously corrected, or to reproduce a "virtual" clock on the remote location. However the initial alignment and the correction of 1 pps timing signal from time to time are still required, besides the highly stabilized clock frequency transfer between distant locations. Here we demonstrate a time synchronization based on an ultra-stable frequency transfer system via 120-km commercial fiber link by transferring an optical frequency comb. Both the phase noise compensation in frequency dissemination and temporal basis alignment in time synchronization were implemented by a feed-forward digital compensation (FFDC) technique. The fractional frequency instability was measured to be 6.18 × 10(-20) at 2000 s. The timing deviation of time synchronization was measured to be 0.6 ps in 1500 s. This technique also can be applied in multi-node fiber network topology.

  1. A current mode feed-forward gain control system for a 0.8 V CMOS hearing aid

    International Nuclear Information System (INIS)

    Li Fanyang; Yang Haigang; Liu Fei; Yin Tao

    2011-01-01

    A current mode feed-forward gain control (CMFGC) technique is presented, which is applied in the front-end system of a hearing aid chip. Compared with conventional automatic gain control (AGC), CMFGC significantly improves the total harmonic distortion (THD) by digital gain control. To attain the digital gain control codes according to the extremely weak output signal from the microphone, a rectifier and a state controller implemented in current mode are proposed. A prototype chip has been designed based on a 0.13 μm standard CMOS process. The measurement results show that the supply voltage can be as low as 0.6 V. And with the 0.8 V supply voltage, the THD is improved and below 0.06% (-64 dB) at the output level of 500 mV p-p , yet the power consumption is limited to 40 μW. In addition, the input referred noise is only 4 μV rms and the maximum gain is maintained at 33 dB. (semiconductor integrated circuits)

  2. Synchronization of coupled metronomes on two layers

    Science.gov (United States)

    Zhang, Jing; Yu, Yi-Zhen; Wang, Xin-Gang

    2017-12-01

    Coupled metronomes serve as a paradigmatic model for exploring the collective behaviors of complex dynamical systems, as well as a classical setup for classroom demonstrations of synchronization phenomena. Whereas previous studies of metronome synchronization have been concentrating on symmetric coupling schemes, here we consider the asymmetric case by adopting the scheme of layered metronomes. Specifically, we place two metronomes on each layer, and couple two layers by placing one on top of the other. By varying the initial conditions of the metronomes and adjusting the friction between the two layers, a variety of synchronous patterns are observed in experiment, including the splay synchronization (SS) state, the generalized splay synchronization (GSS) state, the anti-phase synchronization (APS) state, the in-phase delay synchronization (IPDS) state, and the in-phase synchronization (IPS) state. In particular, the IPDS state, in which the metronomes on each layer are synchronized in phase but are of a constant phase delay to metronomes on the other layer, is observed for the first time. In addition, a new technique based on audio signals is proposed for pattern detection, which is more convenient and easier to apply than the existing acquisition techniques. Furthermore, a theoretical model is developed to explain the experimental observations, and is employed to explore the dynamical properties of the patterns, including the basin distributions and the pattern transitions. Our study sheds new lights on the collective behaviors of coupled metronomes, and the developed setup can be used in the classroom for demonstration purposes.

  3. A two-layer linear piezoelectric micromotor.

    Science.gov (United States)

    Li, Xiaotian; Ci, Penghong; Liu, Guoxi; Dong, Shuxiang

    2015-03-01

    A first bending (B1) mode two-layer piezoelectric ultrasonic linear micromotor has been developed for microoptics driving applications. The piezo-vibrator of the micromotor was composed of two small Pb(Zr,Ti)O3 (PZT-5) plates, with overall dimensions and mass of only 2.0 × 2.0 × 5.0 mm(3) and 0.2 g, respectively. The proposed micromotor could operate either in single-phase voltage (standing wave) mode or two-phase voltage (traveling wave) mode to drive a slider via friction force to provide bidirectional linear motion. A large thrust of up to 0.30 N, which corresponds to a high unit volume direct driving force of 15 mN/mm(3), and a linear movement velocity of up to 230 mm/s were obtained under an applied voltage of 80 Vpp at the B1 mode resonance frequency of 174 kHz.

  4. TWO-LAYER PHASE COMPENSATING INTERFERENCE SYSTEMS

    Directory of Open Access Journals (Sweden)

    Georgiy V. Nikandrov

    2014-09-01

    Full Text Available The paper deals with creation of optical interferential coatings, giving the possibility to form the wave front without the change of energy characteristics of the incident and reflected radiation. Correction is achieved due to the layer, which thickness is a function of coordinate of an optical element surface. Selection technique is suggested for refractive index materials, forming two-layer interference coating that creates a coating with a constant coefficient of reflection on the surface of the optical element. By this procedure the change of coefficient of reflection for the optical element surface, arising because of the variable thickness is eliminated. Magnesium oxide and zirconium dioxide were used as the film-forming materials. The paper presents experimentally obtained thickness distribution of the layer, which is a part of the phase compensating coating. A new class of optical coatings proposed in the paper can find its application for correcting the form of a wave front.

  5. Cell-type specific short-term plasticity at auditory nerve synapses controls feed-forward inhibition in the dorsal cochlear nucleus

    Directory of Open Access Journals (Sweden)

    Miloslav eSedlacek

    2014-07-01

    Full Text Available Feedforward inhibition represents a powerful mechanism by which control of the timing and fidelity of action potentials in local synaptic circuits of various brain regions is achieved. In the cochlear nucleus, the auditory nerve provides excitation to both principal neurons and inhibitory interneurons. Here, we investigated the synaptic circuit associated with fusiform cells (FCs, principal neurons of the dorsal cochlear nucleus (DCN that receive excitation from auditory nerve fibers and inhibition from tuberculoventral cells (TVCs on their basal dendrites in the deep layer of DCN. Despite the importance of these inputs in regulating fusiform cell firing behavior, the mechanisms determining the balance of excitation and feed-forward inhibition in this circuit are not well understood. Therefore, we examined the timing and plasticity of auditory nerve driven feed-forward inhibition (FFI onto FCs. We find that in some FCs, excitatory and inhibitory components of feed-forward inhibition had the same stimulation thresholds indicating they could be triggered by activation of the same fibers. In other FCs, excitation and inhibition exhibit different stimulus thresholds, suggesting FCs and TVCs might be activated by different sets of fibers. In addition we find that during repetitive activation, synapses formed by the auditory nerve onto TVCs and FCs exhibit distinct modes of short-term plasticity. Feed-forward inhibitory post-synaptic currents (IPSCs in FCs exhibit short-term depression because of prominent synaptic depression at the auditory nerve-TVC synapse. Depression of this feedforward inhibitory input causes a shift in the balance of fusiform cell synaptic input towards greater excitation and suggests that fusiform cell spike output will be enhanced by physiological patterns of auditory nerve activity.

  6. A feed-forward circuit linking wingless, fat-dachsous signaling, and the warts-hippo pathway to Drosophila wing growth.

    Directory of Open Access Journals (Sweden)

    Myriam Zecca

    2010-06-01

    Full Text Available During development, the Drosophila wing primordium undergoes a dramatic increase in cell number and mass under the control of the long-range morphogens Wingless (Wg, a Wnt and Decapentaplegic (Dpp, a BMP. This process depends in part on the capacity of wing cells to recruit neighboring, non-wing cells into the wing primordium. Wing cells are defined by activity of the selector gene vestigial (vg and recruitment entails the production of a vg-dependent "feed-forward signal" that acts together with morphogen to induce vg expression in neighboring non-wing cells. Here, we identify the protocadherins Fat (Ft and Dachsous (Ds, the Warts-Hippo tumor suppressor pathway, and the transcriptional co-activator Yorkie (Yki, a YES associated protein, or YAP as components of the feed-forward signaling mechanism, and we show how this mechanism promotes wing growth in response to Wg. We find that vg generates the feed-forward signal by creating a steep differential in Ft-Ds signaling between wing and non-wing cells. This differential down-regulates Warts-Hippo pathway activity in non-wing cells, leading to a burst of Yki activity and the induction of vg in response to Wg. We posit that Wg propels wing growth at least in part by fueling a wave front of Ft-Ds signaling that propagates vg expression from one cell to the next.

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

    International Nuclear Information System (INIS)

    Denby, B.

    1992-04-01

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

  8. Real time monitoring of powder blend bulk density for coupled feed-forward/feed-back control of a continuous direct compaction tablet manufacturing process.

    Science.gov (United States)

    Singh, Ravendra; Román-Ospino, Andrés D; Romañach, Rodolfo J; Ierapetritou, Marianthi; Ramachandran, Rohit

    2015-11-10

    The pharmaceutical industry is strictly regulated, where precise and accurate control of the end product quality is necessary to ensure the effectiveness of the drug products. For such control, the process and raw materials variability ideally need to be fed-forward in real time into an automatic control system so that a proactive action can be taken before it can affect the end product quality. Variations in raw material properties (e.g., particle size), feeder hopper level, amount of lubrication, milling and blending action, applied shear in different processing stages can affect the blend density significantly and thereby tablet weight, hardness and dissolution. Therefore, real time monitoring of powder bulk density variability and its incorporation into the automatic control system so that its effect can be mitigated proactively and efficiently is highly desired. However, real time monitoring of powder bulk density is still a challenging task because of different level of complexities. In this work, powder bulk density which has a significant effect on the critical quality attributes (CQA's) has been monitored in real time in a pilot-plant facility, using a NIR sensor. The sensitivity of the powder bulk density on critical process parameters (CPP's) and CQA's has been analyzed and thereby feed-forward controller has been designed. The measured signal can be used for feed-forward control so that the corrective actions on the density variations can be taken before they can influence the product quality. The coupled feed-forward/feed-back control system demonstrates improved control performance and improvements in the final product quality in the presence of process and raw material variations. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. In vivo spatial frequency domain spectroscopy of two layer media

    Science.gov (United States)

    Yudovsky, Dmitry; Nguyen, John Quan M.; Durkin, Anthony J.

    2012-10-01

    Monitoring of tissue blood volume and local oxygen saturation can inform the assessment of tissue health, healing, and dysfunction. These quantities can be estimated from the contribution of oxyhemoglobin and deoxyhemoglobin to the absorption spectrum of the dermis. However, estimation of blood related absorption in skin can be confounded by the strong absorption of melanin in the epidermis and epidermal thickness and pigmentation varies with anatomic location, race, gender, and degree of disease progression. Therefore, a method is desired that decouples the effect of melanin absorption in the epidermis from blood absorption in the dermis for a large range of skin types and thicknesses. A previously developed inverse method based on a neural network forward model was applied to simulated spatial frequency domain reflectance of skin for multiple wavelengths in the near infrared. It is demonstrated that the optical thickness of the epidermis and absorption and reduced scattering coefficients of the dermis can be determined independently and with minimal coupling. Then, the same inverse method was applied to reflectance measurements from a tissue simulating phantom and in vivo human skin. Oxygen saturation and total hemoglobin concentrations were estimated from the volar forearms of weakly and strongly pigmented subjects using a standard homogeneous model and the present two layer model.

  10. Generalization and capacity of extensively large two-layered perceptrons

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Kanter, Ido; Engel, Andreas

    2002-01-01

    The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, α c , at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different

  11. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...

  12. Aircraft automatic-flight-control system with inversion of the model in the feed-forward path using a Newton-Raphson technique for the inversion

    Science.gov (United States)

    Smith, G. A.; Meyer, G.; Nordstrom, M.

    1986-01-01

    A new automatic flight control system concept suitable for aircraft with highly nonlinear aerodynamic and propulsion characteristics and which must operate over a wide flight envelope was investigated. This exact model follower inverts a complete nonlinear model of the aircraft as part of the feed-forward path. The inversion is accomplished by a Newton-Raphson trim of the model at each digital computer cycle time of 0.05 seconds. The combination of the inverse model and the actual aircraft in the feed-forward path alloys the translational and rotational regulators in the feedback path to be easily designed by linear methods. An explanation of the model inversion procedure is presented. An extensive set of simulation data for essentially the full flight envelope for a vertical attitude takeoff and landing aircraft (VATOL) is presented. These data demonstrate the successful, smooth, and precise control that can be achieved with this concept. The trajectory includes conventional flight from 200 to 900 ft/sec with path accelerations and decelerations, altitude changes of over 6000 ft and 2g and 3g turns. Vertical attitude maneuvering as a tail sitter along all axes is demonstrated. A transition trajectory from 200 ft/sec in conventional flight to stationary hover in the vertical attitude includes satisfactory operation through lift-cure slope reversal as attitude goes from horizontal to vertical at constant altitude. A vertical attitude takeoff from stationary hover to conventional flight is also demonstrated.

  13. Prediction of littoral drift with artificial neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Singh, A.K.; Deo, M.C.; SanilKumar, V.

    of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables...

  14. PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION

    NARCIS (Netherlands)

    COENEGRACHT, PMJ; METTING, HJ; VANLOO, EM; SNOEIJER, GJ; DOORNBOS, DA

    1993-01-01

    A peak tracking method based on a simulated feed-forward neural network with back-propagation is presented. The network uses the normalized UV spectra and peak areas measured in one chromatogram for peak recognition. It suffices to train the network with only one set of spectra recorded in one

  15. Phonematic translation of Polish texts by the neural network

    International Nuclear Information System (INIS)

    Bielecki, A.; Podolak, I.T.; Wosiek, J.; Majkut, E.

    1996-01-01

    Using the back propagation algorithm, we have trained the feed forward neural network to pronounce Polish language, more precisely to translate Polish text into its phonematic counterpart. Depending on the input coding and network architecture, 88%-95% translation efficiency was achieved. (author)

  16. Tests of track segment and vertex finding with neural networks

    International Nuclear Information System (INIS)

    Denby, B.; Lessner, E.; Lindsey, C.S.

    1990-04-01

    Feed forward neural networks have been trained, using back-propagation, to find the slopes of simulated track segments in a straw chamber and to find the vertex of tracks from both simulated and real events in a more conventional drift chamber geometry. Network architectures, training, and performance are presented. 12 refs., 7 figs

  17. Bringing Interpretability and Visualization with Artificial Neural Networks

    Science.gov (United States)

    Gritsenko, Andrey

    2017-01-01

    Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…

  18. Recognition of decays of charged tracks with neural network techniques

    International Nuclear Information System (INIS)

    Stimpfl-Abele, G.

    1991-01-01

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

  19. The effect of structural design parameters on FPGA-based feed-forward space-time trellis coding-orthogonal frequency division multiplexing channel encoders

    Science.gov (United States)

    Passas, Georgios; Freear, Steven; Fawcett, Darren

    2010-08-01

    Orthogonal frequency division multiplexing (OFDM)-based feed-forward space-time trellis code (FFSTTC) encoders can be synthesised as very high speed integrated circuit hardware description language (VHDL) designs. Evaluation of their FPGA implementation can lead to conclusions that help a designer to decide the optimum implementation, given the encoder structural parameters. VLSI architectures based on 1-bit multipliers and look-up tables (LUTs) are compared in terms of FPGA slices and block RAMs (area), as well as in terms of minimum clock period (speed). Area and speed graphs versus encoder memory order are provided for quadrature phase shift keying (QPSK) and 8 phase shift keying (8-PSK) modulation and two transmit antennas, revealing best implementation under these conditions. The effect of number of modulation bits and transmit antennas on the encoder implementation complexity is also investigated.

  20. Cell-type specific short-term plasticity at auditory nerve synapses controls feed-forward inhibition in the dorsal cochlear nucleus.

    Science.gov (United States)

    Sedlacek, Miloslav; Brenowitz, Stephan D

    2014-01-01

    Feed-forward inhibition (FFI) represents a powerful mechanism by which control of the timing and fidelity of action potentials in local synaptic circuits of various brain regions is achieved. In the cochlear nucleus, the auditory nerve provides excitation to both principal neurons and inhibitory interneurons. Here, we investigated the synaptic circuit associated with fusiform cells (FCs), principal neurons of the dorsal cochlear nucleus (DCN) that receive excitation from auditory nerve fibers and inhibition from tuberculoventral cells (TVCs) on their basal dendrites in the deep layer of DCN. Despite the importance of these inputs in regulating fusiform cell firing behavior, the mechanisms determining the balance of excitation and FFI in this circuit are not well understood. Therefore, we examined the timing and plasticity of auditory nerve driven FFI onto FCs. We find that in some FCs, excitatory and inhibitory components of FFI had the same stimulation thresholds indicating they could be triggered by activation of the same fibers. In other FCs, excitation and inhibition exhibit different stimulus thresholds, suggesting FCs and TVCs might be activated by different sets of fibers. In addition, we find that during repetitive activation, synapses formed by the auditory nerve onto TVCs and FCs exhibit distinct modes of short-term plasticity. Feed-forward inhibitory post-synaptic currents (IPSCs) in FCs exhibit short-term depression because of prominent synaptic depression at the auditory nerve-TVC synapse. Depression of this feedforward inhibitory input causes a shift in the balance of fusiform cell synaptic input towards greater excitation and suggests that fusiform cell spike output will be enhanced by physiological patterns of auditory nerve activity.

  1. Two-layer anti-reflection strategies for implant applications

    Science.gov (United States)

    Guerrero, Douglas J.; Smith, Tamara; Kato, Masakazu; Kimura, Shigeo; Enomoto, Tomoyuki

    2006-03-01

    A two-layer bottom anti-reflective coating (BARC) concept in which a layer that develops slowly is coated on top of a bottom layer that develops more rapidly was demonstrated. Development rate control was achieved by selection of crosslinker amount and BARC curing conditions. A single-layer BARC was compared with the two-layer BARC concept. The single-layer BARC does not clear out of 200-nm deep vias. When the slower developing single-layer BARC was coated on top of the faster developing layer, the vias were cleared. Lithographic evaluation of the two-layer BARC concept shows the same resolution advantages as the single-layer system. Planarization properties of a two-layer BARC system are better than for a single-layer system, when comparing the same total nominal thicknesses.

  2. Salient Feature Selection Using Feed-Forward Neural Networks and Signal-to-Noise Ratios with a Focus Toward Network Threat Detection and Classification

    Science.gov (United States)

    2014-03-27

    0.8.0. The virtual machine’s network adapter was set to internal network only to keep any outside traffic from interfering. A MySQL -based query...primary output of Fullstats is the ARFF file format, intended for use with the WEKA Java -based data mining software developed at the University of Waikato

  3. Prediction of gas hydrate saturation throughout the seismic section in Krishna Godavari basin using multivariate linear regression and multi-layer feed forward neural network approach

    Digital Repository Service at National Institute of Oceanography (India)

    Singh, Y.; Nair, R.R.; Singh, H.; Datta, P.; Jaiswal, P.; Dewangan, P.; Ramprasad, T.

    , Goldberg DS, Malinverno A (2014) Natural gas hydrates oc- cupying fractures: a focus on non-vent sites on the Indian continen- tal margin and the northern Gulf of Mexico. Mar Pet Geol 58:278– 291 Dafflon B, Barrash W (2012) 3-D stochastic estimation..., Collett TS (2012) Pore-and fracture-filling gas hydrate reser- voirs in the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II Green Canyon 955 H well. Mar Pet Geol 34:62–71 Lu S, McMechan GA (2004) Elastic imdedance inversion of multichan- nel...

  4. A feed-forward regulation of endothelin receptors by c-Jun in human non-pigmented ciliary epithelial cells and retinal ganglion cells.

    Directory of Open Access Journals (Sweden)

    Junming Wang

    , thereby generating a positive feed-forward loop of endothelin receptor activation and expression. This feed-forward regulation may contribute to RGC death and astrocyte proliferation following ET-1 treatment.

  5. Tracking and vertex finding with drift chambers and neural networks

    International Nuclear Information System (INIS)

    Lindsey, C.

    1991-09-01

    Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs

  6. Optimized Neural Network for Fault Diagnosis and Classification

    International Nuclear Information System (INIS)

    Elaraby, S.M.

    2005-01-01

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  7. Permutation parity machines for neural cryptography.

    Science.gov (United States)

    Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

    2010-06-01

    Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

  8. Permutation parity machines for neural cryptography

    International Nuclear Information System (INIS)

    Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

    2010-01-01

    Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

  9. A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

    NARCIS (Netherlands)

    Sheikhian, Hossein; Delavar, Mahmoud Reza; Stein, Alfred

    2017-01-01

    This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by

  10. Kinetic model-based feed-forward controlled fed-batch fermentation of Lactobacillus rhamnosus for the production of lactic acid from Arabic date juice.

    Science.gov (United States)

    Choi, Minsung; Al-Zahrani, Saeed M; Lee, Sang Yup

    2014-06-01

    Arabic date is overproduced in Arabic countries such as Saudi Arabia and Iraq and is mostly composed of sugars (70-80 wt%). Here we developed a fed-batch fermentation process by using a kinetic model for the efficient production of lactic acid to a high concentration from Arabic date juice. First, a kinetic model of Lactobacillus rhamnosus grown on date juice in batch fermentation was constructed in EXCEL so that the estimation of parameters and simulation of the model can be easily performed. Then, several fed-batch fermentations were conducted by employing different feeding strategies including pulsed feeding, exponential feeding, and modified exponential feeding. Based on the results of fed-batch fermentations, the kinetic model for fed-batch fermentation was also developed. This new model was used to perform feed-forward controlled fed-batch fermentation, which resulted in the production of 171.79 g l(-1) of lactic acid with the productivity and yield of 1.58 and 0.87 g l(-1) h(-1), respectively.

  11. Feed-Forward Inhibition of CD73 and Upregulation of Adenosine Deaminase Contribute to the Loss of Adenosine Neuromodulation in Postinflammatory Ileitis

    Directory of Open Access Journals (Sweden)

    Cátia Vieira

    2014-01-01

    Full Text Available Purinergic signalling is remarkably plastic during gastrointestinal inflammation. Thus, selective drugs targeting the “purinome” may be helpful for inflammatory gastrointestinal diseases. The myenteric neuromuscular transmission of healthy individuals is fine-tuned and controlled by adenosine acting on A2A excitatory receptors. Here, we investigated the neuromodulatory role of adenosine in TNBS-inflamed longitudinal muscle-myenteric plexus of the rat ileum. Seven-day postinflammation ileitis lacks adenosine neuromodulation, which may contribute to acceleration of gastrointestinal transit. The loss of adenosine neuromodulation results from deficient accumulation of the nucleoside at the myenteric synapse despite the fact that the increases in ATP release were observed. Disparity between ATP outflow and adenosine deficit in postinflammatory ileitis is ascribed to feed-forward inhibition of ecto-5′-nucleotidase/CD73 by high extracellular ATP and/or ADP. Redistribution of NTPDase2, but not of NTPDase3, from ganglion cell bodies to myenteric nerve terminals leads to preferential ADP accumulation from released ATP, thus contributing to the prolonged inhibition of muscle-bound ecto-5′-nucleotidase/CD73 and to the delay of adenosine formation at the inflamed neuromuscular synapse. On the other hand, depression of endogenous adenosine accumulation may also occur due to enhancement of adenosine deaminase activity. Both membrane-bound and soluble forms of ecto-5′-nucleotidase/CD73 and adenosine deaminase were detected in the inflamed myenteric plexus. These findings provide novel therapeutic targets for inflammatory gut motility disorders.

  12. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  13. Mass reconstruction with a neural network

    International Nuclear Information System (INIS)

    Loennblad, L.; Peterson, C.; Roegnvaldsson, T.

    1992-01-01

    A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W→qanti q, where W-bosons are produced in panti p reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using 'intelligent' variables in instances when the amount of training instances is limited. (orig.)

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

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  15. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

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

  16. Poetry Feedback That Feeds Forward

    Science.gov (United States)

    Patel, Pooja; Laud, Leslie E.

    2015-01-01

    This article provides a description of three seventh grade English teachers' attempt to augment creativity, reading, and deep understanding, and the standards they used to come up with five essential questions surrounding an eight-week unit on poetry. Each of these questions helps to address the school standards and the Common Core State Standards…

  17. Testing water pollution in a two layer aquifer

    OpenAIRE

    García León, Manuel; Lin Ye, Jue

    2011-01-01

    Water bodies around urban areas may be polluted with chemical elements from urban or industrial activities. We study the case of underground water pollution. This is a serious problem, since under- ground water is high qualified drinkable water in a world where this natural resource is increasingly reduced. This study is focused on a two-layer aquifer. If the superficial layer is contaminated, the deeper layer could be spoiled as well. This contribution checks the equality of the mean or c...

  18. Experimental workflow for developing a feed forward strategy to control biomass growth and exploit maximum specific methane productivity of Methanothermobacter marburgensis in a biological methane production process (BMPP

    Directory of Open Access Journals (Sweden)

    Alexander Krajete

    2016-08-01

    Full Text Available Recently, interests for new biofuel generations allowing conversion of gaseous substrate(s to gaseous product(s arose for power to gas and waste to value applications. An example is biological methane production process (BMPP with Methanothermobacter marburgensis. The latter, can convert carbon dioxide (CO2 and hydrogen (H2, having different origins and purities, to methane (CH4, water and biomass. However, these gas converting bioprocesses are tendentiously gas limited processes and the specific methane productivity per biomass amount (qCH4 tends to be low. Therefore, this contribution proposes a workflow for the development of a feed forward strategy to control biomass, growth (rx and qCH4 in a continuous gas limited BMPP. The proposed workflow starts with a design of experiment (DoE to optimize media composition and search for a liquid based limitation to control selectively growth. From the DoE it came out that controlling biomass growth was possible independently of the dilution and gassing rate applied while not affecting methane evolution rates (MERs. This was done by shifting the process from a natural gas limited state to a controlled liquid limited growth. The latter allowed exploiting the maximum biocatalytic activity for methane formation of Methanothermobacter marburgensis. An increase of qCH4 from 42 to 129 mmolCH4 g−1 h−1 was achieved by applying a liquid limitation compare with the reference state. Finally, a verification experiment was done to verify the feeding strategy transferability to a different process configuration. This evidenced the ratio of the fed KH2PO4 to rx (R(FKH2PO4/rx has an appropriate parameter for scaling feeds in a continuous gas limited BMPP. In the verification experiment CH4 was produced in a single bioreactor step at a methane evolution rate (MER of   132 mmolCH4*L−1*h−1 at a CH4 purity of 93 [Vol.%].

  19. Reconstruction and analysis of transcription factor-miRNA co-regulatory feed-forward loops in human cancers using filter-wrapper feature selection.

    Directory of Open Access Journals (Sweden)

    Chen Peng

    Full Text Available BACKGROUND: As one of the most common types of co-regulatory motifs, feed-forward loops (FFLs control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF and microRNA (miRNA simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis. CONCLUSIONS/SIGNIFICANCE: In this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to

  20. Simulation study on control of spill structure of slow extracted beam from a medical synchrotron with feed-forward and feedback using a fast quadruple magnet and RF-knockout system

    Energy Technology Data Exchange (ETDEWEB)

    Muraoka, Ryo; Nakanishi, Tetsuya, E-mail: nakanishi.tetsuya@nihon-u.ac.jp

    2017-02-21

    A feedback control of the spill structure for the slow beam extraction from the medical synchrotron using a fast quadruple and radio frequency (RF)-knockout (QAR method) is studied to obtain the designed spill structure. In addition the feed-forward control is used so that the feedback control is performed effectively. In this extraction method, the spill of several ms are extracted continuously with an interval time of less than 1 ms. Beam simulation showed that a flat spill structure was effectively obtained with feed-forward and feedback control system as well as a step-wise structure which is useful for the shortening of an irradiation time in a spot scanning operation. The effect of current ripples from main quadruple magnet's power supplies could be also reduced with the feedback control application.

  1. Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks

    Science.gov (United States)

    Nussbaumer, Eric A.; Pinker, Rachel T.

    2012-04-01

    A novel approach for calculating downwelling surface longwave (DSLW) radiation under all sky conditions is presented. The DSLW model (hereafter, DSLW/UMD v2) similarly to its predecessor, DSLW/UMD v1, is driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons is implemented; it is trained with simulations derived from runs of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to DSLW, the cloud base temperature is estimated by using an independent artificial neural network approach of similar architecture as previously mentioned, and parameterizations. The cloud base temperature neural network is trained using spatially and temporally co-located MODIS and CloudSat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. Daily average estimates of DSLW from 2003 to 2009 are compared against ground measurements from the Baseline Surface Radiation Network (BSRN) giving an overall correlation coefficient of 0.98, root mean square error (rmse) of 15.84 W m-2, and a bias of -0.39 W m-2. This is an improvement over an earlier version of the model (DSLW/UMD v1) which for the same time period has an overall correlation coefficient 0.97 rmse of 17.27 W m-2, and bias of 0.73 W m-2.

  2. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  3. A neural network-based optimal spatial filter design method for motor imagery classification.

    Directory of Open Access Journals (Sweden)

    Ayhan Yuksel

    Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

  4. Two-layer type filter for removal of radioactive iodine

    Energy Technology Data Exchange (ETDEWEB)

    Taoki, M

    1976-04-16

    The object is to provide a filter for removing radioactive iodine, which is used for disposal of gaseous waste in an atomic power plant, to particularly hold a pressure loss lower. The filter according to the present invention comprises two layers, which are filled at a front stage with active carbon, which is small in pressure loss and has a good collective efficiency relative to iodine, and at a rear stage with silver zeolite, which has a good collective efficiency relative to both iodine and methyl iodine, whereby respective adsorbent are effectively utilized to minimize pressure loss even if a large quantity of air.

  5. Two-layer type filter for removal of radioactive iodine

    International Nuclear Information System (INIS)

    Taoki, Masafumi.

    1976-01-01

    Object: To provide a filter for removing radioactive iodine, which is used for disposal of gaseous waste in an atomic power plant, to particularly hold a pressure loss lower. Structure: The filter according to the present invention comprises two layers, which are filled at a front stage with active carbon, which is small in pressure loss and has a good collective efficiency relative to iodine, and at a rear stage with silver zeolite, which has a good collective efficiency relative to both iodine and methyl iodine, whereby respective adsorbent are effectively utilized to minimize pressure loss even if a large quantity of air. (Kamimura, M.)

  6. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  7. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  8. Neural network tagging in a toy model

    International Nuclear Information System (INIS)

    Milek, Marko; Patel, Popat

    1999-01-01

    The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

  9. Mass transfer model for two-layer TBP oxidation reactions

    International Nuclear Information System (INIS)

    Laurinat, J.E.

    1994-01-01

    To prove that two-layer, TBP-nitric acid mixtures can be safely stored in the canyon evaporators, it must be demonstrated that a runaway reaction between TBP and nitric acid will not occur. Previous bench-scale experiments showed that, at typical evaporator temperatures, this reaction is endothermic and therefore cannot run away, due to the loss of heat from evaporation of water in the organic layer. However, the reaction would be exothermic and could run away if the small amount of water in the organic layer evaporates before the nitric acid in this layer is consumed by the reaction. Provided that there is enough water in the aqueous layer, this would occur if the organic layer is sufficiently thick so that the rate of loss of water by evaporation exceeds the rate of replenishment due to mixing with the aqueous layer. This report presents measurements of mass transfer rates for the mixing of water and butanol in two-layer, TBP-aqueous mixtures, where the top layer is primarily TBP and the bottom layer is comprised of water or aqueous salt solution. Mass transfer coefficients are derived for use in the modeling of two-layer TBP-nitric acid oxidation experiments. Three cases were investigated: (1) transfer of water into the TBP layer with sparging of both the aqueous and TBP layers, (2) transfer of water into the TBP layer with sparging of just the TBP layer, and (3) transfer of butanol into the aqueous layer with sparging of both layers. The TBP layer was comprised of 99% pure TBP (spiked with butanol for the butanol transfer experiments), and the aqueous layer was comprised of either water or an aluminum nitrate solution. The liquid layers were air sparged to simulate the mixing due to the evolution of gases generated by oxidation reactions. A plastic tube and a glass frit sparger were used to provide different size bubbles. Rates of mass transfer were measured using infrared spectrophotometers provided by SRTC/Analytical Development

  10. Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

    Directory of Open Access Journals (Sweden)

    Migliavacca S.C.P.

    2002-01-01

    Full Text Available Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.

  11. Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

    OpenAIRE

    Chin Kim On; Teo Kein Yau; Rayner Alfred; Jason Teo; Patricia Anthony; Wang Cheng

    2016-01-01

    In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car pla...

  12. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    Kolanoski, H.

    1995-04-01

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

  13. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  14. Fastest learning in small-world neural networks

    International Nuclear Information System (INIS)

    Simard, D.; Nadeau, L.; Kroeger, H.

    2005-01-01

    We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

  15. Control of beam halo-chaos using neural network self-adaptation method

    International Nuclear Information System (INIS)

    Fang Jinqing; Huang Guoxian; Luo Xiaoshu

    2004-11-01

    Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

  16. Beneficial role of noise in artificial neural networks

    International Nuclear Information System (INIS)

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-01-01

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated

  17. Plume Splitting in a Two-layer Stratified Ambient Fluid

    Science.gov (United States)

    Ma, Yongxing; Flynn, Morris; Sutherland, Bruce

    2017-11-01

    A line-source plume descending into a two-layer stratified ambient fluid in a finite sized tank is studied experimentally. Although the total volume of ambient fluid is fixed, lower- and upper-layer fluids are respectively removed and added at a constant rate mimicking marine outfall through diffusers and natural and hybrid ventilated buildings. The influence of the plume on the ambient depends on the value of λ, defined as the ratio of the plume buoyancy to the buoyancy loss of the plume as it crosses the ambient interface. Similar to classical filling-box experiments, the plume can always reach the bottom of the tank if λ > 1 . By contrast, if λ < 1 , an intermediate layer eventually forms as a result of plume splitting. Eventually all of the plume fluid spreads within the intermediate layer. The starting time, tv, and the ending time, tt, of the transition process measured from experiments correlate with the value of λ. A three-layer ambient fluid is observed after transition, and the mean value of the measured densities of the intermediate layer fluid is well predicted using plume theory. Acknowledgments: Funding for this study was provided by NSERC.

  18. Migration of radionuclide through two-layered geologic media

    International Nuclear Information System (INIS)

    Nakayama, Shinichi; Takagi, Ikuji; Nakai, Kunihiro; Higashi, Kunio

    1984-01-01

    For the safety assessment of geologic disposal of high-level radioactive wastes, an analytical solution was obtained for one-dimensional migration of radionuclide through two-layered geologic media without dispersion. By applying it to geologic media composed of granite and soil layers, the effect of interlayer boundary on the discharge profile of radionuclides in decay chains into biological environment is examined. The time-space profiles of radionuclides in the vicinity of interlayer boundary are much complicated as shown in the results of calculation. Those profiles in case that the groundwater flows through granite followed by soil are quite different from those in case that the groundwater flows through soil followed by granite. Each of complicated dependence of profiles on time and space can be physically explained. The characteristic profiles in the vicinity of interlayer boundary have not been discussed previously. Recently, numerical computer codes has been developed to apply to much more realistic geologic situations. However, the numerical accuracies of the codes are necessary to be confirmed. This is achieved by comparing computational results with results from analytical solutions. The analytical solution presented will serve as a bench-mark for numerical accuracy. (author)

  19. First passage time in a two-layer system

    International Nuclear Information System (INIS)

    Lee, J.; Koplik, J.

    1995-01-01

    As a first step in the first passage problem for passive tracer in stratified porous media, we consider the case of a two-dimensional system consisting of two layers with different convection velocities. Using a lattice generating function formalism and a variety of analytic and numerical techniques, we calculate the asymptotic behavior of the first passage time probability distribution. We show analytically that the asymptotic distribution is a simple exponential in time for any choice of the velocities. The decay constant is given in terms of the largest eigenvalue of an operator related to a half-space Green's function. For the anti-symmetric case of opposite velocities in the layers, we show that the decay constant for system length L crosses over from L -2 behavior in the diffusive limit to L -1 behavior in the convective regime, where the crossover length L* is given in terms of the velocities. We also have formulated a general self-consistency relation, from which we have developed a recursive approach which is useful for studying the short-time behavior

  20. 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

  1. Wind Resource Assessment and Forecast Planning with Neural Networks

    Directory of Open Access Journals (Sweden)

    Nicolus K. Rotich

    2014-06-01

    Full Text Available In this paper we built three types of artificial neural networks, namely: Feed forward networks, Elman networks and Cascade forward networks, for forecasting wind speeds and directions. A similar network topology was used for all the forecast horizons, regardless of the model type. All the models were then trained with real data of collected wind speeds and directions over a period of two years in the municipal of Puumala, Finland. Up to 70th percentile of the data was used for training, validation and testing, while 71–85th percentile was presented to the trained models for validation. The model outputs were then compared to the last 15% of the original data, by measuring the statistical errors between them. The feed forward networks returned the lowest errors for wind speeds. Cascade forward networks gave the lowest errors for wind directions; Elman networks returned the lowest errors when used for short term forecasting.

  2. Analysis of the Growth Process of Neural Cells in Culture Environment Using Image Processing Techniques

    Science.gov (United States)

    Mirsafianf, Atefeh S.; Isfahani, Shirin N.; Kasaei, Shohreh; Mobasheri, Hamid

    Here we present an approach for processing neural cells images to analyze their growth process in culture environment. We have applied several image processing techniques for: 1- Environmental noise reduction, 2- Neural cells segmentation, 3- Neural cells classification based on their dendrites' growth conditions, and 4- neurons' features Extraction and measurement (e.g., like cell body area, number of dendrites, axon's length, and so on). Due to the large amount of noise in the images, we have used feed forward artificial neural networks to detect edges more precisely.

  3. Transcriptional dynamics elicited by a short pulse of Notch activation involves feed-forward regulation by E(spl)/Hes genes

    Czech Academy of Sciences Publication Activity Database

    Housden, B. E.; Fu, A. G.; Krejčí, Alena; Bernard, F.; Fischer, B.; Tavaré, S.; Russell, S.; Bray, S. J.

    2013-01-01

    Roč. 9, č. 1 (2013), e1003162 ISSN 1553-7404 Grant - others:BBSRC project grant(GB) BB/F00897X/1; MRC programme(GB) G0800034 Institutional support: RVO:60077344 Keywords : split complex genes * mammalian neural development * T-cell leukemia Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 8.167, year: 2013 http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003162

  4. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  5. Artificial neural networks for plasma spectroscopy analysis

    International Nuclear Information System (INIS)

    Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

    1992-01-01

    Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

  6. Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

    Directory of Open Access Journals (Sweden)

    Yasir Hassan Ali

    2015-01-01

    Full Text Available The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ. The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

  7. Fuel economy and torque tracking in camless engines through optimization of neural networks

    International Nuclear Information System (INIS)

    Ashhab, Moh'd Sami S.

    2008-01-01

    The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The controller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking gurantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller

  8. Generalized Predictive Control and Neural Generalized Predictive Control

    Directory of Open Access Journals (Sweden)

    Sadhana CHIDRAWAR

    2008-12-01

    Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.

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

    OpenAIRE

    Ashhab Moh’d Sami

    2017-01-01

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

  10. Dynamic Pricing in Electronic Commerce Using Neural Network

    Science.gov (United States)

    Ghose, Tapu Kumar; Tran, Thomas T.

    In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.

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

    International Nuclear Information System (INIS)

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

    1999-01-01

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

  12. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  13. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    Joorabian, M.

    1999-05-01

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  14. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  15. Reliability analysis of C-130 turboprop engine components using artificial neural network

    Science.gov (United States)

    Qattan, Nizar A.

    In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

  16. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

    International Nuclear Information System (INIS)

    Wu, Ji; Zhang, Chenbin; Chen, Zonghai

    2016-01-01

    Highlights: • An online RUL estimation method for lithium-ion battery is proposed. • RUL is described by the difference among battery terminal voltage curves. • A feed forward neural network is employed for RUL estimation. • Importance sampling is utilized to select feed forward neural network inputs. - Abstract: An accurate battery remaining useful life (RUL) estimation can facilitate the design of a reliable battery system as well as the safety and reliability of actual operation. A reasonable definition and an effective prediction algorithm are indispensable for the achievement of an accurate RUL estimation result. In this paper, the analysis of battery terminal voltage curves under different cycle numbers during charge process is utilized for RUL definition. Moreover, the relationship between RUL and charge curve is simulated by feed forward neural network (FFNN) for its simplicity and effectiveness. Considering the nonlinearity of lithium-ion charge curve, importance sampling (IS) is employed for FFNN input selection. Based on these results, an online approach using FFNN and IS is presented to estimate lithium-ion battery RUL in this paper. Experiments and numerical comparisons are conducted to validate the proposed method. The results show that the FFNN with IS is an accurate estimation method for actual operation.

  17. On nonlinear dynamics of a dipolar exciton BEC in two-layer graphene

    International Nuclear Information System (INIS)

    Berman, O.L.; Kezerashvili, R.Ya.; Kolmakov, G.V.

    2012-01-01

    The nonlinear dynamics of a Bose–Einstein condensate (BEC) of dipolar excitons in two-layer graphene is studied. It is demonstrated that a steady turbulent state is formed in this system. A comparison between the dynamics of the exciton BEC in two-layer graphene and those in GaAs/AlGaAs coupled quantum wells shows that turbulence is a general effect in a BEC.

  18. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  19. ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Tamara Gvozdenović

    2007-06-01

    Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

  20. A Neural Network Approach for GMA Butt Joint Welding

    DEFF Research Database (Denmark)

    Christensen, Kim Hardam; Sørensen, Torben

    2003-01-01

    This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...... penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least...

  1. Microstructural characterization of materials by neural network technique

    Energy Technology Data Exchange (ETDEWEB)

    Barat, P. [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Chatterjee, A., E-mail: arnomitra@veccal.ernet.i [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Mukherjee, P.; Gayathri, N. [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Jayakumar, T.; Raj, Baldev [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102 (India)

    2010-11-15

    Ultrasonic signals received by pulse echo technique from plane parallel Zircaloy 2 samples of fixed thickness and of three different microstructures, were subjected to signal analysis, as conventional parameters like velocity and attenuation could not reliably discriminate them. The signals, obtained from these samples, were first sampled and digitized. Modified Karhunen Loeve Transform was used to reduce their dimensionality. A multilayered feed forward Artificial Neural Network was trained using a few signals in their reduced domain from the three different microstructures. The rest of the signals from the three samples with different microstructures were classified satisfactorily using this network.

  2. New approach to ECG's features recognition involving neural network

    International Nuclear Information System (INIS)

    Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.

    2001-01-01

    A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5

  3. A Neural Network Approach for GMA Butt Joint Welding

    DEFF Research Database (Denmark)

    Christensen, Kim Hardam; Sørensen, Torben

    2003-01-01

    penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...

  4. The application of artificial neural network in radon disaster model of uranium mining

    International Nuclear Information System (INIS)

    Zhu Yufeng; Zhu Guogen; Zhou Shijian

    2012-01-01

    The structural features, data analysis and learning process of feed-forward neural network (BP ANN) were analyzed at first. Rodon sample from Fuzhou Jinan Uranium Industry Limited Company were used to training the network and make the forecast then, and a forecasting model was established for the radon disaster in uranium mines. The method and effectiveness of BP neural network in predicting radon disaster was discussed. The test of training samples showed that the BP network had gotten fairly satisfied result in predicting mine radon disaster. (authors)

  5. Particle identification with neural networks using a rotational invariant moment representation

    International Nuclear Information System (INIS)

    Sinkus, R.

    1997-01-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

  6. Dislocation Coupling-Induced Transition of Synchronization in Two-Layer Neuronal Networks

    International Nuclear Information System (INIS)

    Qin Hui-Xin; Ma Jun; Wang Chun-Ni; Jin Wu-Yin

    2014-01-01

    The mutual coupling between neurons in a realistic neuronal system is much complex, and a two-layer neuronal network is designed to investigate the transition of electric activities of neurons. The Hindmarsh—Rose neuron model is used to describe the local dynamics of each neuron, and neurons in the two-layer networks are coupled in dislocated type. The coupling intensity between two-layer networks, and the coupling ratio (Pro), which defines the percentage involved in the coupling in each layer, are changed to observe the synchronization transition of collective behaviors in the two-layer networks. It is found that the two-layer networks of neurons becomes synchronized with increasing the coupling intensity and coupling ratio (Pro) beyond certain thresholds. An ordered wave in the first layer is useful to wake up the rest state in the second layer, or suppress the spatiotemporal state in the second layer under coupling by generating target wave or spiral waves. And the scheme of dislocation coupling can be used to suppress spatiotemporal chaos and excite quiescent neurons. (interdisciplinary physics and related areas of science and technology)

  7. Biometrics encryption combining palmprint with two-layer error correction codes

    Science.gov (United States)

    Li, Hengjian; Qiu, Jian; Dong, Jiwen; Feng, Guang

    2017-07-01

    To bridge the gap between the fuzziness of biometrics and the exactitude of cryptography, based on combining palmprint with two-layer error correction codes, a novel biometrics encryption method is proposed. Firstly, the randomly generated original keys are encoded by convolutional and cyclic two-layer coding. The first layer uses a convolution code to correct burst errors. The second layer uses cyclic code to correct random errors. Then, the palmprint features are extracted from the palmprint images. Next, they are fused together by XORing operation. The information is stored in a smart card. Finally, the original keys extraction process is the information in the smart card XOR the user's palmprint features and then decoded with convolutional and cyclic two-layer code. The experimental results and security analysis show that it can recover the original keys completely. The proposed method is more secure than a single password factor, and has higher accuracy than a single biometric factor.

  8. Band splitting and relative spin alignment in two-layer systems

    CERN Document Server

    Ovchinnikov, A A

    2002-01-01

    It is shown that the single-particle spectra of the low Hubbard zone in the two-layer correlated 2D-systems sharply differ in the case of different relative alignment of the layers spin systems. The behavior of the two-layer splitting in the Bi sub 2 Sr sub 2 CaCu sub 2 O sub 8 sub + subdelta gives all reasons for the hypothesis on the possible rearrangement of the F sub z -> AF sub z alignment configuration, occurring simultaneously with the superconducting transition. The effects of the spin alignment on the magnetic excitations spectrum, as the way for studying the spin structure of the two-layer systems, are discussed by the example of homogenous solutions for the effective spin models

  9. Process analysis of two-layered tube hydroforming with analytical and experimental verification

    International Nuclear Information System (INIS)

    Seyedkashi, S. M. Hossein; Panahizadeh R, Valiollah; Xu, Haibin; Kim, Sang Yun; Moon, Young Hoon

    2013-01-01

    Two-layered tubular joints are suitable for special applications. Designing and manufacturing of two layered components require enough knowledge about the tube material behavior during the hydroforming process. In this paper, hydroforming of two-layered tubes is investigated analytically, and the results are verified experimentally. The aim of this study is to derive an analytical model which can be used in the process design. Fundamental equations are written for both of the outer and inner tubes, and the total forming pressure is obtained from these equations. Hydroforming experiments are carried out on two different combinations of materials for inner and outer tubes; case 1: copper/aluminum and case 2: carbon steel/stainless steel. It is observed that experimental results are in good agreement with the theoretical model obtained for estimation of forming pressure able to avoid wrinkling.

  10. Ultrasound evaluation of the cesarean scar: comparison between one- and two layer uterotomy closure

    DEFF Research Database (Denmark)

    Glavind, Julie; Madsen, Lene Duch; Uldbjerg, Niels

    Objectives: To compare the residual myometrial thickness and the size of the cesarean scar defect after one- and two layer uterotomy closure. Methods: From July 2010 a continuous two-layer uterotomy closure technique replaced a continuous one-layer technique after cesarean delivery...... at the Department of Obstetrics and Gynecology at Aarhus University Hospital. A total of 149 consecutively invited women (68 women with one-layer and 81 women with two-layer closure) had their cesarean scar examined with 2D transvaginal sonography (TVS) 6-16 months post partum. Inclusion criteria were non......-pregnant women with one previous elective cesarean, no post-partum uterine infection or uterine re-operation, and no type 1 diabetes. Scar defect width, depth, and residual myometrial thickness were measured on the sagittal plane, and scar defect length was measured on the transverse plane. Results: The median...

  11. Free surface simulation of a two-layer fluid by boundary element method

    Directory of Open Access Journals (Sweden)

    Weoncheol Koo

    2010-09-01

    Full Text Available A two-layer fluid with free surface is simulated in the time domain by a two-dimensional potential-based Numerical Wave Tank (NWT. The developed NWT is based on the boundary element method and a leap-frog time integration scheme. A whole domain scheme including interaction terms between two layers is applied to solve the boundary integral equation. The time histories of surface elevations on both fluid layers in the respective wave modes are verified with analytic results. The amplitude ratios of upper to lower elevation for various density ratios and water depths are also compared.

  12. Neural networks for predicting breeding values and genetic gains

    Directory of Open Access Journals (Sweden)

    Gabi Nunes Silva

    2014-12-01

    Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

  13. Artificial neural networks in the nuclear engineering (Part 2)

    International Nuclear Information System (INIS)

    Baptista Filho, Benedito Dias

    2002-01-01

    The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

  14. Use of artificial neural networks for transport energy demand modeling

    International Nuclear Information System (INIS)

    Murat, Yetis Sazi; Ceylan, Halim

    2006-01-01

    The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

  15. Three-dimensional flow in electromagnetically driven shallow two-layer fluids

    NARCIS (Netherlands)

    Akkermans, R.A.D.; Kamp, L.P.J.; Clercx, H.J.H.; van Heijst, G.J.F.

    2010-01-01

    Recent experiments on a freely evolving dipolar vortex in a homogeneous shallow fluid layer have clearly shown the existence and evolution of complex three-dimensional 3D flow structures. The present contribution focuses on the 3D structures of a dipolar vortex evolving in a stable shallow two-layer

  16. Analysis of Steam Heating of a Two-Layer TBP/N-Paraffin/Nitric Acid Mixtures

    International Nuclear Information System (INIS)

    Laurinat, J.E.; Hassan, N.M.; Rudisill, T.S.; Askew, N.M.

    1998-01-01

    This report presents an analysis of steam heating of a two-layer tri-n-butyl phosphate (TBP)/n-paraffin-nitric acid mixture.The purpose of this study is to determine if the degree of mixing provided by the steam jet or by bubbles generated by the TBP/nitric acid reaction is sufficient to prevent a runaway reaction

  17. Spin transport in two-layer-CVD-hBN/graphene/hBN heterostructures

    Science.gov (United States)

    Gurram, M.; Omar, S.; Zihlmann, S.; Makk, P.; Li, Q. C.; Zhang, Y. F.; Schönenberger, C.; van Wees, B. J.

    2018-01-01

    We study room-temperature spin transport in graphene devices encapsulated between a layer-by-layer-stacked two-layer-thick chemical vapor deposition (CVD) grown hexagonal boron nitride (hBN) tunnel barrier, and a few-layer-thick exfoliated-hBN substrate. We find mobilities and spin-relaxation times comparable to that of SiO2 substrate-based graphene devices, and we obtain a similar order of magnitude of spin relaxation rates for both the Elliott-Yafet and D'Yakonov-Perel' mechanisms. The behavior of ferromagnet/two-layer-CVD-hBN/graphene/hBN contacts ranges from transparent to tunneling due to inhomogeneities in the CVD-hBN barriers. Surprisingly, we find both positive and negative spin polarizations for high-resistance two-layer-CVD-hBN barrier contacts with respect to the low-resistance contacts. Furthermore, we find that the differential spin-injection polarization of the high-resistance contacts can be modulated by dc bias from -0.3 to +0.3 V with no change in its sign, while its magnitude increases at higher negative bias. These features point to the distinctive spin-injection nature of the two-layer-CVD-hBN compared to the bilayer-exfoliated-hBN tunnel barriers.

  18. Direct force-reflecting two-layer approach for passive bilateral teleoperation with time delays

    NARCIS (Netherlands)

    Heck, D.; Saccon, A.; Beerens, R.; Nijmeijer, H.

    2018-01-01

    We propose a two-layer control architecture for bilateral teleoperation with communication delays. The controller is structured with an (inner) performance layer and an (outer) passivity layer. In the performance layer, any traditional controller for bilateral teleoperation can be implemented. The

  19. A two-layer architecture for force-reflecting bilateral teleoperation with time delays

    NARCIS (Netherlands)

    Heck, D.J.F.; Saccon, A.; Nijmeijer, H.

    2015-01-01

    We propose a two-layer control architecture for bilateral teleoperation with communication delays. The controller is structured with an (outer) performance layer and an (inner) passivity layer. In the performance layer, any traditional controller for bilateral teleoperation can be implemented. In

  20. Central-Upwind Schemes for Two-Layer Shallow Water Equations

    KAUST Repository

    Kurganov, Alexander; Petrova, Guergana

    2009-01-01

    We derive a second-order semidiscrete central-upwind scheme for one- and two-dimensional systems of two-layer shallow water equations. We prove that the presented scheme is well-balanced in the sense that stationary steady-state solutions

  1. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  2. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  3. A neural network detection system for lower-hybrid cavities in electron plasma density measured by the FREJA satellite

    International Nuclear Information System (INIS)

    Waldemark, J.; Karlsson, Jan

    1995-03-01

    This paper presents a lower-hybrid cavity detection system, CDS, for measurements of electron plasma density on the FREJA satellite wave experiment. The system can reduce the amount of data to be analysed by as much as 96% and still retain more than 85% of the desired information. The CDS is a combination of a hybrid neural network, HNN and expert rules. The HNN is a Self Organizing Map, SOM, combined with a feed forward back propagation neural net, BP. The CDS can be controlled by the user to operate with various degrees of sensitivity. Maximum detection capability is as high as 95% with data reduction lowered to 85%. 10 refs

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

    International Nuclear Information System (INIS)

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

    2001-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Datta, S.

    1999-10-01

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

  6. Forced phase-locked states and information retrieval in a two-layer network of oscillatory neurons with directional connectivity

    International Nuclear Information System (INIS)

    Kazantsev, Victor; Pimashkin, Alexey

    2007-01-01

    We propose two-layer architecture of associative memory oscillatory network with directional interlayer connectivity. The network is capable to store information in the form of phase-locked (in-phase and antiphase) oscillatory patterns. The first (input) layer takes an input pattern to be recognized and their units are unidirectionally connected with all units of the second (control) layer. The connection strengths are weighted using the Hebbian rule. The output (retrieved) patterns appear as forced-phase locked states of the control layer. The conditions are found and analytically expressed for pattern retrieval in response on incoming stimulus. It is shown that the system is capable to recover patterns with a certain level of distortions or noises in their profiles. The architecture is implemented with the Kuramoto phase model and using synaptically coupled neural oscillators with spikes. It is found that the spiking model is capable to retrieve patterns using the spiking phase that translates memorized patterns into the spiking phase shifts at different time scales

  7. Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

    International Nuclear Information System (INIS)

    Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak

    2003-01-01

    This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data

  8. IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Muthukannan K

    2013-11-01

    Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

  9. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  10. Effect of anisotropy on the magnon energy gap in a two-layer ferromagnetic superlattice

    International Nuclear Information System (INIS)

    Qiu Rongke; Liang Jing; Li Qingfeng; Zhang Zhidong; Song Panpan; Hong Xiaomin

    2009-01-01

    The magnon energy bands or spectra in a two-layer ferromagnetic superlattice are studied. It is found that a modulated energy gap exists in the magnon energy band along K x direction perpendicular to the superlattice plane, which is different from the optical magnon gap at K x =0. The anisotropy, the spin quantum numbers and the interlayer exchange couplings all affect the magnon energy gap. If the anisotropy exists, there will be no acoustic energy branch in the system. There is a competition effect of the anisotropy and the spin quantum number on the magnon energy gap. The competition achieves a balance at the zero energy gap, at which the symmetry of the system is higher. The two energy spectra of the two-layer ferromagnetic superlattice are lowered with increasing temperature.

  11. Two layers LSTM with attention for multi-choice question answering in exams

    Science.gov (United States)

    Li, Yongbin

    2018-03-01

    Question Answering in Exams is typical question answering task that aims to test how accurately the model could answer the questions in exams. In this paper, we use general deep learning model to solve the multi-choice question answering task. Our approach is to build distributed word embedding of question and answers instead of manually extracting features or linguistic tools, meanwhile, for improving the accuracy, the external corpus is introduced. The framework uses a two layers LSTM with attention which get a significant result. By contrast, we introduce the simple long short-term memory (QA-LSTM) model and QA-LSTM-CNN model and QA-LSTM with attention model as the reference. Experiment demonstrate superior performance of two layers LSTM with attention compared to other models in question answering task.

  12. Steady ablation on the surface of a two-layer composite

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Wen-Shan [Chung Shan Institute of Science and Technology, P.O. Box 90008-15-3, Lung-Tan, Tao-Yuan, 32526 Taiwan (China)

    2005-12-01

    Discovered is a quasi-steady ablation phenomenon on the surface of a two-layer composite which is formed by a layer of ablative material and another layer of non-ablative substrate. Theoretical exact solutions of quasi-steady ablation rate, the associated temperature distribution and end-of-ablation time of this two-layer composite are derived. A criterion for the occurrence of quasi-steady ablation is presented also. A one-dimensional transient numerical model is developed to perform a number of numerical experiments and hence to verify the correctness of the above theoretical solutions for the current quasi-steady ablation phenomenon. Based on the current results, a new method of measuring the ablation (or sublimation) heat is also proposed. (author)

  13. FDTD Investigation on Electromagnetic Scattering from Two-Layered Rough Surfaces under UPML Absorbing Condition

    International Nuclear Information System (INIS)

    Juan, Li; Li-Xin, Guo; Hao, Zeng

    2009-01-01

    Electromagnetic scattering from one-dimensional two-layered rough surfaces is investigated by using finite-difference time-domain algorithm (FDTD). The uniaxial perfectly matched layer (UPML) medium is adopted for truncation of FDTD lattices, in which the finite-difference equations can be used for the total computation domain by properly choosing the uniaxial parameters. The rough surfaces are characterized with Gaussian statistics for the height and the autocorrelation function. The angular distribution of bistatic scattering coefficient from single-layered perfect electric conducting and dielectric rough surface is calculated and it is in good agreement with the numerical result with the conventional method of moments. The influence of the relative permittivity, the incident angle, and the correlative length of two-layered rough surfaces on the bistatic scattering coefficient with different polarizations are presented and discussed in detail. (fundamental areas of phenomenology (including applications))

  14. Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models

    Science.gov (United States)

    Xu, L.

    1994-01-01

    A simple two-layer variable infiltration capacity (VIC-2L) land surface model suitable for incorporation in general circulation models (GCMs) is described. The model consists of a two-layer characterization of the soil within a GCM grid cell, and uses an aerodynamic representation of latent and sensible heat fluxes at the land surface. The effects of GCM spatial subgrid variability of soil moisture and a hydrologically realistic runoff mechanism are represented in the soil layers. The model was tested using long-term hydrologic and climatalogical data for Kings Creek, Kansas to estimate and validate the hydrological parameters. Surface flux data from three First International Satellite Land Surface Climatology Project Field Experiments (FIFE) intensive field compaigns in the summer and fall of 1987 in central Kansas, and from the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) in Brazil were used to validate the mode-simulated surface energy fluxes and surface temperature.

  15. Using nanofluids in enhancing the performance of a novel two-layer solar pond

    International Nuclear Information System (INIS)

    Al-Nimr, Moh'd A.; Al-Dafaie, Ameer Mohammed Abbas

    2014-01-01

    A novel two-layer nanofluid solar pond is introduced. A mathematical model that describes the thermal performance of the pond has been developed and solved. The upper layer of the pond is made of mineral oil and the lower layer is made of nanofluid. Nanofluid is known to be an excellent solar radiation absorber, and this has been tested and verified using the mathematical model. Using nanofluid will increase the extinction coefficient of the lower layer and consequently will improve the thermal efficiency and the storage capacity of the pond. The effects of other parameters have been also investigated. - Highlights: • A novel two-layer solar pond is discussed. • Nanofluid as thermal energy storage is used in this pond. • A mathematical model is developed to predict the performance of the pond. • The mathematical model is solved using Green's function. • The pond is simulated for different values of governing parameter

  16. Distributed Coordination of Islanded Microgrid Clusters Using a Two-layer Intermittent Communication Network

    DEFF Research Database (Denmark)

    Lu, Xiaoqing; Lai, Jingang; Yu, Xinghuo

    2018-01-01

    information with its neighbors intermittently in a low-bandwidth communication manner. A two-layer sparse communication network is modeled by pinning one or some DGs (pinned DGs) from the lower network of each MG to constitute an upper network. Under this control framework, the tertiary level generates...... the frequency/voltage references based on the active/reactive power mismatch among MGs while the pinned DGs propagate these references to their neighbors in the secondary level, and the frequency/voltage nominal set-points for each DG in the primary level can be finally adjusted based on the frequency....../voltage errors. Stability analysis of the two-layer control system is given, and sufficient conditions on the upper bound of the sampling period ratio of the tertiary layer to the secondary layer are also derived. The proposed controllers are distributed, and thus allow different numbers of heterogeneous DGs...

  17. Estimation of apparent soil resistivity for two-layer soil structure

    Energy Technology Data Exchange (ETDEWEB)

    Nassereddine, M.; Rizk, J.; Nagrial, M.; Hellany, A. [School of Computing, Engineering and Mathematics, University of Western Sydney (Australia)

    2013-07-01

    High voltage (HV) earthing design is one of the key elements when it comes to safety compliance of a system. High voltage infrastructure exposes workers and people to unsafe conditions. The soil structure plays a vital role in determining the allowable and actual step/touch voltage. This paper presents vital information when working with two-layer soil structure. It shows the process as to when it is acceptable to use a single layer instead of a two-layer structure. It also discusses the simplification of the soil structure approach depending on the reflection coefficient. It introduces the reflection coefficient K interval which determines if single layer approach is acceptable. Multiple case studies are presented to address the new approach and its accuracy.

  18. Forced Vibrations of a Two-Layer Orthotropic Shell with an Incomplete Contact Between Layers

    Science.gov (United States)

    Ghulghazaryan, L. G.; Khachatryan, L. V.

    2018-01-01

    Forced vibrations of a two-layer orthotropic shell, with incomplete contact conditions between layers, when the upper face of the shell is free and the lower one is subjected to a dynamic action are considered. By an asymptotic method, the solution of the corresponding dynamic equations and correlations of a 3D problem of elasticity theory is obtained. The amplitudes of forced vibrations are determined, and resonance conditions are established.

  19. TWO-LAYER SECURE PREVENTION MECHANISM FOR REDUCING E-COMMERCE SECURITY RISKS

    OpenAIRE

    Sen-Tarng Lai

    2015-01-01

    E-commerce is an important information system in the network and digital age. However, the network intrusion, malicious users, virus attack and system security vulnerabilities have continued to threaten the operation of the e-commerce, making e-commerce security encounter serious test. How to improve ecommerce security has become a topic worthy of further exploration. Combining routine security test and security event detection procedures, this paper proposes the Two-Layer Secure ...

  20. Particle-bearing currents in uniform density and two-layer fluids

    Science.gov (United States)

    Sutherland, Bruce R.; Gingras, Murray K.; Knudson, Calla; Steverango, Luke; Surma, Christopher

    2018-02-01

    Lock-release gravity current experiments are performed to examine the evolution of a particle bearing flow that propagates either in a uniform-density fluid or in a two-layer fluid. In all cases, the current is composed of fresh water plus micrometer-scale particles, the ambient fluid is saline, and the current advances initially either over the surface as a hypopycnal current or at the interface of the two-layer fluid as a mesopycnal current. In most cases the tank is tilted so that the ambient fluid becomes deeper with distance from the lock. For hypopycnal currents advancing in a uniform density fluid, the current typically slows as particles rain out of the current. While the loss of particles alone from the current should increase the current's buoyancy and speed, in practice the current's speed decreases because the particles carry with them interstitial fluid from the current. Meanwhile, rather than settling on the sloping bottom of the tank, the particles form a hyperpycnal (turbidity) current that advances until enough particles rain out that the relatively less dense interstitial fluid returns to the surface, carrying some particles back upward. When a hypopycnal current runs over the surface of a two-layer fluid, the particles that rain out temporarily halt their descent as they reach the interface, eventually passing through it and again forming a hyperpycnal current. Dramatically, a mesopycnal current in a two-layer fluid first advances along the interface and then reverses direction as particles rain out below and fresh interstitial fluid rises above.

  1. Four-parametric two-layer algebraic model of transition boundary layer at a planar plate

    International Nuclear Information System (INIS)

    Labusov, A.N.; Lapin, Yu.V.

    1996-01-01

    Consideration is given to four-parametric two-layer algebraic model of transition boundary layer on a plane plate, based on generalization of one-parametric algebraic Prandtl-Loitsjansky-Klauzer-3 model. The algebraic model uses Prandtl formulas for mixing path with Loitsjansky damping multiplier in the internal region and the relation for turbulent viscosity, based on universal scales of external region and named the Klauzer-3 formula. 12 refs., 10 figs

  2. Photobleachable Diazonium Salt-Phenolic Resin Two-Layer Resist System

    Science.gov (United States)

    Uchino, Shou-ichi; Iwayanagi, Takao; Hashimoto, Michiaki

    1988-01-01

    This article describes a new negative two-layer photoresist system formed by a simple, successive spin-coating method. An aqueous acetic acid solution of diazonium salt and poly(N-vinylpyrrolidone) is deposited so as to contact a phenolic resin film spin-coated on a silicon wafer. The diazonium salt diffuses into the phenolic resin layer after standing for several minutes. The residual solution on the phenolic resin film doped with diazonium salt is spun to form the diazonium salt-poly(N-vinylpyrrolidone) top layer. This forms a uniform two-layer resist without phase separation or striation. Upon UV exposure, the diazonium salt in the top layer bleaches to act as a CEL dye, while the diazonium salt in the bottom layer decomposes to cause insolubilization. Half μm line-and-space patterns are obtained with an i-line stepper using 4-diazo-N,N-dimethylaniline chloride zinc chloride double salt as the diazonium salt and a cresol novolac resin for the bottom polymer layer. The resist formation processes, insolubilization mechanism, and the resolution capability of the new two-layer resist are discussed.

  3. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    International Nuclear Information System (INIS)

    Lenhardt, L; Zeković, I; Dramićanin, T; Dramićanin, M D

    2013-01-01

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%. (paper)

  4. Input data preprocessing method for exchange rate forecasting via neural network

    Directory of Open Access Journals (Sweden)

    Antić Dragan S.

    2014-01-01

    Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006

  5. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

  7. Using neural networks with jet shapes to identify b jets in e+e- interactions

    International Nuclear Information System (INIS)

    Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan

    1991-01-01

    A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)

  8. Gas metal arc welding of butt joint with varying gap width based on neural networks

    DEFF Research Database (Denmark)

    Christensen, Kim Hardam; Sørensen, Torben

    2005-01-01

    penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters, has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...

  9. An Artificial Neural Network for Data Forecasting Purposes

    Directory of Open Access Journals (Sweden)

    Catalina Lucia COCIANU

    2015-01-01

    Full Text Available Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.

  10. Gas ultracentrifuge separative parameters modeling using hybrid neural networks

    International Nuclear Information System (INIS)

    Crus, Maria Ursulina de Lima

    2005-01-01

    A hybrid neural network is developed for the calculation of the separative performance of an ultracentrifuge. A feed forward neural network is trained to estimate the internal flow parameters of a gas ultracentrifuge, and then these parameters are applied in the diffusion equation. For this study, a 573 experimental data set is used to establish the relation between the separative performance and the controlled variables. The process control variables considered are: the feed flow rate F, the cut θ and the product pressure Pp. The mechanical arrangements consider the radial waste scoop dimension, the rotating baffle size D s and the axial feed location Z E . The methodology was validated through the comparison of the calculated separative performance with experimental values. This methodology may be applied to other processes, just by adapting the phenomenological procedures. (author)

  11. Surface pressure drag for hydrostatic two-layer flow over axisymmetric mountains

    Energy Technology Data Exchange (ETDEWEB)

    Leutbecher, M.

    2000-07-01

    The effect of partial reflections on surface pressure drag is investigated for hydrostatic gravity waves in two-layer flow with piecewise constant buoyancy frequency. The variation of normalized surface pressure drag with interface height is analyzed for axisymmetric mountains. The results are compared with the familiar solution for infinitely long ridges. The drag for the two-layer flow is normalized with the drag of one-layer flow, which has the buoyancy frequency of the lower layer. An analytical expression for the normalized drag of axisymmetric mountains is derived from linear theory of steady flow. Additionally, two-layer flow over finite-height axisymmetric mountains is simulated numerically for flow with higher stability in the upper layer. The temporal evolution of the surface pressure drag is examined in a series of experiments with different interface and mountain heights. The focus is on the linear regime and the nonlinear regime of nonbreaking gravity waves. The dispersion of gravity waves in flow over isolated mountains prevents that the entire wave spectrum is in resonance at the same interface height, which is the case in hydrostatic flow over infinitely long ridges. In consequence, the oscillation of the normalized drag with interface height is smaller for axisymmetric mountains than for infinitely long ridges. However, even for a reflection coefficient as low as 1/3 the drag of an axisymmetric mountain can be amplified by 50% and reduced by 40%. The nonlinear drag becomes steady in the numerical experiments in which no wave breaking occurs. The steady state nonlinear drag agrees quite well with the prediction of linear theory if the linear drag is computed for a slightly lowered interface. (orig.)

  12. Mass transfer model for two-layer TBP oxidation reactions: Revision 1

    International Nuclear Information System (INIS)

    Laurinat, J.E.

    1994-01-01

    To prove that two-layer, TBP-nitric acid mixtures can be safely stored in the Canyon evaporators, it must be demonstrated that a runaway reaction between TBP and nitric acid will not occur. Previous bench-scale experiments showed that, at typical evaporator temperatures, this reaction is endothermic and therefore cannot run away, due to the loss of heat from evaporation of water in the organic layer. However, the reaction would be exothermic and could run away if the small amount of water in the organic layer evaporates before the nitric acid in this layer is consumed by the reaction. Provided that there is enough water in the aqueous layer, this would occur if the organic layer is sufficiently thick so that the rate of loss of water by evaporation exceeds the rate of replenishment due to mixing with the aqueous layer. Bubbles containing reaction products enhance the rate of transfer of water from the aqueous layer to the organic layer. These bubbles are generated by the oxidation of TBP and its reaction products in the organic layer and by the oxidation of butanol in the aqueous layer. Butanol is formed by the hydrolysis of TBP in the organic layer. For aqueous-layer bubbling to occur, butanol must transfer into the aqueous layer. Consequently, the rate of oxidation and bubble generation in the aqueous layer strongly depends on the rate of transfer of butanol from the organic to the aqueous layer. This report presents measurements of mass transfer rates for the mixing of water and butanol in two-layer, TBP-aqueous mixtures, where the top layer is primarily TBP and the bottom layer is comprised of water or aqueous salt solution. Mass transfer coefficients are derived for use in the modeling of two-layer TBP-nitric acid oxidation experiments

  13. Combined conduction and radiation in a two-layer planar medium with flux boundary condition

    International Nuclear Information System (INIS)

    Ho, C.H.; Ozisik, M.N.

    1987-01-01

    The interaction of conduction and radiation is investigated under both transient and steady-state conditions for an absorbing, emitting, and isotropically scattering two-layer slab having opaque coverings at both boundaries. The slab is subjected to an externally applied constant heat flux at one boundary surface and dissipates heat by radiation into external ambients from both boundary surfaces. An analytic approach is applied to solve the radiation part of the problem, and a finite-difference scheme is used to solve the conduction part. The effects of the conduction-to-radiation parameter, the single scattering albedo, the optical thickness, and the surface emissivity on the temperature distribution are examined

  14. The investigation of a two-layer fluid soliton pair using phase plane analysis

    International Nuclear Information System (INIS)

    Momeni, M.; Moslehi-Fard, M.; Alinejad, H.; Mahmoodi, J.

    2004-01-01

    Nonlinear long waves theory in a two-layer fluid system has been studied. The dynamical equations according to the normalized heights in first order are obtained using the reductive perturbation method and the equations of shallow water in each fluid and taking boundary conditions appropriate into account. Conserve energy form by definition a independent variable is found. By definition a Lyapunov function, the condition for stability are shown. A new technique was used to prove stability as well as existence of soliton pair using phase plane analysis. (author)

  15. A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

    Science.gov (United States)

    Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

    2018-01-01

    In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

  16. The application of artificial neural networks to TLD dose algorithm

    International Nuclear Information System (INIS)

    Moscovitch, M.

    1997-01-01

    We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

  17. Validation of the Two-Layer Model for Correcting Clear Sky Reflectance Near Clouds

    Science.gov (United States)

    Wen, Guoyong; Marshak, Alexander; Evans, K. Frank; Vamal, Tamas

    2014-01-01

    A two-layer model was developed in our earlier studies to estimate the clear sky reflectance enhancement near clouds. This simple model accounts for the radiative interaction between boundary layer clouds and molecular layer above, the major contribution to the reflectance enhancement near clouds for short wavelengths. We use LES/SHDOM simulated 3D radiation fields to valid the two-layer model for reflectance enhancement at 0.47 micrometer. We find: (a) The simple model captures the viewing angle dependence of the reflectance enhancement near cloud, suggesting the physics of this model is correct; and (b) The magnitude of the 2-layer modeled enhancement agree reasonably well with the "truth" with some expected underestimation. We further extend our model to include cloud-surface interaction using the Poisson model for broken clouds. We found that including cloud-surface interaction improves the correction, though it can introduced some over corrections for large cloud albedo, large cloud optical depth, large cloud fraction, large cloud aspect ratio. This over correction can be reduced by excluding scenes (10 km x 10km) with large cloud fraction for which the Poisson model is not designed for. Further research is underway to account for the contribution of cloud-aerosol radiative interaction to the enhancement.

  18. A Novel, Diazonium-Phenolic Resin Two-Layer Resist System Utilizing Photoinduced Interfacial Insolubilization

    Science.gov (United States)

    Uchino, Shou-ichi; Iwayanagi, Takao; Ueno, Takumi; Hashimoto, Michiaki; Nonogaki, Saburo

    1987-08-01

    This paper deals with a negative two-layer photoresist system utilizing a photoinduced insolubilization process at the interface. The bottom layer is a phenolic resin either with or without aromatic azide and the top layer is a photosensitive layer comprised of an aromatic diazonium compound and a water soluble polymer. Upon exposure to light, the diazo compound decomposes to cause insolubilization at the interface between the two layers. The system exhibits high contrast due to the combination of interfacial insolubilization and contrast enhancement by photobleaching of the diazonium compound. Patterns of 0.5 um lines and spaces are obtained using an i-line stepper and a resist system containing 4-diazo-N,N-dimethylaniline chloride zinc chloride in the top layer and 3-(4-azidostyry1)- 5,5-dimethyl- 2-cyclohexen-1-one in the bottom layer. Resists with varying spectral responses from mid-UV to g-line can be designed by selecting the kind of diazo compound used in the top layer.

  19. A two-layer model for buoyant inertial displacement flows in inclined pipes

    Science.gov (United States)

    Etrati, Ali; Frigaard, Ian A.

    2018-02-01

    We investigate the inertial flows found in buoyant miscible displacements using a two-layer model. From displacement flow experiments in inclined pipes, it has been observed that for significant ranges of Fr and Re cos β/Fr, a two-layer, stratified flow develops with the heavier fluid moving at the bottom of the pipe. Due to significant inertial effects, thin-film/lubrication models developed for laminar, viscous flows are not effective for predicting these flows. Here we develop a displacement model that addresses this shortcoming. The complete model for the displacement flow consists of mass and momentum equations for each fluid, resulting in a set of four non-linear equations. By integrating over each layer and eliminating the pressure gradient, we reduce the system to two equations for the area and mean velocity of the heavy fluid layer. The wall and interfacial stresses appear as source terms in the reduced system. The final system of equations is solved numerically using a robust, shock-capturing scheme. The equations are stabilized to remove non-physical instabilities. A linear stability analysis is able to predict the onset of instabilities at the interface and together with numerical solution, is used to study displacement effectiveness over different parametric regimes. Backflow and instability onset predictions are made for different viscosity ratios.

  20. Development of a novel two-layer multiplate magnetorheological clutch for high-power applications

    International Nuclear Information System (INIS)

    Wang, Daoming; Tian, Zuzhi; Meng, Qingrui; Hou, Youfu

    2013-01-01

    A novel magnetorheological (MR) clutch for high-power applications is designed, simulated and tested. The clutch is implemented in a two-layer multiplate transmission form and adopts a two-way liquid cooling method to improve the heat dissipation capability. In this paper, a brief introduction to the transmission form of the proposed MR clutch is given first. Then, theoretical analyses of the output torque, magnetic circuit and temperature characteristic are conducted and further design details are presented and discussed, followed by a magnetostatic simulation of the designed circuit. A prototype of the clutch was fabricated and several tests were carried out to evaluate the torque transmission, time response and steady slip power of the prototype. The results show that the proposed MR clutch can produce a maximum output torque of 1545 N m and possesses a high steady slip power of up to 35 kW. Therefore, the developed two-layer multiplate MR clutch is promising for applications in many high-power situations. (paper)

  1. A design method for two-layer beams consisting of normal and fibered high strength concrete

    International Nuclear Information System (INIS)

    Iskhakov, I.; Ribakov, Y.

    2007-01-01

    Two-layer fibered concrete beams can be analyzed using conventional methods for composite elements. The compressed zone of such beam section is made of high strength concrete (HSC), and the tensile one of normal strength concrete (NSC). The problems related to such type of beams are revealed and studied. An appropriate depth of each layer is prescribed. Compatibility conditions between HSC and NSC layers are found. It is based on the shear deformations equality on the layers border in a section with maximal depth of the compression zone. For the first time a rigorous definition of HSC is given using a comparative analysis of deformability and strength characteristics of different concrete classes. According to this definition, HSC has no download branch in the stress-strain diagram, the stress-strain function has minimum exponent, the ductility parameter is minimal and the concrete tensile strength remains constant with an increase in concrete compression strength. The application fields of two-layer concrete beams based on different static schemes and load conditions make known. It is known that the main disadvantage of HSCs is their low ductility. In order to overcome this problem, fibers are added to the HSC layer. Influence of different fiber volume ratios on structural ductility is discussed. An upper limit of the required fibers volume ratio is found based on compatibility equation of transverse tensile concrete deformations and deformations of fibers

  2. Photoacoustic investigation of the effective diffusivity of two-layer semiconductors

    Energy Technology Data Exchange (ETDEWEB)

    Medina, J; Gurevich, Yu. G; Logvinov G, N; Rodriguez, P; Gonzalez de la Cruz, G. [Instituto Mexicano del Petroleo, Mexico, D.F. (Mexico)

    2001-08-01

    In this work, the problem of the effective thermal diffusivity of two-layer systems is investigated using the photoacoustic spectroscopy. The experimental results are examined in terms of the effective thermal parameters of the composite system determined from an homogeneous material which produces the same physical response under an external perturbation in the detector device. It is shown, that the effective thermal conductivity is not symmetric under exchange of the two layers of the composite; i.e., the effective thermal parameters depend upon which layer is illuminated in the photoacoustic experiments. Particular emphasis is given to the characterization of the interface thermal conductivity between the layer-system. [Spanish] En el presente trabajo se utiliza la espectroscopia fotoacustica para medir la difusividad termica de un sistema de dos capas. Los resultados experimentales son analizados en terminos de los parametros termicos efectivos determinados a partir de un material homogeneo, el cual produce la misma respuesta fisica bajo una perturbacion externa. Se puso particular enfasis en la caracterizacion de los efectos de interfase en el flujo de calor en el sistema de dos capas. Los resultados experimentales se comparan con el modelo teorico propuesto en este trabajo.

  3. Waves propagating over a two-layer porous barrier on a seabed

    Science.gov (United States)

    Lin, Qiang; Meng, Qing-rui; Lu, Dong-qiang

    2018-05-01

    A research of wave propagation over a two-layer porous barrier, each layer of which is with different values of porosity and friction, is conducted with a theoretical model in the frame of linear potential flow theory. The model is more appropriate when the seabed consists of two different properties, such as rocks and breakwaters. It is assumed that the fluid is inviscid and incompressible and the motion is irrotational. The wave numbers in the porous region are complex ones, which are related to the decaying and propagating behaviors of wave modes. With the aid of the eigenfunction expansions, a new inner product of the eigenfunctions in the two-layer porous region is proposed to simplify the calculation. The eigenfunctions, under this new definition, possess the orthogonality from which the expansion coefficients can be easily deduced. Selecting the optimum truncation of the series, we derive a closed system of simultaneous linear equations for the same number of the unknown reflection and transmission coefficients. The effects of several physical parameters, including the porosity, friction, width, and depth of the porous barrier, on the dispersion relation, reflection and transmission coefficients are discussed in detail through the graphical representations of the solutions. It is concluded that these parameters have certain impacts on the reflection and transmission energy.

  4. Modeling a two-layer flow system at the subarctic, subalpine tree line during snowmelt

    Science.gov (United States)

    Leenders, Erica E.; Woo, Ming-Ko

    2002-10-01

    In the subarctic it is common to encounter a two-layer flow system consisting of a porous organic cover overlying frozen or unfrozen mineral soils with much lower hydraulic conductivities. The "simple lumped reservoir parametric," or "semidistributed land-use-based runoff processes" (SLURP), model was adapted to simulate runoff generated by such a flow system from an upland shrub land to an open woodland downslope. A subalpine site in Wolf Creek, Yukon, Canada, was subdivided into two aggregated simulation areas (ASA), each being a unit characterized by a set of parameters. The model computes the vertical water balance and flow generation from several storages, and then routes the water out of the ASA. When applied to the 1999 snowmelt season, the model simulated the very low lateral flow and a large increase in storage in the mineral soil, as was observed in the field. The model was used to assess the sensitivity of the two-layer flow system under a range of temperature, snow cover, and frost conditions. Results show that within the range of possible climatic conditions, the hydrologic system is unlikely to yield significant runoff across the subalpine tree line, but if ground ice is abundant in the soil pores, percolation will be limited and fast flow from the surface layer is enhanced.

  5. Reverse-feeding effect of epidemic by propagators in two-layered networks

    Science.gov (United States)

    Dayu, Wu; Yanping, Zhao; Muhua, Zheng; Jie, Zhou; Zonghua, Liu

    2016-02-01

    Epidemic spreading has been studied for a long time and is currently focused on the spreading of multiple pathogens, especially in multiplex networks. However, little attention has been paid to the case where the mutual influence between different pathogens comes from a fraction of epidemic propagators, such as bisexual people in two separated groups of heterosexual and homosexual people. We here study this topic by presenting a network model of two layers connected by impulsive links, in contrast to the persistent links in each layer. We let each layer have a distinct pathogen and their interactive infection is implemented by a fraction of propagators jumping between the corresponding pairs of nodes in the two layers. By this model we show that (i) the propagators take the key role to transmit pathogens from one layer to the other, which significantly influences the stabilized epidemics; (ii) the epidemic thresholds will be changed by the propagators; and (iii) a reverse-feeding effect can be expected when the infective rate is smaller than its threshold of isolated spreading. A theoretical analysis is presented to explain the numerical results. Project supported by the National Natural Science Foundation of China (Grant Nos. 11135001, 11375066, and 11405059) and the National Basic Key Program of China (Grant No. 2013CB834100).

  6. Reverse-feeding effect of epidemic by propagators in two-layered networks

    International Nuclear Information System (INIS)

    Wu Dayu; Zhao Yanping; Zheng Muhua; Zhou Jie; Liu Zonghua

    2016-01-01

    Epidemic spreading has been studied for a long time and is currently focused on the spreading of multiple pathogens, especially in multiplex networks. However, little attention has been paid to the case where the mutual influence between different pathogens comes from a fraction of epidemic propagators, such as bisexual people in two separated groups of heterosexual and homosexual people. We here study this topic by presenting a network model of two layers connected by impulsive links, in contrast to the persistent links in each layer. We let each layer have a distinct pathogen and their interactive infection is implemented by a fraction of propagators jumping between the corresponding pairs of nodes in the two layers. By this model we show that (i) the propagators take the key role to transmit pathogens from one layer to the other, which significantly influences the stabilized epidemics; (ii) the epidemic thresholds will be changed by the propagators; and (iii) a reverse-feeding effect can be expected when the infective rate is smaller than its threshold of isolated spreading. A theoretical analysis is presented to explain the numerical results. (paper)

  7. Modelling of fast jet formation under explosion collision of two-layer alumina/copper tubes

    Directory of Open Access Journals (Sweden)

    I Balagansky

    2017-09-01

    Full Text Available Under explosion collapse of two-layer tubes with an outer layer of high-modulus ceramics and an inner layer of copper, formation of a fast and dense copper jet is plausible. We have performed a numerical simulation of the explosion collapse of a two-layer alumina/copper tube using ANSYS AUTODYN software. The simulation was performed in a 2D-axis symmetry posting on an Eulerian mesh of 3900x1200 cells. The simulation results indicate two separate stages of the tube collapse process: the nonstationary and the stationary stage. At the initial stage, a non-stationary fragmented jet is moving with the velocity of leading elements up to 30 km/s. The collapse velocity of the tube to the symmetry axis is about 2 km/s, and the pressure in the contact zone exceeds 700 GPa. During the stationary stage, a dense jet is forming with the velocity of 20 km/s. Temperature of the dense jet is about 2000 K, jet failure occurs when the value of effective plastic deformation reaches 30.

  8. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  9. The Rayleigh-Taylor instability in a self-gravitating two-layer fluid sphere

    International Nuclear Information System (INIS)

    Ida, Shigeru; Nakagawa, Yoshitsugu; Nakazawa, Kiyoshi

    1989-01-01

    The Rayleigh-Taylor instability is studied in a self-gravitating two-layer fluid sphere: an inner sphere and an outer layer. The density and the viscosity are assumed to be constant in each region. Analytic expressions of the dispersion relations are obtained in inviscid and viscid cases. This examination aims at the investigation of the Earth's core formation. The fluid sphere corresponds to the proto-Earth in the accretion stage. The instability is examined without rotation of the fluid sphere, while the proto-Earth is rotating. However, it is shown that the Coriolis force does not influence the conclusion in the Earth's core formation problem. 5 refs.; 10 figs

  10. Assessment of Speech in Primary Cleft Palate by Two-layer Closure (Conservative Management).

    Science.gov (United States)

    Jain, Harsha; Rao, Dayashankara; Sharma, Shailender; Gupta, Saurabh

    2012-01-01

    Treatment of the cleft palate has evolved over a long period of time. Various techniques of cleft palate repair that are practiced today are the results of principles learned through many years of modifications. The challenge in the art of modern palatoplasty is no longer successful closure of the cleft palate but an optimal speech outcome without compromising maxillofacial growth. Throughout these periods of evolution in the treatment of cleft palate, the effectiveness of various treatment protocols has been challenged by controversies concerning speech and maxillofacial growth. In this article we have evaluated the results of Pinto's modification of Wardill-Kilner palatoplasty without radical dissection of the levator veli palitini muscle on speech and post-op fistula in two different age groups in 20 patients. Preoperative and 6-month postoperative speech assessment values indicated that two-layer palatoplasty (modified Wardill-Kilner V-Y pushback technique) without an intravelar veloplasty technique was good for speech.

  11. Synthesis of PVA/PVP hydrogels having two-layer by radiation and their physical properties

    International Nuclear Information System (INIS)

    Park, K.R.; Nho, Y.C.

    2003-01-01

    In these studies, two-layer hydrogels which consisted of polyurethane membrane and a mixture of polyvinyl alcohol(PVA)/poly-N-vinylpyrrolidone(PVP)/glycerin/chitosan were made for the wound dressing. Polyurethane was dissolved in solvent, the polyurethane solution was poured on the mould, and then dried to make the thin membrane. Hydrophilic polymer solutions were poured on the polyurethane membranes, they were exposed to gamma irradiation or two steps of 'freezing and thawing' and gamma irradiation doses to make the hydrogels. The physical properties such as gelation, water absorptivity, and gel strength were examined to evaluate the hydrogels for wound dressing. The physical properties of hydrogels such as gelation and gel strength was greatly improved when polyurethane membrane was used as a covering layer of hydrogel, and the evaporation speed of water in hydrogel was reduced

  12. Exposure buildup factors for a cobalt-60 point isotropic source for single and two layer slabs

    International Nuclear Information System (INIS)

    Chakarova, R.

    1992-01-01

    Exposure buildup factors for point isotropic cobalt-60 sources are calculated by the Monte Carlo method with statistical errors ranging from 1.5 to 7% for 1-5 mean free paths (mfp) thick water and iron single slabs and for 1 and 2 mfp iron layers followed by water layers 1-5 mfp thick. The computations take into account Compton scattering. The Monte Carlo data for single slab geometries are approximated by Geometric Progression formula. Kalos's formula using the calculated single slab buildup factors may be applied to reproduce the data for two-layered slabs. The presented results and discussion may help when choosing the manner in which the radiation field gamma irradiation units will be described. (author)

  13. A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

    Science.gov (United States)

    Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

    Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

  14. Central-Upwind Schemes for Two-Layer Shallow Water Equations

    KAUST Repository

    Kurganov, Alexander

    2009-01-01

    We derive a second-order semidiscrete central-upwind scheme for one- and two-dimensional systems of two-layer shallow water equations. We prove that the presented scheme is well-balanced in the sense that stationary steady-state solutions are exactly preserved by the scheme and positivity preserving; that is, the depth of each fluid layer is guaranteed to be nonnegative. We also propose a new technique for the treatment of the nonconservative products describing the momentum exchange between the layers. The performance of the proposed method is illustrated on a number of numerical examples, in which we successfully capture (quasi) steady-state solutions and propagating interfaces. © 2009 Society for Industrial and Applied Mathematics.

  15. Development of analytical theory of the physical libration for a two-layer Moon

    Science.gov (United States)

    Petrova, Natalia; Barkin, Yurii; Gusev, Alexander; Ivanova, Tamara

    2010-05-01

    -project. Prognosis recommendations are made for the future experiment. The model of free rotation of the two-layer Moon is constructed, the periods of the free modes and of the librational motion of a pole are received, effects of influence of a lunar core on behavior of LPhL-harmonics caused by the solid-state rotation of the Moon are deduced. Computer simulating has revealed the sensitivity of the free libration periods to core's ellipticity and to core-mantle boundary dissipation parameters. Geometrical interpretation of the pole motion owing to the free libration is given. For the first time the theoretical model of tidal potential of the Moon is developed, on the basis of the model the analytical formulae for variations of the Stockes coefficients of the 2-nd order and of the speed of the Lunar rotation is received in dependence on time. For a two-layer structure of the Moon and the Mercury Cassini's law were stated at the first time: 1. a two-layer Moon keeps its own stationary rotation; 2. there is a splitting of Cassini nodes and angular momentums of Lunar mantle and core; 3. the same phenomenon will be observed for any two-layer planet (Mercury); 4. the differential rotation of a core and mantle is inherent to a planet as result of a generalized Cassini's Laws. Theoretical and practical methods of construction of the theory of rotation of the Earth have been successfully applied in the development of the theory of rotation of the Moon, in

  16. Two-Layer 16 Tesla Cosθ Dipole Design for the FCC

    Energy Technology Data Exchange (ETDEWEB)

    Holik, Eddie Frank [Fermilab; Ambrosio, Giorgio [Fermilab; Apollinari, G. [Fermilab

    2018-02-13

    The Future Circular Collider or FCC is a study aimed at exploring the possibility to reach 100 TeV total collision energy which would require 16 tesla dipoles. Upon the conclusion of the High Luminosity Upgrade, the US LHC Accelerator Upgrade Pro-ject in collaboration with CERN will have extensive Nb3Sn magnet fabrication experience. This experience includes robust Nb3Sn conductor and insulation scheming, 2-layer cos2θ coil fabrication, and bladder-and-key structure and assembly. By making im-provements and modification to existing technology the feasibility of a two-layer 16 tesla dipole is investigated. Preliminary designs indicate that fields up to 16.6 tesla are feasible with conductor grading while satisfying the HE-LHC and FCC specifications. Key challenges include accommodating high-aspect ratio conductor, narrow wedge design, Nb3Sn conductor grading, and especially quench protection of a 16 tesla device.

  17. Two-layer fragile watermarking method secured with chaotic map for authentication of digital Holy Quran.

    Science.gov (United States)

    Khalil, Mohammed S; Kurniawan, Fajri; Khan, Muhammad Khurram; Alginahi, Yasser M

    2014-01-01

    This paper presents a novel watermarking method to facilitate the authentication and detection of the image forgery on the Quran images. Two layers of embedding scheme on wavelet and spatial domain are introduced to enhance the sensitivity of fragile watermarking and defend the attacks. Discrete wavelet transforms are applied to decompose the host image into wavelet prior to embedding the watermark in the wavelet domain. The watermarked wavelet coefficient is inverted back to spatial domain then the least significant bits is utilized to hide another watermark. A chaotic map is utilized to blur the watermark to make it secure against the local attack. The proposed method allows high watermark payloads, while preserving good image quality. Experiment results confirm that the proposed methods are fragile and have superior tampering detection even though the tampered area is very small.

  18. Modified two-layer social force model for emergency earthquake evacuation

    Science.gov (United States)

    Zhang, Hao; Liu, Hong; Qin, Xin; Liu, Baoxi

    2018-02-01

    Studies of crowd behavior with related research on computer simulation provide an effective basis for architectural design and effective crowd management. Based on low-density group organization patterns, a modified two-layer social force model is proposed in this paper to simulate and reproduce a group gathering process. First, this paper studies evacuation videos from the Luan'xian earthquake in 2012, and extends the study of group organization patterns to a higher density. Furthermore, taking full advantage of the strength in crowd gathering simulations, a new method on grouping and guidance is proposed while using crowd dynamics. Second, a real-life grouping situation in earthquake evacuation is simulated and reproduced. Comparing with the fundamental social force model and existing guided crowd model, the modified model reduces congestion time and truly reflects group behaviors. Furthermore, the experiment result also shows that a stable group pattern and a suitable leader could decrease collision and allow a safer evacuation process.

  19. Initial stresses in two-layer metal domes due to imperfections of their production and assemblage

    Directory of Open Access Journals (Sweden)

    Lebed Evgeniy Vasil’evich

    2015-04-01

    Full Text Available The process of construction of two-layer metal domes is analyzed to illustrate the causes of initial stresses in the bars of their frames. It has been noticed that it is impossible to build such structures with ideal geometric parameters because of imperfections caused by objective reasons. These imperfections cause difficulties in the process of connection of the elements in the joints. The paper demonstrates the necessity of fitting operations during assemblage that involve force fitting and yield initial stresses due to imperfections. The authors propose a special method of computer modeling of enforced elimination of possible imperfections caused by assemblage process and further confirm the method by an analysis of a concrete metal dome.

  20. Cumulative second-harmonic generation of Lamb waves propagating in a two-layered solid plate

    International Nuclear Information System (INIS)

    Xiang Yanxun; Deng Mingxi

    2008-01-01

    The physical process of cumulative second-harmonic generation of Lamb waves propagating in a two-layered solid plate is presented by using the second-order perturbation and the technique of nonlinear reflection of acoustic waves at an interface. In general, the cumulative second-harmonic generation of a dispersive guided wave propagation does not occur. However, the present paper shows that the second-harmonic of Lamb wave propagation arising from the nonlinear interaction of the partial bulk acoustic waves and the restriction of the three boundaries of the solid plates does have a cumulative growth effect if some conditions are satisfied. Through boundary condition and initial condition of excitation, the analytical expression of cumulative second-harmonic of Lamb waves propagation is determined. Numerical results show the cumulative effect of Lamb waves on second-harmonic field patterns. (classical areas of phenomenology)

  1. The Rayleigh-Taylor instability in a self-gravitating two-layer viscous sphere

    Science.gov (United States)

    Mondal, Puskar; Korenaga, Jun

    2018-03-01

    The dispersion relation of the Rayleigh-Taylor instability in the spherical geometry is of profound importance in the context of the Earth's core formation. Here we present a complete derivation of this dispersion relation for a self-gravitating two-layer viscous sphere. Such relation is, however, obtained through the solution of a complex transcendental equation, and it is difficult to gain physical insights directly from the transcendental equation itself. We thus also derive an empirical formula to compute the growth rate, by combining the Monte Carlo sampling of the relevant model parameter space with linear regression. Our analysis indicates that the growth rate of Rayleigh-Taylor instability is most sensitive to the viscosity of inner layer in a physical setting that is most relevant to the core formation.

  2. Three-Dimensional Computer-Assisted Two-Layer Elastic Models of the Face.

    Science.gov (United States)

    Ueda, Koichi; Shigemura, Yuka; Otsuki, Yuki; Fuse, Asuka; Mitsuno, Daisuke

    2017-11-01

    To make three-dimensional computer-assisted elastic models for the face, we decided on five requirements: (1) an elastic texture like skin and subcutaneous tissue; (2) the ability to take pen marking for incisions; (3) the ability to be cut with a surgical knife; (4) the ability to keep stitches in place for a long time; and (5) a layered structure. After testing many elastic solvents, we have made realistic three-dimensional computer-assisted two-layer elastic models of the face and cleft lip from the computed tomographic and magnetic resonance imaging stereolithographic data. The surface layer is made of polyurethane and the inner layer is silicone. Using this elastic model, we taught residents and young doctors how to make several typical local flaps and to perform cheiloplasty. They could experience realistic simulated surgery and understand three-dimensional movement of the flaps.

  3. Analysis of data recorded by the LCTPC equipped with a two layer GEM-system

    CERN Document Server

    Ljunggren, M

    2012-01-01

    wire based readout. The prototype TPC is placed in a 1 Tesla magnet at DESY and tested using an electron beam. Analyses of data taken during two different measurement series, in 2009 and 2010, are presented here. The TPC was instrumented with a two layer GEM system and read out using modified electronics from the ALICE experiment, including the programmable charge sensitive preamp-shaper PCA16. The PCA16 chip has a number of programmable parameters which allows studies to determine the settings optimal to the final TPC. Here, the impact of the shaping time on the space resolution in the drift direction was studied. It was found that a shaping time of 60 ns is the b...

  4. A New, Two-layer Canopy Module For The Detailed Snow Model SNOWPACK

    Science.gov (United States)

    Gouttevin, I.; Lehning, M.; Jonas, T.; Gustafsson, D.; Mölder, M.

    2014-12-01

    A new, two-layer canopy module with thermal inertia for the detailed snow model SNOWPACK is presented. Compared to the old, one-layered canopy formulation with no heat mass, this module now offers a level of physical detail consistent with the detailed snow and soil representation in SNOWPACK. The new canopy model is designed to reproduce the difference in thermal regimes between leafy and woody canopy elements and their impact on the underlying snowpack energy balance. The new model is validated against data from an Alpine and a boreal site. Comparisons of modelled sub-canopy thermal radiations to stand-scale observations at Alptal, Switzerland, demonstrate the improvements induced by our new parameterizations. The main effect is a more realistic simulation of the canopy night-time drop in temperatures. The lower drop is induced by both thermal inertia and the two-layer representation. A specific result is that such a performance cannot be achieved by a single-layered canopy model. The impact of the new parameterizations on the modelled dynamics of the sub-canopy snowpack is analysed and yields consistent results, but the frequent occurrence of mixed-precipitation events at Alptal prevents a conclusive assessment of model performances against snow data.Without specific tuning, the model is also able to reproduce the measured summertime tree trunk temperatures and biomass heat storage at the boreal site of Norunda, Sweden, with an increased accuracy in amplitude and phase. Overall, the SNOWPACK model with its enhanced canopy module constitutes a unique (in its physical process representation) atmosphere-to-soil-through-canopy-and-snow modelling chain.

  5. Precession of a two-layer Earth: contributions of the core and elasticity

    Science.gov (United States)

    Baenas, Tomás; Ferrándiz, José M.; Escapa, Alberto; Getino, Juan; Navarro, Juan F.

    2016-04-01

    The Earth's internal structure contributes to the precession rate in a small but non-negligible amount, given the current accuracy goals demanded by IAG/GGOS to the reference frames, namely 30 μas and 3 μas/yr. These contributions come from a variety of sources. One of those not yet accounted for in current IAU models is associated to the crossed effects of certain nutation-rising terms of a two-layer Earth model; intuitively, it gathers an 'indirect' effect of the core via the NDFW, or FCN, resonance as well as a 'direct' effect arising from terms that account for energy variations depending on the elasticity of the core. Similar order of magnitude reaches the direct effect of the departure of the Earth's rheology from linear elasticity. To compute those effects we work out the problem in a unified way within the Hamiltonian framework developed by Getino and Ferrándiz (2001). It allows a consistent treatment of the problem since all the perturbations are derived from the same tide generating expansion and the crossing effects are rigorously obtained through Hori's canonical perturbation method. The problem admits an asymptotic analytical solution. The Hamiltonian is constructed by considering a two-layer Earth model made up of an anelastic mantle and a fluid core, perturbed by the gravitational action of the Moon and the Sun. The former effects reach some tens of μas/yr in the longitude rate, hence above the target accuracy level. We outline their influence in the estimation of the Earth's dynamical ellipticity, a main parameter factorizing both precession and nutation.

  6. Quantification of the specific yield in a two-layer hard-rock aquifer model

    Science.gov (United States)

    Durand, Véronique; Léonardi, Véronique; de Marsily, Ghislain; Lachassagne, Patrick

    2017-08-01

    Hard rock aquifers (HRA) have long been considered to be two-layer systems, with a mostly capacitive layer just below the surface, the saprolite layer, and a mainly transmissive layer underneath, the fractured layer. Although this hydrogeological conceptual model is widely accepted today within the scientific community, it is difficult to quantify the respective storage properties of each layer with an equivalent porous medium model. Based on an HRA field site, this paper attempts to quantify in a distinct manner the respective values of the specific yield (Sy) in the saprolite and the fractured layer, with the help of a deterministic hydrogeological model. The study site is the Plancoët migmatitic aquifer located in north-western Brittany, France, with piezometric data from 36 observation wells surveyed every two weeks for eight years. Whereas most of the piezometers (26) are located where the water table lies within the saprolite, thus representing the specific yield of the unconfined layer (Sy1), 10 of them are representative of the unconfined fractured layer (Sy2), due to their position where the saprolite is eroded or unsaturated. The two-layer model, based on field observations of the layer geometry, runs with the MODFLOW code. 81 values of the Sy1/Sy2 parameter sets were tested manually, as an inverse calibration was not able to calibrate these parameters. In order to calibrate the storage properties, a new quality-of-fit criterion called ;AdVar; was also developed, equal to the mean squared deviation of the seasonal piezometric amplitude variation. Contrary to the variance, AdVar is able to select the best values for the specific yield in each layer. It is demonstrated that the saprolite layer is about 2.5 times more capacitive than the fractured layer, with Sy1 = 10% (7% < Sy1 < 15%) against Sy2 = 2% (1% < Sy2 < 3%), in this particular example.

  7. Revisiting the two-layer hypothesis: coexistence of alternative functional rooting strategies in savannas.

    Science.gov (United States)

    Holdo, Ricardo M

    2013-01-01

    The two-layer hypothesis of tree-grass coexistence posits that trees and grasses differ in rooting depth, with grasses exploiting soil moisture in shallow layers while trees have exclusive access to deep water. The lack of clear differences in maximum rooting depth between these two functional groups, however, has caused this model to fall out of favor. The alternative model, the demographic bottleneck hypothesis, suggests that trees and grasses occupy overlapping rooting niches, and that stochastic events such as fires and droughts result in episodic tree mortality at various life stages, thus preventing trees from otherwise displacing grasses, at least in mesic savannas. Two potential problems with this view are: 1) we lack data on functional rooting profiles in trees and grasses, and these profiles are not necessarily reflected by differences in maximum or physical rooting depth, and 2) subtle, difficult-to-detect differences in rooting profiles between the two functional groups may be sufficient to result in coexistence in many situations. To tackle this question, I coupled a plant uptake model with a soil moisture dynamics model to explore the environmental conditions under which functional rooting profiles with equal rooting depth but different depth distributions (i.e., shapes) can coexist when competing for water. I show that, as long as rainfall inputs are stochastic, coexistence based on rooting differences is viable under a wide range of conditions, even when these differences are subtle. The results also indicate that coexistence mechanisms based on rooting niche differentiation are more viable under some climatic and edaphic conditions than others. This suggests that the two-layer model is both viable and stochastic in nature, and that a full understanding of tree-grass coexistence and dynamics may require incorporating fine-scale rooting differences between these functional groups and realistic stochastic climate drivers into future models.

  8. Estimation of airway smooth muscle stiffness changes due to length oscillation using artificial neural network.

    Science.gov (United States)

    Al-Jumaily, Ahmed; Chen, Leizhi

    2012-10-07

    This paper presents a novel approach to estimate stiffness changes in airway smooth muscles due to external oscillation. Artificial neural networks are used to model the stiffness changes due to cyclic stretches of the smooth muscles. The nonlinear relationship between stiffness ratios and oscillation frequencies is modeled by a feed-forward neural network (FNN) model. The structure of the FNN is selected through the training and validation using literature data from 11 experiments with different muscle lengths, muscle masses, oscillation frequencies and amplitudes. Data pre-processing methods are used to improve the robustness of the neural network model to match the non-linearity. The validation results show that the FNN model can predict the stiffness ratio changes with a mean square error of 0.0042. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. Artificial neural networks for modeling time series of beach litter in the southern North Sea.

    Science.gov (United States)

    Schulz, Marcus; Matthies, Michael

    2014-07-01

    In European marine waters, existing monitoring programs of beach litter need to be improved concerning litter items used as indicators of pollution levels, efficiency, and effectiveness. In order to ease and focus future monitoring of beach litter on few important litter items, feed-forward neural networks consisting of three layers were developed to relate single litter items to general categories of marine litter. The neural networks developed were applied to seven beaches in the southern North Sea and modeled time series of five general categories of marine litter, such as litter from fishing, shipping, and tourism. Results of regression analyses show that general categories were predicted significantly moderately to well. Measured and modeled data were in the same order of magnitude, and minima and maxima overlapped well. Neural networks were found to be eligible tools to deliver reliable predictions of marine litter with low computational effort and little input of information. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Using a neural network in the search for the Higgs boson

    International Nuclear Information System (INIS)

    Hultqvist, K.; Jacobsson, R.; Johansson, K.E.

    1995-01-01

    The search for the Standard Model Higgs boson in high energy e + e - collisions requires analysis techniques which efficiently discriminate against the very large background. A classifier based on a feed-forward neural network has been extensively used in a search in the channel where the Higgs boson is produced in association with neutrinos. The method has significantly improved the sensitivity of the search. With a simple preselection based on event topology followed by a neural network we have obtained a combined background rejection factor of more than 29 000 and a selection efficiency for Higgs particle events of 54%, assuming a mass of 55 GeV/c 2 for the Higgs boson. We describe here the details of the analysis with emphasis on the neural network. (orig.)

  11. Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Daoyu Lin

    2018-01-01

    Full Text Available Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.

  12. Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network

    Science.gov (United States)

    Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser

    2018-02-01

    The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.

  13. Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications

    KAUST Repository

    Elshurafa, Amro M.; Salama, Khaled N.

    2012-01-01

    In this Letter, the authors fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art single

  14. Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

    Science.gov (United States)

    Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

    2017-02-01

    An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

  15. Prediction of small hydropower plant power production in Himreen Lake dam (HLD using artificial neural network

    Directory of Open Access Journals (Sweden)

    Ali Thaeer Hammid

    2018-03-01

    Full Text Available In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R between the variables of predicted and observed output that would be higher than 0.96. Keywords: Himreen Lake Dam, Small Hydropower plants, Artificial Neural Networks, Feed forward-back propagation model, Generation system's prediction

  16. A novel nature inspired firefly algorithm with higher order neural network: Performance analysis

    Directory of Open Access Journals (Sweden)

    Janmenjoy Nayak

    2016-03-01

    Full Text Available The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others.

  17. Neural network based expert system for fault diagnosis of particle accelerators

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  18. Fabrication and characterization of two-layered nanofibrous membrane for guided bone and tissue regeneration application.

    Science.gov (United States)

    Masoudi Rad, Maryam; Nouri Khorasani, Saied; Ghasemi-Mobarakeh, Laleh; Prabhakaran, Molamma P; Foroughi, Mohammad Reza; Kharaziha, Mahshid; Saadatkish, Niloufar; Ramakrishna, Seeram

    2017-11-01

    Membranes used in dentistry act as a barrier to prevent invasion of intruder cells to defected area and obtains spaces that are to be subsequently filled with new bone and provide required bone volume for implant therapy when there is insufficient volume of healthy bone at implant site. In this study a two-layered bioactive membrane were fabricated by electrospinning whereas one layer provides guided bone regeneration (GBR) and fabricated using poly glycerol sebacate (PGS)/polycaprolactone (PCL) and Beta tri-calcium phosphate (β-TCP) (5, 10 and 15%) and another one containing PCL/PGS and chitosan acts as guided tissue regeneration (GTR). The morphology, chemical, physical and mechanical characterizations of the membranes were studied using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), tensile testing, then biodegradability and bioactivity properties were evaluated. In vitro cell culture study was also carried out to investigate proliferation and mineralization of cells on different membranes. Transmission electron microscope (TEM) and SEM results indicated agglomeration of β-TCP nanoparticles in the structure of nanofibers containing 15% β-TCP. Moreover by addition of β-TCP from 5% to 15%, contact angle decreased due to hydrophilicity of nanoparticles and bioactivity was found to increase. Mechanical properties of the membrane increased by incorporation of 5% and 10% of β-TCP in the structure of nanofibers, while addition of 15% of β-TCP was found to deteriorate mechanical properties of nanofibers. Although the presence of 5% and 10% of nanoparticles in the nanofibers increased proliferation of cells on GBR layer, cell proliferation was observed to decrease by addition of 15% β-TCP in the structure of nanofibers which is likely due to agglomeration of nanoparticles in the nanofiber structure. Our overall results revealed PCL/PGS containing 10% β-TCP could be selected as the optimum GBR membrane

  19. Optical measurements of absorption changes in two-layered diffusive media

    International Nuclear Information System (INIS)

    Fabbri, Francesco; Sassaroli, Angelo; Henry, Michael E; Fantini, Sergio

    2004-01-01

    We have used Monte Carlo simulations for a two-layered diffusive medium to investigate the effect of a superficial layer on the measurement of absorption variations from optical diffuse reflectance data processed by using: (a) a multidistance, frequency-domain method based on diffusion theory for a semi-infinite homogeneous medium; (b) a differential-pathlength-factor method based on a modified Lambert-Beer law for a homogeneous medium and (c) a two-distance, partial-pathlength method based on a modified Lambert-Beer law for a two-layered medium. Methods (a) and (b) lead to a single value for the absorption variation, whereas method (c) yields absorption variations for each layer. In the simulations, the optical coefficients of the medium were representative of those of biological tissue in the near-infrared. The thickness of the first layer was in the range 0.3-1.4 cm, and the source-detector distances were in the range 1-5 cm, which is typical of near-infrared diffuse reflectance measurements in tissue. The simulations have shown that (1) method (a) is mostly sensitive to absorption changes in the underlying layer, provided that the thickness of the superficial layer is ∼0.6 cm or less; (2) method (b) is significantly affected by absorption changes in the superficial layer and (3) method (c) yields the absorption changes for both layers with a relatively good accuracy of ∼4% for the superficial layer and ∼10% for the underlying layer (provided that the absorption changes are less than 20-30% of the baseline value). We have applied all three methods of data analysis to near-infrared data collected on the forehead of a human subject during electroconvulsive therapy. Our results suggest that the multidistance method (a) and the two-distance partial-pathlength method (c) may better decouple the contributions to the optical signals that originate in deeper tissue (brain) from those that originate in more superficial tissue layers

  20. Experimental analysis of two-layered dissimilar metals by roll bonding

    Science.gov (United States)

    Zhao, Guanghui; Li, Yugui; Li, Juan; Huang, Qingxue; Ma, Lifeng

    2018-02-01

    Rolling reduction and base layers thickness have important implications for rolling compounding. A two-layered 304 stainless steel/Q345R low alloyed steel was roll bonded. The roll bonding was performed at the three thickness reductions of 25%, 40% and 55% with base layers of various thicknesses (Q345R). The microstructures of the composite were investigated by the ultra-deep microscope (OM) and scanning electron microscope (SEM) and Transmission electron microscope (TEM). Simultaneously, the mechanical properties of the composite were experimentally measured and the tensile fracture surfaces were observed by SEM. The interfaces were successfully bonded without any cracking or voids, which indicated a good fabrication of the 304/Q345R composite. The rolling reduction rate and thinning increase of the substrate contributed to the bonding effects appearance of the roll bonded sheet. The Cr and Ni enriched diffusion layer was formed by the interface elements diffusion. The Cr and Ni diffusion led to the formation of ˜10 μm wide Cr and Ni layers on the carbon steel side.

  1. Geostrophic tripolar vortices in a two-layer fluid: Linear stability and nonlinear evolution of equilibria

    Science.gov (United States)

    Reinaud, J. N.; Sokolovskiy, M. A.; Carton, X.

    2017-03-01

    We investigate equilibrium solutions for tripolar vortices in a two-layer quasi-geostrophic flow. Two of the vortices are like-signed and lie in one layer. An opposite-signed vortex lies in the other layer. The families of equilibria can be spanned by the distance (called separation) between the two like-signed vortices. Two equilibrium configurations are possible when the opposite-signed vortex lies between the two other vortices. In the first configuration (called ordinary roundabout), the opposite signed vortex is equidistant to the two other vortices. In the second configuration (eccentric roundabouts), the distances are unequal. We determine the equilibria numerically and describe their characteristics for various internal deformation radii. The two branches of equilibria can co-exist and intersect for small deformation radii. Then, the eccentric roundabouts are stable while unstable ordinary roundabouts can be found. Indeed, ordinary roundabouts exist at smaller separations than eccentric roundabouts do, thus inducing stronger vortex interactions. However, for larger deformation radii, eccentric roundabouts can also be unstable. Then, the two branches of equilibria do not cross. The branch of eccentric roundabouts only exists for large separations. Near the end of the branch of eccentric roundabouts (at the smallest separation), one of the like-signed vortices exhibits a sharp inner corner where instabilities can be triggered. Finally, we investigate the nonlinear evolution of a few selected cases of tripoles.

  2. Two-Layer Linear MPC Approach Aimed at Walking Beam Billets Reheating Furnace Optimization

    Directory of Open Access Journals (Sweden)

    Silvia Maria Zanoli

    2017-01-01

    Full Text Available In this paper, the problem of the control and optimization of a walking beam billets reheating furnace located in an Italian steel plant is analyzed. An ad hoc Advanced Process Control framework has been developed, based on a two-layer linear Model Predictive Control architecture. This control block optimizes the steady and transient states of the considered process. Two main problems have been addressed. First, in order to manage all process conditions, a tailored module defines the process variables set to be included in the control problem. In particular, a unified approach for the selection on the control inputs to be used for control objectives related to the process outputs is guaranteed. The impact of the proposed method on the controller formulation is also detailed. Second, an innovative mathematical approach for stoichiometric ratios constraints handling has been proposed, together with their introduction in the controller optimization problems. The designed control system has been installed on a real plant, replacing operators’ mental model in the conduction of local PID controllers. After two years from the first startup, a strong energy efficiency improvement has been observed.

  3. Polarization-selective infrared bandpass filter based on a two-layer subwavelength metallic grating

    Science.gov (United States)

    Hohne, Andrew J.; Moon, Benjamin; Baumbauer, Carol L.; Gray, Tristan; Dilts, James; Shaw, Joseph A.; Dickensheets, David L.; Nakagawa, Wataru

    2017-08-01

    We present the design, fabrication, and characterization of a polarization-selective infrared bandpass filter based on a two-layer subwavelength metallic grating for use in polarimetric imaging. Gold nanowires were deposited via physical vapor deposition (PVD) onto a silicon surface relief grating that was patterned using electron beam lithography (EBL) and fabricated using standard silicon processing techniques. Optical characterization with a broad-spectrum tungsten halogen light source and a grating spectrometer showed normalized peak TM transmission of 53% with a full-width at half-maximum (FWHM) of 122 nm, which was consistent with rigorous coupled-wave analysis (RCWA) simulations. Simulation results suggested that device operation relied on suppression of the TM transmission caused by surface plasmon polariton (SPP) excitation at the gold-silicon interface and an increase in TM transmission caused by a Fabry-Perot (FP) resonance in the cavity between the gratings. TE rejection occurred at the initial air/gold interface. We also present simulation results of an improved design based on a two-dielectric grating where two different SPP resonances allowed us to improve the shape of the passband by suppressing the side lobes. This newer design resulted in improved side-band performance and increased peak TM transmission.

  4. A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Liang

    2014-03-01

    Full Text Available This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient’s ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.

  5. A Two-Layer Method for Sedentary Behaviors Classification Using Smartphone and Bluetooth Beacons.

    Science.gov (United States)

    Cerón, Jesús D; López, Diego M; Hofmann, Christian

    2017-01-01

    Among the factors that outline the health of populations, person's lifestyle is the more important one. This work focuses on the caracterization and prevention of sedentary lifestyles. A sedentary behavior is defined as "any waking behavior characterized by an energy expenditure of 1.5 METs (Metabolic Equivalent) or less while in a sitting or reclining posture". To propose a method for sedentary behaviors classification using a smartphone and Bluetooth beacons considering different types of classification models: personal, hybrid or impersonal. Following the CRISP-DM methodology, a method based on a two-layer approach for the classification of sedentary behaviors is proposed. Using data collected from a smartphones' accelerometer, gyroscope and barometer; the first layer classifies between performing a sedentary behavior and not. The second layer of the method classifies the specific sedentary activity performed using only the smartphone's accelerometer and barometer data, but adding indoor location data, using Bluetooth Low Energy (BLE) beacons. To improve the precision of the classification, both layers implemented the Random Forest algorithm and the personal model. This study presents the first available method for the automatic classification of specific sedentary behaviors. The layered classification approach has the potential to improve processing, memory and energy consumption of mobile devices and wearables used.

  6. A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

    Science.gov (United States)

    Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

    2014-01-01

    This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

  7. Characteristics of phonation onset in a two-layer vocal fold model.

    Science.gov (United States)

    Zhang, Zhaoyan

    2009-02-01

    Characteristics of phonation onset were investigated in a two-layer body-cover continuum model of the vocal folds as a function of the biomechanical and geometric properties of the vocal folds. The analysis showed that an increase in either the body or cover stiffness generally increased the phonation threshold pressure and phonation onset frequency, although the effectiveness of varying body or cover stiffness as a pitch control mechanism varied depending on the body-cover stiffness ratio. Increasing body-cover stiffness ratio reduced the vibration amplitude of the body layer, and the vocal fold motion was gradually restricted to the medial surface, resulting in more effective flow modulation and higher sound production efficiency. The fluid-structure interaction induced synchronization of more than one group of eigenmodes so that two or more eigenmodes may be simultaneously destabilized toward phonation onset. At certain conditions, a slight change in vocal fold stiffness or geometry may cause phonation onset to occur as eigenmode synchronization due to a different pair of eigenmodes, leading to sudden changes in phonation onset frequency, vocal fold vibration pattern, and sound production efficiency. Although observed in a linear stability analysis, a similar mechanism may also play a role in register changes at finite-amplitude oscillations.

  8. Spin-Selective Transmission and Devisable Chirality in Two-Layer Metasurfaces.

    Science.gov (United States)

    Li, Zhancheng; Liu, Wenwei; Cheng, Hua; Chen, Shuqi; Tian, Jianguo

    2017-08-15

    Chirality is a nearly ubiquitous natural phenomenon. Its minute presence in most naturally occurring materials makes it incredibly difficult to detect. Recent advances in metasurfaces indicate that they exhibit devisable chirality in novel forms; this finding offers an effective opening for studying chirality and its features in such nanostructures. These metasurfaces display vast possibilities for highly sensitive chirality discrimination in biological and chemical systems. Here, we show that two-layer metasurfaces based on twisted nanorods can generate giant spin-selective transmission and support engineered chirality in the near-infrared region. Two designed metasurfaces with opposite spin-selective transmission are proposed for treatment as enantiomers and can be used widely for spin selection and enhanced chiral sensing. Specifically, we demonstrate that the chirality in these proposed metasurfaces can be adjusted effectively by simply changing the orientation angle between the twisted nanorods. Our results offer simple and straightforward rules for chirality engineering in metasurfaces and suggest intriguing possibilities for the applications of such metasurfaces in spin optics and chiral sensing.

  9. Clustering Approaches for Pragmatic Two-Layer IoT Architecture

    Directory of Open Access Journals (Sweden)

    J. Sathish Kumar

    2018-01-01

    Full Text Available Connecting all devices through Internet is now practical due to Internet of Things. IoT assures numerous applications in everyday life of common people, government bodies, business, and society as a whole. Collaboration among the devices in IoT to bring various applications in the real world is a challenging task. In this context, we introduce an application-based two-layer architectural framework for IoT which consists of sensing layer and IoT layer. For any real-time application, sensing devices play an important role. Both these layers are required for accomplishing IoT-based applications. The success of any IoT-based application relies on efficient communication and utilization of the devices and data acquired by the devices at both layers. The grouping of these devices helps to achieve the same, which leads to formation of cluster of devices at various levels. The clustering helps not only in collaboration but also in prolonging overall network lifetime. In this paper, we propose two clustering algorithms based on heuristic and graph, respectively. The proposed clustering approaches are evaluated on IoT platform using standard parameters and compared with different approaches reported in literature.

  10. Inferring topologies via driving-based generalized synchronization of two-layer networks

    Science.gov (United States)

    Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

    2016-05-01

    The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

  11. Data Hiding Based on a Two-Layer Turtle Shell Matrix

    Directory of Open Access Journals (Sweden)

    Xiao-Zhu Xie

    2018-02-01

    Full Text Available Data hiding is a technology that embeds data into a cover carrier in an imperceptible way while still allowing the hidden data to be extracted accurately from the stego-carrier, which is one important branch of computer science and has drawn attention of scholars in the last decade. Turtle shell-based (TSB schemes have become popular in recent years due to their higher embedding capacity (EC and better visual quality of the stego-image than most of the none magic matrices based (MMB schemes. This paper proposes a two-layer turtle shell matrix-based (TTSMB scheme for data hiding, in which an extra attribute presented by a 4-ary digit is assigned to each element of the turtle shell matrix with symmetrical distribution. Therefore, compared with the original TSB scheme, two more bits are embedded into each pixel pair to obtain a higher EC up to 2.5 bits per pixel (bpp. The experimental results reveal that under the condition of the same visual quality, the EC of the proposed scheme outperforms state-of-the-art data hiding schemes.

  12. Persufflation Improves Pancreas Preservation When Compared With the Two-Layer Method

    Science.gov (United States)

    Scott, W.E.; O'Brien, T.D.; Ferrer-Fabrega, J.; Avgoustiniatos, E.S.; Weegman, B.P.; Anazawa, T.; Matsumoto, S.; Kirchner, V.A.; Rizzari, M.D.; Murtaugh, M.P.; Suszynski, T.M.; Aasheim, T.; Kidder, L.S.; Hammer, B.E.; Stone, S.G.; Tempelman, L.; Sutherland, D.E.R.; Hering, B.J.; Papas, K.K.

    2010-01-01

    Islet transplantation is emerging as a promising treatment for patients with type 1 diabetes. It is important to maximize viable islet yield for each organ due to scarcity of suitable human donor pancreata, high cost, and the high dose of islets required for insulin independence. However, organ transport for 8 hours using the two-layer method (TLM) frequently results in lower islet yields. Since efficient oxygenation of the core of larger organs (eg, pig, human) in TLM has recently come under question, we investigated oxygen persufflation as an alternative way to supply the pancreas with oxygen during preservation. Porcine pancreata were procured from non–heart-beating donors and preserved by either TLM or persufflation for 24 hours and fixed. Biopsies were collected from several regions of the pancreas, sectioned, stained with hematoxylin and eosin, and evaluated by a histologist. Persufflated tissues exhibited distended capillaries due to gas perfusion and significantly less autolysis/cell death than regions not exposed to persufflation or tissues exposed to TLM. The histology presented here suggests that after 24 hours of preservation, persufflation dramatically improves tissue health when compared with TLM. These results indicate the potential for persufflation to improve viable islet yields and extend the duration of preservation, allowing more donor organs to be utilized. PMID:20692396

  13. Two-layer tissue engineered urethra using oral epithelial and muscle derived cells.

    Science.gov (United States)

    Mikami, Hiroshi; Kuwahara, Go; Nakamura, Nobuyuki; Yamato, Masayuki; Tanaka, Masatoshi; Kodama, Shohta

    2012-05-01

    We fabricated novel tissue engineered urethral grafts using autologously harvested oral cells. We report their viability in a canine model. Oral tissues were harvested by punch biopsy and divided into mucosal and muscle sections. Epithelial cells from mucosal sections were cultured as epithelial cell sheets. Simultaneously muscle derived cells were seeded on collagen mesh matrices to form muscle cell sheets. At 2 weeks the sheets were joined and tubularized to form 2-layer tissue engineered urethras, which were autologously grafted to surgically induced urethral defects in 10 dogs in the experimental group. Tissue engineered grafts were not applied to the induced urethral defect in control dogs. The dogs were followed 12 weeks postoperatively. Urethrogram and histological examination were done to evaluate the grafting outcome. We successfully fabricated 2-layer tissue engineered urethras in vitro and transplanted them in dogs in the experimental group. The 12-week complication-free rate was significantly higher in the experimental group than in controls. Urethrogram confirmed urethral patency without stricture in the complication-free group at 12 weeks. Histologically urethras in the transplant group showed a stratified epithelial layer overlying well differentiated submucosa. In contrast, urethras in controls showed severe fibrosis without epithelial layer formation. Two-layer tissue engineered urethras were engineered using cells harvested by minimally invasive oral punch biopsy. Results suggest that this technique can encourage regeneration of a functional urethra. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  14. A Two-Layer Gene Circuit for Decoupling Cell Growth from Metabolite Production.

    Science.gov (United States)

    Lo, Tat-Ming; Chng, Si Hui; Teo, Wei Suong; Cho, Han-Saem; Chang, Matthew Wook

    2016-08-01

    We present a synthetic gene circuit for decoupling cell growth from metabolite production through autonomous regulation of enzymatic pathways by integrated modules that sense nutrient and substrate. The two-layer circuit allows Escherichia coli to selectively utilize target substrates in a mixed pool; channel metabolic resources to growth by delaying enzymatic conversion until nutrient depletion; and activate, terminate, and re-activate conversion upon substrate availability. We developed two versions of controller, both of which have glucose nutrient sensors but differ in their substrate-sensing modules. One controller is specific for hydroxycinnamic acid and the other for oleic acid. Our hydroxycinnamic acid controller lowered metabolic stress 2-fold and increased the growth rate 2-fold and productivity 5-fold, whereas our oleic acid controller lowered metabolic stress 2-fold and increased the growth rate 1.3-fold and productivity 2.4-fold. These results demonstrate the potential for engineering strategies that decouple growth and production to make bio-based production more economical and sustainable. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  15. Convergence of Extreme Value Statistics in a Two-Layer Quasi-Geostrophic Atmospheric Model

    Directory of Open Access Journals (Sweden)

    Vera Melinda Gálfi

    2017-01-01

    Full Text Available We search for the signature of universal properties of extreme events, theoretically predicted for Axiom A flows, in a chaotic and high-dimensional dynamical system. We study the convergence of GEV (Generalized Extreme Value and GP (Generalized Pareto shape parameter estimates to the theoretical value, which is expressed in terms of the partial information dimensions of the attractor. We consider a two-layer quasi-geostrophic atmospheric model of the mid-latitudes, adopt two levels of forcing, and analyse the extremes of different types of physical observables (local energy, zonally averaged energy, and globally averaged energy. We find good agreement in the shape parameter estimates with the theory only in the case of more intense forcing, corresponding to a strong chaotic behaviour, for some observables (the local energy at every latitude. Due to the limited (though very large data size and to the presence of serial correlations, it is difficult to obtain robust statistics of extremes in the case of the other observables. In the case of weak forcing, which leads to weaker chaotic conditions with regime behaviour, we find, unsurprisingly, worse agreement with the theory developed for Axiom A flows.

  16. Display of the β-effect in the Black Sea Two-Layer Model

    Directory of Open Access Journals (Sweden)

    A.A. Pavlushin

    2016-10-01

    Full Text Available The research is a continuation of a series of numerical experiments on modeling formation of wind currents and eddies in the Black Sea within the framework of a two-layer eddy-resolving model. The main attention is focused on studying the β-effect role. The stationary cyclonic wind is used as an external forcing and the bottom topography is not considered. It is shown that at the β-effect being taken into account, the Rossby waves propagating from east to west are observed both during the currents’ formation and at the statistical equilibrium mode when the mesoscale eddies are formed. In the integral flows’ field the waves are visually manifested in a form of the alternate large-scale cyclonic gyres and zones in which the meso-scale anti-cyclones are formed. This spatial pattern constantly propagates to the west that differs from the results of calculations using the constant Coriolis parameter when the spatially alternate cyclonic and anti-cyclonic vortices are formed, but hold a quasi-stationary position. The waves with the parameters of the Rossby wave first barotropic mode for the closed basin are most clearly pronounced. Interaction of the Rossby waves with large-scale circulation results in intensification of the of the currents’ hydrodynamic instability and in formation of the mesoscale eddies. Significant decrease of kinetic and available potential energy as compared to the values obtained at the constant Coriolis parameter is also a consequence of the eddy formation intensification.

  17. A two-layered forward model of tissue for electrical impedance tomography

    International Nuclear Information System (INIS)

    Kulkarni, Rujuta; Saulnier, Gary J; Kao, Tzu-Jen; Newell, Jonathan C; Boverman, Gregory; Isaacson, David

    2009-01-01

    Electrical impedance tomography is being explored as a technique to detect breast cancer, exploiting the differences in admittivity between normal tissue and tumors. In this paper, the geometry is modeled as an infinite half space under a hand-held probe. A forward solution and a reconstruction algorithm for this geometry were developed previously by Mueller et al (1999 IEEE Trans. Biomed. Eng. 46 1379). In this paper, we present a different approach which uses the decomposition of the forward solution into its Fourier components to obtain the forward solution and the reconstructions. The two approaches are compared in terms of the forward solutions and the reconstructions of experimental tank data. We also introduce a two-layered model to incorporate the presence of the skin that surrounds the body area being imaged. We demonstrate an improvement in the reconstruction of a target in a layered medium using this layered model with finite difference simulated data. We then extend the application of our layered model to human subject data and estimate the skin and the tissue admittivities for data collected on the human abdomen using an ultrasound-like hand-held EIT probe. Lastly, we show that for this set of human subject data, the layered model yields an improvement in predicting the measured voltages of around 81% for the lowest temporal frequency (3 kHz) and around 61% for the highest temporal frequency (1 MHz) applied when compared to the homogeneous model

  18. Synthesis of PVA/PVP hydrogels having two layers by radiation and their physical properties

    International Nuclear Information System (INIS)

    Nho, Y.C.; Park, K.R.

    2002-01-01

    Complete text of publication follows. The radiation can induce chemical reaction to modify polymer under even the solid state or in the low temperature. The radiation crosslinking can be easily adjusted by controlling the radiation dose and is reproducible. The finished product contains no residuals of substances required to initiate the chemical crosslinking that can restrict the application possibilities. In these studies, two layer's hydrogel which consisted of urethane membrane and a mixture of polyvinyl alcohol/poly-N-vinylpyrrolidone /glycerin/chitosan was made by gamma-ray irradiation or two steps of 'freezing and thawing' and gamma-ray irradiation for wound dressing. The physical properties such as gelation, water absorptivity, and gel strength were examined to evaluate the hydrogels for wound dressing. Urethane was dissolved in solvent, the urethane solution was poured on the mould, and then dried to make the thin membrane. Hydrophilic polymer solutions were poured on the urethane membranes, they were exposed to gamma irradiation or 'freezing and thawing' and gamma irradiation doses of 25, 35, 50 and 60 kGy to evaluate the physical properties of hydrogels. The physical properties of hydrogels such as gelation and gel strength were improved, and the evaporation speed of water in hydrogel was low when urethane membrane was used

  19. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  20. Using function approximation to determine neural network accuracy

    International Nuclear Information System (INIS)

    Wichman, R.F.; Alexander, J.

    2013-01-01

    Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

  1. Neural networks within multi-core optic fibers.

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  2. Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach

    Directory of Open Access Journals (Sweden)

    Yan Li

    2018-01-01

    Full Text Available Licensed Assisted Access (LAA is considered one of the latest groundbreaking innovations to provide high performance in future 5G. Coexistence schemes such as Listen Before Talk (LBT and Carrier Sensing and Adaptive Transmission (CSAT have been proven to be good methods to share spectrums, and they are WiFi friendly. In this paper, a modified LBT-based CSAT scheme is proposed which can effectively reduce the collision at the moment when Long Term Evolution (LTE starts to transmit data in CSAT mode. To make full use of the valuable spectrum resources, the throughput of both LAA and WiFi systems should be improved. Thus, a two-layer Coalition-Auction Game-based Transaction (CAGT mechanism is proposed in this paper to optimize the performance of the two systems. In the first layer, a coalition among Access Points (APs is built to balance the WiFi stations and maximize the WiFi throughput. The main idea of the devised coalition forming is to merge the light-loaded APs with heavy-loaded APs into a coalition; consequently, the data of the overloaded APs can be offloaded to the light-loaded APs. Next, an auction game between the LAA and WiFi systems is used to gain a win–win strategy, in which, LAA Base Station (BS is the auctioneer and AP coalitions are bidders. Thus, the throughput of both systems are improved. Simulation results demonstrate that the proposed scheme in this paper can improve the performance of both two systems effectively.

  3. Hydrothermal synthesis of two layered indium oxalates with 12-membered apertures

    International Nuclear Information System (INIS)

    Chen Zhenxia; Zhou Yaming; Weng Linhong; Zhang Haoyu; Zhao Dongyuan

    2003-01-01

    Two layered indium oxalates, In(C 2 O 4 ) 2.5 (C 3 N 2 H 12 )(H 2 O) 3 , I, and In(C 2 O 4 ) 1.5 (H 2 O) 3 , II, have been hydrothermally synthesized. In I, the linkage between indium and oxalate units gives rise to a sheet with a rectangular 12-membered aperture (six indium atoms and six oxalate units). Indium atom of II has an unusual pentagonal bipyramidal coordination arrangement. The connectivity between indium and oxalate units forms a neutral puckered layer with 12- (along a-axis) and eight-membered (along b-axis) apertures. Crystal data for these two indium oxalates are as follows: I, triclinic, space group: P-1 (No. 2), a=8.725(3) A, b=9.170(3) A, c=9.901(3) A, α=98.101(4) deg. , β=97.068(4) deg. , γ=102.403(4) deg. , V=756.3(4) A 3 , Z=2, M=463.0(5), ρ calc =2.042 g/cm 3 , R 1 =0.0377, wR 2 =0.0834. II, monoclinic, space group: P2 1 /c (No. 14), a=10.203(5) A, b=6.638(1) A, c=11.152(7) A, β=95.649(4) deg. , V=751.7(4)A 3 , Z=4, M=300.9(0), ρ calc =2.659 g/cm 3 , R 1 =0.0229, wR 2 =0.0488. TG analyses indicate the water molecules of I can be removed at 150 deg. C. The dehydrated product retains structural integrity

  4. Formulation of two-layer dissolving polymeric microneedle patches for insulin transdermal delivery in diabetic mice.

    Science.gov (United States)

    Lee, I-Chi; Lin, Wei-Ming; Shu, Jwu-Ching; Tsai, Shau-Wei; Chen, Chih-Hao; Tsai, Meng-Tsan

    2017-01-01

    Dissolving microneedles (MNs) display high efficiency in delivering poorly permeable drugs and vaccines. Here, two-layer dissolving polymeric MN patches composed of gelatin and sodium carboxymethyl cellulose (CMC) were fabricated with a two-step casting and centrifuging process to localize the insulin in the needle and achieve efficient transdermal delivery of insulin. In vitro skin insertion capability was determined by staining with tissue-marking dye after insertion, and the real-time penetration depth was monitored using optical coherence tomography. Confocal microscopy images revealed that the rhodamine 6G and fluorescein isothiocyanate-labeled insulin (insulin-FITC) can gradually diffuse from the puncture sites to deeper tissue. Ex vivo drug-release profiles showed that 50% of the insulin was released and penetrated across the skin after 1 h, and the cumulative permeation reached 80% after 5 h. In vivo and pharmacodynamic studies were then conducted to estimate the feasibility of the administration of insulin-loaded dissolving MN patches on diabetic mice for glucose regulation. The total area above the glucose level versus time curve as an index of hypoglycemic effect was 128.4 ± 28.3 (% h) at 0.25 IU/kg. The relative pharmacologic availability and relative bioavailability (RBA) of insulin from MN patches were 95.6 and 85.7%, respectively. This study verified that the use of gelatin/CMC MN patches for insulin delivery achieved a satisfactory RBA compared to traditional hypodermic injection and presented a promising device to deliver poorly permeable protein drugs for diabetic therapy. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 84-93, 2017. © 2016 Wiley Periodicals, Inc.

  5. Two-Layer Hierarchy Optimization Model for Communication Protocol in Railway Wireless Monitoring Networks

    Directory of Open Access Journals (Sweden)

    Xiaoping Ma

    2018-01-01

    Full Text Available The wireless monitoring system is always destroyed by the insufficient energy of the sensors in railway. Hence, how to optimize the communication protocol and extend the system lifetime is crucial to ensure the stability of system. However, the existing studies focused primarily on cluster-based or multihop protocols individually, which are ineffective in coping with the complex communication scenarios in the railway wireless monitoring system (RWMS. This study proposes a hybrid protocol which combines the cluster-based and multihop protocols (CMCP to minimize and balance the energy consumption in different sections of the RWMS. In the first hierarchy, the total energy consumption is minimized by optimizing the cluster quantities in the cluster-based protocol and the number of hops and the corresponding hop distances in the multihop protocol. In the second hierarchy, the energy consumption is balanced through rotating the cluster head (CH in the subnetworks and further optimizing the hops and the corresponding hop distances in the backbone network. On this basis, the system lifetime is maximized with the minimum and balance energy consumption among the sensors. Furthermore, the hybrid particle swarm optimization and genetic algorithm (PSO-GA are adopted to optimize the energy consumption from the two-layer hierarchy. Finally, the effectiveness of the proposed CMCP is verified in the simulation. The performances of the proposed CMCP in system lifetime, residual energy, and the corresponding variance are all superior to the LEACH protocol widely applied in the previous research. The effective protocol proposed in this study can facilitate the application of the wireless monitoring network in the railway system and enhance safety operation of the railway.

  6. Analyzing surface features on icy satellites using a new two-layer analogue model

    Science.gov (United States)

    Morales, K. M.; Leonard, E. J.; Pappalardo, R. T.; Yin, A.

    2017-12-01

    The appearance of similar surface morphologies across many icy satellites suggests potentially unified formation mechanisms. Constraining the processes that shape the surfaces of these icy worlds is fundamental to understanding their rheology and thermal evolution—factors that have implications for potential habitability. Analogue models have proven useful for investigating and quantifying surface structure formation on Earth, but have only been sparsely applied to icy bodies. In this study, we employ an innovative two-layer analogue model that simulates a warm, ductile ice layer overlain by brittle surface ice on satellites such as Europa and Enceladus. The top, brittle layer is composed of fine-grained sand while the ductile, lower viscosity layer is made of putty. These materials were chosen because they scale up reasonably to the conditions on Europa and Enceladus. Using this analogue model, we investigate the role of the ductile layer in forming contractional structures (e.g. folds) that would compensate for the over-abundance of extensional features observed on icy satellites. We do this by simulating different compressional scenarios in the analogue model and analyzing whether the resulting features resemble those on icy bodies. If the resulting structures are similar, then the model can be used to quantify the deformation by calculating strain. These values can then be scaled up to Europa or Enceladus and used to quantity the observed surface morphologies and the amount of extensional strain accommodated by certain features. This presentation will focus on the resulting surface morphologies and the calculated strain values from several analogue experiments. The methods and findings from this work can then be expanded and used to study other icy bodies, such as Triton, Miranda, Ariel, and Pluto.

  7. Dependences of optical properties of spherical two-layered nanoparticles on parameters of gold core and material shell

    International Nuclear Information System (INIS)

    Pustovalov, V.K.; Astafyeva, L.G.; Zharov, V.P.

    2013-01-01

    Modeling of nonlinear dependences of optical properties of spherical two-layered gold core and some material shell nanoparticles (NPs) placed in water on parameters of core and shell was carried out on the basis of the extended Mie theory. Efficiency cross-sections of absorption, scattering and extinction of radiation with wavelength 532 nm by core–shell NPs in the ranges of core radii r 00 =5–40 nm and of relative NP radii r 1 /r 00 =1–8 were calculated (r 1 —radius of two-layered nanoparticle). Shell materials were used with optical indexes in the ranges of refraction n 1 =0.2–1.5 and absorption k 1 =0–3.5 for the presentation of optical properties of wide classes of shell materials (including dielectrics, metals, polymers, vapor shell around gold core). Results show nonlinear dependences of optical properties of two-layered NPs on optical indexes of shell material, core r 00 and relative NP r 1 /r 00 radii. Regions with sharp decrease and increase of absorption, scattering and extinction efficiency cross-sections with changing of core and shell parameters were investigated. These dependences should be taken into account for applications of two-layered NPs in laser nanomedicine and optical diagnostics of tissues. The results can be used for experimental investigation of shell formation on NP core and optical determination of geometrical parameters of core and shell of two-layered NPs. -- Highlights: • Absorption, scattering and extinction of two-layered nanoparticles are studied. • Shell materials change in wide regions of materials (metals, dielectrics, vapor). • Effect of sharp decrease and increase of optical characteristics is established. • Explanation of sharp decreasing and increasing optical characteristics is presented

  8. Randomized trial of four-layer and two-layer bandage systems in the management of chronic venous ulceration.

    Science.gov (United States)

    Moffatt, Christine J; McCullagh, Lynn; O'Connor, Theresa; Doherty, Debra C; Hourican, Catherine; Stevens, Julie; Mole, Trevor; Franks, Peter J

    2003-01-01

    To compare a four-layer bandage system with a two-layer system in the management of chronic venous leg ulceration, a prospective randomized open parallel groups trial was undertaken. In total, 112 patients newly presenting to leg ulcer services with chronic leg ulceration, screened to exclude the presence of arterial disease (ankle brachial pressure index ulceration other than venous disease, were entered into the trial. Patients were randomized to receive either four-layer (Profore) or two-layer (Surepress) high-compression elastic bandage systems. In all, 109 out of 112 patients had at least one follow-up. After 24 weeks, 50 out of 57 (88%) patients randomized to the four-layer bandage system with follow-up had ulcer closure (full epithelialization) compared with 40 out of 52 (77%) on the two-layer bandage, hazard ratio = 1.18 (95% confidence interval 0.69-2.02), p = 0.55. After 12 weeks, 40 out of 57 (70%) patients randomized to the four-layer bandage system with follow-up had ulcer closure compared with 30 out of 52 (58%) on the two-layer bandage, odds ratio = 4.23 (95% confidence interval 1.29-13.86), p = 0.02. Withdrawal rates were significantly greater on the two-layer bandage (30 out of 54; 56%) compared with the four-layer bandage system (8 out of 58; 14%), p bandaging system (15 out of 54; 28%) compared with four-layer bandaging (5 out of 54; 9%), p = 0.01. The higher mean cost of treatment in the two-layer bandaging system arm over 24 weeks ($1374 [ pound 916] vs. $1314 [ pound 876]) was explained by the increased mean number of bandage changes (1.5 vs. 1.1 per week) with the two-layer system. In conclusion, the four-layer bandage offers advantages over the two-layer bandage in terms of reduced withdrawal from treatment, fewer adverse incidents, and lower treatment cost.

  9. Detection of different states of sleep in the rodents by the means of artificial neural networks

    Science.gov (United States)

    Musatov, Viacheslav; Dykin, Viacheslav; Pitsik, Elena; Pisarchik, Alexander

    2018-04-01

    This paper considers the possibility of classification of electroencephalogram (EEG) and electromyogram (EMG) signals corresponding to different phases of sleep and wakefulness of mice by the means of artificial neural networks. A feed-forward artificial neural network based on multilayer perceptron was created and trained on the data of one of the rodents. The trained network was used to read and classify the EEG and EMG data corresponding to different phases of sleep and wakefulness of the same mouse and other mouse. The results show a good recognition quality of all phases for the rodent on which the training was conducted (80-99%) and acceptable recognition quality for the data collected from the same mouse after a stroke.

  10. Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

    Energy Technology Data Exchange (ETDEWEB)

    Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

    2011-03-15

    An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  11. Particle identification with neural networks using a rotational invariant moment representation

    Science.gov (United States)

    Sinkus, Ralph; Voss, Thomas

    1997-02-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

  12. Particle identification with neural networks using a rotational invariant moment representation

    International Nuclear Information System (INIS)

    Sinkus, R.

    1997-01-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-02-01

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

  14. Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

    Directory of Open Access Journals (Sweden)

    Beigi Mohsen

    2017-01-01

    Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

  15. Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    2010-02-01

    This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

  16. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  17. Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

    International Nuclear Information System (INIS)

    Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

    2015-01-01

    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

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

    International Nuclear Information System (INIS)

    Olyaee, Saeed; Hamedi, Samaneh

    2011-01-01

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

  19. Screen-Capturing System with Two-Layer Display for PowerPoint Presentation to Enhance Classroom Education

    Science.gov (United States)

    Lai, Yen-Shou; Tsai, Hung-Hsu; Yu, Pao-Ta

    2011-01-01

    This paper proposes a new presentation system integrating a Microsoft PowerPoint presentation in a two-layer method, called the TL system, to promote learning in a physical classroom. With the TL system, teachers can readily control hints or annotations as a way of making them visible or invisible to students so as to reduce information load. In…

  20. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  1. Application of neural networks to seismic active control

    International Nuclear Information System (INIS)

    Tang, Yu.

    1995-01-01

    An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

  2. THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE

    Directory of Open Access Journals (Sweden)

    António José Silva

    2007-03-01

    Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports

  3. The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

    Science.gov (United States)

    Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

    2017-11-01

    The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

  4. [Single-layer colonic anastomoses using polyglyconate (Maxon) vs. two-layer anastomoses using chromic catgut and silk. Experimental study].

    Science.gov (United States)

    García-Osogobio, Sandra Minerva; Takahashi-Monroy, Takeshi; Velasco, Liliana; Gaxiola, Miguel; Sotres-Vega, Avelina; Santillán-Doherty, Patricio

    2006-01-01

    The safety of an intestinal anastomosis is usually measured by its complication rate, especially the incidence of anastomotic leakage. A wide variety of methods have been described to reestablish intestinal continuity including single-layer continuous or two-layer interrupted anastomosis. To evaluate if the single-layer continuous anastomosis using polygluconate is safer and reliable than two-layer interrupted anastomosis with chromic catgut and silk. A prospective, experimental, randomized and comparative analysis was conducted in 20 dogs. They were divided in two groups; group 1 underwent two-layer interrupted anastomosis and group 2 underwent sigle-layer continuous technique. Anastomoses were timed. Both groups were under observation. Anastomotic leakage, and other complications were evaluated. The animals were sacrified and the anastomosis was taken out together with 10 cm of colon on both sides of the anastomosis. Breaking strength, histologic evaluation and hydroxyproline determination were performed. Ten two-layer anastomosis and ten single-layer anastomosis were performed. A median of 25 minutes (range: 20-30 minutes) was required to construct the anastomoses in group 1 versus 20 minutes (range: 12-25 minutes) in group 2. All animals survived and no leakage was observed. Wound infection ocurred in four dogs (20%). Median breaking strength was 230 mm Hg in group 1 and 210 mm Hg in group 2. Hydroxyproline concentration was 8.94 mg/g in group 1 (range: 5.33-16.71) and 9.94 mg/g in group 2 (range: 2.96-21.87). There was no difference among groups about the inflammatory response evaluated by pathology. There was no statistical significance in any variable evaluated. CONCLUIONS: This study demonstrates that a single-layer continuous is similar in terms of safety to the two-layer technique, but because of its facility to perform, the single-layer technique could be superior.

  5. Application of neural network technique to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt

    Science.gov (United States)

    Elshambaky, Hossam Talaat

    2018-01-01

    Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.

  6. Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

    Science.gov (United States)

    Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

    2018-02-01

    This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

  7. Modeling by artificial neural networks. Application to the management of fuel in a nuclear power plant

    International Nuclear Information System (INIS)

    Gaudier, F.

    1999-01-01

    The determination of the family of optimum core loading patterns for Pressurized Water Reactors (PWRs) involves the assessment of the core attributes, such as the power peaking factor for thousands of candidate loading patterns. Despite the rapid advances in computer architecture, the direct calculation of these attributes by a neutronic code needs a lot of of time and memory. With the goal of reducing the calculation time and optimizing the loading pattern, we propose in this thesis a method based on ideas of neural and statistical learning to provide a feed forward neural network capable of calculating the power peaking corresponding to an eighth core PWR. We use statistical methods to deduct judicious inputs (reduction of the input space dimension) and neural methods to train the model (learning capabilities). Indeed, on one hand, a principal component analysis allows us to characterize more efficiently the fuel assemblies (neural model inputs) and the other hand, the introduction of the a priori knowledge allows us to reducing the number of freedom parameters in the neural network. The model was built using a multi layered perceptron trained with the standard back propagation algorithm. We introduced our neural network in the automatic optimization code FORMOSA, and on EDF real problems we showed an important saving in time. Finally, we propose an hybrid method which combining the best characteristics of the linear local approximator GPT (Generalized Perturbation Theory) and the artificial neural network. (author)

  8. Multistability in bidirectional associative memory neural networks

    International Nuclear Information System (INIS)

    Huang Gan; Cao Jinde

    2008-01-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have 3 n equilibria and 2 n equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results

  9. Multistability in bidirectional associative memory neural networks

    Science.gov (United States)

    Huang, Gan; Cao, Jinde

    2008-04-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

  10. Analysis of surface ozone using a recurrent neural network.

    Science.gov (United States)

    Biancofiore, Fabio; Verdecchia, Marco; Di Carlo, Piero; Tomassetti, Barbara; Aruffo, Eleonora; Busilacchio, Marcella; Bianco, Sebastiano; Di Tommaso, Sinibaldo; Colangeli, Carlo

    2015-05-01

    Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Mean flow generated by an internal wave packet impinging on the interface between two layers of fluid with continuous density

    Energy Technology Data Exchange (ETDEWEB)

    McHugh, John P. [The University of New Hampshire, Department of Mechanical Engineering, Kingsbury Hall, Durham, NH (United States)

    2008-04-15

    Internal waves propagating in an idealized two-layer atmosphere are studied numerically. The governing equations are the inviscid anelastic equations for a perfect gas atmosphere. The numerical formulation eliminates all variables in the linear terms except vertical velocity, which are then treated implicitly. Nonlinear terms are treated explicitly. The basic state is a two-layer flow with continuous density at the interface. Each layer has a unique constant for the Brunt-Vaeisaelae frequency. Waves are forced at the bottom of the domain, are periodic in the horizontal direction, and form a finite wave packet in the vertical. The results show that the wave packet forms a mean flow that is confined to the interface region that persists long after the wave packet has moved away. Large-amplitude waves are forced to break beneath the interface. (orig.)

  12. Non-radiative recombination process in BGaAs/GaAs alloys: Two layer photothermal deflection model

    Energy Technology Data Exchange (ETDEWEB)

    Ilahi, S., E-mail: ilehi_soufiene@yahoo.fr [Université de Carthage, Unité de Recherche de caractérisation photothermique et modélisation, Institut Préparatoire aux Etudes d’Ingénieurs de Nabeul (IPEIN), 8000 Merazka, Nabeul (Tunisia); Baira, M.; Saidi, F. [Université de Monastir, Laboratoire de Micro-Optoélectronique et Nanostructures, Faculté des Sciences de Monastir. Avenue de l’Environnement, Monastir 5019 (Tunisia); Yacoubi, N. [Université de Carthage, Unité de Recherche de caractérisation photothermique et modélisation, Institut Préparatoire aux Etudes d’Ingénieurs de Nabeul (IPEIN), 8000 Merazka, Nabeul (Tunisia); Auvray, L. [Laboratoire Multimateriaux et Interfaces, Université Claude Bernard Lyon I, 43, Boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex (France); Maaref, H. [Université de Monastir, Laboratoire de Micro-Optoélectronique et Nanostructures, Faculté des Sciences de Monastir. Avenue de l’Environnement, Monastir 5019 (Tunisia)

    2013-12-25

    Highlights: •We have developed a two layer photothermal deflection model. •We have determined the electronic properties of BGaAs/GaAs alloys. •We have studied the boron effect in the electronic parameters. -- Abstract: Photo-thermal deflection technique PTD is used to study the nonradiative recombination process in BGaAs/GaAs alloy with boron composition of 3% and 8% grown by metal organic chemical vapor deposition (MOCVD). A two layer theoretical model has been developed taking into account both thermal and electronic contribution in the photothermal signal allowing to extract the electronic parameters namely electronic diffusivity, surface and interface recombination. It is found that the increase of boron composition alters the BGaAs epilayers transport properties.

  13. Preparation of Two-Layer Anion-Exchange Poly(ethersulfone Based Membrane: Effect of Surface Modification

    Directory of Open Access Journals (Sweden)

    Lucie Zarybnicka

    2016-01-01

    Full Text Available The present work deals with the surface modification of a commercial microfiltration poly(ethersulfone membrane by graft polymerization technique. Poly(styrene-co-divinylbenzene-co-4-vinylbenzylchloride surface layer was covalently attached onto the poly(ethersulfone support layer to improve the membrane electrochemical properties. Followed by amination, a two-layer anion-exchange membrane was prepared. The effect of surface layer treatment using the extraction in various solvents on membrane morphological and electrochemical characteristics was studied. The membranes were tested from the point of view of water content, ion-exchange capacity, specific resistance, permselectivity, FT-IR spectroscopy, and SEM analysis. It was found that the two-layer anion-exchange membranes after the extraction using tetrahydrofuran or toluene exhibited smooth and porous surface layer, which resulted in improved ion-exchange capacity, electrical resistance, and permselectivity of the membranes.

  14. Reappraisal of criticality for two-layer flows and its role in the generation of internal solitary waves

    Science.gov (United States)

    Bridges, Thomas J.; Donaldson, Neil M.

    2007-07-01

    A geometric view of criticality for two-layer flows is presented. Uniform flows are classified by diagrams in the momentum-massflux space for fixed Bernoulli energy, and cuspoidal curves on these diagrams correspond to critical uniform flows. Restriction of these surfaces to critical flow leads to new subsurfaces in energy-massflux space. While the connection between criticality and the generation of solitary waves is well known, we find that the nonlinear properties of these bifurcating solitary waves are also determined by the properties of the criticality surfaces. To be specific, the case of two layers with a rigid lid is considered, and application of the theory to other multilayer flows is sketched.

  15. ATLAAS-P2P: a two layer network solution for easing the resource discovery process in unstructured networks

    OpenAIRE

    Baraglia, Ranieri; Dazzi, Patrizio; Mordacchini, Matteo; Ricci, Laura

    2013-01-01

    ATLAAS-P2P is a two-layered P2P architecture for developing systems providing resource aggregation and approximated discovery in P2P networks. Such systems allow users to search the desired resources by specifying their requirements in a flexible and easy way. From the point of view of resource providers, this system makes available an effective solution supporting providers in being reached by resource requests.

  16. Preparation, structures and magnetic properties of Dy/Zr and Ho/Zr two-layers and multi-layers

    International Nuclear Information System (INIS)

    Luche, M.C.

    1993-01-01

    The first part of the report is devoted to the description of the ultra-vacuum evaporation equipment, to the sample preparation conditions and to the characterization of the two-layers and multi-layers through reflection and glancing incidence X diffraction and transmission electron microscopy. In the second part, the magnetic properties of the samples are studied and relations between properties and structures are examined. 37 fig., 35 ref

  17. Distributed Processing System for Restoration of Electric Power Distribution Network Using Two-Layered Contract Net Protocol

    Science.gov (United States)

    Kodama, Yu; Hamagami, Tomoki

    Distributed processing system for restoration of electric power distribution network using two-layered CNP is proposed. The goal of this study is to develop the restoration system which adjusts to the future power network with distributed generators. The state of the art of this study is that the two-layered CNP is applied for the distributed computing environment in practical use. The two-layered CNP has two classes of agents, named field agent and operating agent in the network. In order to avoid conflicts of tasks, operating agent controls privilege for managers to send the task announcement messages in CNP. This technique realizes the coordination between agents which work asynchronously in parallel with others. Moreover, this study implements the distributed processing system using a de-fact standard multi-agent framework, JADE(Java Agent DEvelopment framework). This study conducts the simulation experiments of power distribution network restoration and compares the proposed system with the previous system. We confirmed the results show effectiveness of the proposed system.

  18. Laboratory Research of the Two-Layer Liquid Dynamics at the Wind Surge in a Strait Canal

    Directory of Open Access Journals (Sweden)

    S.F. Dotsenko

    2017-06-01

    Full Text Available The results of laboratory experiments in a straight aerohydrocanal of the rectangular cross-section filled with the two-layer (fresh-salty liquid are represented. The disturbance generator is the air flow directed to the area above the canal. The cases of the two-layer liquid dynamics in the canal with the horizontal flat bottom and in the presence of the bottom obstacle of finite width are considered. It is shown that during the surge in the straight canal, one of the possible exchange mechanisms on the boundary of fresh and salty layers may consist in the salt water emissions (resulted from the Kelvin-Helmholtz instability to the upper freshwater layer. The subsequent eviction can possibly be accompanied by occurrence of undulations at the interface. Besides, the evictions can be followed by formation of the oscillating layer, i.e. the layer with maximum density gradient the oscillations of which propagate to the overlying layers. Presence of the bottom obstacle complicates the structure of the two-layer liquid motions. In particular, it results in emergence of the mixed layers and transformation of the flow behind the obstacle into a turbulent one, formation of the wave-like disturbances over the obstacle, sharp change of the interface position and occurrence of large-scale vortices with the horizontal axes. It is revealed that the maximum peak of the flow velocity horizontal component is shifted upstream from the obstacle.

  19. Effect of Dental Restorative Material Type and Shade on Characteristics of Two-Layer Dental Composite Systems

    Directory of Open Access Journals (Sweden)

    Atefeh Karimzadeh

    Full Text Available Abstract The purpose of this study was to investigate the effects of shade and material type and shape in dental polymer composites on the hardness and shrinkage stress of bulk and two-layered restoration systems. For this purpose, some bulk and layered specimens from three different shades of dental materials were prepared and light-cured. The experiments were carried out on three types of materials: conventional restorative composite, nanohybrid composite and nanocomposite. Micro-indentation experiment was performed on the bulk and also on each layer of layered restoration specimens using a Vicker's indenter. The interface between the two layers was studied by scanning electron microscopy (SEM. The results revealed significant differences between the values of hardness for different shades in the conventional composite and also in the nanohybrid composite. However, no statistically significant difference was observed between the hardness values for different shades in the nanocomposite samples. The layered restoration specimens of different restorative materials exhibited lower hardness values with respect to their bulk specimens. The reduction in the hardness value of the layered conventional composite samples was higher than those of the nanocomposite and nanohybrid composite specimens indicating more shrinkage stresses generated in the conventional composite restorations. According to the SEM images, a gap was observed between the two layers in the layered restorations.

  20. A neural network device for on-line particle identification in cosmic ray experiments

    International Nuclear Information System (INIS)

    Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

    2004-01-01

    On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

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

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

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

  2. Moving image compression and generalization capability of constructive neural networks

    Science.gov (United States)

    Ma, Liying; Khorasani, Khashayar

    2001-03-01

    To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks NNs have been developed. Particularly, several constructive feed-forward neural networks FNNs have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference 2000, we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network OHL-FNN for compressing digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/ or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images, video sequences. The first, or other specified frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a football game.

  3. Precipitation Nowcast using Deep Recurrent Neural Network

    Science.gov (United States)

    Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

    2016-12-01

    An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

  4. Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Tosun Erdi

    2017-01-01

    Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

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

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  6. Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

    Science.gov (United States)

    Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

    2018-05-01

    The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

  7. Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

    Science.gov (United States)

    Manzoor Hussain, M.; Pitchi Raju, V.; Kandasamy, J.; Govardhan, D.

    2018-04-01

    Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction.

  8. Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

    Directory of Open Access Journals (Sweden)

    Mahmoud Akbarian

    2015-07-01

    Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP neural network with scaled conjugate gradient (trainscg back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth among pregnant women with lupus by using identified effective variables.

  9. Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters

    International Nuclear Information System (INIS)

    Stepanova, Marina; Antonova, Elizavieta; Munos-Uribe, F A; Gordo, S L Gomez; Torres-Sanchez, M V

    2008-01-01

    Different kinds of neural networks have established themselves as an effective tool in the prediction of different geomagnetic indices, including the Dst being the most important constituent for determination of the impact of Space Weather on the human life. Feed-forward networks with one hidden layer are used to forecast the Dst variation, using separately the solar wind paramenters, polar cap index, and auroral electrojet index as input parameters. It was found that in all three cases the storm-time intervals were predicted much more precisely as quite time intervals. The majority of cross-correlation coefficients between predicted and observed Dst of strong geomagnetic storms are situated between 0.8 and 0.9. Changes in the neural network architecture, including the number of nodes in the input and hidden layers and the transfer functions between them lead to an improvement of a network performance up to 10%.

  10. Spatial variability of steady-state infiltration into a two-layer soil system on burned hillslopes

    Science.gov (United States)

    Kinner, D.A.; Moody, J.A.

    2010-01-01

    Rainfall-runoff simulations were conducted to estimate the characteristics of the steady-state infiltration rate into 1-m2 north- and south-facing hillslope plots burned by a wildfire in October 2003. Soil profiles in the plots consisted of a two-layer system composed of an ash on top of sandy mineral soil. Multiple rainfall rates (18.4-51.2 mm h-1) were used during 14 short-duration (30 min) and 2 long-duration simulations (2-4 h). Steady state was reached in 7-26 min. Observed spatially-averaged steady-state infiltration rates ranged from 18.2 to 23.8 mm h-1 for north-facing and from 17.9 to 36.0 mm h-1 for south-facing plots. Three different theoretical spatial distribution models of steady-state infiltration rate were fit to the measurements of rainfall rate and steady-state discharge to provided estimates of the spatial average (19.2-22.2 mm h-1) and the coefficient of variation (0.11-0.40) of infiltration rates, overland flow contributing area (74-90% of the plot area), and infiltration threshold (19.0-26 mm h-1). Tensiometer measurements indicated a downward moving pressure wave and suggest that infiltration-excess overland flow is the runoff process on these burned hillslope with a two-layer system. Moreover, the results indicate that the ash layer is wettable, may restrict water flow into the underlying layer, and increase the infiltration threshold; whereas, the underlying mineral soil, though coarser, limits the infiltration rate. These results of the spatial variability of steady-state infiltration can be used to develop physically-based rainfall-runoff models for burned areas with a two-layer soil system. ?? 2010 Elsevier B.V.

  11. Optical characterization of two-layered turbid media for non-invasive, absolute oximetry in cerebral and extracerebral tissue.

    Directory of Open Access Journals (Sweden)

    Bertan Hallacoglu

    Full Text Available We introduce a multi-distance, frequency-domain, near-infrared spectroscopy (NIRS method to measure the optical coefficients of two-layered media and the thickness of the top layer from diffuse reflectance measurements. This method features a direct solution based on diffusion theory and an inversion procedure based on the Levenberg-Marquardt algorithm. We have validated our method through Monte Carlo simulations, experiments on tissue-like phantoms, and measurements on the forehead of three human subjects. The Monte Carlo simulations and phantom measurements have shown that, in ideal two-layered samples, our method accurately recovers the top layer thickness (L, the absorption coefficient (µ a and the reduced scattering coefficient (µ' s of both layers with deviations that are typically less than 10% for all parameters. Our method is aimed at absolute measurements of hemoglobin concentration and saturation in cerebral and extracerebral tissue of adult human subjects, where the top layer (layer 1 represents extracerebral tissue (scalp, skull, dura mater, subarachnoid space, etc. and the bottom layer (layer 2 represents cerebral tissue. Human subject measurements have shown a significantly greater total hemoglobin concentration in cerebral tissue (82±14 µM with respect to extracerebral tissue (30±7 µM. By contrast, there was no significant difference between the hemoglobin saturation measured in cerebral tissue (56%±10% and extracerebral tissue (62%±6%. To our knowledge, this is the first time that an inversion procedure in the frequency domain with six unknown parameters with no other prior knowledge is used for the retrieval of the optical coefficients and top layer thickness with high accuracy on two-layered media. Our absolute measurements of cerebral hemoglobin concentration and saturation are based on the discrimination of extracerebral and cerebral tissue layers, and they can enhance the impact of NIRS for cerebral hemodynamics and

  12. Orthogonal Bases used for Feed Forward Control of Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2011-01-01

    In optimizing wind turbines it can be of a large help to use information of wind speeds at upwind turbine for the control of downwind turbines, it is, however, problematic to use these measurements directly since they are highly influenced by turbulence behind the wind turbine rotor plane. In this....... In this paper an orthogonal basis is use to extract the general trends in the wind signal, which are forward to the down wind turbines. This concept controller is designed and simulated on a generic 4.8 MW wind turbine model, which shows the potential of this proposed scheme....

  13. A state comparison amplifier with feed forward state correction

    Science.gov (United States)

    Mazzarella, Luca; Donaldson, Ross; Collins, Robert; Zanforlin, Ugo; Buller, Gerald; Jeffers, John

    2017-04-01

    The Quantum State Comparison AMPlifier (SCAMP) is a probabilistic amplifier that works for known sets of coherent states. The input state is mixed with a guess state at a beam splitter and one of the output ports is coupled to a detector. The other output contains the amplified state, which is accepted on the condition that no counts are recorded. The system uses only classical resources and has been shown to achieve high gain and repetition rate. However the output fidelity is not high enough for most quantum communication purposes. Here we show how the success probability and fidelity are enhanced by repeated comparison stages, conditioning later state choices on the outcomes of earlier detections. A detector firing at an early stage means that a guess is wrong. This knowledge allows us to correct the state perfectly. The system requires fast-switching between different input states, but still requires only classical resources. Figures of merit compare favourably with other schemes, most notably the probability-fidelity product is higher than for unambiguous state discrimination. Due to its simplicity, the system is a candidate to counteract quantum signal degradation in a lossy fibre or as a quantum receiver to improve the key rate of continuous variable quantum communication. The work was supported by the QComm Project of the UK Engineering and Physical Sciences Research Council (EP/M013472/1).

  14. Crystal surface analysis using matrix textural features classified by a Probabilistic Neural Network

    International Nuclear Information System (INIS)

    Sawyer, C.R.; Quach, V.T.; Nason, D.; van den Berg, L.

    1991-01-01

    A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlappings subimage and features are extracted from each subimage based on statistical measures of the gray tone distribution, according to the method of Haralick [1]. Twenty parameters are derived from each subimage and presented to a Probabilistic Neural Network (PNN) [2] for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities. 6 refs., 4 figs

  15. Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator

    Science.gov (United States)

    Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun

    2010-02-01

    Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.

  16. Design of alluvial Egyptian irrigation canals using artificial neural networks method

    Directory of Open Access Journals (Sweden)

    Hassan Ibrahim Mohamed

    2013-06-01

    Full Text Available In the present study, artificial neural networks method (ANNs is used to estimate the main parameters which used in design of stable alluvial channels. The capability of ANN models to predict the stable alluvial channels dimensions is investigated, where the flow rate and sediment mean grain size were considered as input variables and wetted perimeter, hydraulic radius, and water surface slope were considered as output variables. The used ANN models are based on a back propagation algorithm to train a multi-layer feed-forward network (Levenberg Marquardt algorithm. The proposed models were verified using 311 data sets of field data collected from 61 manmade canals and drains. Several statistical measures and graphical representation are used to check the accuracy of the models in comparison with previous empirical equations. The results of the developed ANN model proved that this technique is reliable in such field compared with previously developed methods.

  17. Condition monitoring of an electro-magnetic brake using an artificial neural network

    Science.gov (United States)

    Gofran, T.; Neugebauer, P.; Schramm, D.

    2017-10-01

    This paper presents a data-driven approach to Condition Monitoring of Electromagnetic brakes without use of additional sensors. For safe and efficient operation of electric motor a regular evaluation and replacement of the friction surface of the brake is required. One such evaluation method consists of direct or indirect sensing of the air-gap between pressure plate and magnet. A larger gap is generally indicative of worn surface(s). Traditionally this has been accomplished by the use of additional sensors - making existing systems complex, cost- sensitive and difficult to maintain. In this work a feed-forward Artificial Neural Network (ANN) is learned with the electrical data of the brake by supervised learning method to estimate the air-gap. The ANN model is optimized on the training set and validated using the test set. The experimental results of estimated air-gap with accuracy of over 95% demonstrate the validity of the proposed approach.

  18. A neural network controller for hydronic heating systems of solar buildings.

    Science.gov (United States)

    Argiriou, Athanassios A; Bellas-Velidis, Ioannis; Kummert, Michaël; André, Philippe

    2004-04-01

    An artificial neural network (ANN)-based controller for hydronic heating plants of buildings is presented. The controller has forecasting capabilities: it includes a meteorological module, forecasting the ambient temperature and solar irradiance, an indoor temperature predictor module, a supply temperature predictor module and an optimizing module for the water supply temperature. All ANN modules are based on the Feed Forward Back Propagation (FFBP) model. The operation of the controller has been tested experimentally, on a real-scale office building during real operating conditions. The operation results were compared to those of a conventional controller. The performance was also assessed via numerical simulation. The detailed thermal simulation tool for solar systems and buildings TRNSYS was used. Both experimental and numerical results showed that the expected percentage of energy savings with respect to a conventional controller is of about 15% under North European weather conditions.

  19. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran

    2017-08-17

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

  20. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

    1998-01-01

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  2. A two layer chaotic encryption scheme of secure image transmission for DCT precoded OFDM-VLC transmission

    Science.gov (United States)

    Wang, Zhongpeng; Chen, Fangni; Qiu, Weiwei; Chen, Shoufa; Ren, Dongxiao

    2018-03-01

    In this paper, a two-layer image encryption scheme for a discrete cosine transform (DCT) precoded orthogonal frequency division multiplexing (OFDM) visible light communication (VLC) system is proposed. Firstly, in the proposed scheme the transmitted image is first encrypted by a chaos scrambling sequence,which is generated from the hybrid 4-D hyper- and Arnold map in the upper-layer. After that, the encrypted image is converted into digital QAM modulation signal, which is re-encrypted by chaos scrambling sequence based on Arnold map in physical layer to further enhance the security of the transmitted image. Moreover, DCT precoding is employed to improve BER performance of the proposed system and reduce the PAPR of OFDM signal. The BER and PAPR performances of the proposed system are evaluated by simulation experiments. The experiment results show that the proposed two-layer chaos scrambling schemes achieve image secure transmission for image-based OFDM VLC. Furthermore, DCT precoding can reduce the PAPR and improve the BER performance of OFDM-based VLC.

  3. Regional evapotranspiration estimation based on a two-layer remote-sensing scheme in Shahe River basin

    International Nuclear Information System (INIS)

    Yin, Jian; Wang, Huixiao

    2014-01-01

    Land surface evapotranspiration (ET) derived from remote sensing data has significant meaning for plant growth monitoring, crop yield assessment, disaster monitoring and understanding energy and water cycle in river basin area and surrounding regions. In the study, we developed a land surface ET remote sensing retrieval system to estimate the daily ET in Shahe river basin using the TM/ETM+ images. The system is based on the two-layer ET model and includes three parts: inversion of the evaporation ration using two-layer model, calculation of total daily net radiation, and estimation of daily ET based on evaporation fraction method. The results show that the average daily ET is about 2.28mm of the typical days in spring, and 2.97mm in summer, 1.59mm in autumn, and 0.5mm in winter. The ET in upstream areas covered by forest is higher than that in the downstream covered by settlement and farmland. In summer the difference of ET between the upper reaches and lower reaches is smaller compared to the other three seasons. The measurements by large aperture scintillometer and eddy correlation instrument were used for validation. By comparing the observed data with the estimated data, we found the estimation system had a high precision with the relative error between 0 and 16% (mean error of 11.1%), and the variance 0.77mm

  4. Preparation and evaluation of tamsulosin hydrochloride sustained-release pellets modified by two-layered membrane techniques

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2015-02-01

    Full Text Available The aim of the present study was to develop tamsulosin hydrochloride sustained-release pellets using two-layered membrane techniques. Centrifugal granulator and fluidized-bed coater were employed to prepare drug-loaded pellets and to employ two-layered membrane coating respectively. The prepared pellets were evaluated for physicochemical characterization, subjected to differential scanning calorimetry (DSC and in vitro release of different pH. Different release models and scanning electron microscopy (SEM were utilized to analyze the release mechanism of Harnual® and home-made pellets. By comparing the dissolution profiles, the ratio and coating weight gain of Eudragit® NE30D and Eudragit® L30D55 which constitute the inside membrane were identified as 18:1 and 10%–11%. The coating amount of outside membrane containing Eudragit® L30D55 was determined to be 0.8%. The similarity factors (f2 of home-made capsule and commercially available product (Harnual® were above 50 in different dissolution media. DSC studies confirmed that drug and excipients had good compatibility and SEM photographs showed the similarities and differences of coating surface between Harnual® and self-made pellets before and after dissolution. According to Ritger-Peppas model, the two dosage form had different release mechanism.

  5. Equivalent circuit models of two-layer flexure beams with excitation by temperature, humidity, pressure, piezoelectric or piezomagnetic interactions

    Directory of Open Access Journals (Sweden)

    U. Marschner

    2014-09-01

    Full Text Available Two-layer flexure beams often serve as basic transducers in actuators and sensors. In this paper a generalized description of their stimuli-influenced mechanical behavior is derived. For small deflection angles this description includes a multi-port circuit or network representation with lumped elements for a beam part of finite length. A number of coupled finite beam parts model the dynamic behavior including the first natural frequencies of the beam. For piezoelectric and piezomagnetic interactions, reversible transducer models are developed. The piezomagnetic two-layer beam model is extended to include solenoid and planar coils. Linear network theory is applied in order to determine network parameters and to simplify the circuit representation. The resulting circuit model is the basis for a fast simulation of the dynamic system behavior with advanced circuit simulators and, thus, the optimization of the system. It is also a useful tool for understanding and explaining this multi-domain system through basic principles of general system theory.

  6. ATLAS-TPX: a two-layer pixel detector setup for neutron detection and radiation field characterization

    International Nuclear Information System (INIS)

    Bergmann, B.; Caicedo, I.; Pospisil, S.; Vykydal, Z.; Leroy, C.

    2016-01-01

    A two-layer pixel detector setup (ATLAS-TPX), designed for thermal and fast neutron detection and radiation field characterization is presented. It consists of two segmented silicon detectors (256 × 256 pixels, pixel pitch 55 μm, thicknesses 300 μm and 500 μm) facing each other. To enhance the neutron detection efficiency a set of converter layers is inserted in between these detectors. The pixelation and the two-layer design allow a discrimination of neutrons against γs by pattern recognition and against charged particles by using the coincidence and anticoincidence information. The neutron conversion and detection efficiencies are measured in a thermal neutron field and fast neutron fields with energies up to 600 MeV. A Geant4 simulation model is presented, which is validated against the measured detector responses. The reliability of the coincidence and anticoincidence technique is demonstrated and possible applications of the detector setup are briefly outlined.

  7. Foot Plantar Pressure Estimation Using Artificial Neural Networks

    OpenAIRE

    Xidias , Elias; Koutkalaki , Zoi; Papagiannis , Panagiotis; Papanikos , Paraskevas; Azariadis , Philip

    2015-01-01

    Part 1: Smart Products; International audience; In this paper, we present a novel approach to estimate the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle. The proposed method is based on Artificial Neural Networks and can be utilized for the determination of the comfort that is related to the sole construction. Input parameters to the proposed neural network are the material properties and the thicknesses of the sole...

  8. Neural networks and particle physics

    CERN Document Server

    Peterson, Carsten

    1993-01-01

    1. Introduction : Structure of the Central Nervous System Generics2. Feed-forward networks, Perceptions, Function approximators3. Self-organisation, Feature Maps4. Feed-back Networks, The Hopfield model, Optimization problems, Feed-back, Networks, Deformable templates, Graph bisection

  9. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  10. Direct process estimation from tomographic data using artificial neural systems

    Science.gov (United States)

    Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

    2001-07-01

    The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

  11. Neural network modeling of chaotic dynamics in nuclear reactor flows

    International Nuclear Information System (INIS)

    Welstead, S.T.

    1992-01-01

    Neural networks have many scientific applications in areas such as pattern classification and time series prediction. The universal approximation property of these networks, however, can also be exploited to provide researchers with tool for modeling observed nonlinear phenomena. It has been shown that multilayer feed forward networks can capture important global nonlinear properties, such as chaotic dynamics, merely by training the network on a finite set of observed data. The network itself then provides a model of the process that generated the data. Characterizations such as the existence and general shape of a strange attractor and the sign of the largest Lyapunov exponent can then be extracted from the neural network model. In this paper, the author applies this idea to data generated from a nonlinear process that is representative of convective flows that can arise in nuclear reactor applications. Such flows play a role in forced convection heat removal from pressurized water reactors and boiling water reactors, and decay heat removal from liquid-metal-cooled reactors, either by natural convection or by thermosyphons

  12. Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

    Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

  13. AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Zainal Ahmad

    2017-05-01

    Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

  14. Gap Filling of Daily Sea Levels by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lyubka Pashova

    2013-06-01

    Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

  15. Neural network stochastic simulation applied for quantifying uncertainties

    Directory of Open Access Journals (Sweden)

    N Foudil-Bey

    2016-09-01

    Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.

  16. Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

    Science.gov (United States)

    Hazza, Muataz Hazza F. Al; Adesta, Erry Y. T.; Riza, Muhammad

    2013-12-01

    High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models.

  17. Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

    International Nuclear Information System (INIS)

    Al Hazza, Muataz Hazza F; Adesta, Erry Y T; Riza, Muhammad

    2013-01-01

    High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models

  18. The spin-3/2 Ising model AFM/AFM two-layer lattice with crystal field

    International Nuclear Information System (INIS)

    Yigit, A.; Albayrak, E.

    2010-01-01

    The spin-3/2 Ising model is investigated for the case of antiferromagnetic (AFM/AFM) interactions on the two-layer Bethe lattice by using the exact recursion relations in a pairwise approach for given coordination numbers q=3, 4 and 6 when the layers are under the influences of equal external magnetic and equal crystal fields. The ground state (GS) phase diagrams are obtained on the different planes in detail and then the temperature dependent phase diagrams of the system are calculated accordingly. It is observed that the system presents both second- and first-order phase transitions for all q, therefore, tricritical points. It was also found that the system exhibits double-critical end points and isolated points. The model also presents two Neel temperatures, TN, and the existence of which leads to the reentrant behavior.

  19. Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications

    KAUST Repository

    Elshurafa, Amro M.

    2012-07-23

    In this Letter, the authors fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art single-layer MEMS fractal capacitors. Explicitly, a capacitor with a woven structure and another with an interleaved configuration were fabricated in the standard PolyMUMPS surface micromachining process and tested at S-band frequencies. The self-resonant frequencies of the fabricated capacitors were close to 10GHz, which is better than that of the parallel-plate capacitor, which measured only 5.5GHz. Further, the presented capacitors provided a higher capacitance when compared with the state-of-the-art-reported MEMS fractal capacitors created using a single layer at the expense of a lower quality factor. © 2012 The Institution of Engineering and Technology.

  20. Two-layer optical model of skin for early, non-invasive detection of wound development on the diabetic foot

    Science.gov (United States)

    Yudovsky, Dmitry; Nouvong, Aksone; Schomacker, Kevin; Pilon, Laurent

    2010-02-01

    Foot ulceration is a debilitating comorbidity of diabetes that may result in loss of mobility and amputation. Optical detection of cutaneous tissue changes due to inflammation and necrosis at the preulcer site could constitute a preventative strategy. A commercial hyperspectral oximetry system was used to measure tissue oxygenation on the feet of diabetic patients. A previously developed predictive index was used to differentiate preulcer tissue from surrounding healthy tissue with a sensitivity of 92% and specificity of 80%. To improve prediction accuracy, an optical skin model was developed treating skin as a two-layer medium and explicitly accounting for (i) melanin content and thickness of the epidermis, (ii) blood content and hemoglobin saturation of the dermis, and (iii) tissue scattering in both layers. Using this forward model, an iterative inverse method was used to determine the skin properties from hyperspectral images of preulcerative areas. The use of this information in lowering the false positive rate was discussed.

  1. Improved lumped models for transient combined convective and radiative cooling of a two-layer spherical fuel element

    International Nuclear Information System (INIS)

    Silva, Alice Cunha da; Su, Jian

    2013-01-01

    The High Temperature Gas cooled Reactor (HTGR) is a fourth generation thermal nuclear reactor, graphite-moderated and helium cooled. The HTGRs have important characteristics making essential the study of these reactors, as well as its fuel element. Examples of these are: high thermal efficiency,low operating costs and construction, passive safety attributes that allow implication of the respective plants. The Pebble Bed Modular Reactor (PBMR) is a HTGR with spherical fuel elements that named the reactor. This fuel element is composed by a particulate region with spherical inclusions, the fuel UO2 particles, dispersed in a graphite matrix and a convective heat transfer by Helium happens on the outer surface of the fuel element. In this work, the transient heat conduction in a spherical fuel element of a pebble-bed high temperature reactor was studied in a transient situation of combined convective and radiative cooling. Improved lumped parameter model was developed for the transient heat conduction in the two-layer composite sphere subjected to combined convective and radiative cooling. The improved lumped model was obtained through two-point Hermite approximations for integrals. Transient combined convective and radiative cooling of the two-layer spherical fuel element was analyzed to illustrate the applicability of the proposed lumped model, with respect to die rent values of the Biot number, the radiation-conduction parameter, the dimensionless thermal contact resistance, the dimensionless inner diameter and coating thickness, and the dimensionless thermal conductivity. It was shown by comparison with numerical solution of the original distributed parameter model that the improved lumped model, with H2,1/H1,1/H0,0 approximation yielded significant improvement of average temperature prediction over the classical lumped model. (author)

  2. Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function

    Science.gov (United States)

    Rodríguez-Quiñonez, J. C.; Sergiyenko, O.; Hernandez-Balbuena, D.; Rivas-Lopez, M.; Flores-Fuentes, W.; Basaca-Preciado, L. C.

    2014-12-01

    Many laser scanners depend on their mechanical construction to guarantee their measurements accuracy, however, the current computational technologies allow us to improve these measurements by mathematical methods implemented in neural networks. In this article we are going to introduce the current laser scanner technologies, give a description of our 3D laser scanner and adjust their measurement error by a previously trained feed forward back propagation (FFBP) neural network with a Widrow-Hoff weight/bias learning function. A comparative analysis with other learning functions such as the Kohonen algorithm and gradient descendent with momentum algorithm is presented. Finally, computational simulations are conducted to verify the performance and method uncertainty in the proposed system.

  3. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  4. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  5. Potential usefulness of an artificial neural network for assessing ventricular size

    International Nuclear Information System (INIS)

    Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

    1995-01-01

    An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

  6. Optimisation of the addition of carrot dietary fibre to a dry fermented sausage (sobrassada) using artificial neural networks.

    Science.gov (United States)

    Eim, Valeria S; Simal, Susana; Rosselló, Carmen; Femenia, Antoni; Bon, José

    2013-07-01

    An optimisation problem was formulated to maximise the amount of carrot dietary fibre (CDF) in a dry fermented sausage, while maintaining product quality, by using 0-12% CDF as the decision variable, and limiting values of several physico-chemical and textural parameters (moisture content, water activity, pH, colour, non-protein nitrogen, free fatty acid, compression work and hardness) as constraints. The evolution of each quality parameter during the ripening process was estimated by developing a multi-layer feed forward artificial neural network (ANN), taking into consideration the CDF concentration and the ripening time as independent variables. Results indicate an optimum CDF concentration of 4.9% with a good correlation between experimental and estimated values (mean relative error≤3.35%). Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

    Directory of Open Access Journals (Sweden)

    J. Prakash Maran

    2013-09-01

    Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

  8. Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

    International Nuclear Information System (INIS)

    Erkaymaz, Okan; Ozer, Mahmut

    2016-01-01

    Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts–Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance.

  9. 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.

  10. Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

    Science.gov (United States)

    Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

    2016-01-01

    Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

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

    International Nuclear Information System (INIS)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

    2015-01-01

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

  12. Thin and Broadband Two-Layer Microwave Absorber in 4-12 GHz with Developed Flaky Cobalt Material

    Science.gov (United States)

    Gill, Neeraj; Singh, Jaydeep; Puthucheri, Smitha; Singh, Dharmendra

    2018-03-01

    Microwave absorbing materials (MAMs) in the frequency range of 2.0-18.0 GHz are essential for the stealth and communication applications. Researchers came up with effective MAMs for the higher frequency regions, i.e., 8.0-18.0 GHz, while absorbers with comparable properties in the lower frequency band are still not in the limelight. Designing a MAM for the lower frequency range is a critical task. It is known that the factors governing the absorption in this frequency predominantly depend on the permeability and conductivity of the material, whereas the shape anisotropy of the particles can initiate different absorption mechanisms like multiple internal reflections, phase cancellations, surface charge polarization and enhanced conductivity that can promote the microwave absorption towards lower frequencies. But the material alone may not serve the purpose of getting broad absorption bandwidth. With the effective use of advanced electromagnetic technique like multi-layering this problem may be solved. Therefore, in this paper, a material with shape anisotropy (cobalt flakes with high shape anisotropy) has been prepared and a two-layer structure is developed which gives the absorption bandwidth in 4.17-12.05 GHz at a coating thickness of 2.66 mm.

  13. TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

    Science.gov (United States)

    Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

    2016-11-01

    The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

  14. Quasi-two-layer morphodynamic model for bedload-dominated problems: bed slope-induced morphological diffusion.

    Science.gov (United States)

    Maldonado, Sergio; Borthwick, Alistair G L

    2018-02-01

    We derive a two-layer depth-averaged model of sediment transport and morphological evolution for application to bedload-dominated problems. The near-bed transport region is represented by the lower (bedload) layer which has an arbitrarily constant, vanishing thickness (of approx. 10 times the sediment particle diameter), and whose average sediment concentration is free to vary. Sediment is allowed to enter the upper layer, and hence the total load may also be simulated, provided that concentrations of suspended sediment remain low. The model conforms with established theories of bedload, and is validated satisfactorily against empirical expressions for sediment transport rates and the morphodynamic experiment of a migrating mining pit by Lee et al. (1993 J. Hydraul. Eng. 119 , 64-80 (doi:10.1061/(ASCE)0733-9429(1993)119:1(64))). Investigation into the effect of a local bed gradient on bedload leads to derivation of an analytical, physically meaningful expression for morphological diffusion induced by a non-zero local bed slope. Incorporation of the proposed morphological diffusion into a conventional morphodynamic model (defined as a coupling between the shallow water equations, Exner equation and an empirical formula for bedload) improves model predictions when applied to the evolution of a mining pit, without the need either to resort to special numerical treatment of the equations or to use additional tuning parameters.

  15. Inverse estimation for temperatures of outer surface and geometry of inner surface of furnace with two layer walls

    International Nuclear Information System (INIS)

    Chen, C.-K.; Su, C.-R.

    2008-01-01

    This study provides an inverse analysis to estimate the boundary thermal behavior of a furnace with two layer walls. The unknown temperature distribution of the outer surface and the geometry of the inner surface were estimated from the temperatures of a small number of measured points within the furnace wall. The present approach rearranged the matrix forms of the governing differential equations and then combined the reversed matrix method, the linear least squares error method and the concept of virtual area to determine the unknown boundary conditions of the furnace system. The dimensionless temperature data obtained from the direct problem were used to simulate the temperature measurements. The influence of temperature measurement errors upon the precision of the estimated results was also investigated. The advantage of this approach is that the unknown condition can be directly solved by only one calculation process without initially guessed temperatures, and the iteration process of the traditional method can be avoided in the analysis of the heat transfer. Therefore, the calculation in this work is more rapid and exact than the traditional method. The result showed that the estimation error of the geometry increased with increasing distance between measured points and inner surface and in preset error, and with decreasing number of measured points. However, the geometry of the furnace inner surface could be successfully estimated by only the temperatures of a small number of measured points within and near the outer surface under reasonable preset error

  16. Experimental study of core bypass flow in a prismatic VHTR based on a two-layer block model

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Huhu, E-mail: huhuwang@tamu.edu; Hassan, Yassin A., E-mail: y-hassan@tamu.edu; Dominguez-Ontiveros, Elvis, E-mail: elvisdom@tamu.edu

    2016-09-15

    Bypass flow in a prismatic very high temperature gas-cooled nuclear reactor (VHTR) plays an important role in determining the coolant distribution in the core region. Efficient removal of heat from the core relies on the majority of coolant passing through the coolant channels instead of the bypass gaps. Consequently, the bypass flow fraction and its flow characteristic are important in the design process of the prismatic VHTR. The objective of this study is to experimentally investigate the flow behavior including the turbulence characteristics inside the bypass gaps using laser Doppler velocimetry (LDV), bypass fraction and pressure drops in the system. The experiment facility constructed at Texas A&M University is a scaled model consisting of two layers of fuel blocks. The distributions of the mean streamwise velocity, turbulence intensity and turbulence kinetic energy within the bypass gap at two different elevations under different Reynolds number were investigated. Uncertainties in the bypass flow fraction estimation were evaluated. The velocity and turbulence study in this work is considered to be unique, and may serve as a benchmark for the related numerical calculations.

  17. Quasi-two-layer morphodynamic model for bedload-dominated problems: bed slope-induced morphological diffusion

    Science.gov (United States)

    Maldonado, Sergio; Borthwick, Alistair G. L.

    2018-02-01

    We derive a two-layer depth-averaged model of sediment transport and morphological evolution for application to bedload-dominated problems. The near-bed transport region is represented by the lower (bedload) layer which has an arbitrarily constant, vanishing thickness (of approx. 10 times the sediment particle diameter), and whose average sediment concentration is free to vary. Sediment is allowed to enter the upper layer, and hence the total load may also be simulated, provided that concentrations of suspended sediment remain low. The model conforms with established theories of bedload, and is validated satisfactorily against empirical expressions for sediment transport rates and the morphodynamic experiment of a migrating mining pit by Lee et al. (1993 J. Hydraul. Eng. 119, 64-80 (doi:10.1061/(ASCE)0733-9429(1993)119:1(64))). Investigation into the effect of a local bed gradient on bedload leads to derivation of an analytical, physically meaningful expression for morphological diffusion induced by a non-zero local bed slope. Incorporation of the proposed morphological diffusion into a conventional morphodynamic model (defined as a coupling between the shallow water equations, Exner equation and an empirical formula for bedload) improves model predictions when applied to the evolution of a mining pit, without the need either to resort to special numerical treatment of the equations or to use additional tuning parameters.

  18. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

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

  19. An analytical model for solute transport through a GCL-based two-layered liner considering biodegradation

    International Nuclear Information System (INIS)

    Guan, C.; Xie, H.J.; Wang, Y.Z.; Chen, Y.M.; Jiang, Y.S.; Tang, X.W.

    2014-01-01

    An analytical model for solute advection and dispersion in a two-layered liner consisting of a geosynthetic clay liner (GCL) and a soil liner (SL) considering the effect of biodegradation was proposed. The analytical solution was derived by Laplace transformation and was validated over a range of parameters using the finite-layer method based software Pollute v7.0. Results show that if the half-life of the solute in GCL is larger than 1 year, the degradation in GCL can be neglected for solute transport in GCL/SL. When the half-life of GCL is less than 1 year, neglecting the effect of degradation in GCL on solute migration will result in a large difference of relative base concentration of GCL/SL (e.g., 32% for the case with half-life of 0.01 year). The 100-year solute base concentration can be reduced by a factor of 2.2 when the hydraulic conductivity of the SL was reduced by an order of magnitude. The 100-year base concentration was reduced by a factor of 155 when the half life of the contaminant in the SL was reduced by an order of magnitude. The effect of degradation is more important in approving the groundwater protection level than the hydraulic conductivity. The analytical solution can be used for experimental data fitting, verification of complicated numerical models and preliminary design of landfill liner systems. - Highlights: •Degradation of contaminants was considered in modeling solute transport in GCL/SL. •Analytical solutions were derived for assessment of GCL/SL with degradation. •Degradation in GCL can be ignored as half-life is larger than 1 year. •Base concentration is more sensitive to half-life of SL than to permeability of SL

  20. An analytical model for solute transport through a GCL-based two-layered liner considering biodegradation

    Energy Technology Data Exchange (ETDEWEB)

    Guan, C. [Institute of Hydrology and Water Resources Engineering, Zhejiang University, Hangzhou 310058 (China); Xie, H.J., E-mail: xiehaijian@zju.edu.cn [Institute of Hydrology and Water Resources Engineering, Zhejiang University, Hangzhou 310058 (China); MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058 (China); Wang, Y.Z.; Chen, Y.M.; Jiang, Y.S.; Tang, X.W. [MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058 (China)

    2014-01-01

    An analytical model for solute advection and dispersion in a two-layered liner consisting of a geosynthetic clay liner (GCL) and a soil liner (SL) considering the effect of biodegradation was proposed. The analytical solution was derived by Laplace transformation and was validated over a range of parameters using the finite-layer method based software Pollute v7.0. Results show that if the half-life of the solute in GCL is larger than 1 year, the degradation in GCL can be neglected for solute transport in GCL/SL. When the half-life of GCL is less than 1 year, neglecting the effect of degradation in GCL on solute migration will result in a large difference of relative base concentration of GCL/SL (e.g., 32% for the case with half-life of 0.01 year). The 100-year solute base concentration can be reduced by a factor of 2.2 when the hydraulic conductivity of the SL was reduced by an order of magnitude. The 100-year base concentration was reduced by a factor of 155 when the half life of the contaminant in the SL was reduced by an order of magnitude. The effect of degradation is more important in approving the groundwater protection level than the hydraulic conductivity. The analytical solution can be used for experimental data fitting, verification of complicated numerical models and preliminary design of landfill liner systems. - Highlights: •Degradation of contaminants was considered in modeling solute transport in GCL/SL. •Analytical solutions were derived for assessment of GCL/SL with degradation. •Degradation in GCL can be ignored as half-life is larger than 1 year. •Base concentration is more sensitive to half-life of SL than to permeability of SL.

  1. Testing the Two-Layer Model for Correcting Near Cloud Reflectance Enhancement Using LES SHDOM Simulated Radiances

    Science.gov (United States)

    Wen, Guoyong; Marshak, Alexander; Varnai, Tamas; Levy, Robert

    2016-01-01

    A transition zone exists between cloudy skies and clear sky; such that, clouds scatter solar radiation into clear-sky regions. From a satellite perspective, it appears that clouds enhance the radiation nearby. We seek a simple method to estimate this enhancement, since it is so computationally expensive to account for all three-dimensional (3-D) scattering processes. In previous studies, we developed a simple two-layer model (2LM) that estimated the radiation scattered via cloud-molecular interactions. Here we have developed a new model to account for cloud-surface interaction (CSI). We test the models by comparing to calculations provided by full 3-D radiative transfer simulations of realistic cloud scenes. For these scenes, the Moderate Resolution Imaging Spectroradiometer (MODIS)-like radiance fields were computed from the Spherical Harmonic Discrete Ordinate Method (SHDOM), based on a large number of cumulus fields simulated by the University of California, Los Angeles (UCLA) large eddy simulation (LES) model. We find that the original 2LM model that estimates cloud-air molecule interactions accounts for 64 of the total reflectance enhancement and the new model (2LM+CSI) that also includes cloud-surface interactions accounts for nearly 80. We discuss the possibility of accounting for cloud-aerosol radiative interactions in 3-D cloud-induced reflectance enhancement, which may explain the remaining 20 of enhancements. Because these are simple models, these corrections can be applied to global satellite observations (e.g., MODIS) and help to reduce biases in aerosol and other clear-sky retrievals.

  2. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  3. Reconstruction of neutron spectra using neural networks starting from the Bonner spheres spectrometric system

    International Nuclear Information System (INIS)

    Ortiz R, J.M.; Martinez B, M.R.; Arteaga A, T.; Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

    2005-01-01

    The artificial neural networks (RN) have been used successfully to solve a wide variety of problems. However to determine an appropriate set of values of the structural parameters and of learning of these, it continues being even a difficult task. Contrary to previous works, here a set of neural networks is designed to reconstruct neutron spectra starting from the counting rates coming from the detectors of the Bonner spheres system, using a systematic and experimental strategy for the robust design of multilayer neural networks of the feed forward type of inverse propagation. The robust design is formulated as a design problem of Taguchi parameters. It was selected a set of 53 neutron spectra, compiled by the International Atomic Energy Agency, the counting rates were calculated that would take place in a Bonner spheres system, the set was arranged according to the wave form of those spectra. With these data and applying the Taguchi methodology to determine the best parameters of the network topology, it was trained and it proved the same one with the spectra. (Author)

  4. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

    Science.gov (United States)

    Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

    2016-12-01

    In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    Fadare, D.A.

    2009-01-01

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  7. Improvement in the determination of elemental concentrations in PIXE analyses using artificial neural system

    International Nuclear Information System (INIS)

    Correa, R.; Dinator, M.I.; Morales, J.R.; Miranda, P.A.; Cancino, S.A.; Vila, I.; Requena, I.

    2008-01-01

    An Artificial Neural System, ANS, has been designed to operate in the analysis of spectra obtained from a PIXE (Proton Induced X-ray Emissions) application. The special designed ANS was used in the calculation of the concentrations of the major elements in the samples. Neural systems using several feed-forward ANN of similar topology working in parallel were trained with error back propagation algorithm using sets of spectra of known elemental concentrations. Following the training phase of the neural networks, other PIXE spectra were analyzed with this methodology providing unknown elemental concentrations. ANS results were compared with results obtained by traditional computer codes like AXIL and GUPIX, obtaining correlations factors close to one. The rather short time required to process each spectrum, of the order of microseconds, allows fast analysis of a large number of samples. Here we present applications of ANS in the PIXE analyses of samples of organic nature like liver, gills and muscle from fishes. ANS results were compared with elemental concentrations obtained in a previous application where a single ANN was used for each analyzed element. PIXE analyses were performed at the Nuclear Physics Laboratory of the University of Chile, using 2.2 MeV proton beams provided by a Van de Graaff accelerator. (author)

  8. Neural attractor network for application in visual field data classification

    International Nuclear Information System (INIS)

    Fink, Wolfgang

    2004-01-01

    The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a ' counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available

  9. Unsupervised neural networks for solving Troesch's problem

    International Nuclear Information System (INIS)

    Raja Muhammad Asif Zahoor

    2014-01-01

    In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. (interdisciplinary physics and related areas of science and technology)

  10. Production of Steel Casts in Two-Layer Moulds with Alkaline Binders Part 2. Facing sand with the alkaline organic binder REZOLIT

    Directory of Open Access Journals (Sweden)

    M. Holtzer

    2011-04-01

    Full Text Available This paper constitutes the second part of the article concerning the implementation of the two-layer mould technology for steel casts inZ.M. POMET. The results of the laboratory examinations of the backing sand with the inorganic binder RUDAL were presented in thefirst part of the paper. Whereas in the second part the results of the laboratory testing of the facing sand with the alkaline resin REZOLITare given. The technology of two-layer moulds was already implemented in Z.M. POMET within the target project. Examples of castingsmade in this technology are shown in the final part of this paper.

  11. Forcast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

    International Nuclear Information System (INIS)

    Vannucci, A.; Oliveira, K.A.; Tajima, T.

    1998-02-01

    A feed-forward neural network with two hidden layers is used in this work to forecast major and minor disruptive instabilities in TEXT discharges. Using soft X-ray signals as input data, the neural net is trained with one disruptive plasma pulse, and a different disruptive discharge is used for validation. After being properly trained the networks, with the same set of weights. is then used to forecast disruptions in two others different plasma pulses. It is observed that the neural net is able to predict the incoming of a disruption more than 3 ms in advance. This time interval is almost three times longer than the one already obtained previously when magnetic signal from a Mirnov coil was used to feed the neural networks with. To our own eye we fail to see any indication of an upcoming disruption from the experimental data this far back from the time of disruption. Finally, from what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism would be associated to an increase of the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards or, in face of the results from this work, the initial perturbation would have occurred first in the central part of the plasma column, within the q = 1 magnetic surface, and then the m = 2 MHD mode would be destabilized afterwards

  12. Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

    Science.gov (United States)

    Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

    2017-09-01

    The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Using a two-layered sphere model to investigate the impact of gas vacuoles on the inherent optical properties of Microcystis aeruginosa

    CSIR Research Space (South Africa)

    Matthews, MW

    2013-01-01

    Full Text Available A two-layered sphere model is used to investigate the impact of gas vacuoles on the inherent optical properties (IOPs) of the cyanophyte Microcystis aeruginosa. Enclosing a vacuole-like particle within a chromatoplasm shell layer significantly...

  14. Modeling by artificial neural networks. Application to the management of fuel in a nuclear power plant; Modelisation par reseaux de neurones. Application a la gestion du combustible dans un reacteur

    Energy Technology Data Exchange (ETDEWEB)

    Gaudier, F

    1999-07-01

    The determination of the family of optimum core loading patterns for Pressurized Water Reactors (PWRs) involves the assessment of the core attributes, such as the power peaking factor for thousands of candidate loading patterns. Despite the rapid advances in computer architecture, the direct calculation of these attributes by a neutronic code needs a lot of of time and memory. With the goal of reducing the calculation time and optimizing the loading pattern, we propose in this thesis a method based on ideas of neural and statistical learning to provide a feed forward neural network capable of calculating the power peaking corresponding to an eighth core PWR. We use statistical methods to deduct judicious inputs (reduction of the input space dimension) and neural methods to train the model (learning capabilities). Indeed, on one hand, a principal component analysis allows us to characterize more efficiently the fuel assemblies (neural model inputs) and the other hand, the introduction of the a priori knowledge allows us to reducing the number of freedom parameters in the neural network. The model was built using a multi layered perceptron trained with the standard back propagation algorithm. We introduced our neural network in the automatic optimization code FORMOSA, and on EDF real problems we showed an important saving in time. Finally, we propose an hybrid method which combining the best characteristics of the linear local approximator GPT (Generalized Perturbation Theory) and the artificial neural network. (author)

  15. Separation prediction in two dimensional boundary layer flows using artificial neural networks

    International Nuclear Information System (INIS)

    Sabetghadam, F.; Ghomi, H.A.

    2003-01-01

    In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

  16. Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

    Science.gov (United States)

    Güntürkün, Rüştü

    2010-08-01

    In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

  17. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

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

  18. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  19. Predicting the Deflections of Micromachined Electrostatic Actuators Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hing Wah LEE

    2009-03-01

    Full Text Available In this study, a general purpose Artificial Neural Network (ANN model based on the feed-forward back-propagation (FFBP algorithm has been used to predict the deflections of a micromachined structures actuated electrostatically under different loadings and geometrical parameters. A limited range of simulation results obtained via CoventorWare™ numerical software will be used initially to train the neural network via back-propagation algorithm. The micromachined structures considered in the analyses are diaphragm, fixed-fixed beams and cantilevers. ANN simulation results are compared with results obtained via CoventorWare™ simulations and existing analytical work for validation purpose. The proposed ANN model accurately predicts the deflections of the micromachined structures with great reduction of simulation efforts, establishing the method superiority. This method can be extended for applications in other sensors particularly for modeling sensors applying electrostatic actuation which are difficult in nature due to the inherent non-linearity of the electro-mechanical coupling response.

  20. An Artificial Neural Networks Approach to Estimate Occupational Accident: A National Perspective for Turkey

    Directory of Open Access Journals (Sweden)

    Hüseyin Ceylan

    2014-01-01

    Full Text Available Occupational accident estimation models were developed by using artificial neural networks (ANNs for Turkey. Using these models the number of occupational accidents and death and permanent incapacity numbers resulting from occupational accidents were estimated for Turkey until the year of 2025 by the three different scenarios. In the development of the models, insured workers, workplace, occupational accident, death, and permanent incapacity values were used as model parameters with data between 1970 and 2012. 2-5-1 neural network architecture was selected as the best network architecture. Sigmoid was used in hidden layers and linear function was used at output layer. The feed forward back propagation algorithm was used to train the network. In order to obtain a useful model, the network was trained between 1970 and 1999 to estimate the values of 2000 to 2012. The result was compared with the real values and it was seen that it is applicable for this aim. The performances of all developed models were evaluated using mean absolute percent errors (MAPE, mean absolute errors (MAE, and root mean square errors (RMSE.

  1. Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

    Directory of Open Access Journals (Sweden)

    F. I. Younis

    2001-01-01

    Full Text Available An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required.

  2. Detection of Pistachio Aflatoxin Using Raman Spectroscopy and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R Mohammadigol

    2015-03-01

    Full Text Available Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive and time-consuming, the main objective of this study was to detect aflatoxin contamination in pistachio by using Raman spectroscopy technique and artificial neural networks. Three sets of samples were prepared: non-contaminated (healthy and contaminated samples with 20 and 100 ppb of the total aflatoxins (B1+B2+G1+G2. After spectral acquisition, considering to the results, spectral data were normalized and then principal components (PCs were extracted to reduce the data dimensions. For classification of the samples spectra, an artificial neural network was used with a feed forward back propagation algorithm for 4 inputs and 3 neurons in hidden layer. Mean overall accuracy was achieved to be 98 percent; therefore, non-liner Raman spectra data modeling by ANN for samples classification was successful.

  3. Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

    International Nuclear Information System (INIS)

    Wang Jiangfeng; Sun Zhixin; Dai Yiping; Ma Shaolin

    2010-01-01

    Supercritical CO 2 power cycle shows a high potential to recover low-grade waste heat due to its better temperature glide matching between heat source and working fluid in the heat recovery vapor generator (HRVG). Parametric analysis and exergy analysis are conducted to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component. The thermodynamic parameters of the supercritical CO 2 power cycle is optimized with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition. An artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training is used to achieve parametric optimization design rapidly. It is shown that the key thermodynamic parameters, such as turbine inlet pressure, turbine inlet temperature and environment temperature have significant effects on the performance of the supercritical CO 2 power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO 2 power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions.

  4. Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Kurt, Hueseyin; Atik, Kemal; Oezkaymak, Mehmet; Recebli, Ziyaddin [Zonguldak Karaelmas University, Karabuk Technical Education Faculty, 78200 Karabuk (Turkey)

    2008-02-15

    Work to date has shown that Artificial Neural Network (ANN) has not been used for predicting thermal performance parameters of a solar cooker. The objective of this study is to predict thermal performance parameters such as absorber plate, enclosure air and pot water temperatures of the experimentally investigated box type solar cooker by using the ANN. Data set is obtained from the box type solar cooker which was tested under various experimental conditions. A feed-forward neural network based on back propagation algorithm was developed to predict the thermal performance of solar cooker with and without reflector. Mathematical formulations derived from the ANN model are presented for each predicting temperatures. The experimental data set consists of 126 values. These were divided into two groups, of which the 96 values were used for training/learning of the network and the rest of the data (30 values) for testing/validation of the network performance. The performance of the ANN predictions was evaluated by comparing the prediction results with the experimental results. The results showed a good regression analysis with the correlation coefficients in the range of 0.9950-0.9987 and mean relative errors (MREs) in the range of 3.925-7.040% for the test data set. The regression coefficients indicated that the ANN model can successfully be used for the prediction of the thermal performance parameters of a box type solar cooker with a high degree of accuracy. (author)

  5. A neural network based methodology to predict site-specific spectral acceleration values

    Science.gov (United States)

    Kamatchi, P.; Rajasankar, J.; Ramana, G. V.; Nagpal, A. K.

    2010-12-01

    A general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject.

  6. Emerging phenomena in neural networks with dynamic synapses and their computational implications

    Directory of Open Access Journals (Sweden)

    Joaquin J. eTorres

    2013-04-01

    Full Text Available In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilisation of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behaviour can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

  7. Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network.

    Science.gov (United States)

    Alves da Rocha, Roney; Paiva, Igor Moura; Anjos, Virgílio; Furtado, Marco Antônio Moreira; Bell, Maria José Valenzuela

    2015-06-01

    In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  8. Estimation of effective connectivity using multi-layer perceptron artificial neural network.

    Science.gov (United States)

    Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

    2018-02-01

    Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

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

    Directory of Open Access Journals (Sweden)

    Hassen T. Dorrah

    2012-06-01

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

  10. An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

    Directory of Open Access Journals (Sweden)

    Antonio del Corte-Valiente

    2017-02-01

    Full Text Available Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

  11. Synthesis, crystal structure, and photocatalytic activity of a new two-layer Ruddlesden-Popper phase, Li2CaTa2O7

    International Nuclear Information System (INIS)

    Liang Zhenhua; Tang Kaibin; Shao Qian; Li Guocan; Zeng Suyuan; Zheng Huagui

    2008-01-01

    A new two-layer Ruddlesden-Popper phase Li 2 CaTa 2 O 7 has been synthesized for the first time. The detailed structure determination of Li 2 CaTa 2 O 7 performed by powder X-ray diffraction (XRD) and electron microscopy (ED) shows that it crystallizes in the space group Fmmm [a∼5.5153(1), b∼5.4646(1), c∼18.2375(3)A]. UV-visible diffuse reflection spectrum of the prepared Li 2 CaTa 2 O 7 indicates that it had absorption in the UV region. The photocatalytic activity of the Li 2 CaTa 2 O 7 powders was evaluated by degradation of RhB molecules in water under ultra visible light irradiation. The results showed that Li 2 CaTa 2 O 7 has high photocatalytic activity at room temperature. Therefore, the preparation and properties studies of Li 2 CaTa 2 O 7 with a two-layer Ruddlesden-Popper structure suggest potential future applications in photocatalysis. - Graphical abstract: Crystal structure of a two-layer Ruddlesden-Popper phase Li 2 CaTa 2 O 7 A new two-layer Ruddlesden-Popper phase Li 2 CaTa 2 O 7 has been synthesized for the first time. Li 2 CaTa 2 O 7 crystallizes in the space group Fmmm determined by powder X-ray and electron diffraction. UV-visible diffuse reflection spectra and the photocatalytic degradation of RhB molecules in water under ultra visible light irradiation show that Li 2 CaTa 2 O 7 is a potential material in photocatalysis

  12. Discrete ordinates solution of coupled conductive radiative heat transfer in a two-layer slab with Fresnel interfaces subject to diffuse and obliquely collimated irradiation

    International Nuclear Information System (INIS)

    Muresan, Cristian; Vaillon, Rodolphe; Menezo, Christophe; Morlot, Rodolphe

    2004-01-01

    The coupled conductive radiative heat transfer in a two-layer slab with Fresnel interfaces subject to diffuse and obliquely collimated irradiation is solved. The collimated and diffuse components problems are treated separately. The solution for diffuse radiation is obtained by using a composite discrete ordinates method and includes the development of adaptive directional quadratures to overcome the difficulties usually encountered at the interfaces. The complete radiation numerical model is validated against the predictions obtained by using the Monte Carlo method

  13. High-Sensitive Two-Layer Photoresistors Based on p-Cd x Hg1- x Te with a Converted Near-Surface Layer

    Science.gov (United States)

    Ismailov, N. D.; Talipov, N. Kh.; Voitsekhovskii, A. V.

    2018-04-01

    The results of an experimental study of photoelectric characteristics of two-layer photoresistors based on p-Cd x Hg1- x Te (x = 0.24-0.28) with a thin near-surface layer of n-type obtained by treatment in atmospheric gas plasma are presented. It is shown that the presence of a potential barrier between the p- and n-regions causes high photosensitivity and speed of operation of such photoresistors at T = 77 K

  14. A two-layer application of the MAGIC model to predict the effects of land use scenarios and reductions in deposition on acid sensitive soils in the UK

    Directory of Open Access Journals (Sweden)

    R. C. Helliwell

    1998-01-01

    Full Text Available A two-layer application of the catchment-based soil and surface water acidification model, MAGIC, was applied to 21 sites in the UK Acid Waters Monitoring Network (AWAMN, and the results were compared with those from a one-layer application of the model. The two-layer model represented typical soil properties more accurately by segregating the organic and mineral horizons into two separate soil compartments. Reductions in sulphur (S emissions associated with the Second S Protocol and different forestry (land use scenarios were modelled, and their effects on soil acidification evaluated. Soil acidification was assessed in terms of base saturation and critical loads for the molar ratio of base cations (CA2+ + MG 2+ + K+ to aluminium (Al in soil solution. The results of the two-layer application indicate that base saturation of the organic compartment was very responsive to changes in land use and deposition compared with the mineral soil. With the two- layer model, the organic soil compartment was particularly sensitive to acid deposition, which resulted in the critical load being predicted to be exceeded at eight sites in 1997 and two sites in 2010. These results indicate that further reductions in S deposition are necessary to raise the base cation (BC:Al ratio above the threshold which is harmful to tree roots. At forested sites BC:Al ratios were generally well below the threshold designated for soil critical loads in Europe and forecasts indicate that forest replanting can adversely affect the acid status of sensitive term objectives of protecting and sustaining soil and water quality. Policy formulation must seek to protect the most sensitive environmental receptor, in this case organic soils. It is clear, therefore, that simply securing protection of surface waters, via the critical loads approach, may not ensure adequate protection of low base status organic soils from the effects of acidification.

  15. High-Sensitive Two-Layer Photoresistors Based on p-Cd x Hg1-x Te with a Converted Near-Surface Layer

    Science.gov (United States)

    Ismailov, N. D.; Talipov, N. Kh.; Voitsekhovskii, A. V.

    2018-04-01

    The results of an experimental study of photoelectric characteristics of two-layer photoresistors based on p-Cd x Hg1-x Te (x = 0.24-0.28) with a thin near-surface layer of n-type obtained by treatment in atmospheric gas plasma are presented. It is shown that the presence of a potential barrier between the p- and n-regions causes high photosensitivity and speed of operation of such photoresistors at T = 77 K

  16. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

    2009-10-15

    In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

  17. Development of an artificial neural network to predict critical heat flux based on the look up tables

    Energy Technology Data Exchange (ETDEWEB)

    Terng, Nilton; Carajilescov, Pedro, E-mail: Nil.terng@gmail.com, E-mail: pedro.carajilescov@ufabc.edu.br [Universidade Federal do ABC (UFABC), Santo Andre, SP (Brazil). Centro de Engenharia, Modelagem e Ciencias Sociais

    2015-07-01

    The critical heat flux (CHF) is one of the principal thermal hydraulic limits of PWR type nuclear reactors. The present work consists in the development of an artificial neural network (ANN) to estimate the CHF, based on Look Up Table CHF data, published by Groeneveld (2006). Three parameters were considered in the development of the ANN: the pressure in the range of 1 to 21 MPa, the mass flux in the range of 50 to 8000 kg m{sup -2} s{sup -1} and the thermodynamic quality in the range of - 0.5 to 0.9. The ANN model considered was a multi feed forward net, which have two feedforward ANN. The first one, called main neural network, is used to calculate the result of CHF, and the second, denominated spacenet, is responsible to modify the main neural network according to the input. Comparing the ANN predictions with the data of the Look Up Table, it was observed an average of the ratio of 0.993 and a root mean square error of 13.3%. With the developed ANN, a parametric study of CHF was performed to observe the influence of each parameter in the CHF. It was possible to note that the CHF decreases with the increase of pressure and thermodynamic quality, while CHF increases with the mass flow rate, as expected. However, some erratic trends were also observed which can be attributed to either unknown aspect of the CHF phenomenon or uncertainties in the data. (author)

  18. A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

    Science.gov (United States)

    Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

    2014-03-01

    The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

  19. The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.

    Science.gov (United States)

    Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

    2016-10-01

    Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

  20. Forecast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

    International Nuclear Information System (INIS)

    Vannucci, A.; Oliveira, K.A.; Tajima, T.; Tajima, Y.J.

    2001-01-01

    A feed-forward neural network is used to forecast major and minor disruptions in TEXT tokamak discharges. Using the experimental data of soft X-ray signals as input data, the neural net is trained with one disruptive plasma discharge, while a different disruptive discharge is used for validation. After proper training, the net works with the same set of weights, it is then used to forecast disruptions in two other different plasma discharges. It is observed that the neural net is capable of predicting the onset of a disruption up to 3.12 ms in advance. From what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism is associated with an increase in the m=2 magnetic island, that disturbs the central part of the plasma column afterwards, or the initial perturbation has first occurred in the central part of the plasma column and then the m=2 MHD mode is destabilized. (author)

  1. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

    International Nuclear Information System (INIS)

    Tahat Amani; Marti Jordi; Khwaldeh Ali; Tahat Kaher

    2014-01-01

    In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)

  2. Electromagnetic field analyses of two-layer power transmission cables consisting of coated conductors with magnetic and non-magnetic substrates and AC losses in their superconductor layers

    International Nuclear Information System (INIS)

    Nakahata, Masaaki; Amemiya, Naoyuki

    2008-01-01

    Two-dimensional electromagnetic field analyses were undertaken using two representative cross sections of two-layer cables consisting of coated conductors with magnetic and non-magnetic substrates. The following two arrangements were used for the coated conductors between the inner and outer layers: (1) tape-on-tape and (2) alternate. The calculated magnetic flux profile around each coated conductor was visualized. In the case of the non-magnetic substrate, the magnetic field to which coated conductors in the outer layer are exposed contains more perpendicular component to the conductor wide face (perpendicular field component) when compared to that in the inner layer. On the other hand, for the tape-on-tape arrangement of coated conductors with a magnetic substrate, the reverse is true. In the case of the alternate arrangement of the coated conductor with a magnetic substrate, the magnetic field to which the coated conductors in the inner and outer layers are exposed experiences a small perpendicular field component. When using a non-magnetic substrate, the AC loss in the superconductor layer of the coated conductors in the two-layer cables is dominated by that in the outer layer, whereas the reverse is true in the case of a magnetic substrate. When comparing the AC losses in superconductor layers of coated conductors with non-magnetic and magnetic substrates in two-layer cables, the latter is larger than the former, but the influence of the magnetism of substrates on AC losses in superconductor layers is not remarkable

  3. An artificial neural network to predict resting energy expenditure in obesity.

    Science.gov (United States)

    Disse, Emmanuel; Ledoux, Séverine; Bétry, Cécile; Caussy, Cyrielle; Maitrepierre, Christine; Coupaye, Muriel; Laville, Martine; Simon, Chantal

    2017-09-01

    The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m -2 ) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m -2 . External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive

  4. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

    Science.gov (United States)

    Lee, Christine K; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime

    2018-04-17

    The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

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

    Science.gov (United States)

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

    2002-01-01

    coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

  6. Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R.; Seyedkashi, S. M. H.

    2011-01-01

    Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

  7. Artificial neural network approach to modeling of alcoholic fermentation of thick juice from sugar beet processing

    Directory of Open Access Journals (Sweden)

    Jokić Aleksandar I.

    2012-01-01

    Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.

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

    Directory of Open Access Journals (Sweden)

    Suhartono Suhartono

    2009-07-01

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

  9. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

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

  10. Influence of the solid-gas interface on the effective thermal parameters of a two-layer structure in photoacoustic experiments

    International Nuclear Information System (INIS)

    Aguirre, N Munoz; Perez, L MartInez; Garibay-Febles, V; Lozada-Cassou, M

    2004-01-01

    From the theoretical point of view, the influence of the solid-gas interface on the effective thermal parameters in a two-layer structure of the photoacoustic technique is discussed. It is shown that the effective thermal parameters depend strongly upon the thermal resistance value associated with the solid-gas interface. New expressions for the effective thermal conductivity and thermal diffusivity in the low frequency limit are obtained. In the high frequency limit, the 'resonant' behaviour of the effective thermal diffusivity is maintained and a new complex dependence on frequency of the effective thermal conductivity is shown

  11. Determination of the Mass Absorption Coefficient in Two-Layer Ti/V and V/Ti Thin Film Systems by the X-Ray Fluorescence Method

    Science.gov (United States)

    Mashin, N. I.; Chernyaeva, E. A.; Tumanova, A. N.; Gafarova, L. M.

    2016-03-01

    A new XRF procedure for the determination of the mass absorption coefficient in thin film Ti/V and V/Ti two-layer systems has been proposed. The procedure uses easy-to-make thin-film layers of sputtered titanium and vanadium on a polymer film substrate. Correction coefficients have been calculated that take into account attenuation of primary radiation of the X-ray tube, as well as attenuation of the spectral line of the bottom layer element in the top layer.

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

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

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

  13. Automatic target recognition using a feature-based optical neural network

    Science.gov (United States)

    Chao, Tien-Hsin

    1992-01-01

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

  14. Magnetooptic effects and Auger electron spectroscopy of two-layer NiFe-Dy and Fe-Dy films with nonuniform layers

    International Nuclear Information System (INIS)

    Ehdel'man, I.S.; Markov, V.V.; Khudyakov, A.E.; Ivantsov, R.D.; Bondarenko, G.V.; Ovchinnikov, S.G.; Kesler, V.G.; Parshin, A.S.; Ronzhin, I.P.

    2001-01-01

    Magneto-optical effects (magnetic circular dichroism and meridional Kerr effect) and element distribution with layer thickness in two-layer NiFe-Dy and Fe-Dy films, prepared by thermal sputtering of component in ultrahigh vacuum, are investigated. It is shown, that Dy in a two-layer film in the temperature range of 80-300 K makes constant contributions to both effects investigated which are approximately equal to the values of the effects observed in an isolated Dy film only at temperatures below the temperature T c of Dy transition into a ferromagnetic state (T c ∼ 100 K for the films under study). This behaviour of magneto-optical effects is assumed to be due to the influence of a NiFe layer spin system on magnetic state of a Dy layer, this influence is enhanced by the deep penetration of Ni and Fe ions into Dy layer as it follows from the data obtained using Auger electron spectroscopy [ru

  15. Efficient calculation of potential distribution in two-layer earth; Niso kozo daichikei ni okeru denki tansa no tame no koritsuteki den`i keisan shuho

    Energy Technology Data Exchange (ETDEWEB)

    Kataoka, M; Okamoto, Y [Chiba Institute of Technology, Chiba (Japan); Endo, M; Noguchi, K [Waseda University, Tokyo (Japan); Teramachi, Y; Akabane, H [University of Industrial Technology, Kanagawa (Japan); Agu, M [Ibaraki University, Ibaraki (Japan)

    1997-05-27

    An efficient calculation method of potential distribution in the presence of an embedded body in multi-layer earth has been proposed by expanding the method of image with a consideration of multiple reflection between the ground surface and each underground boundary. For this method, when solving boundary integral equation with the potential of embedded body surface as only one unknown, i.e., when obtaining discretization equation, ordinary boundary element program developed for analyzing the finite closed region can be used. As an example, numerical calculation was conducted for the two-layer earth. The analysis expression of potential distribution in the case of the certain embedded body in two-layer earth has never published. Accordingly, the calculated results were compared with those by the integral equation method. As a result, it was concluded that the primary potential obtained from the present method agreed well with that obtained from the integral equation method. However, there was a disregarded difference in the secondary potential. For confirming the effectiveness, it was necessary to compare with another numerical calculation method, such as finite element method. 5 refs., 5 figs.

  16. A two-layered diffusion model traces the dynamics of information processing in the valuation-and-choice circuit of decision making.

    Science.gov (United States)

    Piu, Pietro; Fargnoli, Francesco; Innocenti, Alessandro; Rufa, Alessandra

    2014-01-01

    A circuit of evaluation and selection of the alternatives is considered a reliable model in neurobiology. The prominent contributions of the literature to this topic are reported. In this study, valuation and choice of a decisional process during Two-Alternative Forced-Choice (TAFC) task are represented as a two-layered network of computational cells, where information accrual and processing progress in nonlinear diffusion dynamics. The evolution of the response-to-stimulus map is thus modeled by two linked diffusive modules (2LDM) representing the neuronal populations involved in the valuation-and-decision circuit of decision making. Diffusion models are naturally appropriate for describing accumulation of evidence over the time. This allows the computation of the response times (RTs) in valuation and choice, under the hypothesis of ex-Wald distribution. A nonlinear transfer function integrates the activities of the two layers. The input-output map based on the infomax principle makes the 2LDM consistent with the reinforcement learning approach. Results from simulated likelihood time series indicate that 2LDM may account for the activity-dependent modulatory component of effective connectivity between the neuronal populations. Rhythmic fluctuations of the estimate gain functions in the delta-beta bands also support the compatibility of 2LDM with the neurobiology of DM.

  17. The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

    Science.gov (United States)

    Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

    2017-01-01

    The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

  18. Quasi-static analysis of flexible pavements based on predicted frequencies using Fast Fourier Transform and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ali Reza Ghanizadeh

    2018-01-01

    Full Text Available New trend in design of flexible pavements is mechanistic-empirical approach. The first step for applying this method is analyzing the pavement structure for several times and computation of critical stresses and strains, which needs a fast analysis method with good accuracy. This paper aims to introduce a new rapid pavement analysis approach, which can consider the history of loading and rate effect. To this end, 1200 flexible pavement sections were analyzed, and equivalent frequencies (EF were calculated using Fast Fourier Transform (FFT method at various depths of asphalt layer. A nonlinear regression equation has been presented for determining EF at different depths of asphalt layer. For more accurate predicting of EF at low frequencies, a feed-forward Artificial Neural Network (ANN was employed, which allows accurate prediction of EF. The frequencies obtained by the proposed regression equation and ANN were compared with frequencies observed in Virginia Smart Road project, and it was found that there is a good agreement between observed and predicted frequencies. Comparison of quasi-static analysis of flexible pavements by frequencies obtained using FFT method and full dynamic analysis by 3D-Move program approves that the critical responses of pavement computed by proposed quasi-static analysis approach are comparable to critical responses computed using full dynamic analysis. Keywords: Equivalent frequency, Fast Fourier Transform (FFT, Pavement quasi-static analysis, Dynamic modulus, Artificial Neural Network (ANN

  19. Prediction Analysis of Weld-Bead and Heat Affected Zone in TIG welding using Artificial Neural Networks

    Science.gov (United States)

    Saldanha, Shamith L.; Kalaichelvi, V.; Karthikeyan, R.

    2018-04-01

    TIG Welding is a high quality form of welding which is very popular in industries. It is one of the few types of welding that can be used to join dissimilar metals. Here a weld joint is formed between stainless steel and monel alloy. It is desired to have control over the weld geometry of such a joint through the adjustment of experimental parameters which are welding current, wire feed speed, arc length and the shielding gas flow rate. To facilitate the automation of the same, a model of the welding system is needed. However the underlying welding process is complex and non-linear, and analytical methods are impractical for industrial use. Therefore artificial neural networks (ANN) are explored for developing the model, as they are well-suited for modelling non-linear multi-variate data. Feed-forward neural networks with backpropagation training algorithm are used, and the data for training the ANN taken from experimental work. There are four outputs corresponding to the weld geometry. Different training and testing phases were carried out using MATLAB software and ANN approximates the given data with minimum amount of error.

  20. Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nami, Faezeh [Department of Chemistry, Shahid Beheshti University, G.C., Evin-Tehran 1983963113 (Iran, Islamic Republic of); Deyhimi, Farzad, E-mail: f-deyhimi@sbu.ac.i [Department of Chemistry, Shahid Beheshti University, G.C., Evin-Tehran 1983963113 (Iran, Islamic Republic of)

    2011-01-15

    To our knowledge, this work illustrates for the first time the ability of artificial neural network (ANN) to predict activity coefficients at infinite dilution for organic solutes in ionic liquids (ILs). Activity coefficient at infinite dilution ({gamma}{sup {infinity}}) is a useful parameter which can be used for the selection of effective solvent in the separation processes. Using a multi-layer feed-forward network with Levenberg-Marquardt optimization algorithm, the resulting ANN model generated activity coefficient at infinite dilution data over a temperature range of 298 to 363 K. The unavailable input data concerning softness (S) of organic compounds (solutes) and dipole moment ({mu}) of ionic liquids were calculated using GAMESS suites of quantum chemistry programs. The resulting ANN model and its validation are based on the investigation of up to 24 structurally different organic compounds (alkanes, alkenes, alkynes, cycloalkanes, aromatics, and alcohols) in 16 common imidazolium-based ionic liquids, at different temperatures within the range of 298 to 363 K (i.e. a total number of 914 {gamma}{sub Solute}{sup {infinity}}for each IL data point). The results show a satisfactory agreement between the predicted ANN and experimental data, where, the root mean square error (RMSE) and the determination coefficient (R{sup 2}) of the designed neural network were found to be 0.103, 0.996 for training data and 0.128, 0.994 for testing data, respectively.

  1. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2014-10-01

    Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

  2. Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

    Science.gov (United States)

    Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

    2016-11-01

    This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. An approach to unfold the response of a multi-element system using an artificial neural network

    International Nuclear Information System (INIS)

    Cordes, E.; Fehrenbacher, G.; Schuetz, R.; Sprunck, M.; Hahn, K.; Hofmann, R.; Wahl, W.

    1998-01-01

    An unfolding procedure is proposed which aims at obtaining spectral information of a neutron radiation field by the analysis of the response of a multi-element system consisting of converter type semiconductors. For the unfolding procedure an artificial neural network (feed forward network), trained by the back-propagation method, was used. The response functions of the single elements to neutron radiation were calculated by application of a computational model for an energy range from 10 -2 eV to 10 MeV. The training of the artificial neural network was based on the computation of responses of a six-element system for a set of 300 neutron spectra and the application of the back-propagation method. The validation was performed by the unfolding of 100 computed responses. Two unfolding examples were pointed out for the determination of the neutron spectra. The spectra resulting from the unfolding procedure agree well with the original spectra used for the response computation

  4. pH prediction by artificial neural networks for the drinking water of the distribution system of Hyderabad city

    International Nuclear Information System (INIS)

    Memon, N.A.; Unar, M.A.; Ansari, A.K.

    2012-01-01

    In this research, feed forward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm. The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%. Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results. (author)

  5. Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by artificial neural network

    International Nuclear Information System (INIS)

    Nami, Faezeh; Deyhimi, Farzad

    2011-01-01

    To our knowledge, this work illustrates for the first time the ability of artificial neural network (ANN) to predict activity coefficients at infinite dilution for organic solutes in ionic liquids (ILs). Activity coefficient at infinite dilution (γ ∞ ) is a useful parameter which can be used for the selection of effective solvent in the separation processes. Using a multi-layer feed-forward network with Levenberg-Marquardt optimization algorithm, the resulting ANN model generated activity coefficient at infinite dilution data over a temperature range of 298 to 363 K. The unavailable input data concerning softness (S) of organic compounds (solutes) and dipole moment (μ) of ionic liquids were calculated using GAMESS suites of quantum chemistry programs. The resulting ANN model and its validation are based on the investigation of up to 24 structurally different organic compounds (alkanes, alkenes, alkynes, cycloalkanes, aromatics, and alcohols) in 16 common imidazolium-based ionic liquids, at different temperatures within the range of 298 to 363 K (i.e. a total number of 914 γ Solute ∞ for each IL data point). The results show a satisfactory agreement between the predicted ANN and experimental data, where, the root mean square error (RMSE) and the determination coefficient (R 2 ) of the designed neural network were found to be 0.103, 0.996 for training data and 0.128, 0.994 for testing data, respectively.

  6. Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water.

    Science.gov (United States)

    Salari, Marjan; Salami Shahid, Esmaeel; Afzali, Seied Hosein; Ehteshami, Majid; Conti, Gea Oliveri; Derakhshan, Zahra; Sheibani, Solmaz Nikbakht

    2018-04-22

    Today, due to the increase in the population, the growth of industry and the variety of chemical compounds, the quality of drinking water has decreased. Five important river water quality properties such as: dissolved oxygen (DO), total dissolved solids (TDS), total hardness (TH), alkalinity (ALK) and turbidity (TU) were estimated by parameters such as: electric conductivity (EC), temperature (T), and pH that could be measured easily with almost no costs. Simulate water quality parameters were examined with two methods of modeling include mathematical and Artificial Neural Networks (ANN). Mathematical methods are based on polynomial fitting with least square method and ANN modeling algorithms are feed-forward networks. All conditions/circumstances covered by neural network modeling were tested for all parameters in this study, except for Alkalinity. All optimum ANN models developed to simulate water quality parameters had precision value as R-value close to 0.99. The ANN model extended to simulate alkalinity with R-value equals to 0.82. Moreover, Surface fitting techniques were used to refine data sets. Presented models and equations are reliable/useable tools for studying water quality parameters at similar rivers, as a proper replacement for traditional water quality measuring equipment's. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. 3-D inversion of borehole-to-surface electrical data using a back-propagation neural network

    Science.gov (United States)

    Ho, Trong Long

    2009-08-01

    The "fluid-flow tomography", an advanced technique for geoelectrical survey based on the conventional mise-à-la-masse measurement, has been developed by Exploration Geophysics Laboratory at the Kyushu University. This technique is proposed to monitor fluid-flow behavior during water injection and production in a geothermal field. However data processing of this technique is very costly. In this light, this paper will discuss the solution to cost reduction by applying a neural network in the data processing. A case study in the Takigami geothermal field in Japan will be used to illustrate this. The achieved neural network in this case study is three-layered and feed-forward. The most successful learning algorithm in this network is the Resilient Propagation (RPROP). Consequently, the study advances the pragmatism of the "fluid-flow tomography" technique which can be widely used for geothermal fields. Accuracy of the solution is then verified by using root mean square (RMS) misfit error as an indicator.

  8. Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor

    International Nuclear Information System (INIS)

    Martinez-Martinez, Sinuhe; Messai, Nadhir; Jeannot, Jean-Philippe; Nuzillard, Danielle

    2015-01-01

    The total instantaneous blockage (TIB) of an assembly in the core of a sodium-cooled fast reactor (SFR) is investigated. Such incident could appear as an abnormal rise in temperature on the assemblies neighbouring the blockage. Its detection relies on a dataset of temperature measurements of the assemblies making up the core of the French Phenix Nuclear Reactor. The data are provided by the French Commission of Atomic and Alternatives Energies (CEA). Here, two strategies are proposed depending on whether the sensor measurement of the suspected assembly is reliable or not. The proposed methodology implements a time-lagged feed-forward neural (TLFFN) Network in order to predict the one-step-ahead temperature of a given assembly. The incident is declared if the difference between the predicted process and the actual one exceeds a threshold. In these simulated conditions, the method is efficient to detect small gradients as expected in reality. - Highlights: • We study the total instantaneous blockage (TIB) of a sodium-cooled fast reactor. • The TIB symptom is simulated as an abrupt rise on temperature (0.1–1 °C/s). • The goal is to improve the early detection of the incident. • Two strategies laying on neural networks are proposed. • TIB is detected in 3 s for 1 °C/s and 18–21 s for 0.1 °C/s

  9. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    Science.gov (United States)

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  10. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    Science.gov (United States)

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.

  11. Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

    Science.gov (United States)

    Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

    2018-04-01

    In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

  12. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    Science.gov (United States)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  13. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    International Nuclear Information System (INIS)

    Pendashteh, Ali Reza; Fakhru'l-Razi, A.; Chaibakhsh, Naz; Abdullah, Luqman Chuah; Madaeni, Sayed Siavash; Abidin, Zurina Zainal

    2011-01-01

    Highlights: → Hypersaline oily wastewater was treated in a membrane bioreactor. → The effects of salinity and organic loading rate were evaluated. → The system was modeled by neural network and optimized by genetic algorithm. → The model prediction agrees well with experimental values. → The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m 3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m 3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  14. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Pendashteh, Ali Reza [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Environmental Research Institute, Iranian Academic Center for Education, Culture and Research (ACECR), Rasht (Iran, Islamic Republic of); Fakhru' l-Razi, A., E-mail: fakhrul@eng.upm.edu.my [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Chaibakhsh, Naz [Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Abdullah, Luqman Chuah [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Madaeni, Sayed Siavash [Chemical Engineering Department, Razi University, Kermanshah (Iran, Islamic Republic of); Abidin, Zurina Zainal [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia)

    2011-08-30

    Highlights: {yields} Hypersaline oily wastewater was treated in a membrane bioreactor. {yields} The effects of salinity and organic loading rate were evaluated. {yields} The system was modeled by neural network and optimized by genetic algorithm. {yields} The model prediction agrees well with experimental values. {yields} The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m{sup 3} day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m{sup 3} day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  15. An artificial neural network estimation of gait balance control in the elderly using clinical evaluations.

    Directory of Open Access Journals (Sweden)

    Vipul Lugade

    Full Text Available The use of motion analysis to assess balance is essential for determining the underlying mechanisms of falls during dynamic activities. Clinicians evaluate patients using clinical examinations of static balance control, gait performance, cognition, and neuromuscular ability. Mapping these data to measures of dynamic balance control, and the subsequent categorization and identification of community dwelling elderly fallers at risk of falls in a quick and inexpensive manner is needed. The purpose of this study was to demonstrate that given clinical measures, an artificial neural network (ANN could determine dynamic balance control, as defined by the interaction of the center of mass (CoM with the base of support (BoS, during gait. Fifty-six elderly adults were included in this study. Using a feed-forward neural network with back propagation, combinations of five functional domains, the number of hidden layers and error goals were evaluated to determine the best parameters to assess dynamic balance control. Functional domain input parameters included subject characteristics, clinical examinations, cognitive performance, muscle strength, and clinical balance performance. The use of these functional domains demonstrated the ability to quickly converge to a solution, with the network learning the mapping within 5 epochs, when using up to 30 hidden nodes and an error goal of 0.001. The ability to correctly identify the interaction of the CoM with BoS demonstrated correlation values up to 0.89 (P<.001. On average, using all clinical measures, the ANN was able to estimate the dynamic CoM to BoS distance to within 1 cm and BoS area to within 75 cm2. Our results demonstrated that an ANN could be trained to map clinical variables to biomechanical measures of gait balance control. A neural network could provide physicians and patients with a cost effective means to identify dynamic balance issues and possible risk of falls from routinely collected clinical

  16. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  17. Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@lmp.ufrj.b [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Gomes Pinheiro, Christiano Jorge [State University of Rio de Janeiro, RJ (Brazil); Rancoita, Paola [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Mathematics Department, Universita degli Studi di Milano (Italy); Schaul, Tom; Gambardella, Luca Maria [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Schirru, Roberto [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Barroso, Regina Cely; Oliveira, Luis Fernando de [State University of Rio de Janeiro, RJ (Brazil)

    2010-09-21

    Micro-computed tomography ({mu}CT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on {mu}CT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-{mu}CT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-{mu}CT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-{mu}CT medical images.

  18. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Directory of Open Access Journals (Sweden)

    Mohd Taufiq Muslim

    Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  19. Application of artificial neural networks for the prediction of traction performance parameters

    Directory of Open Access Journals (Sweden)

    Hamid Taghavifar

    2014-01-01

    Full Text Available This study handles artificial neural networks (ANN modeling to predict tire contact area and rolling resistance due to the complex and nonlinear interactions between soil and wheel that mathematical, numerical and conventional models fail to investigate multivariate input and output relationships with nonlinear and complex characteristics. Experimental data acquisitioning was carried out using a soil bin facility with single-wheel tester at seven inflation pressures of tire (i.e. 100–700 kPa and seven different wheel loads (1–7 KN with two soil textures and two tire types. The experimental datasets were used to develop a feed-forward with back propagation ANN model. Four criteria (i.e. R-value, T value, mean squared error, and model simplicity were used to evaluate model’s performance. A well-trained optimum 4-6-2 ANN provided the best accuracy in modeling contact area and rolling resistance with regression coefficients of 0.998 and 0.999 and T value and MSE of 0.996 and 2.55 × 10−12, respectively. It was found that ANNs due to faster, more precise, and considerably reliable computation of multivariable, nonlinear, and complex computations are highly appropriate for soil–wheel interaction modeling.

  20. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

    Science.gov (United States)

    Jose, K V Jovan; Artrith, Nongnuch; Behler, Jörg

    2012-05-21

    An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.