Minimizing Hexapod Robot Foot Deviations Using Multilayer Perceptron

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Vytautas Valaitis

2015-12-01

Full Text Available Rough-terrain traversability is one of the most valuable characteristics of walking robots. Even despite their slower speeds and more complex control algorithms, walking robots have far wider usability than wheeled or tracked robots. However, efficient movement over irregular surfaces can only be achieved by eliminating all possible difficulties, which in many cases are caused by a high number of degrees of freedom, feet slippage, frictions and inertias between different robot parts or even badly developed inverse kinematics (IK. In this paper we address the hexapod robot-foot deviation problem. We compare the foot-positioning accuracy of unconfigured inverse kinematics and Multilayer Perceptron-based (MLP methods via theory, computer modelling and experiments on a physical robot. Using MLP-based methods, we were able to significantly decrease deviations while reaching desired positions with the hexapod's foot. Furthermore, this method is able to compensate for deviations of the robot arising from any possible reason.

Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

International Nuclear Information System (INIS)

Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

2008-01-01

This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

Generation of hourly irradiation synthetic series using the neural network multilayer perceptron

Energy Technology Data Exchange (ETDEWEB)

Hontoria, L.; Aguilera, J. [Universidad de Jaen, Linares-Jaen (Spain). Dpto. de Electronica; Zufiria, P. [Ciudad Universitaria, Madrid (Spain). Grupo de Redes Neuronales

2002-05-01

In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed. (author)

Online learning dynamics of multilayer perceptrons with unidentifiable parameters

Energy Technology Data Exchange (ETDEWEB)

Park, Hyeyoung [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Inoue, Masato [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507 (Japan); ' Intelligent Cooperation and Control' , PRESTO, JST, c/o RIKEN BSI, Saitama 351-0198 (Japan); Okada, Masato [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan)

2003-11-28

In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures.

Online learning dynamics of multilayer perceptrons with unidentifiable parameters

International Nuclear Information System (INIS)

Park, Hyeyoung; Inoue, Masato; Okada, Masato

2003-01-01

In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

DEFF Research Database (Denmark)

Míguez González, M; López Peña, F.; Díaz Casás, V.

2011-01-01

Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowle......Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been...... acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network...

Wind speed estimation using multilayer perceptron

International Nuclear Information System (INIS)

Velo, Ramón; López, Paz; Maseda, Francisco

2014-01-01

Highlights: • We present a method for determining the average wind speed using neural networks. • We use data from that site in the short term and data from other nearby stations. • The inputs used in the ANN were wind speed and direction data from a station. • The method allows knowing the wind speed without topographical data. - Abstract: Wind speed knowledge is prerequisite in the siting of wind turbines. In consequence the wind energy use requires meticulous and specified knowledge of the wind characteristics at a location. This paper presents a method for determining the annual average wind speed at a complex terrain site by using neural networks, when only short term data are available for that site. This information is useful for preliminary calculations of the wind resource at a remote area having only a short time period of wind measurements measurement in a site. Artificial neural networks are useful for implementing non-linear process variables over time, and therefore are a useful tool for estimating the wind speed. The neural network used is multilayer perceptron with three layers and the supervised learning algorithm used is backpropagation. The inputs used in the neural network were wind speed and direction data from a single station, and the training patterns used correspond to sixty days data. The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations with correlation coefficients above 0.5 were satisfactory, compared with actual values. Reliable estimations were obtained, with errors below 6%

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

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Kayri, Murat

2015-01-01

The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery.

Science.gov (United States)

Tamouridou, Afroditi A; Alexandridis, Thomas K; Pantazi, Xanthoula E; Lagopodi, Anastasia L; Kashefi, Javid; Kasampalis, Dimitris; Kontouris, Georgios; Moshou, Dimitrios

2017-10-11

Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

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Mohammad Fathian

2012-04-01

Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

Identification of determinants for globalization of SMEs using multi-layer perceptron neural networks

International Nuclear Information System (INIS)

Draz, U.; Jahanzaib, M.; Asghar, G.

2016-01-01

SMEs (Small and Medium Sized Enterprises) sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network) has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that firm registered with OEM (Original Equipment Manufacturer) and size of firm are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies. (author)

River flow simulation using a multilayer perceptron-firefly algorithm model

Science.gov (United States)

Darbandi, Sabereh; Pourhosseini, Fatemeh Akhoni

2018-06-01

River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose

Science.gov (United States)

Pan, Chih-Heng; Tang, Kea-Tiong

2011-09-01

This article presents an analog multilayer perceptron (MLP) neural network circuit with on-chip back propagation learning. This low power and small area analog MLP circuit is proposed to implement as a classifier in an electronic nose (E-nose). Comparing with the E-nose using microprocessor or FPGA as a classifier, the E-nose applying analog circuit as a classifier can be faster and much smaller, demonstrate greater power efficiency and be capable of developing a portable E-nose [1]. The system contains four inputs, four hidden neurons, and only one output neuron; this simple structure allows the circuit to have a smaller area and less power consumption. The circuit is fabricated using TSMC 0.18 μm 1P6M CMOS process with 1.8 V supply voltage. The area of this chip is 1.353×1.353 mm2 and the power consumption is 0.54 mW. Post-layout simulations show that the proposed analog MLP circuit can be successively trained to identify three kinds of fruit odors.

Estimates of Storage Capacity of Multilayer Perceptron with Threshold Logic Hidden Units.

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Kowalczyk, Adam

1997-11-01

We estimate the storage capacity of multilayer perceptron with n inputs, h(1) threshold logic units in the first hidden layer and 1 output. We show that if the network can memorize 50% of all dichotomies of a randomly selected N-tuple of points of R(n) with probability 1, then Nmemory capacity (in the sense of Cover) between nh(1)+1 and 2(nh(1)+1) input patterns and for the most efficient networks in this class between 1 and 2 input patterns per connection. Comparing these results with the recent estimates of VC-dimension we find that in contrast to a single neuron case, the VC-dimension exceeds the capacity for a sufficiently large n and h(1). The results are based on the derivation of an explicit expression for the number of dichotomies which can be implemented by such a network for a special class of N-tuples of input patterns which has a positive probability of being randomly chosen.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

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Shalin Savalia

2018-05-01

Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

Science.gov (United States)

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Simulation of a Multidimensional Input Quantum Perceptron

Science.gov (United States)

Yamamoto, Alexandre Y.; Sundqvist, Kyle M.; Li, Peng; Harris, H. Rusty

2018-06-01

In this work, we demonstrate the improved data separation capabilities of the Multidimensional Input Quantum Perceptron (MDIQP), a fundamental cell for the construction of more complex Quantum Artificial Neural Networks (QANNs). This is done by using input controlled alterations of ancillary qubits in combination with phase estimation and learning algorithms. The MDIQP is capable of processing quantum information and classifying multidimensional data that may not be linearly separable, extending the capabilities of the classical perceptron. With this powerful component, we get much closer to the achievement of a feedforward multilayer QANN, which would be able to represent and classify arbitrary sets of data (both quantum and classical).

Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Directory of Open Access Journals (Sweden)

Ali Mansourkhaki

2018-01-01

Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network

DEFF Research Database (Denmark)

Kucuk, Nil; Manohara, S.R.; Hanagodimath, S.M.

2013-01-01

In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15Me......V, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA...

On electron and pion identification using a multilayer perceptron in the transition radiation detector of the CBM experiment

International Nuclear Information System (INIS)

Akishina, T.P.; Denisova, O.Yu.; Ivanov, V.V.

2009-01-01

The problem of pion-electron identification based on their energy losses in the TRD is considered in the frame of the CBM experiment. For particles identification an artificial neural network (ANN) was used, a multilayer perceptron realized in JETNET and ROOT packages. It is demonstrated that, in order to get correct and comparable results, it is important to define the network structure correctly. The recommendations for such a selection are given. In order to achieve an acceptable level of pions suppression, the energy losses need to be transformed to more 'effective' variables. The dependency of ANN output threshold for a fixed portion of electron loss on the particle momentum is presented

Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

Directory of Open Access Journals (Sweden)

Mohammad Subhi Al-batah

2015-01-01

Full Text Available Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP and Cascade Forward Neural Network (CFNN, are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

An application of the multilayer perceptron: Solar radiation maps in Spain

Energy Technology Data Exchange (ETDEWEB)

Hontoria, L.; Aguilera, J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica, de Telecomunicaciones y Automatica, Escuela Politecnica Superior de Jaen, Campus de las Lagunillas, Universidad de Jaen, 23071 Jaen (Spain); Zufiria, P. [Grupo de Redes Neuronales, Dpto. de Matematica Aplicada a las Tecnologias de la Informacion, ETSI Telecomunicaciones, UPM Ciudad Universitaria s/n, 28040 Madrid (Spain)

2005-11-01

In this work an application of a methodology to obtain solar radiation maps is presented. This methodology is based on a neural network system [Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22] called Multi-Layer Perceptron (MLP) [Haykin, S., 1994. Neural Networks. A Comprehensive Foundation. Macmillan Publishing Company; Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366]. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the zone of the map where it is going to be drawn. For most of the locations all over the world the records of these data (solar radiation in whatever scale, daily or hourly values) are non-existent. Only very few locations have the privilege of having good meteorological stations where records of solar radiation have being registered. But even in those locations with historical records of solar data, the quality of these solar series is not as good as it should be for most purposes. In addition, to draw solar radiation maps the number of points on the maps (real sites) that it is necessary to work with makes this problem difficult to solve. Nevertheless, with the application of the methodology proposed in this paper, this problem has been solved and solar radiation maps have been obtained for a small region of Spain: Jaen province, a southern province of Spain between parallels 38{sup o}25' N and 37{sup o}25' N, and meridians 4{sup o}10' W and 2{sup o}10' W, and for a larger region: Andalucia, the most southern region of Spain situated between parallels 38{sup o}40' N and 36{sup o}00' N, and meridians 7{sup o}30' W and 1{sup o}40' W. (author)

Analysis of 7Be behaviour in the air by using a multilayer perceptron neural network

International Nuclear Information System (INIS)

Samolov, A.; Dragović, S.; Daković, M.; Bačić, G.

2014-01-01

A multilayer perceptron artificial neural network (ANN) model for the prediction of the 7 Be behaviour in the air as the function of meteorological parameters was developed. The model was optimized and tested using 7 Be activity concentrations obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009–2011 and meteorological data for the same period. Good correlation (r = 0.91) between experimental values of 7 Be activity concentrations and those predicted by ANN was obtained. The good performance of the model in prediction of 7 Be activity concentrations could provide basis for construction of models which would forecast behaviour of other airborne radionuclides. - Highlights: • Neural network analysis was used to predict airborne 7 Be activity using meteorological parameters as inputs. • Strong correlation between calculated and measured activities was found. • Obtained results can help in construction of a general model of 7 Be activity variation in air

A Multilayer Perceptron-Based Impulsive Noise Detector with Application to Power-Line-Based Sensor Networks

KAUST Repository

Chien, Ying-Ren

2018-04-10

For power-line-based sensor networks, impulsive noise (IN) will dramatically degrade the data transmission rate in the power line. In this paper, we present a multilayer perceptron (MLP)-based approach to detect IN in orthogonal frequency-division multiplexing (OFDM)-based baseband power line communications (PLCs). Combining the MLP-based IN detection method with the outlier detection theory allows more accurate identification of the harmful residual IN. For OFDM-based PLC systems, the high peak-to-average power ratio (PAPR) of the received signal makes detection of harmful residual IN more challenging. The detection mechanism works in an iterative receiver that contains a pre-IN mitigation and a post-IN mitigation. The pre-IN mitigation is meant to null the stronger portion of IN, while the post-IN mitigation suppresses the residual portion of IN using an iterative process. Compared with previously reported IN detectors, the simulation results show that our MLP-based IN detector improves the resulting bit error rate (BER) performance.

Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons.

Science.gov (United States)

Jäger, Jörg M; Schöllhorn, Wolfgang I

2012-04-01

Offensive and defensive systems of play represent important aspects of team sports. They include the players' positions at certain situations during a match, i.e., when players have to be on specific positions on the court. Patterns of play emerge based on the formations of the players on the court. Recognition of these patterns is important to react adequately and to adjust own strategies to the opponent. Furthermore, the ability to apply variable patterns of play seems to be promising since they make it harder for the opponent to adjust. The purpose of this study is to identify different team tactical patterns in volleyball and to analyze differences in variability. Overall 120 standard situations of six national teams in women's volleyball are analyzed during a world championship tournament. Twenty situations from each national team are chosen, including the base defence position (start configuration) and the two players block with middle back deep (end configuration). The shapes of the defence formations at the start and end configurations during the defence of each national team as well as the variability of these defence formations are statistically analyzed. Furthermore these shapes data are used to train multilayer perceptrons in order to test whether artificial neural networks can recognize the teams by their tactical patterns. Results show significant differences between the national teams in both the base defence position at the start and the two players block with middle back deep at the end of the standard defence situation. Furthermore, the national teams show significant differences in variability of the defence systems and start-positions are more variable than the end-positions. Multilayer perceptrons are able to recognize the teams at an average of 98.5%. It is concluded that defence systems in team sports are highly individual at a competitive level and variable even in standard situations. Artificial neural networks can be used to recognize

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.

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

Energy Technology Data Exchange (ETDEWEB)

Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

A learning rule for very simple universal approximators consisting of a single layer of perceptrons.

Science.gov (United States)

Auer, Peter; Burgsteiner, Harald; Maass, Wolfgang

2008-06-01

One may argue that the simplest type of neural networks beyond a single perceptron is an array of several perceptrons in parallel. In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal approximators for arbitrary continuous functions with values in [0,1] if one views the fraction of perceptrons that output 1 as the analog output of the parallel perceptron. Note that in contrast to the familiar model of a "multi-layer perceptron" the parallel perceptron that we consider here has just binary values as outputs of gates on the hidden layer. For a long time one has thought that there exists no competitive learning algorithm for these extremely simple neural networks, which also came to be known as committee machines. It is commonly assumed that one has to replace the hard threshold gates on the hidden layer by sigmoidal gates (or RBF-gates) and that one has to tune the weights on at least two successive layers in order to achieve satisfactory learning results for any class of neural networks that yield universal approximators. We show that this assumption is not true, by exhibiting a simple learning algorithm for parallel perceptrons - the parallel delta rule (p-delta rule). In contrast to backprop for multi-layer perceptrons, the p-delta rule only has to tune a single layer of weights, and it does not require the computation and communication of analog values with high precision. Reduced communication also distinguishes our new learning rule from other learning rules for parallel perceptrons such as MADALINE. Obviously these features make the p-delta rule attractive as a biologically more realistic alternative to backprop in biological neural circuits, but also for implementations in special purpose hardware. We show that the p-delta rule also implements gradient descent-with regard to a suitable error measure

Using multilayer perceptron and a satellite image for the estimation of soil salinity

International Nuclear Information System (INIS)

Lau, A.; Ruiz, M.E.; Garcia, E.

2008-01-01

Applying the model of the Perceptron multilayer with momentum of an artificial neural network particularly and a multispectral image of high resolution spatial and radiometric, for the first time estimated the salinity of the soil cultivated with sugar cane. The study area is the UBPC 'Lazaro Romero' of the sugar company 'Hector Molina' of the locality San Nicolas de Bari, Havana province, located at 22° 44' North latitude and 81 ° 56' longitude West. The experiments were made in the framework of the El-479 project funded by the Inter universities Council of Flanders, Belgium. 36 samples geo referenced of soils were taken at 3 depths in each of the 4 sugar cane selected blocks, which determined the electrical conductivity of the saturation extract; half of that amount of data was used for the training of the network and the other half for control in a computer program of the artificial neural network created to that effect, together with the reflectance of vegetation indexes for the image, which were maps of electrical conductivity of each block and bands. They were compared with those obtained by simple linear regression between the normalized difference vegetation index and electrical conductivity, Ndv with the approach of the neuronal network, the correlation coefficient was 0.78 to 0.83, while the linear regression was between 0.65 to 0.75

A novel single neuron perceptron with universal approximation and XOR computation properties.

Science.gov (United States)

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

Application of a multilayer perceptron neural network to phytoplankton concentration using marine reflectance measures

Science.gov (United States)

Su, Feng-Chun; Ho, Chung-Ru; Kuo, Nan-Jung

2005-01-01

The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.

Simulating the dynamics of the neutron flux in a nuclear reactor by locally recurrent neural networks

International Nuclear Information System (INIS)

Cadini, F.; Zio, E.; Pedroni, N.

2007-01-01

In this paper, a locally recurrent neural network (LRNN) is employed for approximating the temporal evolution of a nonlinear dynamic system model of a simplified nuclear reactor. To this aim, an infinite impulse response multi-layer perceptron (IIR-MLP) is trained according to a recursive back-propagation (RBP) algorithm. The network nodes contain internal feedback paths and their connections are realized by means of IIR synaptic filters, which provide the LRNN with the necessary system state memory

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

International Nuclear Information System (INIS)

Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam

2007-01-01

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported

Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

International Nuclear Information System (INIS)

Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

2017-01-01

As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

International Nuclear Information System (INIS)

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

2014-01-01

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its ''black box'' aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where ''all'' configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA

Evaluation of multilayer perceptron algorithms for an analysis of network flow data

Science.gov (United States)

Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz

2016-09-01

The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

Science.gov (United States)

Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2012-01-01

Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

Daily global solar radiation modelling using multi-layer perceptron neural networks in semi-arid region

Directory of Open Access Journals (Sweden)

Mawloud GUERMOUI

2016-07-01

Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

1995-01-01

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

First steps towards the realization of a double layer perceptron based on organic memristive devices

Science.gov (United States)

Emelyanov, A. V.; Lapkin, D. A.; Demin, V. A.; Erokhin, V. V.; Battistoni, S.; Baldi, G.; Dimonte, A.; Korovin, A. N.; Iannotta, S.; Kashkarov, P. K.; Kovalchuk, M. V.

2016-11-01

Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

First steps towards the realization of a double layer perceptron based on organic memristive devices

Directory of Open Access Journals (Sweden)

A. V. Emelyanov

2016-11-01

Full Text Available Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.

Perceptron Mistake Bounds

OpenAIRE

Mohri, Mehryar; Rostamizadeh, Afshin

2013-01-01

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.

Second-Order Learning Methods for a Multilayer Perceptron

International Nuclear Information System (INIS)

Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.

1994-01-01

First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

Science.gov (United States)

Heddam, Salim

2016-09-01

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

Core reactivity estimation in space reactors using recurrent dynamic networks

Science.gov (United States)

Parlos, Alexander G.; Tsai, Wei K.

1991-01-01

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron.

Science.gov (United States)

Maldonato, Nelson M; Sperandeo, Raffaele; Moretto, Enrico; Dell'Orco, Silvia

2018-01-01

Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence) and character dimensions (cooperativeness and self directedness). In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called "multilayer perceptron," was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89%) to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a greater influence than

A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron

Directory of Open Access Journals (Sweden)

Nelson M. Maldonato

2018-04-01

Full Text Available Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence and character dimensions (cooperativeness and self directedness. In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called “multilayer perceptron,” was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89% to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a

Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys

Energy Technology Data Exchange (ETDEWEB)

Johns, Jesse M., E-mail: jesse.johns@pnnl.gov; Burkes, Douglas, E-mail: douglas.burkes@pnnl.gov

2017-07-15

In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model's ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.

Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network.

Science.gov (United States)

LeMoyne, Robert; Mastroianni, Timothy

2016-08-01

Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise

International Nuclear Information System (INIS)

Jun, Sung Chan; Pearlmutter, Barak A.; Nolte, Guido

2002-01-01

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location. (author) )

Temperature profile retrieval in axisymmetric combustion plumes using multilayer perceptron modeling and spectral feature selection in the infrared CO2 emission band.

Science.gov (United States)

García-Cuesta, Esteban; de Castro, Antonio J; Galván, Inés M; López, Fernando

2014-01-01

In this work, a methodology based on the combined use of a multilayer perceptron model fed using selected spectral information is presented to invert the radiative transfer equation (RTE) and to recover the spatial temperature profile inside an axisymmetric flame. The spectral information is provided by the measurement of the infrared CO2 emission band in the 3-5 μm spectral region. A guided spectral feature selection was carried out using a joint criterion of principal component analysis and a priori physical knowledge of the radiative problem. After applying this guided feature selection, a subset of 17 wavenumbers was selected. The proposed methodology was applied over synthetic scenarios. Also, an experimental validation was carried out by measuring the spectral emission of the exhaust hot gas plume in a microjet engine with a Fourier transform-based spectroradiometer. Temperatures retrieved using the proposed methodology were compared with classical thermocouple measurements, showing a good agreement between them. Results obtained using the proposed methodology are very promising and can encourage the use of sensor systems based on the spectral measurement of the CO2 emission band in the 3-5 μm spectral window to monitor combustion processes in a nonintrusive way.

Memristive Perceptron for Combinational Logic Classification

Directory of Open Access Journals (Sweden)

Lidan Wang

2013-01-01

Full Text Available The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR is confirmed by MATLAB simulation.

Finite Size Scaling of Perceptron

OpenAIRE

Korutcheva, Elka; Tonchev, N.

2000-01-01

We study the first-order transition in the model of a simple perceptron with continuous weights and large, bit finite value of the inputs. Making the analogy with the usual finite-size physical systems, we calculate the shift and the rounding exponents near the transition point. In the case of a general perceptron with larger variety of inputs, the analysis only gives bounds for the exponents.

Empirical modeling of nuclear power plants using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.; Chong, K.T.

1991-01-01

A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Directory of Open Access Journals (Sweden)

Chudech Losiri

2016-07-01

Full Text Available Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR is the urbanized and agglomerated area of Bangkok Metropolis (BM and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC and Multi-Layer Perceptron-Markov Chain (MLP-MC to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD. It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

Nonlinear identification of process dynamics using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, A.F.; Chong, K.T.

1992-01-01

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

On-line learning through simple perceptron learning with a margin.

Science.gov (United States)

Hara, Kazuyuki; Okada, Masato

2004-03-01

We analyze a learning method that uses a margin kappa a la Gardner for simple perceptron learning. This method corresponds to the perceptron learning when kappa = 0 and to the Hebbian learning when kappa = infinity. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and found that it was the same as for perceptron learning. We also investigated an adaptive margin control method.

Optimal properties of analog perceptrons with excitatory weights.

Directory of Open Access Journals (Sweden)

Claudia Clopath

Full Text Available The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF to Purkinje Cell (PC synapses is guided by the Climbing fibers (CF, which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally.

On-line learning through simple perceptron with a margin

OpenAIRE

Hara, Kazuyuki; Okada, Masato

2003-01-01

We analyze a learning method that uses a margin $\\kappa$ {\\it a la} Gardner for simple perceptron learning. This method corresponds to the perceptron learning when $\\kappa=0$, and to the Hebbian learning when $\\kappa \\to \\infty$. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and...

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

Science.gov (United States)

Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda

2010-07-01

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

International Nuclear Information System (INIS)

Azimi, R.; Ghayekhloo, M.; Ghofrani, M.

2016-01-01

Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

Parallel strategy for optimal learning in perceptrons

International Nuclear Information System (INIS)

Neirotti, J P

2010-01-01

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

Evaluation of Süleymanköy (Diyarbakir, Eastern Turkey) and Seferihisar (Izmir, Western Turkey) Self Potential Anomalies with Multilayer Perceptron Neural Networks

Science.gov (United States)

Kaftan, Ilknur; Sindirgi, Petek

2013-04-01

Self-potential (SP) is one of the oldest geophysical methods that provides important information about near-surface structures. Several methods have been developed to interpret SP data using simple geometries. This study investigated inverse solution of a buried, polarized sphere-shaped self-potential (SP ) anomaly via Multilayer Perceptron Neural Networks ( MLPNN ). The polarization angle ( α ) and depth to the centre of sphere ( h )were estimated. The MLPNN is applied to synthetic and field SP data. In order to see the capability of the method in detecting the number of sources, MLPNN was applied to different spherical models at different depths and locations.. Additionally, the performance of MLPNN was tested by adding random noise to the same synthetic test data. The sphere model successfully obtained similar parameters under different S/N ratios. Then, MLPNN method was applied to two field examples. The first one is the cross section taken from the SP anomaly map of the Ergani-Süleymanköy (Turkey) copper mine. MLPNN was also applied to SP data from Seferihisar Izmir (Western Turkey) geothermal field. The MLPNN results showed good agreement with the original synthetic data set. The effect of The technique gave satisfactory results following the addition of 5% and 10% Gaussian noise levels. The MLPNN results were compared to other SP interpretation techniques, such as Normalized Full Gradient (NFG), inverse solution and nomogram methods. All of the techniques showed strong similarity. Consequently, the synthetic and field applications of this study show that MLPNN provides reliable evaluation of the self potential data modelled by the sphere model.

ERROR VS REJECTION CURVE FOR THE PERCEPTRON

OpenAIRE

PARRONDO, JMR; VAN DEN BROECK, Christian

1993-01-01

We calculate the generalization error epsilon for a perceptron J, trained by a teacher perceptron T, on input patterns S that form a fixed angle arccos (J.S) with the student. We show that the error is reduced from a power law to an exponentially fast decay by rejecting input patterns that lie within a given neighbourhood of the decision boundary J.S = 0. On the other hand, the error vs. rejection curve epsilon(rho), where rho is the fraction of rejected patterns, is shown to be independent ...

Perceptron Genetic to Recognize Openning Strategy Ruy Lopez

Science.gov (United States)

Azmi, Zulfian; Mawengkang, Herman

2018-01-01

The application of Perceptron method is not effective for coding on hardware based systems because it is not real time learning. With Genetic algorithm approach in calculating and searching the best weight (fitness value) system will do learning only one iteration. And the results of this analysis were tested in the case of the introduction of the opening pattern of chess Ruy Lopez. The Analysis with Perceptron Model with Algorithm Approach Genetics from group Artificial Neural Network for open Ruy Lopez. The data is processed with base open chess, with step eight a position white Pion from end open chess. Using perceptron method have many input and one output process many weight and refraction until output equal goal. Data trained and test with software Matlab and system can recognize the chess opening Ruy Lopez or Not open Ruy Lopez with Real time.

Learning unlearnable problems with perceptrons

Science.gov (United States)

Watkin, Timothy L. H.; Rau, Albrecht

1992-03-01

We study how well perceptrons learn to solve problems for which there is no perfect answer (the usual case), taking as examples a rule with a threshold, a rule in which the answer is not a monotonic function of the overlap between question and teacher, and a rule with many teachers (a ``hard'' unlearnable problem). In general there is a tendency for first-order transitions, even using spherical perceptrons, as networks compromise between conflicting requirements. Some existing learning schemes fail completely-occasionally even finding the worst possible solution; others are more successful. High-temperature learning seems more satisfactory than zero-temperature algorithms and avoids ``overlearning'' and ``overfitting,'' but care must be taken to avoid ``trapping'' in spurious free-energy minima. For some rules examples alone are not enough to learn from, and some prior information is required.

"Accelerated Perceptron": A Self-Learning Linear Decision Algorithm

OpenAIRE

Zuev, Yu. A.

2003-01-01

The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...

Learning and generalization errors for the 2D binary perceptron

NARCIS (Netherlands)

Klymovskiy, A.

2005-01-01

The statistical mechanics model of the binary perceptron learning is considered. It is proved that under the regularity conditions learning and generalization errors for the binary perceptron with two inputs tend to 0 at the average; the first term of the asymptotics is provided; its behavior with

Direct Kernel Perceptron (DKP): ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation.

Science.gov (United States)

Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José

2014-02-01

The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way

On-line learning of non-monotonic rules by simple perceptron

OpenAIRE

Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.

Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.

Science.gov (United States)

Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D; Crampin, Edmund J

2013-01-01

In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, Amir; Chong, K.T.

1991-01-01

A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

Directory of Open Access Journals (Sweden)

Michael R W Dawson

Full Text Available Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

Science.gov (United States)

2017-01-01

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. PMID:28212422

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

Science.gov (United States)

Dawson, Michael R W; Gupta, Maya

2017-01-01

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.

Science.gov (United States)

Mohebian, Mohammad R; Marateb, Hamid R; Mansourian, Marjan; Mañanas, Miguel Angel; Mokarian, Fariborz

2017-01-01

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR Using Optimized Ensemble Learning

Directory of Open Access Journals (Sweden)

Mohammad R. Mohebian

Full Text Available Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3% were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT. The proper combination of selected categorical features and also the weight (importance of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy. This algorithm is thus a promising online tool for the prediction of breast cancer recurrence. Keywords: Breast cancer, Cancer recurrence, Computer-assisted diagnosis, Machine learning, Prognosis

Learning rate and attractor size of the single-layer perceptron

International Nuclear Information System (INIS)

Singleton, Martin S.; Huebler, Alfred W.

2007-01-01

We study the simplest possible order one single-layer perceptron with two inputs, using the delta rule with online learning, in order to derive closed form expressions for the mean convergence rates. We investigate the rate of convergence in weight space of the weight vectors corresponding to each of the 14 out of 16 linearly separable rules. These vectors follow zigzagging lines through the piecewise constant vector field to their respective attractors. Based on our studies, we conclude that a single-layer perceptron with N inputs will converge in an average number of steps given by an Nth order polynomial in (t/l), where t is the threshold, and l is the size of the initial weight distribution. Exact values for these averages are provided for the five linearly separable classes with N=2. We also demonstrate that the learning rate is determined by the attractor size, and that the attractors of a single-layer perceptron with N inputs partition R N +R N

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

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

The application of deep confidence network in the problem of image recognition

Directory of Open Access Journals (Sweden)

Chumachenko О.І.

2016-12-01

Full Text Available In order to study the concept of deep learning, in particular the substitution of multilayer perceptron on the corresponding network of deep confidence, computer simulations of the learning process to test voters was carried out. Multi-layer perceptron has been replaced by a network of deep confidence, consisting of successive limited Boltzmann machines. After training of a network of deep confidence algorithm of layer-wise training it was found that the use of networks of deep confidence greatly improves the accuracy of multilayer perceptron training by method of reverse distribution errors.

Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

Science.gov (United States)

Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Annweiler, Cédric; Fantino, Bruno; Beauchet, Olivier

2015-04-01

Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults. Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis. Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI 4, vision score 9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP. NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms

Science.gov (United States)

Kaluza, Pablo; Urdapilleta, Eugenio

2014-10-01

Perceptrons are one of the fundamental paradigms in artificial neural networks and a key processing scheme in supervised classification tasks. However, the algorithm they provide is given in terms of unrealistically simple processing units and connections and therefore, its implementation in real neural networks is hard to be fulfilled. In this work, we present a neural circuit able to perform perceptron's computation based on realistic models of neurons and synapses. The model uses Wang-Buzsáki neurons with coupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The main characteristics of the feedforward perceptron operation are conserved, which allows to combine both approaches: whereas the classical artificial system can be used to learn a particular problem, its solution can be directly implemented in this neural circuit. As a result, we propose a biologically-inspired system able to work appropriately in a wide range of frequencies and system parameters, while keeping robust to noise and error.

Using recurrent neural network models for early detection of heart failure onset.

Science.gov (United States)

Choi, Edward; Schuetz, Andy; Stewart, Walter F; Sun, Jimeng

2017-03-01

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Using the Perceptron Algorithm to Find Consistent Hypotheses

OpenAIRE

Anthony, M.; Shawe-Taylor, J.

1993-01-01

The perceptron learning algorithm yields quite naturally an algorithm for finding a linearly separable boolean function consistent with a sample of such a function. Using the idea of a specifying sample, we give a simple proof that this algorithm is not efficient, in general.

SISTEM CERDAS DIAGNOSA PENYAKIT DALAM MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN METODE PERCEPTRON

Directory of Open Access Journals (Sweden)

Usman Usman

2017-12-01

Full Text Available Aplikasi Jaringan Syaraf Tiruan (JST untuk diagnosa  Penyakit Dalam dengan metode perceptron. Aplikasi ini dibuat untuk mengetahui keakuratan diagnosa Penyakit Dalam menggunakan JST perceptron dan mengimplementasikan JST perceptron berdasarkan gejala-gejala Penyakit Dalam ke dalam matlab dengan tampilan Graphical User Interface (GUI. Penyakit Dalam yang dibahas sebanyak 9 Penyakit Dalam. Yaitu penyakit  Asma bronchial, Anemia, Demam berdarah, Diabetes mellitus, Gagal Jantung, Tetanus, Hipertensi, Hepatitis, dan  Tuberkolosis paru. Gejala penyakit dalam yang diambil berdasarkan data rekam medis penderita Penyakit Dalam dan data pasien yang berobat  di Poli Penyakit Dalam RSUD Puri Husada Tembilahan. Dari hasil pelatihan (training terhadap 48 data, kecocokan keluaran jaringan dan target yang di inginkan yaitu penyakit dalam 100% dapat dikenali/sesuai dengan target yang di inginkan. Dan hasil pengujian data baru sebanyak 10 kali pengujian menghasilkan keluaran sekitar 78,9 % yang sesuai dengan target dan 21,1% yang tidak sesuai dengan target. Hasil pengujian dan pelatihan di implementasikan ke dalam GUI sebagai aplikasi dan user interface.

Experimental characterization of the perceptron laser rangefinder

Science.gov (United States)

Kweon, I. S.; Hoffman, Regis; Krotkov, Eric

1991-01-01

In this report, we characterize experimentally a scanning laser rangefinder that employs active sensing to acquire three-dimensional images. We present experimental techniques applicable to a wide variety of laser scanners, and document the results of applying them to a device manufactured by Perceptron. Nominally, the sensor acquires data over a 60 deg x 60 deg field of view in 256 x 256 pixel images at 2 Hz. It digitizes both range and reflectance pixels to 12 bits, providing a maximum range of 40 m and a depth resolution of 1 cm. We present methods and results from experiments to measure geometric parameters including the field of view, angular scanning increments, and minimum sensing distance. We characterize qualitatively problems caused by implementation flaws, including internal reflections and range drift over time, and problems caused by inherent limitations of the rangefinding technology, including sensitivity to ambient light and surface material. We characterize statistically the precision and accuracy of the range measurements. We conclude that the performance of the Perceptron scanner does not compare favorably with the nominal performance, that scanner modifications are required, and that further experimentation must be conducted.

Out-of-equilibrium dynamical mean-field equations for the perceptron model

Science.gov (United States)

Agoritsas, Elisabeth; Biroli, Giulio; Urbani, Pierfrancesco; Zamponi, Francesco

2018-02-01

Perceptrons are the building blocks of many theoretical approaches to a wide range of complex systems, ranging from neural networks and deep learning machines, to constraint satisfaction problems, glasses and ecosystems. Despite their applicability and importance, a detailed study of their Langevin dynamics has never been performed yet. Here we derive the mean-field dynamical equations that describe the continuous random perceptron in the thermodynamic limit, in a very general setting with arbitrary noise and friction kernels, not necessarily related by equilibrium relations. We derive the equations in two ways: via a dynamical cavity method, and via a path-integral approach in its supersymmetric formulation. The end point of both approaches is the reduction of the dynamics of the system to an effective stochastic process for a representative dynamical variable. Because the perceptron is formally very close to a system of interacting particles in a high dimensional space, the methods we develop here can be transferred to the study of liquid and glasses in high dimensions. Potentially interesting applications are thus the study of the glass transition in active matter, the study of the dynamics around the jamming transition, and the calculation of rheological properties in driven systems.

Analysis of ensemble learning using simple perceptrons based on online learning theory

Science.gov (United States)

Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato

2005-03-01

Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

A perceptron network theorem prover for the propositional calculus

NARCIS (Netherlands)

Drossaers, M.F.J.

In this paper a short introduction to neural networks and a design for a perceptron network theorem prover for the propositional calculus are presented. The theorem prover is a representation of a variant of the semantic tableau method, called the parallel tableau method, by a network of

Quantum perceptron over a field and neural network architecture selection in a quantum computer.

Science.gov (United States)

da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

2016-04-01

In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

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.

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

Science.gov (United States)

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

2001-03-10

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

Vibration Based Damage Assessment of a Cantilever using a Neural Network

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Rytter, A.

In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated.......In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated....

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

Efficient learning algorithm for quantum perceptron unitary weights

OpenAIRE

Seow, Kok-Leong; Behrman, Elizabeth; Steck, James

2015-01-01

For the past two decades, researchers have attempted to create a Quantum Neural Network (QNN) by combining the merits of quantum computing and neural computing. In order to exploit the advantages of the two prolific fields, the QNN must meet the non-trivial task of integrating the unitary dynamics of quantum computing and the dissipative dynamics of neural computing. At the core of quantum computing and neural computing lies the qubit and perceptron, respectively. We see that past implementat...

Practical Application of Neural Networks in State Space Control

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon

the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening......In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train...

A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

OpenAIRE

Bologna, Guido; Hayashi, Yoichi

2018-01-01

One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experime...

MLP-RBF

International Nuclear Information System (INIS)

Proriol, J.

1993-01-01

A cooperative multi-modular neural network architecture is presented: a Multi-Layer Perceptron (MLP), followed by a Radial Basis Function network (RBF). It is shown that, in the LEP experiment of electron-positron collision run at CERN, this architecture was able to outperform both a simple multi-layer perceptron, a multi-modular MLP+LVQ (LVQ: Learning Vector Quantization) and MLP+RBF trained sequentially and a conventional technique (Discriminant Analysis). (author). 10 refs., 2 figs

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

Directory of Open Access Journals (Sweden)

S. N. Kale

2009-01-01

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

Higher-order probabilistic perceptrons as Bayesian inference engines

International Nuclear Information System (INIS)

Clark, J.W.; Ristig, M.L.

1994-08-01

This letter makes explicit a structural connection between the Bayes optimal classifier operating on K binary input variables and corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. Given a large and unbiased training set and an effective learning algorithm, such a neural network should be able to learn the statistics of the classification problem and match the a posteriori probabilities given by the Bayes optimal classifier. (author). 18 refs

Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra

2018-01-01

Roč. 29, č. 7 (2018), s. 305-315 ISSN 0941-0643 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices Subject RIV: IN - Informatics, Computer Science Impact factor: 2.505, year: 2016

Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2017-01-01

Roč. 91, July (2017), s. 34-41 ISSN 0893-6080 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow networks * perceptrons * model complexity * lower bounds on approximation rates * Chernoff-Hoeffding bounds Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.287, year: 2016

Hard synchrotron radiation scattering from a nonideal surface grating from multilayer X-ray mirrors

International Nuclear Information System (INIS)

Punegov, V.I.; Nesterets, Ya.I.; Mytnichenko, S.V.; Kovalenko, N.V.; Chernov, V.A.

2003-01-01

The hard synchrotron radiation scattering from a multilayer surface grating is theoretically and experimentally investigated. The numerical calculations of angular distribution of scattering intensity from X-ray mirror Ni/C are executed with use of recurrence formulae and statistical dynamical theory of diffraction. It is shown, that the essential role in formation of a diffraction pattern plays a diffuse scattering caused by structure imperfection of a multilayer grating [ru

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

Science.gov (United States)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons.

Science.gov (United States)

Buhusi, Catalin V; Oprisan, Sorinel A

2013-05-01

In most species, interval timing is time-scale invariant: errors in time estimation scale up linearly with the estimated duration. In mammals, time-scale invariance is ubiquitous over behavioral, lesion, and pharmacological manipulations. For example, dopaminergic drugs induce an immediate, whereas cholinergic drugs induce a gradual, scalar change in timing. Behavioral theories posit that time-scale invariance derives from particular computations, rules, or coding schemes. In contrast, we discuss a simple neural circuit, the perceptron, whose output neurons fire in a clockwise fashion based on the pattern of coincidental activation of its input neurons. We show numerically that time-scale invariance emerges spontaneously in a perceptron with realistic neurons, in the presence of noise. Under the assumption that dopaminergic drugs modulate the firing of input neurons, and that cholinergic drugs modulate the memory representation of the criterion time, we show that a perceptron with realistic neurons reproduces the pharmacological clock and memory patterns, and their time-scale invariance, in the presence of noise. These results suggest that rather than being a signature of higher order cognitive processes or specific computations related to timing, time-scale invariance may spontaneously emerge in a massively connected brain from the intrinsic noise of neurons and circuits, thus providing the simplest explanation for the ubiquity of scale invariance of interval timing. Copyright © 2013 Elsevier B.V. All rights reserved.

Learning from correlated patterns by simple perceptrons

Energy Technology Data Exchange (ETDEWEB)

Shinzato, Takashi; Kabashima, Yoshiyuki [Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502 (Japan)], E-mail: shinzato@sp.dis.titech.ac.jp, E-mail: kaba@dis.titech.ac.jp

2009-01-09

Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.

Learning from correlated patterns by simple perceptrons

Science.gov (United States)

Shinzato, Takashi; Kabashima, Yoshiyuki

2009-01-01

Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.

Learning from correlated patterns by simple perceptrons

International Nuclear Information System (INIS)

Shinzato, Takashi; Kabashima, Yoshiyuki

2009-01-01

Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance

Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

Science.gov (United States)

Graves, Alex; Schmidhuber, Jürgen

2005-01-01

In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.

Learning by random walks in the weight space of the Ising perceptron

International Nuclear Information System (INIS)

Huang, Haiping; Zhou, Haijun

2010-01-01

Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of α≈0.63 for pattern length N = 101 and α≈0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of α≈0.80 for N = 101 and α≈0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density α of the learning task; at a fixed value of α, the width of the Hamming distance distribution decreases with N

Low-cost autonomous perceptron neural network inspired by quantum computation

Science.gov (United States)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

A comparison between wavelet based static and dynamic neural network approaches for runoff prediction

Science.gov (United States)

Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.; Khan, Mudasser Muneer

2016-04-01

In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of neural network models are commonly used in hydrology. Furthermore, the wavelet coupled multilayer perceptron neural network (MLPNN) models has also been found superior relative to the simple neural network models which are not coupled with wavelet. However, the MLPNN models are considered as static and memory less networks and lack the ability to examine the temporal dimension of data. Recurrent neural network models, on the other hand, have the ability to learn from the preceding conditions of the system and hence considered as dynamic models. This study for the first time explores the potential of wavelet coupled time lagged recurrent neural network (TLRNN) models for runoff prediction using rainfall data. The Discrete Wavelet Transformation (DWT) is employed in this study to decompose the input rainfall data using six of the most commonly used wavelet functions. The performance of the simple and the wavelet coupled static MLPNN models is compared with their counterpart dynamic TLRNN models. The study found that the dynamic wavelet coupled TLRNN models can be considered as alternative to the static wavelet MLPNN models. The study also investigated the effect of memory depth on the performance of static and dynamic neural network models. The memory depth refers to how much past information (lagged data) is required as it is not known a priori. The db8 wavelet function is found to yield the best results with the static MLPNN models and with the TLRNN models having small memory depths. The performance of the wavelet coupled TLRNN models with large memory depths is found insensitive to the selection of the wavelet function as all wavelet functions have similar performance.

Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.

Science.gov (United States)

Wan-Mamat, Wan Mohd Fahmi; Isa, Nor Ashidi Mat; Wahab, Habibah A; Wan-Mamat, Wan Mohd Fairuz

2009-01-01

An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.

Identification of abnormal movement state and avoidance strategy for mobile robots

Institute of Scientific and Technical Information of China (English)

CAI Zi-xing; DUAN Zhuo-hua; ZHANG Hui-tuan; YU Jin-xia

2006-01-01

Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence of abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer perceptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally,avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

Science.gov (United States)

Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin

2009-10-01

Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

Directory of Open Access Journals (Sweden)

Alireza Alemi

2015-08-01

Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the

Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

Science.gov (United States)

Benrekia, Fayçal; Attari, Mokhtar; Bouhedda, Mounir

2013-01-01

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases. PMID:23529119

Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

Science.gov (United States)

Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha

2018-01-01

Combined sewer overflow causes severe water pollution, urban flooding and reduced treatment plant efficiency. Understanding the behavior of CSO structures is vital for urban flooding prevention and overflow control. Neural networks have been extensively applied in water resource related fields. In this study, we collect data from an Internet of Things monitoring CSO structure and build different neural network models for simulating and predicting the water level of the CSO structure. Through a comparison of four different neural networks, namely multilayer perceptron (MLP), wavelet neural network (WNN), long short-term memory (LSTM) and gated recurrent unit (GRU), the LSTM and GRU present superior capabilities for multi-step-ahead time series prediction. Furthermore, GRU achieves prediction performances similar to LSTM with a quicker learning curve.

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-02-01

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

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

International Nuclear Information System (INIS)

Olyaee, Saeed; Hamedi, Samaneh

2011-01-01

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

Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition

Science.gov (United States)

Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku

2015-03-01

This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

Magnetic multilayer structure

Science.gov (United States)

Herget, Philipp; O'Sullivan, Eugene J.; Romankiw, Lubomyr T.; Wang, Naigang; Webb, Bucknell C.

2016-07-05

A mechanism is provided for an integrated laminated magnetic device. A substrate and a multilayer stack structure form the device. The multilayer stack structure includes alternating magnetic layers and diode structures formed on the substrate. Each magnetic layer in the multilayer stack structure is separated from another magnetic layer in the multilayer stack structure by a diode structure.

A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes.

Science.gov (United States)

Narasingarao, M R; Manda, R; Sridhar, G R; Madhu, K; Rao, A A

2009-02-01

Diabetes mellitus is an increasingly common life-style disorder whose management outcomes are measured in symptomatic, biochemical as well as psychological areas. Well being as an outcome of treatment is being increasingly recognized as a crucial component of treatment. There is little published literature on psychosocial outcomes and the factors influencing them. Therefore we have developed a neural network system which is trained to predict the well being in diabetes, using data generated in real life. We developed a Multi Layer Perceptron Neural Network model, which had been trained by back propagation algorithm. Data was used from a cohort of 241 individuals with diabetes. We used age, gender, weight, fasting plasma glucose as a set of inputs and predicted measures of well-being (depression, anxiety, energy and positive well-being). It was observed that female patients report significantly higher levels of depression than their male counter parts. Some slight high or no significant differences are observed between males and female patients with regard to the number of persons with whom they share their anxieties and fears regarding diabetes. There is not much difference has been observed in energy levels of both males and females. Also, Males have higher pwb value when compared with the female counterparts. Also, this may be due to women tend to react more emotionally to disease and hence experience more difficulty in coping with it. The present sample of women being predominantly house wives may be worrying more about their health and its problems. Also, it is observed that, gender differences are significant with regard to total general well-being. With five inputs (age, sex, weight, fasting plasma glucose, bias), four outputs are four (depression, anxiety, energy and positive well-being) the momentum rate was 0.9, the learning rate 0.7, using a sample of 50. the maximum individual error is 0.001 when the number of iterations were 500, number of hidden layers

Multilayer Brain Networks

Science.gov (United States)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

Fulltext PDF

Indian Academy of Sciences (India)

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan,. Pakistan. 1325. Ahmed Sajjad .... Mashhad city, NE Iran. 1417 ..... 993. Vyshnavi S. Water–rock interaction on the development of granite.

Advances in Artificial Neural Networks – Methodological Development and Application

Directory of Open Access Journals (Sweden)

Yanbo Huang

2009-08-01

Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

Sistema de análise de ativos através de redes neurais de múltiplas camadas. Asset analysis system using multilayer neural networks

Directory of Open Access Journals (Sweden)

Vânia Medianeira Flores Costa

2012-04-01

Full Text Available When investors decide to “adventure” through stock markets they search for a method to provide safety on making decision. In fact, there is no precise way to know which stocks will became a profitable investiment. Technical analysis is a discipline that support the investors on making decisions. Such a discipline uses a set of tools and statistical methods to forecast the market’s movement. Such a paper presents the develpment of a robotical Trade System, using a heuristic method. The system has a Neural Network multilayer perceptron, trained with an algorithm for back propagation error. Thus, approaching to the technical analysis without emotional aspects, using the Neural Network forecast on supporting the decisions of a investor on stock market. In analyzing the results of the neural network can be seen that the neural network got a result of 42.6% higher than the diagnostic of the technical analysis.Quando investidores decidem se “aventurar” pelo mercado de renda variável, como pelo mercado de ações, buscam um método de ter mais segurança na tomada de decisão. Na prática, não há como saber quais ativos tornar-se-ão um investimento lucrativo. No mercado acionário, a Análise Técnica procura auxiliar o investidor na tomada de decisão. Para isso, utiliza-se de ferramentas e de métodos estatísticos para tentar predizer os movimentos do mercado. Este artigo apresenta o desenvolvimento de um Trade System robótico, utilizando um método heurístico. O sistema conta com uma rede neural multilayer perceptron, treinada com o algoritmo de retro propagação de erro, aproximando-se da análise técnica sem o fator emoção. Ao avaliar os resultados da rede neural, pode ser visto que a mesma obteve um resultado de 42,6% maior do que o diagnóstico da análise técnica.

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

Science.gov (United States)

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

2010-12-01

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

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Fabrication of multilayer nanowires

Energy Technology Data Exchange (ETDEWEB)

Kaur, Jasveer, E-mail: kaurjasveer89@gmail.com; Singh, Avtar; Kumar, Davinder [Department of Physics, Punjabi University Patiala, 147002, Punjab (India); Thakur, Anup; Kaur, Raminder, E-mail: raminder-k-saini@yahoo.com [Department of Basic and Applied Sciences, Punjabi University Patiala, 147002, Punjab (India)

2016-05-06

Multilayer nanowires were fabricated by potentiostate ectrodeposition template synthesis method into the pores of polycarbonate membrane. In present work layer by layer deposition of two different metals Ni and Cu in polycarbonate membrane having pore size of 600 nm were carried out. It is found that the growth of nanowires is not constant, it varies with deposition time. Scanning electron microscopy (SEM) is used to study the morphology of fabricated multilayer nanowires. An energy dispersive X-ray spectroscopy (EDS) results confirm the composition of multilayer nanowires. The result shows that multilayer nanowires formed is dense.

Fabrication of multilayer nanowires

International Nuclear Information System (INIS)

Kaur, Jasveer; Singh, Avtar; Kumar, Davinder; Thakur, Anup; Kaur, Raminder

2016-01-01

Multilayer nanowires were fabricated by potentiostate ectrodeposition template synthesis method into the pores of polycarbonate membrane. In present work layer by layer deposition of two different metals Ni and Cu in polycarbonate membrane having pore size of 600 nm were carried out. It is found that the growth of nanowires is not constant, it varies with deposition time. Scanning electron microscopy (SEM) is used to study the morphology of fabricated multilayer nanowires. An energy dispersive X-ray spectroscopy (EDS) results confirm the composition of multilayer nanowires. The result shows that multilayer nanowires formed is dense.

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)

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.

Three-dimensional sound localisation with a lizard peripheral auditory model

DEFF Research Database (Denmark)

Kjær Schmidt, Michael; Shaikh, Danish

the networks learned a transfer function that translated the three-dimensional non-linear mapping into estimated azimuth and elevation values for the acoustic target. The neural network with two hidden layers as expected performed better than that with only one hidden layer. Our approach assumes that for any...... location of an acoustic target in three dimensions. Our approach utilises a model of the peripheral auditory system of lizards [Christensen-Dalsgaard and Manley 2005] coupled with a multi-layer perceptron neural network. The peripheral auditory model’s response to sound input encodes sound direction...... information in a single plane which by itself is insufficient to localise the acoustic target in three dimensions. A multi-layer perceptron neural network is used to combine two independent responses of the model, corresponding to two rotational movements, into an estimate of the sound direction in terms...

Statistical Pattern Recognition: Application to νμ→ντ Oscillation Searches Based on Kinematic Criteria

Science.gov (United States)

Bueno, A.; Martinez de la Ossa, A.; Navas, S.; Rubbia, A.

2004-11-01

Classic statistical techniques (like the multi-dimensional likelihood and the Fisher discriminant method) together with Multi-layer Perceptron and Learning Vector Quantization Neural Networks have been systematically used in order to find the best sensitivity when searching for νμ→ντ oscillations. We discovered that for a general direct ντ appearance search based on kinematic criteria: (a) An optimal discrimination power is obtained using only three variables (Evisible, PTmiss and ρl) and their correlations. Increasing the number of variables (or combinations of variables) only increases the complexity of the problem, but does not result in a sensible change of the expected sensitivity. (b) The multi-layer perceptron approach offers the best performance. As an example to assert numerically those points, we have considered the problem of ντ appearance at the CNGS beam using a Liquid Argon TPC detector.

A neural network based seafloor classification using acoustic backscatter

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.

This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...

A Multievidence Approach for Crop Discrimination Using Multitemporal WorldView-2 Imagery

DEFF Research Database (Denmark)

Chellasamy, Menaka; Zielinski, Rafal Tomasz; Greve, Mogens Humlekrog

2014-01-01

summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based...

Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control

CSIR Research Space (South Africa)

Adeleke, Jude Adekunle

2017-04-01

Full Text Available in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show...

Advances in Artificial Neural Networks - Methodological Development and Application

Science.gov (United States)

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

Directory of Open Access Journals (Sweden)

Nisrine Jrad

2009-01-01

rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

APLICACIÓN DE LA PERCEPTRÓN EN EL GRÁFICO DE CONTROL DE MEDICIONES INDIVIDUALES // IMPLEMENTATION OF THE PERCEPTRON IN THE CONTROL CHART FOR INDIVIDUAL

Directory of Open Access Journals (Sweden)

José Antonio Vázquez-López

2012-06-01

Full Text Available In this article the Perceptron artificial neural network is applied as a classifier system of out of control points, in the field of contrlol chart for individual measurements. The use of geometric properties of the Perceptron as a training method is introduced, replacing in consequence to the known training methods. Some experiments with numerical databases contaminated with altered data in global average was performed, and the ability of the detection of \\out of control points" of the control chart with the implementation of the Perceptron trained by geometry was compared. The results reveal greater capacity in the Perceptron. This approach can be generalized to other types of control charts and patterns of natural and special variation, not considered in this research. // RESUMEN: En este artículo se aplica la red neuronal artificial Perceptrón como sistema clasificador de puntos fuera de control en el ámbito de la carta de control de mediciones individuales. Se introduce el uso de las propiedades geométricas de la Perceptrón como método de entrenamiento para sustituir, en consecuencia, a los métodos de entrenamiento conocidos. Se experimentó con bases de datos numéricas contaminadas con datos alterados en su media global y se comparó la capacidad de la detección de puntos fuera de control de la carta de control con la aplicación de la Perceptrón entrenada por geometría. Los resultados revelan mayor capacidad en la Perceptrón en diferentes porcentajes de contaminación. Esta propuesta puede ser generalizada a otros tipos de gráficos de control y a patrones de variación especial y natural no considerados en esta investigación.

Intelligent control of HVAC systems. Part II: perceptron performance analysis

Directory of Open Access Journals (Sweden)

Ioan URSU

2013-09-01

Full Text Available This is the second part of a paper on intelligent type control of Heating, Ventilating, and Air-Conditioning (HVAC systems. The whole study proposes a unified approach in the design of intelligent control for such systems, to ensure high energy efficiency and air quality improving. In the first part of the study it is considered as benchmark system a single thermal space HVAC system, for which it is assigned a mathematical model of the controlled system and a mathematical model(algorithm of intelligent control synthesis. The conception of the intelligent control is of switching type, between a simple neural network, a perceptron, which aims to decrease (optimize a cost index,and a fuzzy logic component, having supervisory antisaturating role for neuro-control. Based on numerical simulations, this Part II focuses on the analysis of system operation in the presence only ofthe neural control component. Working of the entire neuro-fuzzy system will be reported in a third part of the study.

Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Rytter, A.

In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Petr Maca

2016-01-01

Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

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H. Elçiçek

2014-01-01

Full Text Available Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals.

Multilayer perceptron neural network for downscaling rainfall in arid ...

Indian Academy of Sciences (India)

3Research Center for Climate Change, Ministry of Water Resources, Nanjing, ... system models (ESMs) are considered as the ... downscaling methods, regression models, which are ...... a decision support tool for the assessment of regional.

Hybrid combination of multi-layer perceptron and neutron activation ...

Indian Academy of Sciences (India)

2017-01-04

Jan 4, 2017 ... 1Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. 2Radiation ... method for the qualitative and quantitative analyses of ... ferent types of cement using reactor, inertial elec-.

Rainfall prediction methodology with binary multilayer perceptron neural networks

Science.gov (United States)

Esteves, João Trevizoli; de Souza Rolim, Glauco; Ferraudo, Antonio Sergio

2018-05-01

Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a soft computing technique to forecast the occurrence of rainfall in short ranges of time by artificial neural networks (ANNs) in accumulated periods from 3 to 7 days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from ten agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60 years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have an direct impact on the accuracy of the ANNs. The models have peak performance in well defined seasons, but looses its accuracy in transitional seasons and places under influence of macro-climatic and mesoclimatic effects, which indicates that this technique can be used to indicate the eminence of rainfall with some limitations.

Incorporating a priori knowledge into initialized weights for neural classifier

NARCIS (Netherlands)

Chen, Zhe; Feng, T.J.; Feng, Tian-Jin; Houkes, Z.

2000-01-01

Artificial neural networks (ANN), especially, multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge in the design of ANNs is still an open problem. The paper tries to give some insight on this topic

Universal LD50 predictions using deep learning

Science.gov (United States)

NICEATM Predictive Models for Acute Oral Systemic Toxicity LD50 entry Risa R. Sayre (sayre.risa@epa.gov) & Christopher M. Grulke Our approach uses an ensemble of multilayer perceptron regressions to predict rat acute oral LD50 values from chemical features. Features were genera...

New developments in Ni/Ti multilayers

Energy Technology Data Exchange (ETDEWEB)

Anderson, I; Hoghoj, P [Institut Max von Laue - Paul Langevin (ILL), 38 - Grenoble (France)

1997-04-01

It is now 20 years since super-mirrors were first used as a neutron optical element. Since then the field of multilayer neutron-optics has matured with multilayers finding their way to application in many neutron scattering instruments. However, there is still room for progress in terms of multilayer quality, performance and application. Along with work on multilayers for neutron polarisation Ni/Ti super-mirrors have been optimised. The state-of-the-art Ni/Ti super-mirror performance and the results obtained in two neutron-optics applications of Ni/Ti multilayers are presented. (author).

Neutron optics with multilayer monochromators

International Nuclear Information System (INIS)

Saxena, A.M.; Majkrzak, C.F.

1984-01-01

A multilayer monochromator is made by depositing thin films of two materials in an alternating sequence on a glass substrate. This makes a multilayer periodic in a direction perpendicular to the plane of the films, with a d-spacing equal to the thickness of one bilayer. Neutrons of wavelength λ incident on a multilayer will be reflected at an angle phi given by the Bragg relation nλ = 2d sinphi, where n is the order of reflection. The use of thin-film multilayers for monochromating neutrons is discussed. Because of the low flux of neutrons, the samples have to be large, and the width of the incident beam can be as much as 2 cm. Multilayers made earlier were fabricated by resistive heating of the materials in a vacuum chamber. Because of geometrical constraints imposed by the size of the vacuum chamber, limits on the amount of material that can be loaded in a boat, and finite life of the boats, this method of preparation limits the length of a multilayer to ∼ 15 cm and the total number of bilayers in a multilayer to about 200. This paper discusses a thin-film deposition system using RF sputtering for depositing films

Aphasia Classification Using Neural Networks

DEFF Research Database (Denmark)

Axer, H.; Jantzen, Jan; Berks, G.

2000-01-01

A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Earth System Science; Volume 124; Issue 6. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan. Kamal Ahmed Shamsuddin Shahid Sobri Bin Haroon Wang Xiao-Jun. Volume 124 Issue 6 August 2015 pp 1325-1341 ...

Temporal difference learning for the game Tic-Tac-Toe 3D : applying structure to neural networks

NARCIS (Netherlands)

van de Steeg, M.; Drugan, M.M.; Wiering, M.

2015-01-01

When reinforcement learning is applied to large state spaces, such as those occurring in playing board games, the use of a good function approximator to learn to approximate the value function is very important. In previous research, multi-layer perceptrons have often been quite successfully used as

A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

OpenAIRE

Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

2010-01-01

The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...

Modeling of an industrial drying process by artificial neural networks

Directory of Open Access Journals (Sweden)

E. Assidjo

2008-09-01

Full Text Available A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN, precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Smita K Magdum

2017-10-01

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

A Neuro Solution for Economic Diagnosis and Prediction

Directory of Open Access Journals (Sweden)

Nicolae Morariu

2013-07-01

Full Text Available The paper present a solution for the economic activity evolution diagnostic and prediction by means of a set of indicators. Starting from the indicators set, there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indicators set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostic and prediction the following tools are used: pattern recognition and multilayer perceptron implemented in the REFORME software written by the author and the results of the experiment obtained with this software for macroeconomic diagnostic and prediction during the years 2005-2012 for diagnostic and 2013-2014 for prediction. Keywords: pattern recognition, neural network, multilayer perceptron, indicators, diagnostic, prediction.

MIMO transmit scheme based on morphological perceptron with competitive learning.

Science.gov (United States)

Valente, Raul Ambrozio; Abrão, Taufik

2016-08-01

This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme. Copyright © 2016 Elsevier Ltd. All rights reserved.

Wedged multilayer Laue lens

International Nuclear Information System (INIS)

Conley, Ray; Liu Chian; Qian Jun; Kewish, Cameron M.; Macrander, Albert T.; Yan Hanfei; Maser, Joerg; Kang, Hyon Chol; Stephenson, G. Brian

2008-01-01

A multilayer Laue lens (MLL) is an x-ray focusing optic fabricated from a multilayer structure consisting of thousands of layers of two different materials produced by thin-film deposition. The sequence of layer thicknesses is controlled to satisfy the Fresnel zone plate law and the multilayer is sectioned to form the optic. An improved MLL geometry can be created by growing each layer with an in-plane thickness gradient to form a wedge, so that every interface makes the correct angle with the incident beam for symmetric Bragg diffraction. The ultimate hard x-ray focusing performance of a wedged MLL has been predicted to be significantly better than that of a nonwedged MLL, giving subnanometer resolution with high efficiency. Here, we describe a method to deposit the multilayer structure needed for an ideal wedged MLL and report our initial deposition results to produce these structures

Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

Energy Technology Data Exchange (ETDEWEB)

Kabashima, Y [Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502 (Japan)], E-mail: kaba@dis.titech.ac.jp

2008-01-15

A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.

Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

International Nuclear Information System (INIS)

Kabashima, Y

2008-01-01

A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown

Inference from correlated patterns: a unified theory for perceptron learning and linear vector channels

Science.gov (United States)

Kabashima, Y.

2008-01-01

A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.

Interfacial effects in multilayers

International Nuclear Information System (INIS)

Barbee, T.W. Jr.

1998-01-01

Interfacial structure and the atomic interactions between atoms at interfaces in multilayers or nano-laminates have significant impact on the physical properties of these materials. A technique for the experimental evaluation of interfacial structure and interfacial structure effects is presented and compared to experiment. In this paper the impact of interfacial structure on the performance of x-ray, soft x-ray and extreme ultra-violet multilayer optic structures is emphasized. The paper is concluded with summary of these results and an assessment of their implications relative to multilayer development and the study of buried interfaces in solids in general

I - Multivariate Classification and Machine Learning in HEP

CERN Multimedia

CERN. Geneva

2016-01-01

Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Earth System Science. Shamsuddin Shahid. Articles written in Journal of Earth System Science. Volume 124 Issue 6 August 2015 pp 1325-1341. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan · Kamal Ahmed Shamsuddin Shahid ...

Emotion of Physiological Signals Classification Based on TS Feature Selection

Institute of Scientific and Technical Information of China (English)

Wang Yujing; Mo Jianlin

2015-01-01

This paper propose a method of TS-MLP about emotion recognition of physiological signal.It can recognize emotion successfully by Tabu search which selects features of emotion’s physiological signals and multilayer perceptron that is used to classify emotion.Simulation shows that it has achieved good emotion classification performance.

The modeling of response indicators of integrated water resources ...

African Journals Online (AJOL)

The results indicate that the feed forward multilayer perceptron models with back propagation are useful tools to define and prioritize the most effective response variable on water resources mobilization to intervene and solve water problems. The model evaluation shows that the correlation coefficients are more than 96% ...

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior.

Science.gov (United States)

Gao, Zhong-Ke; Dang, Wei-Dong; Yang, Yu-Xuan; Cai, Qing

2017-03-01

The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.

Figure correction of multilayer coated optics

Science.gov (United States)

Chapman; Henry N. , Taylor; John S.

2010-02-16

A process is provided for producing near-perfect optical surfaces, for EUV and soft-x-ray optics. The method involves polishing or otherwise figuring the multilayer coating that has been deposited on an optical substrate, in order to correct for errors in the figure of the substrate and coating. A method such as ion-beam milling is used to remove material from the multilayer coating by an amount that varies in a specified way across the substrate. The phase of the EUV light that is reflected from the multilayer will be affected by the amount of multilayer material removed, but this effect will be reduced by a factor of 1-n as compared with height variations of the substrate, where n is the average refractive index of the multilayer.

Resting State Network Estimation in Individual Subjects

Science.gov (United States)

Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

2014-01-01

Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

Gait recognition using kinect and locally linear embedding ...

African Journals Online (AJOL)

This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person's identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification ...

Tagging b quark events in ALEPH with neural networks

International Nuclear Information System (INIS)

Proriol, J.; Jousset, J.; Guicheney, C.; Falvard, A.; Henrard, P.; Pallin, D.; Perret, P.; Brandl, B.

1991-01-01

Comparison of different methods to tag b quark events are presented: multilayered perceptron (MLP), Learning Vector Quantization (LVQ), discriminant analysis, combination of any two of the above methods. The sample events come from the ALEPH Monte Carlo and data, from the 1990 ALEPH runs. (authors) 12 refs., 16 figs., 5 tabs

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan · Kamal Ahmed Shamsuddin Shahid Sobri Bin Haroon Wang Xiao-Jun · More Details Abstract Fulltext PDF. Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and ...

Sectioning of multilayers to make a multilayer Laue lens

International Nuclear Information System (INIS)

Kang, Hyon Chol; Stephenson, G. Brian; Liu Chian; Conley, Ray; Khachatryan, Ruben; Wieczorek, Michael; Macrander, Albert T.; Yan Hanfei; Maser, Joerg; Hiller, Jon; Koritala, Rachel

2007-01-01

We report a process to fabricate multilayer Laue lenses (MLL's) by sectioning and thinning multilayer films. This method can produce a linear zone plate structure with a very large ratio of zone depth to width (e.g., >1000), orders of magnitude larger than can be attained with photolithography. Consequently, MLL's are advantageous for efficient nanofocusing of hard x rays. MLL structures prepared by the technique reported here have been tested at an x-ray energy of 19.5 keV, and a diffraction-limited performance was observed. The present article reports the fabrication techniques that were used to make the MLL's

Failure and reliability prediction by support vector machines regression of time series data

International Nuclear Information System (INIS)

Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

2011-01-01

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

Finding overlapping communities in multilayer networks.

Science.gov (United States)

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Controlling light with plasmonic multilayers

DEFF Research Database (Denmark)

Orlov, Alexey A.; Zhukovsky, Sergei; Iorsh, Ivan V.

2014-01-01

metamaterials and describe their use for light manipulation at the nanoscale. While demonstrating the recently emphasized hallmark effect of hyperbolic dispersion, we put special emphasis to the comparison between multilayered hyperbolic metamaterials and more broadly defined plasmonic-multilayer metamaterials...

Higher-order corrections to the effective potential close to the jamming transition in the perceptron model

Science.gov (United States)

Altieri, Ada

2018-01-01

In view of the results achieved in a previously related work [A. Altieri, S. Franz, and G. Parisi, J. Stat. Mech. (2016) 093301], 10.1088/1742-5468/2016/09/093301, regarding a Plefka-like expansion of the free energy up to the second order in the perceptron model, we improve the computation here focusing on the role of third-order corrections. The perceptron model is a simple example of constraint satisfaction problem, falling in the same universality class as hard spheres near jamming and hence allowing us to get exact results in high dimensions for more complex settings. Our method enables to define an effective potential (or Thouless-Anderson-Palmer free energy), namely a coarse-grained functional, which depends on the generalized forces and the effective gaps between particles. The analysis of the third-order corrections to the effective potential reveals that, albeit irrelevant in a mean-field framework in the thermodynamic limit, they might instead play a fundamental role in considering finite-size effects. We also study the typical behavior of generalized forces and we show that two kinds of corrections can occur. The first contribution arises since the system is analyzed at a finite distance from jamming, while the second one is due to finite-size corrections. We nevertheless show that third-order corrections in the perturbative expansion vanish in the jamming limit both for the potential and the generalized forces, in agreement with the isostaticity argument proposed by Wyart and coworkers. Finally, we analyze the relevant scaling solutions emerging close to the jamming line, which define a crossover regime connecting the control parameters of the model to an effective temperature.

Interfacial behaviour of biopolymer multilayers

NARCIS (Netherlands)

Corstens, Meinou N.; Osorio Caltenco, Lilia A.; Vries, de Renko; Schroën, Karin; Berton-Carabin, Claire C.

2017-01-01

Although multilayered emulsions have been related to reduced lipolysis, the involved interfacial phenomena have never been studied directly. In this work, we systematically built multilayers of whey protein and pectin, which we further subjected to digestive conditions, using two different

A refined model for characterizing x-ray multilayers

International Nuclear Information System (INIS)

Oren, A.L.; Henke, B.L.

1987-12-01

The ability to quickly and accurately characterize arbitrary multilayers is very valuable for not only can we use the characterizations to predict the reflectivity of a multilayer for any soft x-ray wavelength, we also can generalize the results to apply to other multilayers of the same type. In addition, we can use the characterizations as a means of evaluating various sputtering environments and refining sputtering techniques to obtain better multilayers. In this report we have obtained improved characterizations for sample molybdenum-silicon and vanadium-silicon multilayers. However, we only examined five crystals overall, so the conclusions that we could draw about the structure of general multilayers is limited. Research involving many multilayers manufactured under the same sputtering conditions is clearly in order. In order to best understand multilayer structures it may be necessary to further refine our model, e.g., adopting a Gaussian form for the interface regions. With such improvements we can expect even better agreement with experimental values and continued concurrence with other characterization techniques. 18 refs., 30 figs., 7 tabs

A Recurrent Neural Network Approach to Rear Vehicle Detection Which Considered State Dependency

Directory of Open Access Journals (Sweden)

Kayichirou Inagaki

2003-08-01

Full Text Available Experimental vision-based detection often fails in cases when the acquired image quality is reduced by changing optical environments. In addition, the shape of vehicles in images that are taken from vision sensors change due to approaches by vehicle. Vehicle detection methods are required to perform successfully under these conditions. However, the conventional methods do not consider especially in rapidly varying by brightness conditions. We suggest a new detection method that compensates for those conditions in monocular vision-based vehicle detection. The suggested method employs a Recurrent Neural Network (RNN, which has been applied for spatiotemporal processing. The RNN is able to respond to consecutive scenes involving the target vehicle and can track the movements of the target by the effect of the past network states. The suggested method has a particularly beneficial effect in environments with sudden, extreme variations such as bright sunlight and shield. Finally, we demonstrate effectiveness by state-dependent of the RNN-based method by comparing its detection results with those of a Multi Layered Perceptron (MLP.

Multi-Layer E-Textile Circuits

Science.gov (United States)

Dunne, Lucy E.; Bibeau, Kaila; Mulligan, Lucie; Frith, Ashton; Simon, Cory

2012-01-01

Stitched e-textile circuits facilitate wearable, flexible, comfortable wearable technology. However, while stitched methods of e-textile circuits are common, multi-layer circuit creation remains a challenge. Here, we present methods of stitched multi-layer circuit creation using accessible tools and techniques.

Base Metal Co-Fired Multilayer Piezoelectrics

Directory of Open Access Journals (Sweden)

Lisheng Gao

2016-03-01

Full Text Available Piezoelectrics have been widely used in different kinds of applications, from the automobile industry to consumer electronics. The novel multilayer piezoelectrics, which are inspired by multilayer ceramic capacitors, not only minimize the size of the functional parts, but also maximize energy efficiency. Development of multilayer piezoelectric devices is at a significant crossroads on the way to achieving low costs, high efficiency, and excellent reliability. Concerning the costs of manufacturing multilayer piezoelectrics, the trend is to replace the costly noble metal internal electrodes with base metal materials. This paper discusses the materials development of metal co-firing and the progress of integrating current base metal chemistries. There are some significant considerations in metal co-firing multilayer piezoelectrics: retaining stoichiometry with volatile Pb and alkaline elements in ceramics, the selection of appropriate sintering agents to lower the sintering temperature with minimum impact on piezoelectric performance, and designing effective binder formulation for low pO2 burnout to prevent oxidation of Ni and Cu base metal.

Multilayer Stochastic Block Models Reveal the Multilayer Structure of Complex Networks

Directory of Open Access Journals (Sweden)

Toni Vallès-Català

2016-03-01

Full Text Available In complex systems, the network of interactions we observe between systems components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs, a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate-observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.

FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel

International Nuclear Information System (INIS)

Mekki, H.; Belhout, K.; Mellit, A.; Salhi, H.

2008-01-01

The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel

Multilayer Nanoporous Graphene Membranes for Water Desalination.

Science.gov (United States)

Cohen-Tanugi, David; Lin, Li-Chiang; Grossman, Jeffrey C

2016-02-10

While single-layer nanoporous graphene (NPG) has shown promise as a reverse osmosis (RO) desalination membrane, multilayer graphene membranes can be synthesized more economically than the single-layer material. In this work, we build upon the knowledge gained to date toward single-layer graphene to explore how multilayer NPG might serve as a RO membrane in water desalination using classical molecular dynamic simulations. We show that, while multilayer NPG exhibits similarly promising desalination properties to single-layer membranes, their separation performance can be designed by manipulating various configurational variables in the multilayer case. This work establishes an atomic-level understanding of the effects of additional NPG layers, layer separation, and pore alignment on desalination performance, providing useful guidelines for the design of multilayer NPG membranes.

Mechanical properties of highly textured Cu/Ni multilayers

International Nuclear Information System (INIS)

Liu, Y.; Bufford, D.; Wang, H.; Sun, C.; Zhang, X.

2011-01-01

We report on the synthesis of highly (1 1 1) and (1 0 0) textured Cu/Ni multilayers with individual layer thicknesses, h, varying from 1 to 200 nm. When, h, decreases to 5 nm or less, X-ray diffraction spectra show epitaxial growth of Cu/Ni multilayers. High resolution transmission electron microscopy studies show the coexistence of nanotwins and coherent layer interfaces in highly (1 1 1) textured Cu/Ni multilayers with smaller h. Hardnesses of multilayer films increase with decreasing h, approach a maximum at h of a few nanometers, and show softening thereafter at smaller h. The influence of layer interfaces as well as twin interfaces on strengthening mechanisms of multilayers and the formation of twins in Ni in multilayers are discussed.

Gender classification in children based on speech characteristics: using fundamental and formant frequencies of Malay vowels.

Science.gov (United States)

Zourmand, Alireza; Ting, Hua-Nong; Mirhassani, Seyed Mostafa

2013-03-01

Speech is one of the prevalent communication mediums for humans. Identifying the gender of a child speaker based on his/her speech is crucial in telecommunication and speech therapy. This article investigates the use of fundamental and formant frequencies from sustained vowel phonation to distinguish the gender of Malay children aged between 7 and 12 years. The Euclidean minimum distance and multilayer perceptron were used to classify the gender of 360 Malay children based on different combinations of fundamental and formant frequencies (F0, F1, F2, and F3). The Euclidean minimum distance with normalized frequency data achieved a classification accuracy of 79.44%, which was higher than that of the nonnormalized frequency data. Age-dependent modeling was used to improve the accuracy of gender classification. The Euclidean distance method obtained 84.17% based on the optimal classification accuracy for all age groups. The accuracy was further increased to 99.81% using multilayer perceptron based on mel-frequency cepstral coefficients. Copyright © 2013 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

Directory of Open Access Journals (Sweden)

Panchanand Jha

2014-07-01

Full Text Available Inverse kinematic is one of the most interesting problems of industrial robot. The inverse kinematics problem in robotics is about the determination of joint angles for a desired Cartesian position of the end effector. It comprises of the computation need to find the joint angles for a given Cartesian position and orientation of the end effectors to control a robot arm. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network is one such technique which can be gainfully used to yield the acceptable results. This paper proposes a structured artificial neural network (ANN model to find the inverse kinematics solution of a 4-dof SCARA manipulator. The ANN model used is a multi-layered perceptron neural network (MLPNN, wherein gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that multi-layered perceptron neural network gives minimum mean square error.

Extended asymmetric-cut multilayer X-ray gratings.

Science.gov (United States)

Prasciolu, Mauro; Haase, Anton; Scholze, Frank; Chapman, Henry N; Bajt, Saša

2015-06-15

The fabrication and characterization of a large-area high-dispersion blazed grating for soft X-rays based on an asymmetric-cut multilayer structure is reported. An asymmetric-cut multilayer structure acts as a perfect blazed grating of high efficiency that exhibits a single diffracted order, as described by dynamical diffraction throughout the depth of the layered structure. The maximum number of grating periods created by cutting a multilayer deposited on a flat substrate is equal to the number of layers deposited, which limits the size of the grating. The size limitation was overcome by depositing the multilayer onto a substrate which itself is a coarse blazed grating and then polish it flat to reveal the uniformly spaced layers of the multilayer. The number of deposited layers required is such that the multilayer thickness exceeds the step height of the substrate structure. The method is demonstrated by fabricating a 27,060 line pairs per mm blazed grating (36.95 nm period) that is repeated every 3,200 periods by the 120-μm period substrate structure. This preparation technique also relaxes the requirements on stress control and interface roughness of the multilayer film. The dispersion and efficiency of the grating is demonstrated for soft X-rays of 13.2 nm wavelength.

Soft X-ray multilayers and filters

CERN Document Server

Wang Zhan Shan; Tang Wei Xing; Qin Shuji; Zhou Bing; Chen Ling Ya

2002-01-01

The periodic and non-periodic multilayers were designed by using a random number to change each layer and a suitable merit function. Ion beam sputtering and magnetron sputtering were used to fabricate various multilayers and beam splitters in soft X-ray range. The characterization of multilayers by small angle X-ray diffraction, Auger electron spectroscopy, Rutherford back scattering spectroscopy and reflectivity illustrated the multilayers had good structures and smooth interlayers. The reflectivity and transmission of a beam splitter is about 5%. The fabrication and transmission properties of Ag, Zr were studied. The Rutherford back scattering spectroscopy and auger electron spectroscopy were used to investigate the contents and distributions of impurities and influence on qualities of filters. The attenuation coefficients were corrected by the data obtained by measurements

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.

Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control

DEFF Research Database (Denmark)

Madsen, Per Printz

-Layer Perceptron net. Der er opstillet koncepter/metoder til såvel feedforward regulering som feedback regulering. Multi-Layer Perceptronen er i stand til at regulere et ulineært, multivariabelt og dynamisk system, således at der opnås følgende: 1. Systemet lineariseres således, at der opnås ensartet steprespons i...

Generator Approach to Evolutionary Optimization of Catalysts and its Integration with Surrogate Modeling

Czech Academy of Sciences Publication Activity Database

Holeňa, Martin; Linke, D.; Rodemerck, U.

2011-01-01

Roč. 159, č. 1 (2011), s. 84-95 ISSN 0920-5861 R&D Projects: GA ČR GA201/08/0802 Institutional research plan: CEZ:AV0Z10300504 Keywords : optimization of catalytic materials * evolutionary optimization * surrogate modeling * artificial neural networks * multilayer perceptron * regression boosting Subject RIV: IN - Informatics, Computer Science Impact factor: 3.407, year: 2011

Magnetoresistive multilayers deposited on the AAO membranes

International Nuclear Information System (INIS)

Malkinski, Leszek M.; Chalastaras, Athanasios; Vovk, Andriy; Jung, Jin-Seung; Kim, Eun-Mee; Jun, Jong-Ho; Ventrice, Carl A.

2005-01-01

Silicon and GaAs wafers are the most commonly used substrates for deposition of giant magnetoresistive (GMR) multilayers. We explored a new type of a substrate, prepared electrochemically by anodization of aluminum sheets, for deposition of GMR multilayers. The surface of this AAO substrate consists of nanosized hemispheres organized in a regular hexagonal array. The current applied along the substrate surface intersects many magnetic layers in the multilayered structure, which results in enhancement of giant magnetoresistance effect. The GMR effect in uncoupled Co/Cu multilayers was significantly larger than the magnetoresistance of similar structures deposited on Si

Heat stability evaluations of Co/SiO2 multilayers

International Nuclear Information System (INIS)

Ishino, Masahiko; Koike, Masato; Kanehira, Mika; Satou, Futami; Terauchi, Masami; Sano, Kazuo

2008-01-01

The heat stability of Co/SiO 2 multilayers was evaluated. Co/SiO 2 multilayer samples were deposited on Si substrate by means of an ion beam sputtering method, and annealed at temperatures from 100degC to 600degC in a vacuum furnace. For the structural and optical evaluations, small angle x-ray diffraction (XRD) measurements, soft x-ray reflectivity measurements, and transmission electron microscopy (TEM) observations were carried out. As the results, the Co/SiO 2 multilayer samples annealed up to 400degC maintained the initial multilayer structures, and kept almost the same soft x-ray reflectivities as that of the as-deposited Co/SiO 2 multilayer sample. A deterioration of the multilayer structure caused by the growth of Co grains was found on the Co/SiO 2 multilayer samples annealed over 500degC, and the soft x-ray reflectivity dropped in accordance with the deterioration of the multilayer structure. (author)

Numerical simulation and experiment on multilayer stagger-split die.

Science.gov (United States)

Liu, Zhiwei; Li, Mingzhe; Han, Qigang; Yang, Yunfei; Wang, Bolong; Sui, Zhou

2013-05-01

A novel ultra-high pressure device, multilayer stagger-split die, has been constructed based on the principle of "dividing dies before cracking." Multilayer stagger-split die includes an encircling ring and multilayer assemblages, and the mating surfaces of the multilayer assemblages are mutually staggered between adjacent layers. In this paper, we investigated the stressing features of this structure through finite element techniques, and the results were compared with those of the belt type die and single split die. The contrast experiments were also carried out to test the bearing pressure performance of multilayer stagger-split die. It is concluded that the stress distributions are reasonable and the materials are utilized effectively for multilayer stagger-split die. And experiments indicate that the multilayer stagger-split die can bear the greatest pressure.

EUV multilayer mirrors with enhanced stability

Science.gov (United States)

Benoit, Nicolas; Yulin, Sergiy; Feigl, Torsten; Kaiser, Norbert

2006-08-01

The application of multilayer optics in EUV lithography requires not only the highest possible normal-incidence reflectivity but also a long-term thermal and radiation stability at operating temperatures. This requirement is most important in the case of the collector mirror of the illumination system close to the EUV source where a short-time decrease in reflectivity is most likely. Mo/Si multilayer mirrors, designed for high normal reflectivity at the wavelength of 13.5 nm and deposited by dc magnetron sputtering, were directly exposed to EUV radiation without mitigation system. They presented a loss of reflectivity of more than 18% after only 8 hours of irradiation by a Xe-discharge source. Another problem of Mo/Si multilayers is the instability of reflectivity and peak wavelength under high heat load. It becomes especially critical at temperatures above 200°C, where interdiffusion between the molybdenum and the silicon layers is observed. The development of high-temperature multilayers was focused on two alternative Si-based systems: MoSi II/Si and interface engineered Mo/C/Si/C multilayer mirrors. The multilayer designs as well as the deposition parameters of all systems were optimized in terms of high peak reflectivity (>= 60 %) at a wavelength of 13.5 nm and high thermal stability. Small thermally induced changes of the MoSi II/Si multilayer properties were found but they were independent of the annealing time at all temperatures examined. A wavelength shift of -1.7% and a reflectivity drop of 1.0% have been found after annealing at 500°C for 100 hours. The total degradation of optical properties above 650°C can be explained by a recrystallization process of MoSi II layers.

Magnetic surfaces, thin films, and multilayers

International Nuclear Information System (INIS)

Parkin, S.S.P.; Renard, J.P.; Shinjo, T.; Zinn, W.

1992-01-01

This paper details recent developments in the magnetism of surfaces, thin films and multilayers. More than 20 invited contributions and more than 60 contributed papers attest to the great interest and vitality of this subject. In recent years the study of magnetic surfaces, thin films and multilayers has undergone a renaissance, partly motivated by the development of new growth and characterization techniques, but perhaps more so by the discovery of many exciting new properties, some quite unanticipated. These include, most recently, the discovery of enormous values of magnetoresistance in magnetic multilayers far exceeding those found in magnetic single layer films and the discovery of oscillatory interlayer coupling in transition metal multilayers. These experimental studies have motivated much theoretical work. However these developments are to a large extent powered by materials engineering and our ability to control and understand the growth of thin layers just a few atoms thick. The preparation of single crystal thin film layers and multilayers remains important for many studies, in particular, for properties dependent. These studies obviously require engineering not just a layer thicknesses but of lateral dimensions as well. The properties of such structures are already proving to be a great interest

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

Science.gov (United States)

D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

2010-03-09

It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

Compositionally Graded Multilayer Ceramic Capacitors.

Science.gov (United States)

Song, Hyun-Cheol; Zhou, Jie E; Maurya, Deepam; Yan, Yongke; Wang, Yu U; Priya, Shashank

2017-09-27

Multilayer ceramic capacitors (MLCC) are widely used in consumer electronics. Here, we provide a transformative method for achieving high dielectric response and tunability over a wide temperature range through design of compositionally graded multilayer (CGML) architecture. Compositionally graded MLCCs were found to exhibit enhanced dielectric tunability (70%) along with small dielectric losses (filters and power converters.

Nondestructive diagnosis of multilayer electronic plates

International Nuclear Information System (INIS)

Matvienko, A.N.; Savin, D.O.; Yas'ko, A.V.

1992-01-01

Methods of non-destructive tomographic investigation into multilayer printed plates using x radiation are described. Mathematic problem setting is given, experimental facility and methods for source data ecquisition are described. A special attention is paid to the consideration of the main factors differing the actual problem setting from the idealized one. Methods for accounting and correction of these factors are described. The efficiency of the approach proposed is demonstrated using the actual problems of reducing separate layers of multilayer printed plate metallization. The method developed is useful when exersizing control over multilayer printed plate production

Porous germanium multilayers

Energy Technology Data Exchange (ETDEWEB)

Garralaga Rojas, Enrique; Hensen, Jan; Brendel, Rolf [Institut fuer Solarenergieforschung Hameln (ISFH), Emmerthal (Germany); Carstensen, Juergen; Foell, Helmut [Chair for General Materials Science, Faculty of Engineering, Christian-Albrechts-University of Kiel (Germany)

2011-06-15

We present the reproducible fabrication of porous germanium (PGe) single- and multilayers. Mesoporous layers form on heavily doped 4'' p-type Ge wafers by electrochemical etching in highly concentrated HF-based electrolytes with concentrations in a range of 30-50 wt.%. Direct PGe formation is accompanied by a constant dissolution of the already-formed porous layer at the electrolyte/PGe interface, hence yielding a thinner substrate after etching. This effect inhibits multilayer formation as the starting layer is etched while forming the second layer. We avoid dissolution of the porous layer by alternating the etching bias from anodic to cathodic. PGe formation occurs during anodic etching whereas the cathodic step passivates pore walls with H-atoms and avoids electropolishing. The passivation lasts a limited time depending on the etching current density and electrolyte concentration, necessitating a repetition of the cathodic step at suitable intervals. With optimized alternating bias mesoporous multilayer production is possible. We control the porosity of each single layer by varying the etching current density and the electrolyte (copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

75 FR 79019 - Multilayered Wood Flooring From China

Science.gov (United States)

2010-12-17

...)] Multilayered Wood Flooring From China Determinations On the basis of the record \\1\\ developed in the subject... imports from China of multilayered wood flooring, provided for in subheadings 4409.10, 4409.29, 4412.31... multilayered wood flooring. The following companies are members of the CAHP: Anderson Hardwood Floors, LLC...

Wolter type I x-ray focusing mirror using multilayer coatings

International Nuclear Information System (INIS)

Chon, Kwon Su; Namba, Yoshiharu; Yoon, Kwon-Ha

2006-01-01

A multilayer coating is a useful addition to a mirror in the x-ray region and has been applied to normal incidence mirrors used with soft x rays. When a multilayer coating is used on grazing incidence optics, higher performance can be achieved than without it.Cr/Sc multilayers coated on a Wolter type I mirror substrate for a soft x-ray microscope are considered. The reflectivity and effective solid angle are calculated for Wolter type I mirrors with uniform and laterally graded multilayer coatings. The laterally graded multilayer mirror showed superior x-ray performance, and the multilayer tolerances were relaxed. This multilayer mirror could be especially useful in the soft x-ray microscope intended for biological applications

Irradiated multilayer film for primal meat packaging

International Nuclear Information System (INIS)

Lustig, S.; Schuetz, J.M.; Vicik, S.J.

1987-01-01

This patent deals with a heat-shrinkable, multilayer film suitable for use in fabricating bags for packaging primal and sub-primal meat cuts and processed meats. The multilayer film has a first outer layer of an ethylene-vinyl acetate copolymer, a core layer of a barrier film comprising vinylidene chloride-methyl acrylate copolymer, and a second outer layer of an ethylene-vinyl acetate copolymer. The multilayer film is preferably made by co-extrusion of the layers, and then it is biaxially stretched. After biaxial stretching, the multilayer film is irradiated to a dosage level of between 1 megarad and 5 megarads and heat-sealed in the form of a bag. The bag has improved storage stability characteristics

Spectral tailoring of nanoscale EUV and soft x-ray multilayer optics

Science.gov (United States)

Huang, Qiushi; Medvedev, Viacheslav; van de Kruijs, Robbert; Yakshin, Andrey; Louis, Eric; Bijkerk, Fred

2017-03-01

Extreme ultraviolet and soft X-ray (XUV) multilayer optics have experienced significant development over the past few years, particularly on controlling the spectral characteristics of light for advanced applications like EUV photolithography, space observation, and accelerator- or lab-based XUV experiments. Both planar and three dimensional multilayer structures have been developed to tailor the spectral response in a wide wavelength range. For the planar multilayer optics, different layered schemes are explored. Stacks of periodic multilayers and capping layers are demonstrated to achieve multi-channel reflection or suppression of the reflective properties. Aperiodic multilayer structures enable broadband reflection both in angles and wavelengths, with the possibility of polarization control. The broad wavelength band multilayer is also used to shape attosecond pulses for the study of ultrafast phenomena. Narrowband multilayer monochromators are delivered to bridge the resolution gap between crystals and regular multilayers. High spectral purity multilayers with innovated anti-reflection structures are shown to select spectrally clean XUV radiation from broadband X-ray sources, especially the plasma sources for EUV lithography. Significant progress is also made in the three dimensional multilayer optics, i.e., combining micro- and nanostructures with multilayers, in order to provide new freedom to tune the spectral response. Several kinds of multilayer gratings, including multilayer coated gratings, sliced multilayer gratings, and lamellar multilayer gratings are being pursued for high resolution and high efficiency XUV spectrometers/monochromators, with their advantages and disadvantages, respectively. Multilayer diffraction optics are also developed for spectral purity enhancement. New structures like gratings, zone plates, and pyramids that obtain full suppression of the unwanted radiation and high XUV reflectance are reviewed. Based on the present achievement

Multilayer Graphene for Waveguide Terahertz Modulator

DEFF Research Database (Denmark)

Khromova, I.; Andryieuski, Andrei; Lavrinenko, Andrei

2014-01-01

We study terahertz to infrared electromagnetic properties of multilayer graphene/dielectric artificial medium and present a novel concept of terahertz modulation at midinfrared wavelengths. This approach allows the realization of high-speed electrically controllable terahertz modulators based...... on hollow waveguide sections filled with multilayer graphene....

An X-ray grazing incidence phase multilayer grating

CERN Document Server

Chernov, V A; Mytnichenko, S V

2001-01-01

An X-ray grazing incidence phase multilayer grating, representing a thin grating placed on a multilayer mirror, is proposed. A high efficiency of grating diffraction can be obtained by the possibility of changing the phase shift of the wave diffracted from the multilayer under the Bragg and total external reflection conditions. A grazing incidence phase multilayer grating consisting of Pt grating stripes on a Ni/C multilayer and optimized for the hard X-ray range was fabricated. Its diffraction properties were studied at photon energies of 7 and 8 keV. The obtained maximum value of the diffraction efficiency of the +1 grating order was 9% at 7 keV and 6.5% at 8 keV. The data obtained are in a rather good accordance with the theory.

Multi-modular neural networks for the classification of e+e- hadronic events

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

Superconductivity in multilayer perovskite. Weak coupling analysis

International Nuclear Information System (INIS)

Koikegami, Shigeru; Yanagisawa, Takashi

2006-01-01

We investigate the superconductivity of a three-dimensional d-p model with a multilayer perovskite structure on the basis of the second-order perturbation theory within the weak coupling framework. Our model has been designed with multilayer high-T c superconducting cuprates in mind. In our model, multiple Fermi surfaces appear, and the component of a superconducting gap function develops on each band. We have found that the multilayer structure can stabilize the superconductivity in a wide doping range. (author)

Artificial neural networks applied to forecasting time series.

Science.gov (United States)

Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

2011-04-01

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

Exponential H(infinity) synchronization of general discrete-time chaotic neural networks with or without time delays.

Science.gov (United States)

Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

2010-08-01

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

The characterization of multilayers analyzers: Models and measurements

International Nuclear Information System (INIS)

Henke, B.L.; Vejio, J.Y.; Tackaberry, R.E.; Yamada, H.T.

1985-01-01

A procedure is described for the detailed characterization of multilayer analyzers which can be effectively applied to their design, optimization and application for absolute x-ray spectrometry. An accurate analytical model has been developed that is based upon a simple modification of the dynamical Darwin-Prins theory to extend its application to finite multilayer systems. Its equivalence to the optical E and M solution of the Fresnel equations at each interface is demonstrated by detailed calculation comparisons for the reflectivity of a multilayer throughout the angular range of incidence of 0 to 90 0 . A special spectrograph and experimental method is described for the measurement of the absolute reflectivity characteristics of the multilayer. The experimental measurements at three photon energies in the 100-200 eV region are fit by the analytical modified Darwin-Prins equation (MDP) for I(θ), generating a detailed characterization of two ''state of the art'' multilayers, a sputtered tungsten-carbon of 2d ≅ 70 A and a molecular lead separate of 2d ≅ 100 A. The fitting parameters that are determined in this procedure are applied to help establish the structural characteristics of the particular multilayer

ALGORITMO PARA O RECONHECIMENTO DE CARACTERES MANUSCRITOS

Directory of Open Access Journals (Sweden)

Rafael Arthur Rocha Miranda

2013-12-01

Full Text Available The handwritten character recognition in digital images is an important and challenging area of study in Computer Vision, with several possibilities for applications to facilitate the daily work of the people. This paper presents an algorithm for handwritten character recognition with two proposed approaches. The first proposal complements earlier work by some of the authors of this article, including 290 new attributes, based on histograms, Zoning and transformed Hit-or-Miss. The second proposal uses 79 attributes, obtained from frequency information, distance-edge character and densities, which performs classification using an approach based on maximum and minimum values of each attribute for each character type, and a neural network Multilayer Perceptron. The large number of attributes contributes to a more precise discrimination of characters, on the other hand, the extraction of these descriptors is easy because only performs the pixels counting. Thus, the processing time in this task is reduced. Although the classification using a Multilayer Perceptron neural network achieved a higher hit rate, the processing time of the maximum and minimum limits based classification is smaller, allowing its use in applications where the processing time is critical.

Neural networks in front-end processing and control

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.

1992-01-01

Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper the authors illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. The authors also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. The authors outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. The authors also present some of the difficulties encountered in applying these networks

Neural networks in front-end processing and control

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.

1991-07-01

Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper we illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. We also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. We outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. We also present some of the difficulties encountered in applying these networks. (author) 13 figs., 9 refs

Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics

International Nuclear Information System (INIS)

Dong, Zibo; Yang, Dazhi; Reindl, Thomas; Walsh, Wilfred M.

2014-01-01

Highlights: • Satellite image analysis is performed and cloud cover index is classified using self-organizing maps (SOM). • The ESSS model is used to forecast cloud cover index. • Solar irradiance is estimated using multi-layer perceptron (MLP). • The proposed model shows better accuracy than other investigated models. - Abstract: We forecast hourly solar irradiance time series using satellite image analysis and a hybrid exponential smoothing state space (ESSS) model together with artificial neural networks (ANN). Since cloud cover is the major factor affecting solar irradiance, cloud detection and classification are crucial to forecast solar irradiance. Geostationary satellite images provide cloud information, allowing a cloud cover index to be derived and analysed using self-organizing maps (SOM). Owing to the stochastic nature of cloud generation in tropical regions, the ESSS model is used to forecast cloud cover index. Among different models applied in ANN, we favour the multi-layer perceptron (MLP) to derive solar irradiance based on the cloud cover index. This hybrid model has been used to forecast hourly solar irradiance in Singapore and the technique is found to outperform traditional forecasting models

Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation

Directory of Open Access Journals (Sweden)

E. Nazemi

2016-02-01

Full Text Available Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas–liquid two-phase flows by using γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-ray attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.

Predictive models reduce talent development costs in female gymnastics.

Science.gov (United States)

Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

2017-04-01

This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

Aid decision algorithms to estimate the risk in congenital heart surgery.

Science.gov (United States)

Ruiz-Fernández, Daniel; Monsalve Torra, Ana; Soriano-Payá, Antonio; Marín-Alonso, Oscar; Triana Palencia, Eddy

2016-04-01

In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Ordered organic-organic multilayer growth

Science.gov (United States)

Forrest, Stephen R; Lunt, Richard R

2015-01-13

An ordered multilayer crystalline organic thin film structure is formed by depositing at least two layers of thin film crystalline organic materials successively wherein the at least two thin film layers are selected to have their surface energies within .+-.50% of each other, and preferably within .+-.15% of each other, whereby every thin film layer within the multilayer crystalline organic thin film structure exhibit a quasi-epitaxial relationship with the adjacent crystalline organic thin film.

Localization of multilayer networks by optimized single-layer rewiring.

Science.gov (United States)

Jalan, Sarika; Pradhan, Priodyuti

2018-04-01

We study localization properties of principal eigenvectors (PEVs) of multilayer networks (MNs). Starting with a multilayer network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multilayer network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that rewiring only one layer is enough to attain a MN having a highly localized PEV. Our investigation reveals that a single edge rewiring of the optimized MN can lead to the complete delocalization of a highly localized PEV. This sensitivity in the localization behavior of PEVs is accompanied with the second largest eigenvalue lying very close to the largest one. This observation opens an avenue to gain a deeper insight into the origin of PEV localization of networks. Furthermore, analysis of multilayer networks constructed using real-world social and biological data shows that the localization properties of these real-world multilayer networks are in good agreement with the simulation results for the model multilayer network. This paper is relevant to applications that require understanding propagation of perturbation in multilayer networks.

Neutron diffraction studies of thin film multilayer structures

International Nuclear Information System (INIS)

Majkrzak, C.F.

1985-01-01

The application of neutron diffraction methods to the study of the microscopic chemical and magnetic structures of thin film multilayers is reviewed. Multilayer diffraction phenomena are described in general and in particular for the case in which one of the materials of a bilayer is ferromagnetic and the neutron beam polarized. Recent neutron diffraction measurements performed on some interesting multilayer systems are discussed. 70 refs., 5 figs

Design and fabrication of heat resistant multilayers

International Nuclear Information System (INIS)

Thorne, J.M.; Knight, L.V.; Peterson, B.G.; Perkins, R.T.; Gray, K.J.

1986-01-01

Many promising applications of multilayer x-ray optical elements subject them to intense radiation. This paper discusses the selection of optimal pairs of materials to resist heat damage and presents simulations of multilayer performance under extreme heat loadings

Refractive index contrast in porous silicon multilayers

Energy Technology Data Exchange (ETDEWEB)

Nava, R.; Mora, M.B. de la; Tagueena-Martinez, J. [Centro de Investigacion en Energia, Universidad Nacional Autonoma de Mexico, Temixco, Morelos (Mexico); Rio, J.A. del [Centro de Investigacion en Energia, Universidad Nacional Autonoma de Mexico, Temixco, Morelos (Mexico); Centro Morelense de Innovacion y Transferencia Tecnologica, Consejo de Ciencia y Tecnologia del Estado de Morelos (Mexico)

2009-07-15

Two of the most important properties of a porous silicon multilayer for photonic applications are flat interfaces and a relative large refractive index contrast between layers in the optical wavelength range. In this work, we studied the effect of the current density and HF electrolyte concentration on the refractive index of porous silicon. With the purpose of increasing the refractive index contrast in a multilayer, the refractive index of porous silicon produced at low current was studied in detail. The current density applied to produce the low porosity layers was limited in order to keep the electrolyte flow through the multilayer structure and to avoid deformation of layer interfaces. We found that an electrolyte composed of hydrofluoric acid, ethanol and glycerin in a ratio of 3:7:1 gives a refractive index contrast around 1.3/2.8 at 600 nm. Several multilayer structures with this refractive index contrast were fabricated, such as dielectric Bragg mirrors and microcavities. Reflectance spectra of the structures show the photonic quality of porous silicon multilayers produced under these electrochemical conditions. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

Advances in polyelectrolyte multilayer nanofilms as tunable drug delivery systems

Science.gov (United States)

Jiang, Bingbing; Barnett, John B; Li, Bingyun

2009-01-01

There has been considerable interest in polyelectrolyte multilayer nanofilms, which have a variety of applications ranging from optical and electrochemical materials to biomedical devices. Polyelectrolyte multilayer nanofilms are constructed from aqueous solutions using electrostatic layer-by-layer self-assembly of oppositely-charged polyelectrolytes on a solid substrate. Multifunctional polyelectrolyte multilayer nanofilms have been studied using charged dyes, metal and inorganic nanoparticles, DNA, proteins, and viruses. In the past few years, there has been increasing attention to developing polyelectrolyte multilayer nanofilms as drug delivery vehicles. In this mini-review, we present recent developments in polyelectrolyte multilayer nanofilms with tunable drug delivery properties, with particular emphasis on the strategies in tuning the loading and release of drugs in polyelectrolyte multilayer nanofilms as well as their applications. PMID:24198464

Recurrent varicocele

Directory of Open Access Journals (Sweden)

Katherine Rotker

2016-01-01

Full Text Available Varicocele recurrence is one of the most common complications associated with varicocele repair. A systematic review was performed to evaluate varicocele recurrence rates, anatomic causes of recurrence, and methods of management of recurrent varicoceles. The PubMed database was evaluated using keywords "recurrent" and "varicocele" as well as MESH criteria "recurrent" and "varicocele." Articles were not included that were not in English, represented single case reports, focused solely on subclinical varicocele, or focused solely on a pediatric population (age <18. Rates of recurrence vary with the technique of varicocele repair from 0% to 35%. Anatomy of recurrence can be defined by venography. Management of varicocele recurrence can be surgical or via embolization.

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

OpenAIRE

Chen, Pin-Yu; Hero, Alfred O.

2017-01-01

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal plus noise model, we provide a phase transition analys...

Electron/pion identification in the CBM TRD using a multilayer perceptron

International Nuclear Information System (INIS)

Akishina, E.P.; Akishina, T.P.; Ivanov, V.V.; Maevskaya, A.I.; Afanas'ev, O.A.

2008-01-01

The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. A possibility to solve such a problem by applying an artificial neural network (ANN) is considered. As input information for the network we used both the samples of energy losses of pions or electrons in the TRD absorbers and the 'clever' variable obtained on the basis of the original data. We show that usage of this new variable permits one to reach a reliable level of particle recognition no longer than after 10-20 training epochs; there are practically no fluctuations against the trend, and the needed level of pions suppression is obtained under the condition of a minimal loss of electrons

Particle identification using artificial neural networks at BESIII

International Nuclear Information System (INIS)

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

2008-01-01

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

Desktop aligner for fabrication of multilayer microfluidic devices.

Science.gov (United States)

Li, Xiang; Yu, Zeta Tak For; Geraldo, Dalton; Weng, Shinuo; Alve, Nitesh; Dun, Wu; Kini, Akshay; Patel, Karan; Shu, Roberto; Zhang, Feng; Li, Gang; Jin, Qinghui; Fu, Jianping

2015-07-01

Multilayer assembly is a commonly used technique to construct multilayer polydimethylsiloxane (PDMS)-based microfluidic devices with complex 3D architecture and connectivity for large-scale microfluidic integration. Accurate alignment of structure features on different PDMS layers before their permanent bonding is critical in determining the yield and quality of assembled multilayer microfluidic devices. Herein, we report a custom-built desktop aligner capable of both local and global alignments of PDMS layers covering a broad size range. Two digital microscopes were incorporated into the aligner design to allow accurate global alignment of PDMS structures up to 4 in. in diameter. Both local and global alignment accuracies of the desktop aligner were determined to be about 20 μm cm(-1). To demonstrate its utility for fabrication of integrated multilayer PDMS microfluidic devices, we applied the desktop aligner to achieve accurate alignment of different functional PDMS layers in multilayer microfluidics including an organs-on-chips device as well as a microfluidic device integrated with vertical passages connecting channels located in different PDMS layers. Owing to its convenient operation, high accuracy, low cost, light weight, and portability, the desktop aligner is useful for microfluidic researchers to achieve rapid and accurate alignment for generating multilayer PDMS microfluidic devices.

Asynchronous cracking with dissimilar paths in multilayer graphene.

Science.gov (United States)

Jang, Bongkyun; Kim, Byungwoon; Kim, Jae-Hyun; Lee, Hak-Joo; Sumigawa, Takashi; Kitamura, Takayuki

2017-11-16

Multilayer graphene consists of a stack of single-atomic-thick monolayer graphene sheets bound with π-π interactions and is a fascinating model material opening up a new field of fracture mechanics. In this study, fracture behavior of single-crystalline multilayer graphene was investigated using an in situ mode I fracture test under a scanning electron microscope, and abnormal crack propagation in multilayer graphene was identified for the first time. The fracture toughness of graphene was determined from the measured load-displacement curves and the realistic finite element modelling of specimen geometries. Nonlinear fracture behavior of the multilayer graphene is discussed based on nonlinear elastic fracture mechanics. In situ scanning electron microscope images obtained during the fracture test showed asynchronous crack propagation along independent paths, causing interlayer shear stress and slippages. We also found that energy dissipation by interlayer slippages between the graphene layers is the reason for the enhanced fracture toughness of multilayer graphene. The asynchronous cracking with independent paths is a unique cracking and toughening mechanism for single-crystalline multilayer graphene, which is not observed for the monolayer graphene. This could provide a useful insight for the design and development of graphene-based composite materials for structural applications.

Recycling of Polymer-Based Multilayer Packaging: A Review

Directory of Open Access Journals (Sweden)

Katharina Kaiser

2017-12-01

Full Text Available Polymer-based multilayer packaging materials are commonly used in order to combine the respective performance of different polymers. By this approach, the tailored functionality of packaging concepts is created to sufficiently protect sensitive food products and thus obtain extended shelf life. However, because of their poor recyclability, most multilayers are usually incinerated or landfilled, counteracting the efforts towards a circular economy and crude oil independency. This review depicts the current state of the European multilayer packaging market and sketches the current end-of-life situation of postconsumer multilayer packaging waste in Germany. In the main section, a general overview of the state of research about material recycling of different multilayer packaging systems is provided. It is divided into two subsections, whereby one describes methods to achieve a separation of the different components, either by delamination or the selective dissolution–reprecipitation technique, and the other describes methods to achieve recycling by compatibilization of nonmiscible polymer types. While compatibilization methods and the technique of dissolution–reprecipitation are already extensively studied, the delamination of packaging has not been investigated systematically. All the presented options are able to recycle multilayer packaging, but also have drawbacks like a limited scope or a high expenditure of energy.

Robust giant magnetoresistive effect type multilayer sensor

NARCIS (Netherlands)

Lenssen, K.M.H.; Kuiper, A.E.T.; Roozeboom, F.

2002-01-01

A robust Giant Magneto Resistive effect type multilayer sensor comprising a free and a pinned ferromagnetic layer, which can withstand high temperatures and strong magnetic fields as required in automotive applications. The GMR multi-layer has an asymmetric magneto-resistive curve and enables

Irradiated multilayer film for primal meat packaging

International Nuclear Information System (INIS)

Lustig, S.; Schuetz, J.M.; Vicik, S.J.

1987-01-01

This patent deals with a heat-shrinkable, multilayer film suitable for use in fabricating bags for packaging primal and sub-primal meat cuts and processed meats. The multilayer film has a first outer layer of a first ethylene-vinyl acetate copolymer, a core layer of a polyvinylidene chloride-vinyl chloride copolymer containing between about 70 weight percent and about 90 weight percent vinylidene chloride as a barrier film, and a second outer layer of a second ethylene-vinyl acetate copolymer. The multilayer film is preferably made by co-extrusion of the layers, and then it is biaxially stretched. After biaxial stretching, the entire multilayer film is substantially uniformly irradiated to a dosage level of between about 2 megarads and about 3 megarads and heat-sealed in the form of a bag. The film is not significantly discoloured by the irradiation and the bag has improved toughness properties and heat-sealing characteristics

Transfer matrices for multilayer structures

International Nuclear Information System (INIS)

Baquero, R.

1988-08-01

We consider four of the transfer matrices defined to deal with multilayer structures. We deduce algorithms to calculate them numerically, in a simple and neat way. We illustrate their application to semi-infinite systems using SGFM formulae. These algorithms are of fast convergence and allow a calculation of bulk-, surface- and inner-layers band structure in good agreement with much more sophisticated calculations. Supermatrices, interfaces and multilayer structures can be calculated in this way with a small computational effort. (author). 10 refs

Polymerization of vinyl stearate multilayers by electron beam irradiation

International Nuclear Information System (INIS)

Nishii, Masanobu; Hatada, Motoyoshi

1975-01-01

Studies on the radiation-induced polymerization of vinyl stearate (VST) multilayers were carried out. The VST multilayers built-up on an aluminum plated glass plate by Langmuir-Blodgett technique were irradiated with electron beams from a Van de Graaff electron accelerator in nitrogen atmosphere. The structure of the multilayers and the effects of irradiation were investigated by X-ray diffractometry, contact angle measurement, multireflection infrared spectroscopy, and scanning electron microscopy. The VST multilayers became insoluble to methanol by the irradiation, and the multi-reflection infrared spectrum of the VST multilayers turned into that of poly (VST) with increasing dosage. The polymerization proceeded during the irradiation at the temperature range between -10 0 and 10 0 C, and the conversion attained to 90% within 2.5 minutes (total dose, 5.6 Mrads). The multilayers irradiated above 13 Mrads turned into the polymer film insoluble to benzene, indicating that the polymer chains were cross-linked by the irradiation. Stearic acid which was formed by the irradiation of VST at nitrogen-water interface as a hydrolysis product was not detected in this system. (auth.)

Corrugated grating on organic multilayer Bragg reflector

Science.gov (United States)

Jaquet, Sylvain; Scharf, Toralf; Herzig, Hans Peter

2007-08-01

Polymeric multilayer Bragg structures are combined with diffractive gratings to produce artificial visual color effects. A particular effect is expected due to the angular reflection dependence of the multilayer Bragg structure and the dispersion caused by the grating. The combined effects can also be used to design particular filter functions and various resonant structures. The multilayer Bragg structure is fabricated by spin-coating of two different low-cost polymer materials in solution on a cleaned glass substrate. These polymers have a refractive index difference of about 0.15 and permit multilayer coatings without interlayer problems. Master gratings of different periods are realized by laser beam interference and replicated gratings are superimposed on the multilayer structure by soft embossing in a UV curing glue. The fabrication process requires only polymer materials. The obtained devices are stable and robust. Angular dependent reflection spectrums for the visible are measured. These results show that it is possible to obtain unexpected reflection effects. A rich variety of color spectra can be generated, which is not possible with a single grating. This can be explained by the coupling of transmission of grating orders and the Bragg reflection band. A simple model permits to explain some of the spectral vs angular dependence of reflected light.

Inkjet-printed Polyvinyl Alcohol Multilayers.

Science.gov (United States)

Salaoru, Iulia; Zhou, Zuoxin; Morris, Peter; Gibbons, Gregory J

2017-05-11

Inkjet printing is a modern method for polymer processing, and in this work, we demonstrate that this technology is capable of producing polyvinyl alcohol (PVOH) multilayer structures. A polyvinyl alcohol aqueous solution was formulated. The intrinsic properties of the ink, such as surface tension, viscosity, pH, and time stability, were investigated. The PVOH-based ink was a neutral solution (pH 6.7) with a surface tension of 39.3 mN/m and a viscosity of 7.5 cP. The ink displayed pseudoplastic (non-Newtonian shear thinning) behavior at low shear rates, and overall, it demonstrated good time stability. The wettability of the ink on different substrates was investigated, and glass was identified as the most suitable substrate in this particular case. A proprietary 3D inkjet printer was employed to manufacture polymer multilayer structures. The morphology, surface profile, and thickness uniformity of inkjet-printed multilayers were evaluated via optical microscopy.

Transmission fingerprints in quasiperiodic magnonic multilayers

Energy Technology Data Exchange (ETDEWEB)

Coelho, I.P. [Departamento de Ensino Superior, Instituto Federal de Educacao, Ciencia e Tecnologia do Maranhao, Imperatriz-MA 65919-050 (Brazil); Departamento de Fisica, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil); Vasconcelos, M.S. [Escola de Ciencias e Tecnologia, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil); Bezerra, C.G., E-mail: cbezerra@dfte.ufrn.br [Departamento de Fisica, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil)

2011-12-15

In this paper we investigated the influence of mirror symmetry on the transmission spectra of quasiperiodic magnonic multilayers arranged according to Fibonacci, Thue-Morse and double period quasiperiodic sequences. We consider that the multilayers composed of two simple cubic Heisenberg ferromagnets with bulk exchange constants J{sub A} and J{sub B} and spin quantum numbers S{sub A} and S{sub B}, respectively. The multilayer structure is surrounded by two semi-infinite slabs of a third Heisenberg ferromagnetic material with exchange constant J{sub C} and spin quantum number S{sub C}. For simplicity, the lattice constant has the same value a in each material, corresponding to epitaxial growth at the interfaces. The transfer matrix treatment was used for the exchange-dominated regime, taking into account the random phase approximation (RPA). Our numerical results illustrate the effects of mirror symmetry on (i) transmission spectra and (ii) transmission fingerprints. - Highlights: > We model quasiperiodic magnetic multilayers presenting mirror symmetry. > We investigated the allowed and forbidden bands of magnonic transmission. > Transmission return maps show the influence of mirror symmetry. > Mirror symmetry has no effect on the Fibonacci case. > Mirror symmetry does have effect on the Thue-Morse and double period cases.

Transmission fingerprints in quasiperiodic magnonic multilayers

International Nuclear Information System (INIS)

Coelho, I.P.; Vasconcelos, M.S.; Bezerra, C.G.

2011-01-01

In this paper we investigated the influence of mirror symmetry on the transmission spectra of quasiperiodic magnonic multilayers arranged according to Fibonacci, Thue-Morse and double period quasiperiodic sequences. We consider that the multilayers composed of two simple cubic Heisenberg ferromagnets with bulk exchange constants J A and J B and spin quantum numbers S A and S B , respectively. The multilayer structure is surrounded by two semi-infinite slabs of a third Heisenberg ferromagnetic material with exchange constant J C and spin quantum number S C . For simplicity, the lattice constant has the same value a in each material, corresponding to epitaxial growth at the interfaces. The transfer matrix treatment was used for the exchange-dominated regime, taking into account the random phase approximation (RPA). Our numerical results illustrate the effects of mirror symmetry on (i) transmission spectra and (ii) transmission fingerprints. - Highlights: → We model quasiperiodic magnetic multilayers presenting mirror symmetry. → We investigated the allowed and forbidden bands of magnonic transmission. → Transmission return maps show the influence of mirror symmetry. → Mirror symmetry has no effect on the Fibonacci case. → Mirror symmetry does have effect on the Thue-Morse and double period cases.

EUV multilayer mirror, optical system including a multilayer mirror and method of manufacturing a multilayer mirror

NARCIS (Netherlands)

Huang, Qiushi; Louis, Eric; Bijkerk, Frederik; de Boer, Meint J.; von Blanckenhagen, G.

2016-01-01

A multilayer mirror (M) reflecting extreme ultraviolet (EUV) radiation from a first wave-length range in a EUV spectral region comprises a substrate (SUB) and a stack of layers (SL) on the substrate, the stack of layers comprising layers comprising a low index material and a high index material, the

High-frequency characteristics of glass/ceramic composite and alumina multilayer structures

International Nuclear Information System (INIS)

Niwa, K.; Suzuki, H.; Yokoyama, H.; Kamechara, N.; Tsubone, K.; Tanisawa, H.; Sugiki, H.

1990-01-01

This paper reports the transmission characteristics of glass/ceramic composite (borosilicate glass/alumina) and alumina multilayer structures examined. The triplate stripline formed in the glass/ceramic multilayer shows low conductor and dielectric loss. Alumina multilayer, however, has twice the transmission loss at 10 GHz, because the resistivity of W in the alumina multilayer is higher than the Cu in the glass/ceramic multilayer. Crosstalk between striplines in the glass/ceramics is less than -80 dB up to 11 GHz and 9 GHz for alumina

Multilayer optics for x-ray analysis: design - fabrication - application

International Nuclear Information System (INIS)

Dietsch, R.; Holz, Th.; Bruegemann, L.

2002-01-01

Full text: The use of multilayer optics induced a decisive extension of opportunities in laboratory based X-ray analysis. With the growing number of different applications, more and more dedicated X-ray optics are required, optimized for the spectral range they are intended to be used for. Both the characteristic of the used X-ray source and the design of the multilayer optics finally define the performance of the conditioned incident beam for the application. In any case, qualified spacer and absorber materials have to be selected for the deposition of the multilayer in respect to the designated X-ray wavelength. X-ray optical devices based on uniform multilayers have the advantage of a wide acceptance angle but show chromatic aberrations. This effect can be avoided by synthesizing a multilayer with a lateral thickness gradient. The gradient ensures that any beam of a certain wavelength emitted from an infinite narrow X-ray source impinging the multilayer optics fulfills the Bragg condition. Three different types of curvature of laterally graded multilayer mirrors are used for X-ray analysis experiments: parabolic, elliptic and planar, which result in parallel, focusing and divergent beam conditions, respectively. Furthermore, the X-ray beam characteristics: intensity, monochromasy, divergence, beam width and brilliance can be additionally conditioned by combining one multilayer optics with either a different optic and/or with a crystal monochromator. The deposition of nanometer-multilayers, used as X-ray optical components, result in extraordinary requirements of the deposition process concerning precision, reproducibility and long term stability. Across a stack of more than 150 individual layers with thicknesses in the range between 1 to 10 nm, a variation of single layer thickness considerably lower than σ D = 0.1 nm and an interface roughness below σ R = 0.25 nm have to be achieved. Thickness homogeneity Δd/d -8 have to be guaranteed across macroscopic

Bonded Multilayer Laue Lens for focusing hard X-rays

International Nuclear Information System (INIS)

Liu Chian; Conley, R.; Qian, J.; Kewish, C.M.; Macrander, A.T.; Maser, J.; Kang, H.C.; Yan, H.; Stephenson, G.B.

2007-01-01

We have fabricated partial Multilayer Laue Lens (MLL) linear zone plate structures with thousands of alternating WSi 2 and Si layers and various outermost zone widths according to the Fresnel zone plate formula. Using partial MLL structures, we were able to focus hard X-rays to line foci with a width of 30 nm and below. Here, we describe challenges and approaches used to bond these multilayers to achieve line and point focusing. Bonding was done by coating two multilayers with AuSn and heating in a vacuum oven at 280-300 o C. X-ray reflectivity measurements confirmed that there was no change in the multilayers after heating to 350 o C. A bonded MLL was polished to a 5-25 μm wedge without cracking. SEM image analyses found well-positioned multilayers after bonding. These results demonstrate the feasibility of a bonded full MLL for focusing hard X-rays

Multilayer cladding with hyperbolic dispersion for plasmonic waveguides

DEFF Research Database (Denmark)

Babicheva, Viktoriia; Shalaginov, Mikhail Y.; Ishii, Satoshi

2015-01-01

We study the properties of plasmonic waveguides with a dielectric core and multilayer metal-dielectric claddings that possess hyperbolic dispersion. The waveguides hyperbolic multilayer claddings show better performance in comparison to conventional plasmonic waveguides. © OSA 2015....

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

OpenAIRE

Rodan, Ali; Fayyoumi, Ayham; Faris, Hossam; Alsakran, Jamal; Al-Kadi, Omar

2015-01-01

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons ...

Multilayer scaffolds in orthopaedic tissue engineering.

Science.gov (United States)

Atesok, Kivanc; Doral, M Nedim; Karlsson, Jon; Egol, Kenneth A; Jazrawi, Laith M; Coelho, Paulo G; Martinez, Amaury; Matsumoto, Tomoyuki; Owens, Brett D; Ochi, Mitsuo; Hurwitz, Shepard R; Atala, Anthony; Fu, Freddie H; Lu, Helen H; Rodeo, Scott A

2016-07-01

The purpose of this study was to summarize the recent developments in the field of tissue engineering as they relate to multilayer scaffold designs in musculoskeletal regeneration. Clinical and basic research studies that highlight the current knowledge and potential future applications of the multilayer scaffolds in orthopaedic tissue engineering were evaluated and the best evidence collected. Studies were divided into three main categories based on tissue types and interfaces for which multilayer scaffolds were used to regenerate: bone, osteochondral junction and tendon-to-bone interfaces. In vitro and in vivo studies indicate that the use of stratified scaffolds composed of multiple layers with distinct compositions for regeneration of distinct tissue types within the same scaffold and anatomic location is feasible. This emerging tissue engineering approach has potential applications in regeneration of bone defects, osteochondral lesions and tendon-to-bone interfaces with successful basic research findings that encourage clinical applications. Present data supporting the advantages of the use of multilayer scaffolds as an emerging strategy in musculoskeletal tissue engineering are promising, however, still limited. Positive impacts of the use of next generation scaffolds in orthopaedic tissue engineering can be expected in terms of decreasing the invasiveness of current grafting techniques used for reconstruction of bone and osteochondral defects, and tendon-to-bone interfaces in near future.

Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers

International Nuclear Information System (INIS)

Baydush, Alan H.; Catarious, David M. Jr.; Lo, Joseph Y.; Abbey, Craig K.; Floyd, Carey E. Jr.

2001-01-01

We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (A Z ). The classifier was optimized by adding additional features until the A Z declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an A Z of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1%. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives

Advanced materials for multilayer mirrors for extreme ultraviolet solar astronomy.

Science.gov (United States)

Bogachev, S A; Chkhalo, N I; Kuzin, S V; Pariev, D E; Polkovnikov, V N; Salashchenko, N N; Shestov, S V; Zuev, S Y

2016-03-20

We provide an analysis of contemporary multilayer optics for extreme ultraviolet (EUV) solar astronomy in the wavelength ranges: λ=12.9-13.3  nm, λ=17-21  nm, λ=28-33  nm, and λ=58.4  nm. We found new material pairs, which will make new spaceborne experiments possible due to the high reflection efficiencies, spectral resolution, and long-term stabilities of the proposed multilayer coatings. In the spectral range λ=13  nm, Mo/Be multilayer mirrors were shown to demonstrate a better ratio of reflection efficiency and spectral resolution compared with the commonly used Mo/Si. In the spectral range λ=17-21  nm, a new multilayer structure Al/Si was proposed, which had higher spectral resolution along with comparable reflection efficiency compared with the commonly used Al/Zr multilayer structures. In the spectral range λ=30  nm, the Si/B4C/Mg/Cr multilayer structure turned out to best obey reflection efficiency and long-term stability. The B4C and Cr layers prevented mutual diffusion of the Si and Mg layers. For the spectral range λ=58  nm, a new multilayer Mo/Mg-based structure was developed; its reflection efficiency and long-term stability have been analyzed. We also investigated intrinsic stresses inherent for most of the multilayer structures and proposed possibilities for stress elimination.

Identifying key nodes in multilayer networks based on tensor decomposition.

Science.gov (United States)

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

Magnetic studies in evaporated Ni/Pd multilayers

International Nuclear Information System (INIS)

Chafai, K.; Salhi, H.; Lassri, H.; Yamkane, Z.; Lassri, M.; Abid, M.; Hlil, E.K.; Krishnan, R.

2011-01-01

The magnetic properties of Ni/Pd multilayers, prepared by sequential evaporation in ultrahigh vacuum, have been studied. The Ni thickness dependence of the magnetization and magnetic anisotropy is discussed. The temperature dependence of the spontaneous magnetization is well described by a T 3/2 law in all multilayers. A spin-wave theory has been used to explain the temperature dependence of the spontaneous magnetization, and the approximate values for the exchange interactions for various Ni layer thicknesses have been obtained. - Research highlights: → The magnetic properties of Ni/Pd multilayers, prepared by sequential evaporation in ultrahigh vacuum, have been studied. → The temperature dependence of the spontaneous magnetization is well described by a T 3/2 law in Ni/Pd multilayers. → The spin-wave constant B was observed to depend on t Ni nonmonotonically. → A spin-wave theory has been used to explain the temperature dependence of the spontaneous magnetization. → The approximate values for the exchange interactions for various Ni layer thicknesses have been obtained.

Fabrication and performance characterization of Al/Ni multilayer energetic films

Science.gov (United States)

Yang, Cheng; Hu, Yan; Shen, Ruiqi; Ye, Yinghua; Wang, Shouxu; Hua, Tianli

2014-02-01

Al/Ni multilayer bridge films, which were composed of alternate Al and Ni layers with bilayer thicknesses of 50, 100 and 200 nm, were prepared by RF magnetron sputtering. In each bilayer, the thickness ratio of Al to Ni was maintained at 3:2 to obtain an overall 1:1 atomic composition. The total thickness of Al/Ni multilayer films was 2 μm. XRD measurements show that the compound of AlNi is the final product of the exothermic reactions. DSC curves show that the values of heat release in Al/Ni multilayer films with bilayer thicknesses of 50, 100 and 200 nm are 389.43, 396.69 and 409.92 J g-1, respectively. The temperatures of Al/Ni multilayer films were obviously higher than those of Al bridge film and Ni bridge film. Al/Ni multilayer films with modulation of 50 nm had the highest electrical explosion temperature of 7000 K. The exothermic reaction in Al/Ni multilayer films leads to a more intense electric explosion. Al/Ni multilayer bridge films with modulation period of 50 nm explode more rapidly and intensely than other bridge films because decreasing the bilayer thickness results in an increased reaction velocity.

Oromucosal multilayer films for tailor-made, controlled drug delivery.

Science.gov (United States)

Lindert, Sandra; Breitkreutz, Jörg

2017-11-01

The oral mucosa has recently become increasingly important as an alternative administration route for tailor-made, controlled drug delivery. Oromucosal multilayer films, assigned to the monograph oromucosal preparations in the Ph.Eur. may be a promising dosage form to overcome the requirements related to this drug delivery site. Areas covered: We provide an overview of multilayer films as drug delivery tools, and discuss manufacturing processes and characterization methods. We focus on the suitability of characterization methods for particular requirements of multilayer films. A classification was performed covering indication areas and APIs incorporated in multilayer film systems for oromucosal use in order to provide a summary of data published in this field. Expert opinion: The shift in drug development to high molecular weight drugs will influence the field of pharmaceutical development and delivery technologies. For a high number of indication areas, such as hormonal disorders, cardiovascular diseases or local treatment of infections, the flexible layer design of oromucosal multilayer films provides a promising option for tailor-made, controlled delivery of APIs to or through defined surfaces in the oral cavity. However, there is a lack of discriminating or standardized testing methods to assess the quality of multilayer films in a reliable way.

Design of a normal incidence multilayer imaging X-ray microscope

Science.gov (United States)

Shealy, David L.; Gabardi, David R.; Hoover, Richard B.; Walker, Arthur B. C., Jr.; Lindblom, Joakim F.

Normal incidence multilayer Cassegrain X-ray telescopes were flown on the Stanford/MSFC Rocket X-ray Spectroheliograph. These instruments produced high spatial resolution images of the sun and conclusively demonstrated that doubly reflecting multilayer X-ray optical systems are feasible. The images indicated that aplanatic imaging soft X-ray/EUV microscopes should be achievable using multilayer optics technology. A doubly reflecting normal incidence multilayer imaging X-ray microscope based on the Schwarzschild configuration has been designed. The design of the microscope and the results of the optical system ray trace analysis are discussed. High resolution aplanatic imaging X-ray microscopes using normal incidence multilayer X-ray mirrors should have many important applications in advanced X-ray astronomical instrumentation, X-ray lithography, biological, biomedical, metallurgical, and laser fusion research.

78 FR 30329 - Multilayered Wood Flooring from China

Science.gov (United States)

2013-05-22

...)] Multilayered Wood Flooring from China AGENCY: United States International Trade Commission. ACTION: Notice of...-1179 (Final) concerning multilayered wood flooring (``MLWF'') from China. For further information... reconsider ``its decision not to investigate domestic producers of hardwood plywood used for flooring'' 2. to...

POLYELECTROLYTE MULTILAYER STAMPING IN AQUEOUS PHASE AND NON-CONTACT MODE

Science.gov (United States)

Mehrotra, Sumit; Lee, Ilsoon; Liu, Chun; Chan, Christina

2011-01-01

Polyelectrolyte multilayer (PEM) transfer printing has been previously achieved by stamping under dry conditions. Here, we show for the first time, that PEM can be transferred from a stamp to the base substrate under aqueous conditions whereby the two surfaces are in a non-contact mode. Degradable multilayers of (PAA/PEG)10.5 followed by non-degradable multilayers of (PDAC/SPS)80.5 were fabricated under acidic pH conditions on either PDMS or glass (stamp), and subsequently transferred over top of another multilayer prepared on a different substrate (base substrate), with a spacing of ~ 200 μm between the stamping surface and the base substrate. This multilayer transfer was performed under physiological pH conditions. This process is referred to herein as non-contact, aqueous-phase multilayer (NAM) transfer. NAM transfer can be useful for applications such as fabricating three-dimensional (3-D) cellular scaffolds. We attempted to create a 3-D cellular scaffold using NAM transfer, and characterized the scaffolds with conventional and fluorescence microscopy. PMID:21860540

Zone compensated multilayer laue lens and apparatus and method of fabricating the same

Science.gov (United States)

Conley, Raymond P.; Liu, Chian Qian; Macrander, Albert T.; Yan, Hanfei; Maser, Jorg; Kang, Hyon Chol; Stephenson, Gregory Brian

2015-07-14

A multilayer Laue Lens includes a compensation layer formed in between a first multilayer section and a second multilayer section. Each of the first and second multilayer sections includes a plurality of alternating layers made of a pair of different materials. Also, the thickness of layers of the first multilayer section is monotonically increased so that a layer adjacent the substrate has a minimum thickness, and the thickness of layers of the second multilayer section is monotonically decreased so that a layer adjacent the compensation layer has a maximum thickness. In particular, the compensation layer of the multilayer Laue lens has an in-plane thickness gradient laterally offset by 90.degree. as compared to other layers in the first and second multilayer sections, thereby eliminating the strict requirement of the placement error.

Multilayer Integrated Film Bulk Acoustic Resonators

CERN Document Server

Zhang, Yafei

2013-01-01

Multilayer Integrated Film Bulk Acoustic Resonators mainly introduces the theory, design, fabrication technology and application of a recently developed new type of device, multilayer integrated film bulk acoustic resonators, at the micro and nano scale involving microelectronic devices, integrated circuits, optical devices, sensors and actuators, acoustic resonators, micro-nano manufacturing, multilayer integration, device theory and design principles, etc. These devices can work at very high frequencies by using the newly developed theory, design, and fabrication technology of nano and micro devices. Readers in fields of IC, electronic devices, sensors, materials, and films etc. will benefit from this book by learning the detailed fundamentals and potential applications of these advanced devices. Prof. Yafei Zhang is the director of the Ministry of Education’s Key Laboratory for Thin Films and Microfabrication Technology, PRC; Dr. Da Chen was a PhD student in Prof. Yafei Zhang’s research group.

Turbidity forecasting at a karst spring using combined machine learning and wavelet multiresolution analysis.

Science.gov (United States)

Savary, M.; Massei, N.; Johannet, A.; Dupont, J. P.; Hauchard, E.

2016-12-01

25% of the world populations drink water extracted from karst aquifer. The comprehension and the protection of these aquifers appear as crucial due to an increase of drinking water needs. In Normandie(North-West of France), the principal exploited aquifer is the chalk aquifer. The chalk aquifer highly karstified is an important water resource, regionally speaking. Connections between surface and underground waters thanks to karstification imply turbidity that decreases water quality. Both numerous parameters and phenomenons, and the non-linearity of the rainfall/turbidity relation influence the turbidity causing difficulties to model and forecast turbidity peaks. In this context, the Yport pumping well provides half of Le Havreconurbation drinking water supply (236 000 inhabitants). The aim of this work is thus to perform prediction of the turbidity peaks in order to help pumping well managers to decrease the impact of turbidity on water treatment. Database consists in hourly rainfalls coming from six rain gauges located on the alimentation basin since 2009 and hourly turbidity since 1993. Because of the lack of accurate physical description of the karst system and its surface basin, the systemic paradigm is chosen and a black box model: a neural network model is chosen. In a first step, correlation analyses are used to design the original model architecture by identifying the relation between output and input. The following optimization phases bring us four different architectures. These models were experimented to forecast 12h ahead turbidity and threshold surpassing. The first model is a simple multilayer perceptron. The second is a two-branches model designed to better represent the fast (rainfall) and low (evapotranspiration) dynamics. Each kind of model is developed using both a recurrent and feed-forward architecture. This work highlights that feed-forward multilayer perceptron is better to predict turbidity peaks when feed-forward two-branches model is

The viability of neural network for modeling the impact of individual job satisfiers on work commitment in Indian manufacturing unit

Directory of Open Access Journals (Sweden)

Therasa Chandrasekar

2015-10-01

Full Text Available This paper provides an exposition about application of neural networks in the context of research to find out the contribution of individual job satisfiers towards work commitment. The purpose of the current study is to build a predictive model to estimate the normalized importance of individual job satisfiers towards work commitment of employees working in TVS Group, an Indian automobile company. The study is based on the tool developed by Spector (1985 and Sue Hayday (2003.The input variable of the study consists of nine independent individual job satisfiers which includes Pay, Promotion, Supervision, Benefits, Rewards, Operating procedures, Co-workers, Work-itself and Communication of Spector (1985 and dependent variable as work commitment of Sue Hayday (2003.The primary data has been collected using a closed-ended questionnaire based on simple random sampling approach. This study employed the multilayer Perceptron neural network model to envisage the level of job satisfiers towards work commitment. The result from the multilayer Perceptron neural network model displayed with four hidden layer with correct classification rate of 70% and 30% for training and testing data set. The normalized importance shows high value for coworkers, superior satisfaction and communication and which acts as most significant attributes of job satisfiers that predicts the overall work commitment of employees.

Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

Directory of Open Access Journals (Sweden)

Mutasem Sh. Alkhasawneh

2013-01-01

Full Text Available Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.

Tunable drug loading and release from polypeptide multilayer nanofilms

Science.gov (United States)

Jiang, Bingbing; Li, Bingyun

2009-01-01

Polypeptide multilayer nanofilms were prepared using electrostatic layer-by-layer self-assembly nanotechnology. Small charged drug molecules (eg, cefazolin, gentamicin, and methylene blue) were loaded in polypeptide multilayer nanofilms. Their loading and release were found to be pH-dependent and could also be controlled by changing the number of film layers and drug incubation time, and applying heat-treatment after film formation. Antibioticloaded polypeptide multilayer nanofilms showed controllable antibacterial properties against Staphylococcus aureus. The developed biodegradable polypeptide multilayer nanofilms are capable of loading both positively- and negatively-charged drug molecules and promise to serve as drug delivery systems on biomedical devices for preventing biomedical device-associated infection, which is a significant clinical complication for both civilian and military patients. PMID:19421369

Magnons in ultrahigh vacuum deposited Fe/Ag multilayers

International Nuclear Information System (INIS)

El Kiadi, I.; Lassri, H.; Benkirane, K.; Bensassi, B.

2007-01-01

We have grown Fe/Ag multilayers with Ag buffer layer, by evaporation under UHV conditions on glass substrates. The magnetic properties of Fe/Ag multilayers are examined as a function of Fe layer thickness t Fe . The temperature dependence of the spontaneous magnetization M(T) is well described by a T 3/2 law in all multilayers. A spin-wave theory has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J b and surface exchange interaction J s for various Fe layer thicknesses have been obtained

Covalently attached multilayer assemblies of diazo-resins and binuclear cobalt phthalocyanines

International Nuclear Information System (INIS)

Li Xiaofang; Zhao Shuang; Yang Min; Sun Changqing; Guo, Liping

2005-01-01

By using the ionic self-assembly technique, ordered multilayer thin films composed of diazo-resin (DAR) as polycation and water-soluble binuclear cobalt phthalocyaninehexasulfonate (Bi-CoPc) as polyanion were alternately fabricated on quartz, CaF 2 and glassy carbon electrodes (GCEs). Upon ultraviolet irradiation, the adjacent interface of the multilayer films reacted to form a covalently cross-linking structure. The obtained thin films were characterized by ultraviolet (UV)-vis, Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), atomic force microscope (AFM), surface photovoltage spectra (SPS), and cyclic voltammetry. The results show that the uniform, highly stable and ordered multilayer thin films were formed. The linkage nature between the adjacent interface of the multilayer films converts from ionic to covalent, and, as a result, the stability of the multilayer thin films dramatically improved. The multilayer thin films on GCEs also exhibited excellent electrochemical behavior

Covalently attached multilayer assemblies of diazo-resins and binuclear cobalt phthalocyanines

Energy Technology Data Exchange (ETDEWEB)

Li Xiaofang [Key Lab of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130023 (China); Zhao Shuang [Key Lab of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130023 (China); Yang Min [Key Lab of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130023 (China); Sun Changqing [Key Lab of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130023 (China)]. E-mail: sunchq@mail.jlu.edu.cn; Guo, Liping [Department of Chemistry, Northeast Normal University, Changchun 130024 (China)

2005-05-01

By using the ionic self-assembly technique, ordered multilayer thin films composed of diazo-resin (DAR) as polycation and water-soluble binuclear cobalt phthalocyaninehexasulfonate (Bi-CoPc) as polyanion were alternately fabricated on quartz, CaF{sub 2} and glassy carbon electrodes (GCEs). Upon ultraviolet irradiation, the adjacent interface of the multilayer films reacted to form a covalently cross-linking structure. The obtained thin films were characterized by ultraviolet (UV)-vis, Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), atomic force microscope (AFM), surface photovoltage spectra (SPS), and cyclic voltammetry. The results show that the uniform, highly stable and ordered multilayer thin films were formed. The linkage nature between the adjacent interface of the multilayer films converts from ionic to covalent, and, as a result, the stability of the multilayer thin films dramatically improved. The multilayer thin films on GCEs also exhibited excellent electrochemical behavior.

Multilayer thin films: sequential assembly of nanocomposite materials

National Research Council Canada - National Science Library

Decher, Gero; Schlenoff, Joseph B

2003-01-01

... polymeric or nanoparticulate building blocks, understanding the polymer physical chemistry of multilayers, or characterizing their optical, electrical or biological activities. The reasons for the intense interest in the field are also clearly evident: multilayers bridge the gap between monolayers and spun-on or dip-coated films, ...

Design and performance of capping layers for extreme-ultraviolet multilayer mirrors

International Nuclear Information System (INIS)

Bajt, Sasa; Chapman, Henry N.; Nguyen, Nhan; Alameda, Jennifer; Robinson, Jeffrey C.; Malinowski, Michael; Gullikson, Eric; Aquila, Andrew; Tarrio, Charles; Grantham, Steven

2003-01-01

Multilayer lifetime has emerged as one of the major issues for the commercialization of extreme-ultraviolet lithography (EUVL). We describe the performance of an oxidation-resistant capping layer of Ru atop multilayers that results in a reflectivity above 69% at 13.2 nm, which is suitable for EUVL projection optics and has been tested with accelerated electron-beam and extreme-ultraviolet (EUV) light in a water-vapor environment. Based on accelerated exposure results, we calculated multilayer lifetimes for all reflective mirrors in a typical commercial EUVL tool and concluded that Ru-capped multilayers have ∼40x longer lifetimes than Si-capped multilayers, which translates to 3 months to many years, depending on the mirror dose

Magnetic damping phenomena in ferromagnetic thin-films and multilayers

Science.gov (United States)

Azzawi, S.; Hindmarch, A. T.; Atkinson, D.

2017-11-01

Damped ferromagnetic precession is an important mechanism underpinning the magnetisation processes in ferromagnetic materials. In thin-film ferromagnets and ferromagnetic/non-magnetic multilayers, the role of precession and damping can be critical for spintronic device functionality and as a consequence there has been significant research activity. This paper presents a review of damping in ferromagnetic thin-films and multilayers and collates the results of many experimental studies to present a coherent synthesis of the field. The terms that are used to define damping are discussed with the aim of providing consistent definitions for damping phenomena. A description of the theoretical basis of damping is presented from early developments to the latest discussions of damping in ferromagnetic thin-films and multilayers. An overview of the time and frequency domain methods used to study precessional magnetisation behaviour and damping in thin-films and multilayers is also presented. Finally, a review of the experimental observations of magnetic damping in ferromagnetic thin-films and multilayers is presented with the most recent explanations. This brings together the results from many studies and includes the effects of ferromagnetic film thickness, the effects of composition on damping in thin-film ferromagnetic alloys, the influence of non-magnetic dopants in ferromagnetic films and the effects of combining thin-film ferromagnets with various non-magnetic layers in multilayered configurations.

Thermal-Insulation Properties of Multilayer Textile Packages

Directory of Open Access Journals (Sweden)

Matusiak Małgorzata

2014-12-01

Full Text Available Thermal-insulation properties of textile materials play a significant role in material engineering of protective clothing. Thermal-insulation properties are very important from the point of view of thermal comfort of the clothing user as well as the protective efficiency against low or high temperature. Thermal protective clothing usually is a multilayer construction. Its thermal insulation is a resultant of a number of layers and their order, as well as the thermalinsulation properties of a single textile material creating particular layers. The aim of the presented work was to investigate the relationships between the thermal-insulation properties of single materials and multilayer textile packages composed of these materials. Measurement of the thermal-insulation properties of single and multilayer textile materials has been performed with the Alambeta. The following properties have been investigated: thermal conductivity, resistance and absorptivity. Investigated textile packages were composed of two, three and four layers made of woven and knitted fabrics, as well as nonwovens. On the basis of the obtained results an analysis has been carried out in order to assess the dependency of the resultant values of the thermal-insulation properties of multilayer packages on the appropriate values of particular components.

Recurrent Meningitis.

Science.gov (United States)

Rosenberg, Jon; Galen, Benjamin T

2017-07-01

Recurrent meningitis is a rare clinical scenario that can be self-limiting or life threatening depending on the underlying etiology. This review describes the causes, risk factors, treatment, and prognosis for recurrent meningitis. As a general overview of a broad topic, the aim of this review is to provide clinicians with a comprehensive differential diagnosis to aide in the evaluation and management of a patient with recurrent meningitis. New developments related to understanding the pathophysiology of recurrent meningitis are as scarce as studies evaluating the treatment and prevention of this rare disorder. A trial evaluating oral valacyclovir suppression after HSV-2 meningitis did not demonstrate a benefit in preventing recurrences. The data on prophylactic antibiotics after basilar skull fractures do not support their use. Intrathecal trastuzumab has shown promise in treating leptomeningeal carcinomatosis from HER-2 positive breast cancer. Monoclonal antibodies used to treat cancer and autoimmune diseases are new potential causes of drug-induced aseptic meningitis. Despite their potential for causing recurrent meningitis, the clinical entities reviewed herein are not frequently discussed together given that they are a heterogeneous collection of unrelated, rare diseases. Epidemiologic data on recurrent meningitis are lacking. The syndrome of recurrent benign lymphocytic meningitis described by Mollaret in 1944 was later found to be closely related to HSV-2 reactivation, but HSV-2 is by no means the only etiology of recurrent aseptic meningitis. While the mainstay of treatment for recurrent meningitis is supportive care, it is paramount to ensure that reversible and treatable causes have been addressed for further prevention.

Clustering multilayer omics data using MuNCut.

Science.gov (United States)

Teran Hidalgo, Sebastian J; Ma, Shuangge

2018-03-14

Omics profiling is now a routine component of biomedical studies. In the analysis of omics data, clustering is an essential step and serves multiple purposes including for example revealing the unknown functionalities of omics units, assisting dimension reduction in outcome model building, and others. In the most recent omics studies, a prominent trend is to conduct multilayer profiling, which collects multiple types of genetic, genomic, epigenetic and other measurements on the same subjects. In the literature, clustering methods tailored to multilayer omics data are still limited. Directly applying the existing clustering methods to multilayer omics data and clustering each layer first and then combing across layers are both "suboptimal" in that they do not accommodate the interconnections within layers and across layers in an informative way. In this study, we develop the MuNCut (Multilayer NCut) clustering approach. It is tailored to multilayer omics data and sufficiently accounts for both across- and within-layer connections. It is based on the novel NCut technique and also takes advantages of regularized sparse estimation. It has an intuitive formulation and is computationally very feasible. To facilitate implementation, we develop the function muncut in the R package NcutYX. Under a wide spectrum of simulation settings, it outperforms competitors. The analysis of TCGA (The Cancer Genome Atlas) data on breast cancer and cervical cancer shows that MuNCut generates biologically meaningful results which differ from those using the alternatives. We propose a more effective clustering analysis of multiple omics data. It provides a new venue for jointly analyzing genetic, genomic, epigenetic and other measurements.

75 FR 66126 - Multilayered Wood Flooring From China

Science.gov (United States)

2010-10-27

...)] Multilayered Wood Flooring From China AGENCY: United States International Trade Commission. ACTION: Institution... flooring, provided for in subheadings 4409.10, 4409.29, 4412.31, 4412.32, 4412.39, 4412.94, 4412.99, 4418... multilayered wood flooring. The following companies are members of the CAHP: Anderson Hardwood Floors, LLC...

Simulation of reflectivity spectrum for non-absorbing multilayer ...

Indian Academy of Sciences (India)

Reflectivity simulation is an essential tool for the design and optimization of optical thin ... with the experimental results of the multilayer optical thin films grown by electron-beam evaporation ... beam splitters [4] and various optical filters. ... thickness (QWOT) layer AR coating and multilayer HR coating using electron- beam ...

Reference Models for Multi-Layer Tissue Structures

Science.gov (United States)

2016-09-01

function of multi-layer tissues (etiology and management of pressure ulcers ). What was the impact on other disciplines? As part of the project, a data...simplification to develop cost -effective models of surface manipulation of multi-layer tissues. Deliverables. Specimen- (or subject) and region-specific...simplification to develop cost -effective models of surgical manipulation. Deliverables. Specimen-specific surrogate models of upper legs confirmed against data

Interface-engineered spin-dependent transport in perpendicular Co/Pt multilayers

Energy Technology Data Exchange (ETDEWEB)

Jiang, Shao-Long; Yang, Guang; Teng, Jiao, E-mail: tengjiao@mater.ustb.edu.cn; Guo, Qi-Xun; Li, Lei-Lei; Yu, Guang-Hua, E-mail: ghyu@mater.ustb.edu.cn

2016-11-30

Highlights: • The anomalous Hall effect in Co/Pt multilayers is studied. • Thermally stable AHE feature is obtained in [Pt/Co]{sub 3}/Ta/MgO multilayers. • Good thermal stability is due to enhanced side-jump and intrinsic contributions. - Abstract: The improvement of anomalous Hall effect (AHE) has been obtained through the introduction of a Ta metallic layer at the Co/MgO interface in perpendicular [Pt/Co]{sub 3}/MgO multilayers. It is exhibited that the saturation anomalous Hall resistivity is 42% larger than that in Co/Pt multilayers without Ta insertion. More meaningfully, thermally stable AHE feature is gained in perpendicular [Pt/Co]{sub 3}/Ta/MgO multilayers despite Co-Pt interdiffusion. The AHE is enhanced for sample [Pt/Co]{sub 3}/Ta/MgO after annealing, mainly due to the enhancement of the side-jump and intrinsic contributions.

Terrain Mapping and Classification in Outdoor Environments Using Neural Networks

OpenAIRE

Alberto Yukinobu Hata; Denis Fernando Wolf; Gustavo Pessin; Fernando Osório

2009-01-01

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstra...

EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

OpenAIRE

Cukic, Milena; Pokrajac, David; Stokic, Miodrag; Simic, slobodan; Radivojevic, Vlada; Ljubisavljevic, Milos

2018-01-01

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linea...

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

Energy Technology Data Exchange (ETDEWEB)

Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

2012-11-01

Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

Structural integrity of ceramic multilayer capacitor materials and ceramic multilayer capacitors

NARCIS (Netherlands)

With, de G.

1993-01-01

An review with 61 refs. is given of the fracture of and stress situation in ceramic capacitor materials and ceramic multilayer capacitors. A brief introduction to the relevant concepts is given first. Next the data for capacitor materials and the data for capacitors are discussed. The materials data

Optical measurement of thermal deformation of multilayer optics under synchrotron radiation

International Nuclear Information System (INIS)

Revesz, P.; Kazimirov, A.; Bazarov, I.

2007-01-01

An in situ optical technique to visualize surface distortions of the first monochromator crystal under synchrotron beam heat loading has been developed and applied to measure surface profiles of multilayer optics under white wiggler beam at the CHESS A2 beamline. Two identical multilayer structures deposited on Si and SiC substrates have been tested. Comparison of the reconstructed 3D heatbump profiles showed the surface distortions of the multilayer on SiC a factor of two smaller than the same multilayer on a Si substrate

Optical measurement of thermal deformation of multilayer optics under synchrotron radiation

Energy Technology Data Exchange (ETDEWEB)

Revesz, P. [Cornell University, CHESS, Ithaca, NY 14850 (United States)], E-mail: pr20@cornell.edu; Kazimirov, A.; Bazarov, I. [Cornell University, CHESS, Ithaca, NY 14850 (United States)

2007-11-11

An in situ optical technique to visualize surface distortions of the first monochromator crystal under synchrotron beam heat loading has been developed and applied to measure surface profiles of multilayer optics under white wiggler beam at the CHESS A2 beamline. Two identical multilayer structures deposited on Si and SiC substrates have been tested. Comparison of the reconstructed 3D heatbump profiles showed the surface distortions of the multilayer on SiC a factor of two smaller than the same multilayer on a Si substrate.

Phosphorus-based compounds for EUV multilayer optics materials

NARCIS (Netherlands)

Medvedev, Viacheslav; Yakshin, Andrey; van de Kruijs, Robbert Wilhelmus Elisabeth; Bijkerk, Frederik

2015-01-01

We have evaluated the prospects of phosphorus-based compounds in extreme ultraviolet multilayer optics. Boron phosphide (BP) is suggested to be used as a spacer material in reflective multilayer optics operating just above the L-photoabsorption edge of P (λ ≈9.2 nm). Mo, Ag, Ru, Rh, and Pd were

Heteroepitaxial growth of strained multilayer thin films of high-temperature superconductors

International Nuclear Information System (INIS)

Gross, R.; Gupta, A.; Olsson, E.; Segmueller, A.; Koren, G.

1991-01-01

Recently, the heteroepitaxial growth of multilayer structures of different copper oxide superconductors has been reported by several groups. In general, two different types of multilayer structures should be distinguished. The first kind of mulitlayer is formed by high-T c materials having the same crystal structure and almost the same lattice constants, as for example ReBa 2 Cu 3 O 7 (Re=rare earth) multilayers with alternating Re-elements. In these multilayers the two different rare earth copper oxides (Y/Dy, Y/Pr) have the same orthorhombic unit cell. Due to the very similar lattice constants, the misfit strain is easily accommodated without the formation of defects. The second kind of multilayer is formed by layers of materials having different crystal structure and lattice parameters. In these multilayers the misfit can be coherently accommodated below a critical modulation thickness as discussed below. This renders possible the heteroepitaxial growth of strained multilayer structures, both of two copper oxides of different crystal structure, as has been demonstrated recently for the system YBa 2 Cu 3 O 7-δ /Nd 1.83 Ce 0.17 CuO x , and of superconducting copper oxides and insulating materials. For multilayers of different copper oxides, a combination of almost all high-Tc materials should be possible, since the presence of the CuO 2 sheets in these materials results in similar lattice constants in their basal planes ('a' and 'b'). (orig./BHO)

76 FR 76435 - Multilayered Wood Flooring From China

Science.gov (United States)

2011-12-07

...)] Multilayered Wood Flooring From China Determinations On the basis of the record \\1\\ developed in the subject... multilayered wood flooring, provided for in subheadings 4409.10, 4409.29, 4412.31, 4412.32, 4412.39, 4412.94... flooring. The following companies are members of the CAHP: Anderson Hardwood Floors, LLC, Fountain Inn, SC...

Magnetic studies of spin wave excitations in Ni/Au multilayers

International Nuclear Information System (INIS)

Salhi, H.; Chafai, K.; Benkirane, K.; Lassri, H.; Abid, M.; Hlil, E.K.

2010-01-01

Ni/Au multilayers were prepared by the electron beam evaporation method under ultra high vacuum conditions. The multilayer films have a coherent structure with (1 1 1) texture. The magnetic properties of Ni/Au multilayers are examined as a function of Ni layer thickness t Ni . The temperature dependence of the spontaneous magnetization M(T) is well described by a T 3/2 law in all multilayers. A spin wave theory has been used to explain the magnetization versus temperature. Based on this theory, the approximate values for the bulk exchange interaction J b , surface exchange interaction J S and the interlayer coupling strength J I have been obtained for various Ni layer thicknesses.

Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

Science.gov (United States)

Wind, Galina; Platnick, Steven; King, Michael D.; Hubanks, Paul A,; Pavolonis, Michael J.; Heidinger, Andrew K.; Yang, Ping; Baum, Bryan A.

2009-01-01

Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Earth Observing System EOS Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that presents difficulties for retrieving cloud effective radius using single layer plane-parallel cloud models. The algorithm uses the MODIS 0.94 micron water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94 micron methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases

Characterization of Mo/Si multilayer growth on stepped topographies

NARCIS (Netherlands)

van den Boogaard, Toine; Louis, Eric; Zoethout, E.; Goldberg, K.A.; Bijkerk, Frederik

2011-01-01

Mo/Si multilayer mirrors with nanoscale bilayer thicknesses have been deposited on stepped substrate topographies, using various deposition angles. The multilayer morphology at the step-edge region was studied by cross section transmission electron microscopy. A transition from a continuous- to

A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 1. MODEL FORMULATION

Science.gov (United States)

A multilayer biochemical dry deposition model has been developed based on the NOAA Multilayer Model (MLM) to study gaseous exchanges between the soil, plants, and the atmosphere. Most of the parameterizations and submodels have been updated or replaced. The numerical integration ...

Hot-rolled Process of Multilayered Composite Metal Plate

Directory of Open Access Journals (Sweden)

YU Wei

2017-02-01

Full Text Available For multi-layer plate, it is a difficult problem to increase product yield rate and improve bonding interface quality. A high yield hot-rolled method of multilayered plate was proposed. The raw strips and plate were fixed by argon arc welding. The combined billet was put into a metal box and vacuum pumped, and then heated and rolled by multi passes at the temperature of 1000-1200℃. The 67 layered plate with the thickness of 2.5mm was successfully produced. The interfacial microstructures and diffusion behavior were investigated and analyzed by optical microscopy and scan electronic microscopy. The tensile and shear strength were tested,and the shear fractures were analyzed. The results show that the multilayered plate yield rate is more than 90% by two steps billet combination method and rolling process optimization. The good bonding interface quality is obtained, the shear strength of multilayered plate reaches 241 MPa. Nickel interlayer between 9Cr18 and 1Cr17 can not only prevent the diffusion of carbon, but also improve the microstructure characteristics.

Tunable photonic multilayer sensors from photo-crosslinkable polymers

Science.gov (United States)

Chiappelli, Maria; Hayward, Ryan

2014-03-01

The fabrication of tunable photonic multilayer sensors from stimuli-responsive, photo-crosslinkable polymers will be described. Benzophenone is covalently incorporated as a pendent photo-crosslinker, allowing for facile preparation of multilayer films by sequential spin-coating and crosslinking processes. Copolymer chemistries and layer thicknesses are selected to provide robust multilayer sensors which can show color changes across nearly the full visible spectrum due to the specific stimulus-responsive nature of the hydrated film stack. We will describe how this approach is extended to alternative sensor designs by tailoring the thickness and chemistry of each layer independently, allowing for the preparation of sensors which depend not only on the shift in wavelength of a reflectance peak, but also on the transition between Bragg mirrors and filters. Device design is optimized by photo-patterning sensor arrays on a single substrate, providing more efficient fabrication time as well as multi-functional sensors. Finally, radiation-sensitive multilayers, designed by choosing polymers which will preferentially degrade or crosslink under ionizing radiation, will also be described.

Comparison of RF spectrum prediction methods for dynamic spectrum access

Science.gov (United States)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

RECEIVER OPERATING CHARACTERISTICS MEASURE FOR THE RECOGNITION OF STUTTERING DYSFLUENCIES USING LINE SPECTRAL FREQUENCIES

Directory of Open Access Journals (Sweden)

Nahrul Khair Alang Rashid

2017-05-01

Full Text Available Stuttering is a motor-speech disorder, having common features with other motor control disorders such as dystonia, Parkinson’s disease and Tourette’s syndrome. Stuttering results from complex interactions between factors such as motor, language, emotional and genetic. This study used Line Spectral Frequency (LSF for the feature extraction, while using three classifiers for the identification purpose, Multilayer Perceptron (MLP, Recurrent Neural Network (RNN and Radial Basis Function (RBF. The UCLASS (University College London Archive of Stuttered Speech release 1 was used as database in this research. These recordings were from people of ages 12y11m to 19y5m, who were referred to clinics in London for assessment of their stuttering. The performance metrics used for interpreting the results are sensitivity, accuracy, precision and misclassification rate. Only M1 and M2 had below 100% sensitivity for RBF. The sensitivity of M1 was found to be between 40 & 60%, therefore categorized as moderate, while that of M2 falls between 60 & 80%, classed as substantial. Overall, RBF outperforms the two other classifiers, MLP and RNN for all the performance metrics considered.

Young’s modulus of multi-layer microcantilevers

Directory of Open Access Journals (Sweden)

Zhikang Deng

2017-12-01

Full Text Available A theoretical model for calculating the Young’s modulus of multi-layer microcantilevers with a coating is proposed, and validated by a three-dimensional (3D finite element (FE model using ANSYS parametric design language (APDL and atomic force microscopy (AFM characterization. Compared with typical theoretical models (Rayleigh-Ritz model, Euler-Bernoulli (E-B beam model and spring mass model, the proposed theoretical model can obtain Young’s modulus of multi-layer microcantilevers more precisely. Also, the influences of coating’s geometric dimensions on Young’s modulus and resonant frequency of microcantilevers are discussed. The thickness of coating has a great influence on Young’s modulus and resonant frequency of multi-layer microcantilevers, and the coating should be considered to calculate Young’s modulus more precisely, especially when fairly thicker coating is employed.

Experimental analysis on stress wave in inhomogeneous multi-layered structures

International Nuclear Information System (INIS)

Cho, Yun Ho; Ham, Hyo Sick

1998-01-01

The guided wave propagation in inhomogeneous multi-layered structures is experimentally explored based on theoretical dispersion curves. It turns out that proper selection of incident angle and frequency is critical for guided wave generation in multi-layered structures. Theoretical dispersion curves greatly depend on adhesive zone thickness, layer thickness and material properties. It was possible to determine the adhesive zone thickness of an inhomogeneous multi-layered structure by monitoring experimentally the change of dispersion curves.

Thermally induced delamination of multilayers

DEFF Research Database (Denmark)

Sørensen, Bent F.; Sarraute, S.; Jørgensen, O.

1998-01-01

Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion coefficie...... coefficients may be an effective way of reducing the delamination energy release rate. Uneven layer thickness and increasing elastic mismatch are shown to raise the energy release rate. Experimental work confirms important trends of the model.......Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion...

Ultrahard Multilayer Coatings

International Nuclear Information System (INIS)

Chrzan, D.C.; Dugger, M.; Follstaedt, D.M.; Friedman, Lawrence H.; Friedmann, T.A.; Knapp, J.A.; McCarty, K.F.; Medlin, D.L.; Mirkarimi, P.B.; Missert, N.; Newcomer, P.P.; Sullivan, J.P.; Tallant, D.R.

1999-01-01

We have developed a new multilayer a-tC material that is thick stress-free, adherent, low friction, and with hardness and stiffness near that of diamond. The new a-tC material is deposited by J pulsed-laser deposition (PLD) at room temperature, and fully stress-relieved by a short thermal anneal at 600 ampersand deg;C. A thick multilayer is built up by repeated deposition and annealing steps. We measured 88 GPa hardness, 1100 GPa Young's modulus, and 0.1 friction coefficient (under high load). Significantly, these results are all well within the range reported for crystalline diamond. In fact, this material, if considered separate from crystalline diamond, is the 2nd hardest material known to man. Stress-free a-tC also has important advantages over thin film diamond; namely, it is smooth, processed at lower temperature, and can be grown on a much broader range of substrates. This breakthrough will enable a host of applications that we are actively pursuing in MEMs, sensors, LIGA, etc

Multilayered sulphonated polysulfone/silica composite membranes for fuel cell applications

International Nuclear Information System (INIS)

Padmavathi, Rajangam; Karthikumar, Rajendhiran; Sangeetha, Dharmalingam

2012-01-01

Highlights: ► Multilayered membranes were fabricated with SPSu. ► Aminated polysulfone and silica were used as the layers in order to prevent the crossover of methanol. ► The methanol permeability and selectivity ratio proved a strong influence on DMFC application. ► The suitability of the multilayered membranes was studied in the lab made set-ups of PEMFC and DMFC. - Abstract: Polymer electrolyte membranes used in proton exchange membrane fuel cell (PEMFC) and direct methanol fuel cell (DMFC) suffer from low dimensional stability. Hence multilayered membranes using sulfonated polysulfone (SPSu) and silica (SiO 2 ) were fabricated to alter such properties. The introduction of an SiO 2 layer between two layers of SPSu to form the multilayered composite membrane enhanced its dimensional stability, but slightly lowered its proton conductivity when compared to the conventional SPSu/SiO 2 composite membrane. Additionally, higher water absorption, lower methanol permeability and higher flame retardancy were also observed in this newly fabricated multilayered membrane. The performance evaluation of the 2 wt% SiO 2 loaded multilayered membrane in DMFC showed a maximum power density of 86.25 mW cm −2 , which was higher than that obtained for Nafion 117 membrane (52.8 mW cm −2 ) in the same single cell test assembly. Hence, due to the enhanced dimensional stability, reduced methanol permeability and higher maximum power density, the SPSu/SiO 2 /SPSu multilayered membrane can be a viable and a promising candidate for use as an electrolyte membrane in DMFC applications, when compared to Nafion.

The multilayer nanoparticles formed by layer by layer approach for cancer-targeting therapy.

Science.gov (United States)

Oh, Keun Sang; Lee, Hwanbum; Kim, Jae Yeon; Koo, Eun Jin; Lee, Eun Hee; Park, Jae Hyung; Kim, Sang Yoon; Kim, Kwangmeyung; Kwon, Ick Chan; Yuk, Soon Hong

2013-01-10

The multilayer nanoparticles (NPs) were prepared for cancer-targeting therapy using the layer by layer approach. When drug-loaded Pluronic NPs were mixed with vesicles (liposomes) in the aqueous medium, Pluronic NPs were incorporated into the vesicles to form the vesicle NPs. Then, the multilayer NPs were formed by freeze-drying the vesicle NPs in a Pluronic aqueous solution. The morphology and size distribution of the multilayer NPs were observed using a TEM and a particle size analyzer. In order to apply the multilayer NPs as a delivery system for docetaxel (DTX), which is a model anticancer drug, the release pattern of the DTX was observed and the tumor growth was monitored by injecting the multilayer NPs into the tail veins of tumor (squamous cell carcinoma)-bearing mice. The cytotoxicity of free DTX (commercial DTX formulation (Taxotere®)) and the multilayer NPs was evaluated using MTT assay. We also evaluated the tumor targeting ability of the multilayer NPs using magnetic resonance imaging. The multilayer NPs showed excellent tumor targetability and antitumor efficacy in tumor-bearing mice, caused by the enhanced permeation and retention (EPR) effect. These results suggest that the multilayer NPs could be a potential drug delivery system for cancer-targeting therapy. Copyright © 2012 Elsevier B.V. All rights reserved.

Hot-electrons-induced ultrafast demagnitization in Co/Pt multilayers

NARCIS (Netherlands)

Bergeard, N.; Hehn, M.; Mangin, S.; Lengaigne, G.; Montaigne, F.; Lalieu, M. L. M.; Koopmans, B.; Malinowski, G.

2016-01-01

Using specially engineered structures to tailor the optical absorption in a metallic multilayer, we analyze the magnetization dynamics of a Co/Pt multilayer buried below a thick Cu layer. We demonstrate that hot electrons alone can very efficiently induce ultrafast demagnetization. Simulations based

Giant magnetoresistance of hysteresis-free Cu/Co-based multilayers

International Nuclear Information System (INIS)

Huetten, A.; Hempel, T.; Schepper, W.; Kleineberg, U.; Reiss, G.

2001-01-01

It has been demonstrated that hysteresis-free multilayers based on {Cu/Co} and {Cu/Ni 57 Co 43 } can be experimentally realized obtaining room temperature GMR effect amplitudes from 6.5% up to 20%. A critical window for the layer thickness for hysteresis-free GMR curves can be achieved for both systems, ranging from 0.38 to 0.45 nm and 0.59 to 0.7 nm, respectively. The corresponding sensitivities range from 0.075 up to 0.114%/Oe, but are still below that of normal {Cu/Co} multilayers. Hysteresis-free multilayers based on these systems are stable up to 180 deg. C upon isochronal annealing. It is shown that hysteresis-free {Cu/Co or Ni 57 Co 43 }-multilayers are neither a solution to achieve good temperature stability nor a higher sensitivity compared with normal ones and hence are not candidates for application

Self-assembled metal nano-multilayered film prepared by co-sputtering method

Science.gov (United States)

Xie, Tianle; Fu, Licai; Qin, Wen; Zhu, Jiajun; Yang, Wulin; Li, Deyi; Zhou, Lingping

2018-03-01

Nano-multilayered film is usually prepared by the arrangement deposition of different materials. In this paper, a self-assembled nano-multilayered film was deposited by simultaneous sputtering of Cu and W. The Cu/W nano-multilayered film was accumulated by W-rich layer and Cu-rich layer. Smooth interfaces with consecutive composition variation and semi-coherent even coherent relationship were identified, indicating that a spinodal-like structure with a modulation wavelength of about 20 nm formed during co-deposition process. The participation of diffusion barrier element, such as W, is believed the essential to obtain the nano-multilayered structure besides the technological parameters.

Analytic theory of alternate multilayer gratings operating in single-order regime.

Science.gov (United States)

Yang, Xiaowei; Kozhevnikov, Igor V; Huang, Qiushi; Wang, Hongchang; Hand, Matthew; Sawhney, Kawal; Wang, Zhanshan

2017-07-10

Using the coupled wave approach (CWA), we introduce the analytical theory for alternate multilayer grating (AMG) operating in the single-order regime, in which only one diffraction order is excited. Differing from previous study analogizing AMG to crystals, we conclude that symmetrical structure, or equal thickness of the two multilayer materials, is not the optimal design for AMG and may result in significant reduction in diffraction efficiency. The peculiarities of AMG compared with other multilayer gratings are analyzed. An influence of multilayer structure materials on diffraction efficiency is considered. The validity conditions of analytical theory are also discussed.

Multilayer porous UHMWPE scaffolds for bone defects replacement

International Nuclear Information System (INIS)

Maksimkin, A.V.; Senatov, F.S.; Anisimova, N.Yu.; Kiselevskiy, M.V.; Zalepugin, D.Yu.; Chernyshova, I.V.; Tilkunova, N.A.; Kaloshkin, S.D.

2017-01-01

Reconstruction of the structural integrity of the damaged bone tissue is an urgent problem. UHMWPE may be potentially used for the manufacture of porous implants simulating as closely as possible the porous cancellous bone tissue. But the extremely high molecular weight of the polymer does not allow using traditional methods of foaming. Porous and multilayer UHMWPE scaffolds with nonporous bulk layer and porous layer that mimics cancellous bone architecture were obtained by solid-state mixing, thermopressing and washing in subcritical water. Structural and mechanical properties of the samples were studied. Porous UHMWPE samples were also studied in vitro and in vivo. The pores of UHMWPE scaffold are open and interconnected. Volume porosity of the obtained samples was 79 ± 2%; the pore size range was 80–700 μm. Strong connection of the two layers in multilayer UHMWPE scaffolds was observed with decreased number of fusion defects. Functionality of implants based on multilayer UHMWPE scaffolds is provided by the fixation of scaffolds in the bone defect through ingrowths of the connective tissue into the pores, which ensures the maintenance of the animals' mobility - Highlights: • Porous UHMWPE scaffold mimics cancellous bone architecture, maintaining its flexibility. • Multilayer UHMWPE scaffold is able to simulate different types of bone tissue. • Fixation of scaffolds in the bone provides through ingrowths of the connective tissue into pores. • Multilayer UHMWPE scaffolds can be used for the formation of bone implants.

Multilayer porous UHMWPE scaffolds for bone defects replacement

Energy Technology Data Exchange (ETDEWEB)

Maksimkin, A.V. [National University of Science and Technology “MISIS”, Moscow (Russian Federation); Senatov, F.S., E-mail: senatov@misis.ru [National University of Science and Technology “MISIS”, Moscow (Russian Federation); Anisimova, N.Yu.; Kiselevskiy, M.V. [National University of Science and Technology “MISIS”, Moscow (Russian Federation); N.N. Blokhin Russian Cancer Research Center, Moscow (Russian Federation); Zalepugin, D.Yu.; Chernyshova, I.V.; Tilkunova, N.A. [State Plant of Medicinal Drugs, Moscow (Russian Federation); Kaloshkin, S.D. [National University of Science and Technology “MISIS”, Moscow (Russian Federation)

2017-04-01

Reconstruction of the structural integrity of the damaged bone tissue is an urgent problem. UHMWPE may be potentially used for the manufacture of porous implants simulating as closely as possible the porous cancellous bone tissue. But the extremely high molecular weight of the polymer does not allow using traditional methods of foaming. Porous and multilayer UHMWPE scaffolds with nonporous bulk layer and porous layer that mimics cancellous bone architecture were obtained by solid-state mixing, thermopressing and washing in subcritical water. Structural and mechanical properties of the samples were studied. Porous UHMWPE samples were also studied in vitro and in vivo. The pores of UHMWPE scaffold are open and interconnected. Volume porosity of the obtained samples was 79 ± 2%; the pore size range was 80–700 μm. Strong connection of the two layers in multilayer UHMWPE scaffolds was observed with decreased number of fusion defects. Functionality of implants based on multilayer UHMWPE scaffolds is provided by the fixation of scaffolds in the bone defect through ingrowths of the connective tissue into the pores, which ensures the maintenance of the animals' mobility - Highlights: • Porous UHMWPE scaffold mimics cancellous bone architecture, maintaining its flexibility. • Multilayer UHMWPE scaffold is able to simulate different types of bone tissue. • Fixation of scaffolds in the bone provides through ingrowths of the connective tissue into pores. • Multilayer UHMWPE scaffolds can be used for the formation of bone implants.

Elastic properties of suspended multilayer WSe{sub 2}

Energy Technology Data Exchange (ETDEWEB)

Zhang, Rui, E-mail: rui.zhang@ed.ac.uk; Cheung, Rebecca [Scottish Microelectronics Centre, Alexander Crum Brown Road, The University of Edinburgh, King' s Buildings, Edinburgh EH9 3FF (United Kingdom); Koutsos, Vasileios [Institute for Materials and Processes, School of Engineering, The University of Edinburgh, King' s Buildings, Edinburgh EH9 3FB (United Kingdom)

2016-01-25

We report the experimental determination of the elastic properties of suspended multilayer WSe{sub 2}, a promising two-dimensional (2D) semiconducting material combined with high optical quality. The suspended WSe{sub 2} membranes have been fabricated by mechanical exfoliation of bulk WSe{sub 2} and transfer of the exfoliated multilayer WSe{sub 2} flakes onto SiO{sub 2}/Si substrates pre-patterned with hole arrays. Then, indentation experiments have been performed on these membranes with an atomic force microscope. The results show that the 2D elastic modulus of the multilayer WSe{sub 2} membranes increases linearly while the prestress decreases linearly as the number of layers increases. The interlayer interaction in WSe{sub 2} has been observed to be strong enough to prevent the interlayer sliding during the indentation experiments. The Young's modulus of multilayer WSe{sub 2} (167.3 ± 6.7 GPa) is statistically independent of the thickness of the membranes, whose value is about two thirds of other most investigated 2D semiconducting transition metal dichalcogenides, namely, MoS{sub 2} and WS{sub 2}. Moreover, the multilayer WSe{sub 2} can endure ∼12.4 GPa stress and ∼7.3% strain without fracture or mechanical degradation. The 2D WSe{sub 2} can be an attractive semiconducting material for application in flexible optoelectronic devices and nano-electromechanical systems.

Artificial neural network application for predicting soil distribution coefficient of nickel

International Nuclear Information System (INIS)

Falamaki, Amin

2013-01-01

The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in

Layer-by-layer strippable Ag multilayer films fabricated by modular assembly.

Science.gov (United States)

Li, Yan; Chen, Xiaoyan; Li, Qianqian; Song, Kai; Wang, Shihui; Chen, Xiaoyan; Zhang, Kai; Fu, Yu; Jiao, Yong-Hua; Sun, Ting; Liu, Fu-Chun; Han, En-Hou

2014-01-21

We have developed a new method to fabricate multilayer films, which uses prepared thin films as modular blocks and transfer as operation mode to build up multilayer structures. In order to distinguish it from the in situ fabrication manner, this method is called modular assembly in this study. On the basis of such concept, we have fabricated a multilayer film using the silver mirror film as the modular block and poly(lactic acid) as the transfer tool. Due to the special double-layer structure of the silver mirror film, the resulting multilayer film had a well-defined stratified architecture with alternate porous/compact layers. As a consequence of the distinct structure, the interaction between the adjacent layers was so weak that the multilayer film could be layer-by-layer stripped. In addition, the top layer in the film could provide an effective protection on the morphology and surface property of the underlying layers. This suggests that if the surface of the film was deteriorated, the top layer could be peeled off and the freshly exposed surface would still maintain the original function. The successful preparation of the layer-by-layer strippable silver multilayer demonstrates that modular assembly is a feasible and effective method to build up multilayer films capable of creating novel and attractive micro/nanostructures, having great potential in the fabrication of nanodevices and coatings.

Swift heavy ion irradiation effects in Pt/C and Ni/C multilayers

Science.gov (United States)

Gupta, Ajay; Pandita, Suneel; Avasthi, D. K.; Lodha, G. S.; Nandedkar, R. V.

1998-12-01

Irradiation effects of 100 MeV Ag ion irradiation on Ni/C and Pt/C multilayers have been studied using X-ray reflectivity measurements. Modifications are observed in both the multilayers at (dE/dx)e values much below the threshold values for Ni and Pt. This effect is attributed to the discontinuous nature of the metal layers. In both the multilayers interfacial roughness increases with irradiation dose. While Ni/C multilayers exhibit large ion-beam induced intermixing, no observable intermixing is observed in the case of Pt/C multilayer. This difference in the behavior of the two systems suggests a significant role for chemically guided defect motion in the mixing process associated with swift heavy ion irradiation.

Multiperiodicity in plasmonic multilayers: General description and diversity of topologies

DEFF Research Database (Denmark)

Orlov, Alexey A.; Krylova, Anastasia K.; Zhukovsky, Sergei

2014-01-01

We introduce multiperiodicity in periodicmetal-dielectric multilayers by stacking more than two types of metal and/or dielectric layers into the unit cell. A simple way to characterize arbitrary multiperiodic multilayers using permutation vectors is suggested and employed. Effects of multiperiodi...... of multiperiodicity up to its fourth order are investigated. We demonstrate that various topologies of multiple-sheet isofrequency and dispersion surfaces exist for such plasmonic multilayers, including a photonic realization of nontrivial isolated Dirac cones....

Docetaxel-loaded multilayer nanoparticles with nanodroplets for cancer therapy.

Science.gov (United States)

Oh, Keun Sang; Kim, Kyungim; Yoon, Byeong Deok; Lee, Hye Jin; Park, Dal Yong; Kim, Eun-Yeong; Lee, Kiho; Seo, Jae Hong; Yuk, Soon Hong

2016-01-01

A mixture of docetaxel (DTX) and Solutol(®) HS 15 (Solutol) transiently formed nanodroplets when it was suspended in an aqueous medium. However, nanodroplets that comprised DTX and Solutol showed a rapid precipitation of DTX because of their unstable characteristics in the aqueous medium. The incorporation of nanodroplets that comprised DTX and Solutol through vesicle fusion and subsequent stabilization was designed to prepare multilayer nanoparticles (NPs) with a DTX-loaded Solutol nanodroplet (as template NPs) core for an efficient delivery of DTX as a chemotherapeutic drug. As a result, the DTX-loaded Solutol nanodroplets (~11.7 nm) were observed to have an increased average diameter (from 11.7 nm to 156.1 nm) and a good stability of the hydrated NPs without precipitation of DTX by vesicle fusion and multilayered structure, respectively. Also, a long circulation of the multilayer NPs was observed, and this was due to the presence of Pluronic F-68 on the surface of the multilayer NPs. This led to an improved antitumor efficacy based on the enhanced permeation and retention effect. Therefore, this study indicated that the multilayer NPs have a considerable potential as a drug delivery system with an enhanced therapeutic efficacy by blood circulation and with low side effects.

Self-Propagating Reactive Fronts in Compacts of Multilayered Particles

International Nuclear Information System (INIS)

Sraj, I.; Vohra, M.; Alawieh, L.; Weihs, T.P.; Knio, O.M.

2013-01-01

Reactive multilayered foils in the form of thin films have gained interest in various applications such as joining, welding, and ignition. Typically, thin film multilayers support self-propagating reaction fronts with speeds ranging from 1 to 20 m/s. In some applications, however, reaction fronts with much smaller velocities are required. This recently motivated Fritz et al. (2011) to fabricate compacts of regular sized/shaped multilayered particles and demonstrate self-sustained reaction fronts having much smaller velocities than thin films with similar layering. In this work, we develop a simplified numerical model to simulate the self-propagation of reactive fronts in an idealized compact, comprising identical Ni/Al multilayered particles in thermal contact. The evolution of the reaction in the compact is simulated using a two-dimensional transient model, based on a reduced description of mixing, heat release, and thermal transport. Computed results reveal that an advancing reaction front can be substantially delayed as it crosses from one particle to a neighboring particle, which results in a reduced mean propagation velocity. A quantitative analysis is thus conducted on the dependence of these phenomena on the contact area between the particles, the thermal contact resistance, and the arrangement of the multilayered particles.

Self-Propagating Reactive Fronts in Compacts of Multilayered Particles

Directory of Open Access Journals (Sweden)

Ihab Sraj

2013-01-01

Full Text Available Reactive multilayered foils in the form of thin films have gained interest in various applications such as joining, welding, and ignition. Typically, thin film multilayers support self-propagating reaction fronts with speeds ranging from 1 to 20 m/s. In some applications, however, reaction fronts with much smaller velocities are required. This recently motivated Fritz et al. (2011 to fabricate compacts of regular sized/shaped multilayered particles and demonstrate self-sustained reaction fronts having much smaller velocities than thin films with similar layering. In this work, we develop a simplified numerical model to simulate the self-propagation of reactive fronts in an idealized compact, comprising identical Ni/Al multilayered particles in thermal contact. The evolution of the reaction in the compact is simulated using a two-dimensional transient model, based on a reduced description of mixing, heat release, and thermal transport. Computed results reveal that an advancing reaction front can be substantially delayed as it crosses from one particle to a neighboring particle, which results in a reduced mean propagation velocity. A quantitative analysis is thus conducted on the dependence of these phenomena on the contact area between the particles, the thermal contact resistance, and the arrangement of the multilayered particles.

Decomposition of multilayer benzene and n-hexane films on vanadium.

Science.gov (United States)

Souda, Ryutaro

2015-09-21

Reactions of multilayer hydrocarbon films with a polycrystalline V substrate have been investigated using temperature-programmed desorption and time-of-flight secondary ion mass spectrometry. Most of the benzene molecules were dissociated on V, as evidenced by the strong depression in the thermal desorption yields of physisorbed species at 150 K. The reaction products dehydrogenated gradually after the multilayer film disappeared from the surface. Large amount of oxygen was needed to passivate the benzene decomposition on V. These behaviors indicate that the subsurface sites of V play a role in multilayer benzene decomposition. Decomposition of the n-hexane multilayer films is manifested by the desorption of methane at 105 K and gradual hydrogen desorption starting at this temperature, indicating that C-C bond scission precedes C-H bond cleavage. The n-hexane dissociation temperature is considerably lower than the thermal desorption temperature of the physisorbed species (140 K). The n-hexane multilayer morphology changes at the decomposition temperature, suggesting that a liquid-like phase formed after crystallization plays a role in the low-temperature decomposition of n-hexane.

Stacked Heterogeneous Neural Networks for Time Series Forecasting

Directory of Open Access Journals (Sweden)

Florin Leon

2010-01-01

Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Improvement of the detection response time of gas sensors using the association of artificial neural networks with pattern recognition technique; Amelioration de la reponse temporelle de capteurs de gaz par reconnaissance de forme a l'aide de reseaux de neurones

Energy Technology Data Exchange (ETDEWEB)

Bordieu, Ch.; Rebiere, D. [Bordeaux-1 Univ., Lab. IXL, UMR CNRS 5818, 33 (France); Pistre, J.; Planata, R. [Centre d' Etudes du Bouchet, 91 - Vert-le-Petit (France)

1999-07-01

The association of artificial neural networks (multilayer perceptrons) with a real time pattern recognition technique (shifting windows) allowed the development of systems for the detection and the quantification of gases. Shifting window technique is presented and offers an interesting way to improve the detection response time. The partial detector characterization with regard to its parameters was realized. Applications dealing with the detection of gas compounds using surface acoustic sensors permit to show the shifting window technique feasibility. (author)

Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR

OpenAIRE

Hagen, Astrid; Morris, Andrew

2000-01-01

In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood--based MGMs...

Clustering network layers with the strata multilayer stochastic block model.

Science.gov (United States)

Stanley, Natalie; Shai, Saray; Taylor, Dane; Mucha, Peter J

2016-01-01

Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

Multilayer composition coatings for cutting tools: formation and performance properties

Science.gov (United States)

Tabakov, Vladimir P.; Vereschaka, Anatoly S.; Vereschaka, Alexey A.

2018-03-01

The paper considers the concept of a multi-layer architecture of the coating in which each layer has a predetermined functionality. Latest generation of coatings with multi-layered architecture for cutting tools secure a dual nature of the coating, in which coatings should not only improve the mechanical and physical characteristics of the cutting tool material, but also reduce the thermo-mechanical effect on the cutting tool determining wear intensity. Here are presented the results of the development of combined methods of forming multi-layer coatings with improved properties. Combined method of forming coatings using a pulsed laser allowed reducing excessively high levels of compressive residual stress and increasing micro hardness of the multilayered coatings. The results in testing coated HSS tools showed that the use of additional pulse of laser processing increases tool life up to 3 times. Using filtered cathodic vacuum arc deposition for the generation of multilayer coatings based on TiAlN compound has increased the wear-resistance of carbide tools by 2 fold compared with tool life of cutting tool with commercial TiN coatings. The aim of this study was to develop an innovative methodological approach to the deposition of multilayer coatings for cutting tools with functional architectural selection, properties and parameters of the coating based on sound knowledge of coating failure in machining process.

Multiple analysis of an unknown optical multilayer coating

International Nuclear Information System (INIS)

Dobrowolski, J.A.; Ho, F.C.; Waldorf, A.

1985-01-01

Results are given of the analysis at five different laboratories of an unknown optical multilayer coating. In all, eleven different analytical and laboratory techniques were applied to the problem. The multilayer nominally consisted of three dielectric and two metallic layers. It was demonstrated convincingly that with present day techniques it is possible to determine the basic structure of such a coating

Enhancement of the spin Peltier effect in multilayers

Science.gov (United States)

Uchida, K.; Iguchi, R.; Daimon, S.; Ramos, R.; Anadón, A.; Lucas, I.; Algarabel, P. A.; Morellón, L.; Aguirre, M. H.; Ibarra, M. R.; Saitoh, E.

2017-05-01

The spin Peltier effect (SPE), heat-current generation as a result of spin-current injection, has been investigated in alternately stacked Pt/Fe3O4 multilayer films. The temperature modulation induced by the SPE in the [Pt/Fe3O4]×n films was found to be significantly enhanced with increasing the number of Pt/Fe3O4 bilayers n . This SPE enhancement is much greater than that expected for a simple stack of independent Pt/Fe3O4 bilayers. The observed n dependence of the SPE can be explained by introducing spin-current redistribution in the multilayer films in the thickness direction, in a manner similar to the enhancement of the spin Seebeck effect in multilayers.

Neutron spin quantum precession using multilayer spin splitters and a phase-spin echo interferometer

International Nuclear Information System (INIS)

Ebisawa, Toru; Tasaki, Seiji; Kawai, Takeshi; Hino, Masahiro; Akiyoshi, Tsunekazu; Achiwa, Norio; Otake, Yoshie; Funahashi, Haruhiko.

1996-01-01

Neutron spin quantum precession by multilayer spin splitter has been demonstrated using a new spin interferometer. The multilayer spin splitter consists of a magnetic multilayer mirror on top, followed by a gap layer and a non magnetic multilayer mirror which are evaporated on a silicon substrate. Using the multilayer spin splitter, a polarized neutron wave in a magnetic field perpendicular to the polarization is split into two spin eigenstates with a phase shift in the direction of the magnetic field. The spin quantum precession is equal to the phase shift, which depends on the effective thickness of the gap layer. The demonstration experiments verify the multilayer spin splitter as a neutron spin precession device as well as the coherent superposition principle of the two spin eigenstates. We have developed a new phase-spin echo interferometer using the multilayer spin splitters. We present successful performance tests of the multilayer spin splitter and the phase-spin echo interferometer. (author)

Stress in tungsten carbide-diamond like carbon multilayer coatings

NARCIS (Netherlands)

Pujada, B.R.; Tichelaar, F.D.; Janssen, G.C.A.M.

2007-01-01

Tungsten carbide-diamond like carbon (WC-DLC) multilayer coatings have been prepared by sputter deposition from a tungsten-carbide target and periodic switching on and off of the reactive acetylene gas flow. The stress in the resulting WC-DLC multilayers has been studied by substrate curvature.

Photo-crosslinkable polymers for fabrication of photonic multilayer sensors

Science.gov (United States)

Chiappelli, Maria; Hayward, Ryan C.

2013-03-01

We have used photo-crosslinkable polymers to fabricate photonic multilayer sensors. Benzophenone is utilized as a covalently incorporated pendent photo-crosslinker, providing a convenient means of fabricating multilayer films by sequential spin-coating and crosslinking processes. Colorimetric temperature sensors were designed from thermally-responsive, low-refractive index poly(N-isopropylacrylamide) (PNIPAM) and high-refractive index poly(para-methyl styrene) (P pMS). Copolymer chemistries and layer thicknesses were selected to provide robust multilayer sensors which show color changes across nearly the full visible spectrum due to changes in temperature of the hydrated film stack. We have characterized the uniformity and interfacial broadening within the multilayers, the kinetics of swelling and de-swelling, and the reversibility over multiple hydration/dehydration cycles. We also describe how the approach can be extended to alternative sensor designs through the ability to tailor each layer independently, as well as to additional stimuli by selecting alternative copolymer chemistries.

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.

Science.gov (United States)

Araújo, Ricardo de A

2012-04-01

Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.

Highly efficient blazed grating with multilayer coating for tender X-ray energies

NARCIS (Netherlands)

Senf, F.; Bijkerk, Frederik; Eggenstein, F.; Gwalt, G.; Huang, Qiushi; van de Kruijs, Robbert Wilhelmus Elisabeth; Kutz, O.; Lemke, S.; Louis, Eric; Mertin, M.; Packe, I.; Rudolph, I.; Schafers, F.; Siewert, F.; Sokolov, A.; Sturm, Jacobus Marinus; Waberski, C.; Wang, Z.; Wolf, J.; Zeschke, T.; Erko, A.

2016-01-01

For photon energies of 1 – 5 keV, blazed gratings with multilayer coating are ideally suited for the suppression of stray and higher orders light in grating monochromators. We developed and characterized a blazed 2000 lines/mm grating coated with a 20 period Cr/C- multilayer. The multilayer

Thermal stress prediction in mirror and multilayer coatings.

Science.gov (United States)

Cheng, Xianchao; Zhang, Lin; Morawe, Christian; Sanchez Del Rio, Manuel

2015-03-01

Multilayer optics for X-rays typically consist of hundreds of periods of two types of alternating sub-layers which are coated on a silicon substrate. The thickness of the coating is well below 1 µm (tens or hundreds of nanometers). The high aspect ratio (∼10(7)) between the size of the optics and the thickness of the multilayer can lead to a huge number of elements (∼10(16)) for the numerical simulation (by finite-element analysis using ANSYS code). In this work, the finite-element model for thermal-structural analysis of multilayer optics has been implemented using the ANSYS layer-functioned elements. The number of meshed elements is considerably reduced and the number of sub-layers feasible for the present computers is increased significantly. Based on this technique, single-layer coated mirrors and multilayer monochromators cooled by water or liquid nitrogen are studied with typical parameters of heat-load, cooling and geometry. The effects of cooling-down of the optics and heating of the X-ray beam are described. It is shown that the influences from the coating on temperature and deformation are negligible. However, large stresses are induced in the layers due to the different thermal expansion coefficients between the layer and the substrate materials, which is the critical issue for the survival of the optics. This is particularly true for the liquid-nitrogen cooling condition. The material properties of thin multilayer films are applied in the simulation to predict the layer thermal stresses with more precision.

An evaluation of factors predicting breast recurrence and prognosis after recurrence, on distinguishing intramammary and extramammary recurrence, in breast-conserving surgery

Energy Technology Data Exchange (ETDEWEB)

Nishimura, Reiki; Nagao, Kazuharu; Miyayama, Haruhiko [Kumamoto City Hospital (Japan)] (and others)

2001-06-01

Recurrence of cancer in the breast is an important problem in breast-conserving therapy. We evaluated risk factors for recurrence from the viewpoint of recurrence type and outcome after recurrence. Of 533 cases of breast cancer treated with breast-conserving surgery from April 1989 through July 2000, disease in 66 recurred (12.4%) and were classified as 23 cases of breast recurrence only, 16 cases of both breast recurrence and distant metastasis, and 27 cases of distant metastasis only. The clinical factors examined included age, lymphatic invasion, nodal status, extensive intraductal component (EIC), proliferative activity, and estrogen receptor (ER) status. Of the 39 cases of breast recurrence, 19 had intramammary tumors and 20 had extramammary tumors of the skin, subcutaneous tissue, or muscle, including 8 cases with inflammatory breast recurrence. Multivariate analysis showed that factors correlated with breast recurrence were age, ER status, proliferative activity, and surgical margin. EIC-comedo was related to intramammary recurrence, whereas lymphatic invasion and nodal status were related to extramammary recurrence. Postoperative irradiation was an effective treatment for tumors in young women and tumors with positive margins or a comedo component. Outcome after breast recurrence depended on nodal status at primary operation, and survival rates were worst in patients with inflammatory breast recurrence. In conclusion, age, EIC-comedo status, the surgical margin, and negative ER status were correlated with breast recurrence. Countermeasures against these factors should be investigated. (author)

An evaluation of factors predicting breast recurrence and prognosis after recurrence, on distinguishing intramammary and extramammary recurrence, in breast-conserving surgery

International Nuclear Information System (INIS)

Nishimura, Reiki; Nagao, Kazuharu; Miyayama, Haruhiko

2001-01-01

Recurrence of cancer in the breast is an important problem in breast-conserving therapy. We evaluated risk factors for recurrence from the viewpoint of recurrence type and outcome after recurrence. Of 533 cases of breast cancer treated with breast-conserving surgery from April 1989 through July 2000, disease in 66 recurred (12.4%) and were classified as 23 cases of breast recurrence only, 16 cases of both breast recurrence and distant metastasis, and 27 cases of distant metastasis only. The clinical factors examined included age, lymphatic invasion, nodal status, extensive intraductal component (EIC), proliferative activity, and estrogen receptor (ER) status. Of the 39 cases of breast recurrence, 19 had intramammary tumors and 20 had extramammary tumors of the skin, subcutaneous tissue, or muscle, including 8 cases with inflammatory breast recurrence. Multivariate analysis showed that factors correlated with breast recurrence were age, ER status, proliferative activity, and surgical margin. EIC-comedo was related to intramammary recurrence, whereas lymphatic invasion and nodal status were related to extramammary recurrence. Postoperative irradiation was an effective treatment for tumors in young women and tumors with positive margins or a comedo component. Outcome after breast recurrence depended on nodal status at primary operation, and survival rates were worst in patients with inflammatory breast recurrence. In conclusion, age, EIC-comedo status, the surgical margin, and negative ER status were correlated with breast recurrence. Countermeasures against these factors should be investigated. (author)

Status and limitations of multilayer X-ray interference structures

International Nuclear Information System (INIS)

Kortright, J.B.

1996-01-01

Trends in the performance of x-ray multilayer interference structures with periods ranging from 9 to 130 (angstrom) are reviewed. Analysis of near-normal incidence reflectance data vs photon energy reveals that the effective interface with σ in a static Debye-Waller model, describing interdiffusion and roughness, decreases as the multilayer period decreases, and reaches a lower limit of roughly 2 (angstrom). Specular reflectance and diffuse scattering from uncoated and multilayer-coated substrates having different roughness suggest that this lower limit results largely from substrate roughness. The increase in interface width with period thus results from increasing roughness of interdiffusion as the layer thickness increases

Multiresolution forecasting for futures trading using wavelet decompositions.

Science.gov (United States)

Zhang, B L; Coggins, R; Jabri, M A; Dersch, D; Flower, B

2001-01-01

We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades.

STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Mayra Luiza Marques da Silva Binoti

2015-03-01

Full Text Available The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80% and generalization (20%. Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.

Technique for etching monolayer and multilayer materials

Science.gov (United States)

Bouet, Nathalie C. D.; Conley, Raymond P.; Divan, Ralu; Macrander, Albert

2015-10-06

A process is disclosed for sectioning by etching of monolayers and multilayers using an RIE technique with fluorine-based chemistry. In one embodiment, the process uses Reactive Ion Etching (RIE) alone or in combination with Inductively Coupled Plasma (ICP) using fluorine-based chemistry alone and using sufficient power to provide high ion energy to increase the etching rate and to obtain deeper anisotropic etching. In a second embodiment, a process is provided for sectioning of WSi.sub.2/Si multilayers using RIE in combination with ICP using a combination of fluorine-based and chlorine-based chemistries and using RF power and ICP power. According to the second embodiment, a high level of vertical anisotropy is achieved by a ratio of three gases; namely, CHF.sub.3, Cl.sub.2, and O.sub.2 with RF and ICP. Additionally, in conjunction with the second embodiment, a passivation layer can be formed on the surface of the multilayer which aids in anisotropic profile generation.

Rocket flight of a multilayer coated high-density EUV toroidal grating

Science.gov (United States)

Keski-Kuha, Ritva A. M.; Thomas, Roger J.; Davila, Joseph M.

1992-01-01

A multilayer coated high density toroidal grating was flown on a sounding rocket experiment in the Solar EUV Rocket Telescope and Spectrograph (SERTS) instrument. To our knowledge this is the first space flight of a multilayer coated grating. Pre-flight performance evaluation showed that the application of a 10-layer Ir/Si multilayer coating to the 3600 l/mm blazed toroidal replica grating produced a factor of 9 enhancement in peak efficiency near the design wavelength around 30 nm in first order over the standard gold coating, with a measured EUV efficiency that peaked at 3.3 percent. In addition, the grating's spectral resolution of better than 5000 was maintained. The region of enhanced grating efficiency due to the multilayer coating is clearly evident in the flight data. Within the bandpass of the multilayer coating, the recorded film densities were roughly equivalent to those obtained with a factor of six longer exposure on the previous flight of the SERTS instrument.

Aneurysm Recurrence Volumetry Is More Sensitive than Visual Evaluation of Aneurysm Recurrences.

Science.gov (United States)

Schönfeld, M H; Schlotfeldt, V; Forkert, N D; Goebell, E; Groth, M; Vettorazzi, E; Cho, Y D; Han, M H; Kang, H-S; Fiehler, J

2016-03-01

Considerable inter-observer variability in the visual assessment of aneurysm recurrences limits its use as an outcome parameter evaluating new coil generations. The purpose of this study was to compare visual assessment of aneurysm recurrences and aneurysm recurrence volumetry with an example dataset of HydroSoft coils (HSC) versus bare platinum coils (BPC). For this retrospective study, 3-dimensional time-of-flight magnetic resonance angiography datasets acquired 6 and 12 months after endovascular therapy using BPC only or mainly HSC were analyzed. Aneurysm recurrence volumes were visually rated by two observersas well as quantified by subtraction of the datasets after intensity-based rigid registration. A total of 297 aneurysms were analyzed (BPC: 169, HSC: 128). Recurrences were detected by aneurysm recurrence volumetry in 9 of 128 (7.0 %) treated with HSC and in 24 of 169 (14.2 %) treated with BPC (odds ratio: 2.39, 95 % confidence interval: 1.05-5.48; P = 0.039). Aneurysm recurrence volumetry revealed an excellent correlation between observers (Cronbach's alpha = 0.93). In contrast, no significant difference in aneurysm recurrence was found for visual assessment (3.9 % in HSC cases and 4.7 % in BPC cases). Recurrences were observed in aneurysms smaller than the sample median in 10 of 33 (30.3 %) by aneurysm recurrence volumetry and in 1 of 13 (7.7 %) by visual assessment. Aneurysm recurrences were detected more frequently by aneurysm recurrence volumetry when compared with visual assessment. By using aneurysm recurrence volumetry, differences between treatment groups were detected with higher sensitivity and inter-observer validity probably because of the higher detection rate of recurrences in small aneurysms.

Mathematical Formulation of Multilayer Networks

Science.gov (United States)

De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; Arenas, Alex

2013-10-01

A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing “traditional” network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.

A novel multilayer model with controllable mechanical properties for magnesium-based bone plates.

Science.gov (United States)

Zhou, Juncen; Huang, Wanru; Li, Qing; She, Zuxin; Chen, Funan; Li, Longqin

2015-04-01

Proper mechanical properties are essential for the clinical application of magnesium-based implants. In the present work, a novel multilayer model composed of three layers with desirable features was developed. The modulus of the multilayer model can be adjusted by changing the thickness of each layer. To combine three layers and improve the corrosion resistance of the whole multilayer model, the polycaprolactone coating was employed. In the immersion test, pH values, the concentration of released magnesium ions, and weight loss indicate that the corrosion rate of multilayer models is considerable lower than that of the one-layer bare substrate. The three-point bending test, which is used to examine models' mechanical properties, shows that the flexural modulus of multilayer models is reduced effectively. In addition, the mechanical degradation of multilayer models is more stable, compared to the one-layer substrate.

The NSLS-II Multilayer Laue Lens Deposition System

International Nuclear Information System (INIS)

Conley, R.; Bouet, N.; Biancarosa, J.; Shen, Q.; Boas, L.; Feraca, J.; Rosenbaum, L.

2009-01-01

The NSLS-II(1) program has a requirement for an unprecedented level of x-ray nanofocusing and has selected the wedged multilayer Laue lens(2,3) (MLL) as the optic of choice to meet this goal. In order to fabricate the MLL a deposition system is required that is capable of depositing depth-graded and laterally-graded multilayers with precise thickness control over many thousands of layers, with total film growth in one run up to 100 m thick or greater. This machine design expounds on the positive features of a rotary deposition system(4) constructed previously for MLLs and will contain multiple stationary, horizontally-oriented magnetron sources where a transport will move a substrate back and forth in a linear fashion over shaped apertures at well-defined velocities to affect a multilayer coating.

Electron with arbitrary pseudo-spins in multilayer graphene

Institute of Scientific and Technical Information of China (English)

Worasak Prarokijjak; Bumned Soodchomshom

2015-01-01

Using the low-energy effective Hamiltonian of the ABC-stacked multilayer graphene, the pseudo-spin coupling to real orbital angular momentum of electrons in multilayer graphene is investigated. We show that the electron wave function in N-layer graphene mimics the behavior of a particle with a spin of N × (}/2), where N={1, 2, 3, . . .}. It is said that for N>1 the low-energy effective Hamiltonian for ABC-stacked graphene cannot be used to describe pseudo-spin-1/2 particles. The wave function of electrons in multilayer graphene may behave like fermionic (or bosonic) particle for N being odd (or even). In this paper, we propose a theory of graphene serving as a host material of electrons with arbitrary pseudo-spins tunable by changing the number of graphene layers.

Electron with arbitrary pseudo-spins in multilayer graphene

International Nuclear Information System (INIS)

Prarokijjak Worasak; Soodchomshom Bumned

2015-01-01

Using the low-energy effective Hamiltonian of the ABC-stacked multilayer graphene, the pseudo-spin coupling to real orbital angular momentum of electrons in multilayer graphene is investigated. We show that the electron wave function in N-layer graphene mimics the behavior of a particle with a spin of N × (ħ/2), where N = {1, 2, 3,…}. It is said that for N > 1 the low-energy effective Hamiltonian for ABC-stacked graphene cannot be used to describe pseudo-spin-1/2 particles. The wave function of electrons in multilayer graphene may behave like fermionic (or bosonic) particle for N being odd (or even). In this paper, we propose a theory of graphene serving as a host material of electrons with arbitrary pseudo-spins tunable by changing the number of graphene layers. (paper)

Multilayer network decoding versatility and trust

Science.gov (United States)

Sarkar, Camellia; Yadav, Alok; Jalan, Sarika

2016-01-01

In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real-world systems. We analyze massive time-varying social data drawn from the largest film industry of the world under a multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher-order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. The analysis of weak ties of the industry uncovers nodes from a lower-degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real-world systems.

Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

Science.gov (United States)

Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

2018-03-01

In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

Directory of Open Access Journals (Sweden)

Guido Bologna

2018-01-01

Full Text Available One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP, experiments were performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP architecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs, and Support Vector Machines (SVM. The complexity of rulesets was measured with the average number of generated rules and average number of antecedents per rule. From the 25 used classification problems, the most complex rulesets were generated from BSTs trained by “gentle boosting” and “real boosting.” Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules generated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest fidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to those reported in the literature proved to be competitive.

Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Becerra-Alonso, David

2012-01-01

Full Text Available The level of default in financial institutions is a key piece of information in the activity of these organizations and reveals their level of risk. This in turn explains the growing attention given to variables of this kind, during the crisis of these last years. This paper presents a method to estimate the default rate using the non-linear model defined by standard Multilayer Perceptron (MLP neural networks trained with a novel methodology called Extreme Learning Machine (ELM. The experimental results are promising, and show a good performance when comparing the MLP model trained with the Leverberg-Marquard algorithm. || La morosidad en las entidades financieras es un dato muy importante de la actividad de estas instituciones pues permite conocer el nivel de riesgo asumido por éstas. Esto a su vez explica la creciente atención otorgada a esta variable, especialmente en los últimos años de crisis. Este artículo presenta un método para estimar el nivel de la tasa de morosidad por medio de un modelo no lineal definido por la red neuronal Multilayer Perceptron (MLP entrenada con una nueva metodología llamada Extreme Learning Machine (ELM. Los resultados experimentales son prometedores, mostrando un buen resultado si se compara con el modelo MLP entrenado con el algoritmo de Leverberg-Marquard.

Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

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Murat Cuhadar

2014-03-01

Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

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Fernando Castaño

2017-09-01

Full Text Available Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.. The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.

Science.gov (United States)

Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E; Artuñedo, Antonio

2017-09-14

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE

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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

Investigation of aperiodic W/C multi-layer mirror for X-ray optics

International Nuclear Information System (INIS)

Wang Zhanshan; Cheng Xinbin; Zhu Jingtao; Huang Qiushi; Zhang Zhong; Chen Lingyan

2011-01-01

Design, fabrication and characterization of aperiodic tungsten/carbon (W/C) multi-layer mirror were studied. W/C multi-layer was designed as a broad-angle reflective supermirror for Cu-Kα line (λ = 0.154 nm) in the grazing incident angular range (0.9-1.1 deg.) using simulated annealing algorithm. To deposit the W/C depth-graded multi-layer mirror accurately, we introduce an effective layer growth rate as a function of layer thickness. This method greatly improves the reflectivity curve compared to the conventional multi-layer mirror prepared with constant growth rate. The deposited multi-layer mirror exhibits an average reflectivity of 19% over the grazing incident angle range of 0.88-1.08 deg. which mainly coincides with the designed value. Furthermore, the physical mechanisms were discussed and the re-sputtering process of light-atom layers is accounted for the modification of layer thicknesses which leads to the effective growth rates. Using this calibration method, the aperiodic multi-layer mirrors can be better fabricated for X-ray optics.

Measure of Node Similarity in Multilayer Networks

DEFF Research Database (Denmark)

Møllgaard, Anders; Zettler, Ingo; Dammeyer, Jesper

2016-01-01

university.Our analysis is based on data obtained using smartphones equipped with customdata collection software, complemented by questionnaire-based data. The networkof social contacts is represented as a weighted multilayer network constructedfrom different channels of telecommunication as well as data...... might bepresent in one layer of the multilayer network and simultaneously be absent inthe other layers. For a variable such as gender, our measure reveals atransition from similarity between nodes connected with links of relatively lowweight to dis-similarity for the nodes connected by the strongest...

Exchange interactions in Fe/Y multilayers

International Nuclear Information System (INIS)

Elkabil, R.; Elkaidi, I.; Annouar, F.; Lassri, H.; Hamdoun, A.; Bensassi, B.; Berrada, A.; Krishnan, R.

2005-01-01

The magnetization of Fe/Y multilayers has been measured as a function of temperature. A bulk-like T 3/2 temperature dependence of the magnetization is observed for all multilayers in the temperature range 5-300 K. The spin-wave constant B is found to decrease inversely with t Fe . A simple theoretical model with exchange interactions only, and with non-interacting magnons, has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J b , surface exchange interaction J s and the interlayer exchange interaction J I for various Fe layer thicknesses have been obtained

Domain structures and magnetization reversal in Co/Pd and CoFeB/Pd multilayers

Energy Technology Data Exchange (ETDEWEB)

Sbiaa, R., E-mail: rachid@squ.edu.om [Department of Physics, Sultan Qaboos University, P.O. Box 36, PC 123 (Oman); Ranjbar, M. [Physics Department, University of Gothenburg, 412 96 Gothenburg (Sweden); Åkerman, J. [Physics Department, University of Gothenburg, 412 96 Gothenburg (Sweden); Materials Physics, School of ICT, Royal Institute of Technology (KTH), 164 40 Kista (Sweden)

2015-05-07

Domain structures and magnetization reversal of (Co/Pd) and (CoFeB/Pd) multilayers with 7 and 14 repeats were investigated. The Co-based multilayers show much larger coercivities, a better squareness, and a sharper magnetization switching than CoFeB-based multilayers. From magnetic force microscopy observations, both structures show strong reduction in domains size as the number of repeats increases but the magnetic domains for Co-based multilayers are more than one order of magnitude larger than for CoFeB-based multilayers. By imaging domains at different times, breaks in the (CoFeB/Pd) multilayer stripes were observed within only few hours, while no change could be seen for (Co/Pd) multilayers. Although CoFeB single layers are suitable for magnetoresistive devices due to their large spin polarization and low damping constants, their lamination with Pd suffers mainly from thermal instability.

Finite-width plasmonic waveguides with hyperbolic multilayer cladding

DEFF Research Database (Denmark)

Babicheva, Viktoriia; Shalaginov, Mikhail Y.; Ishii, Satoshi

2015-01-01

Engineering plasmonic metamaterials with anisotropic optical dispersion enables us to tailor the properties of metamaterial-based waveguides. We investigate plasmonic waveguides with dielectric cores and multilayer metal-dielectric claddings with hyperbolic dispersion. Without using any homogeniz......Engineering plasmonic metamaterials with anisotropic optical dispersion enables us to tailor the properties of metamaterial-based waveguides. We investigate plasmonic waveguides with dielectric cores and multilayer metal-dielectric claddings with hyperbolic dispersion. Without using any...

Local Recurrence After Uveal Melanoma Proton Beam Therapy: Recurrence Types and Prognostic Consequences

International Nuclear Information System (INIS)

Caujolle, Jean-Pierre; Paoli, Vincent; Chamorey, Emmanuel; Maschi, Celia; Baillif, Stéphanie; Herault, Joël; Gastaud, Pierre; Hannoun-Levi, Jean Michel

2013-01-01

Purpose: To study the prognosis of the different types of uveal melanoma recurrences treated by proton beam therapy (PBT). Methods and Materials: This retrospective study analyzed 61 cases of uveal melanoma local recurrences on a total of 1102 patients treated by PBT between June 1991 and December 2010. Survival rates have been determined by using Kaplan-Meier curves. Prognostic factors have been evaluated by using log-rank test or Cox model. Results: Our local recurrence rate was 6.1% at 5 years. These recurrences were divided into 25 patients with marginal recurrences, 18 global recurrences, 12 distant recurrences, and 6 extrascleral extensions. Five factors have been identified as statistically significant risk factors of local recurrence in the univariate analysis: large tumoral diameter, small tumoral volume, low ratio of tumoral volume over eyeball volume, iris root involvement, and safety margin inferior to 1 mm. In the local recurrence-free population, the overall survival rate was 68.7% at 10 years and the specific survival rate was 83.6% at 10 years. In the local recurrence population, the overall survival rate was 43.1% at 10 years and the specific survival rate was 55% at 10 years. The multivariate analysis of death risk factors has shown a better prognosis for marginal recurrences. Conclusion: Survival rate of marginal recurrences is superior to that of the other recurrences. The type of recurrence is a clinical prognostic value to take into account. The influence of local recurrence retreatment by proton beam therapy should be evaluated by novel studies

Local Recurrence After Uveal Melanoma Proton Beam Therapy: Recurrence Types and Prognostic Consequences

Energy Technology Data Exchange (ETDEWEB)

Caujolle, Jean-Pierre, E-mail: ncaujolle@aol.com [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Paoli, Vincent [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Chamorey, Emmanuel [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France); Department of Biostatistics and Epidemiology, Centre Antoine Lacassagne, Nice (France); Maschi, Celia; Baillif, Stéphanie [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Herault, Joël [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France); Gastaud, Pierre [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Hannoun-Levi, Jean Michel [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France)

2013-04-01

Purpose: To study the prognosis of the different types of uveal melanoma recurrences treated by proton beam therapy (PBT). Methods and Materials: This retrospective study analyzed 61 cases of uveal melanoma local recurrences on a total of 1102 patients treated by PBT between June 1991 and December 2010. Survival rates have been determined by using Kaplan-Meier curves. Prognostic factors have been evaluated by using log-rank test or Cox model. Results: Our local recurrence rate was 6.1% at 5 years. These recurrences were divided into 25 patients with marginal recurrences, 18 global recurrences, 12 distant recurrences, and 6 extrascleral extensions. Five factors have been identified as statistically significant risk factors of local recurrence in the univariate analysis: large tumoral diameter, small tumoral volume, low ratio of tumoral volume over eyeball volume, iris root involvement, and safety margin inferior to 1 mm. In the local recurrence-free population, the overall survival rate was 68.7% at 10 years and the specific survival rate was 83.6% at 10 years. In the local recurrence population, the overall survival rate was 43.1% at 10 years and the specific survival rate was 55% at 10 years. The multivariate analysis of death risk factors has shown a better prognosis for marginal recurrences. Conclusion: Survival rate of marginal recurrences is superior to that of the other recurrences. The type of recurrence is a clinical prognostic value to take into account. The influence of local recurrence retreatment by proton beam therapy should be evaluated by novel studies.

Docetaxel-loaded multilayer nanoparticles with nanodroplets for cancer therapy

Directory of Open Access Journals (Sweden)

Oh KS

2016-03-01

Full Text Available Keun Sang Oh,1,* Kyungim Kim,1,* Byeong Deok Yoon,1 Hye Jin Lee,1 Dal Yong Park,1 Eun-yeong Kim,1 Kiho Lee,1 Jae Hong Seo,2 Soon Hong Yuk1,2 1College of Pharmacy, Korea University, Sejong, 2Biomedical Research Center, Korea University Guro Hospital, Guro-gu, Seoul, Republic of Korea *These authors contributed equally to this work Abstract: A mixture of docetaxel (DTX and Solutol® HS 15 (Solutol transiently formed nanodroplets when it was suspended in an aqueous medium. However, nanodroplets that comprised DTX and Solutol showed a rapid precipitation of DTX because of their unstable characteristics in the aqueous medium. The incorporation of nanodroplets that comprised DTX and Solutol through vesicle fusion and subsequent stabilization was designed to prepare multilayer nanoparticles (NPs with a DTX-loaded Solutol nanodroplet (as template NPs core for an efficient delivery of DTX as a chemotherapeutic drug. As a result, the DTX-loaded Solutol nanodroplets (~11.7 nm were observed to have an increased average diameter (from 11.7 nm to 156.1 nm and a good stability of the hydrated NPs without precipitation of DTX by vesicle fusion and multilayered structure, respectively. Also, a long circulation of the multilayer NPs was observed, and this was due to the presence of Pluronic F-68 on the surface of the multilayer NPs. This led to an improved antitumor efficacy based on the enhanced permeation and retention effect. Therefore, this study indicated that the multilayer NPs have a considerable potential as a drug delivery system with an enhanced therapeutic efficacy by blood circulation and with low side effects. Keywords: multilayer nanoparticles, Solutol, Pluronic F-68, docetaxel, cancer therapy

Multilayer mirrors as power filters in insertion device beamlines

International Nuclear Information System (INIS)

Kortright, J.B.; DiGennaro, R.S.

1988-08-01

The power-filtering capabilities of multilayer band-pass x-ray mirrors relative to total reflection low-pass mirrors is presented. Results are based on calculations assuming proposed wiggler sources on the upcoming generation of low energy (1.5 GeV) and high energy (7.0 GeV) synchrotron radiation sources. Results show that multilayers out-perform total reflection mirrors in terms of reduction in reflected power by roughly an order of magnitude, with relatively small increases in total absorbed power and power density over total reflection mirrors, and with comparable reflected flux values. Various aspects of this potential application of multilayer x-ray optics are discussed. 13 refs., 3 figs., 1 tab

Anomalously Weak Scattering in Metal-Semiconductor Multilayer Hyperbolic Metamaterials

Directory of Open Access Journals (Sweden)

Hao Shen

2015-05-01

Full Text Available In contrast to strong plasmonic scattering from metal particles or structures in metal films, we show that patterns of arbitrary shape fabricated out of multilayer hyperbolic metamaterials become invisible within a chosen band of optical frequencies. This is due to anomalously weak scattering when the in-plane permittivity of the multilayer hyperbolic metamaterials is tuned to match with the surrounding medium. This new phenomenon is described theoretically and demonstrated experimentally by optical characterization of various patterns in Au-Si multilayer hyperbolic metamaterials. This anomalously weak scattering is insensitive to pattern sizes, shapes, and incident angles, and has potential applications in scattering cross-section engineering, optical encryption, low-observable conductive probes, and optoelectric devices.

Tunable Multilayer Graphene Metamaterials for Terahertz/Infrared Waveguide Modulators

DEFF Research Database (Denmark)

Khromova, Irina; Andryieuski, Andrei; Lavrinenko, Andrei

regimes of multilayer graphene-dielectric artificial metamaterials. The interplay between interband and intraband transitions in graphene allows converting the structure into a transparent and/or electromagnetically dense artificial medium. The gate voltage can be used to electrically control...... the concentration of carriers in the graphene sheets and, thus, efficiently change the dispersion of the whole structure. Placed inside a hollow waveguide, a multilayer graphene/dielectric metamaterial provides high-speed modulation and tunable bandpass filtering. The absence of scattered radiation enables dense...... the latter to shift its central frequency by 1:25% per every meV graphene Fermi energy change. We believe that graphene-dielectric multilayer metamaterials will constitute the functional platform for THz-IR waveguide-integrated devices....

Multilayer composite material and method for evaporative cooling

Science.gov (United States)

Buckley, Theresa M. (Inventor)

2002-01-01

A multilayer composite material and method for evaporative cooling of a person employs an evaporative cooling liquid that changes phase from a liquid to a gaseous state to absorb thermal energy. The evaporative cooling liquid is absorbed into a superabsorbent material enclosed within the multilayer composite material. The multilayer composite material has a high percentage of the evaporative cooling liquid in the matrix. The cooling effect can be sustained for an extended period of time because of the high percentage of phase change liquid that can be absorbed into the superabsorbent. Such a composite can be used for cooling febrile patients by evaporative cooling as the evaporative cooling liquid in the matrix changes from a liquid to a gaseous state to absorb thermal energy. The composite can be made with a perforated barrier material around the outside to regulate the evaporation rate of the phase change liquid. Alternatively, the composite can be made with an imperveous barrier material or semipermeable membrane on one side to prevent the liquid from contacting the person's skin. The evaporative cooling liquid in the matrix can be recharged by soaking the material in the liquid. The multilayer composite material can be fashioned into blankets, garments and other articles.

Multilayer tape cast SOFC – Effect of anode sintering temperature

DEFF Research Database (Denmark)

Hauch, Anne; Birkl, Christoph; Brodersen, Karen

2012-01-01

Multilayer tape casting (MTC) is considered a promising, cost-efficient, up-scalable shaping process for production of planar anode supported solid oxide fuel cells (SOFC). Multilayer tape casting of the three layers comprising the half cell (anode support/active anode/electrolyte) can potentially...

Boundary element method in dynamic interaction of structures with multilayers media

International Nuclear Information System (INIS)

Mihalache, N.; Poterasu, V.F.

1993-01-01

The paper presents the problems of dynamic interaction between the multilayers media and structure by means of B.E.M., using Green's functions. The structure considered by the authors as a particular problem concerns a reinforced concrete shear wall and soil foundation of three layers having different thickness and mechanical characteristics. The authors will present comparatively the stresses and the displacements in static and dynamic regime interaction response of the structure. Theoretical part of the paper presents: Green's functions for the multilayers media in dynamic regime, stiffness matrices, stresses and displacements in the multilayers media exprimed by means of the Green's functions induced by the shear and horizontal forces, computer program, consideration for dynamic, structure-foundation-multilayers soil foundation interaction. (author)

Discrete Orthogonal Transforms and Neural Networks for Image Interpolation

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J. Polec

1999-09-01

Full Text Available In this contribution we present transform and neural network approaches to the interpolation of images. From transform point of view, the principles from [1] are modified for 1st and 2nd order interpolation. We present several new interpolation discrete orthogonal transforms. From neural network point of view, we present interpolation possibilities of multilayer perceptrons. We use various configurations of neural networks for 1st and 2nd order interpolation. The results are compared by means of tables.

Gas demand forecasting by a new artificial intelligent algorithm

Science.gov (United States)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Multivalent-Counterion-Induced Surfactant Multilayer Formation at Hydrophobic and Hydrophilic Solid-Solution Interfaces.

Science.gov (United States)

Penfold, Jeffrey; Thomas, Robert K; Li, Peixun; Xu, Hui; Tucker, Ian M; Petkov, Jordan T; Sivia, Devinderjit S

2015-06-23

Surface multilayer formation from the anionic-nonionic surfactant mixture of sodium dodecyl dioxyethylene sulfate, SLES, and monododecyl dodecaethylene glycol, C12E12, by the addition of multivalent Al(3+) counterions at the solid-solution interface is observed and characterized by neutron reflectivity, NR. The ability to form surface multilayer structures on hydrophobic and hydrophilic silica and cellulose surfaces is demonstrated. The surface multilayer formation is more pronounced and more well developed on the hydrophilic and hydrophobic silica surfaces than on the hydrophilic and hydrophobic cellulose surfaces. The less well developed multilayer formation on the cellulose surfaces is attributed to the greater surface inhomogeneities of the cellulose surface which partially inhibit lateral coherence and growth of the multilayer domains at the surface. The surface multilayer formation is associated with extreme wetting properties and offers the potential for the manipulation of the solid surfaces for enhanced adsorption and control of the wetting behavior.

Domain wall theory and exchange stiffness in Co/Pd multilayers

NARCIS (Netherlands)

Kambersky, V.; Kambersky, V.; de Haan, P.; Simsova, J.; Porthun, S.; Porthun, S.; Gemperle, R.; Lodder, J.C.

1996-01-01

The stripe model of domain structure in multilayers is studied by micromagnetic simulation. The results indicate a strong reduction of the effective domain wall energy (by dipolar effects). Domain width measurements on sputtered Co/Pd multilayers are compared with the theory. The estimated exchange

Issues in the use of neural networks in information retrieval

CERN Document Server

Iatan, Iuliana F

2017-01-01

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

Durable Corrosion Resistance of Copper Due to Multi-Layer Graphene

Directory of Open Access Journals (Sweden)

Abhishek Tiwari

2017-09-01

Full Text Available Ultra-thin graphene coating has been reported to provide considerable resistance against corrosion during short-term exposures, however, there is great variability in the corrosion resistance due to graphene coating in different studies. It may be possible to overcome the problem of hampered corrosion protection ability of graphene that is caused due to defective single layer graphene by applying multilayer graphene. Systematic electrochemical characterization showed that the multilayer graphene coating developed in the study provided significant corrosion resistance in a chloride solution and the corrosion resistance was sustained for long durations (~400 h, which is attributed to the multilayer graphene.

Characterization of multilayer nitride coatings by electron microscopy and modulus mapping

International Nuclear Information System (INIS)

Pemmasani, Sai Pramod; Rajulapati, Koteswararao V.; Ramakrishna, M.; Valleti, Krishna; Gundakaram, Ravi C.; Joshi, Shrikant V.

2013-01-01

This paper discusses multi-scale characterization of physical vapour deposited multilayer nitride coatings using a combination of electron microscopy and modulus mapping. Multilayer coatings with a triple layer structure based on TiAlN and nanocomposite nitrides with a nano-multilayered architecture were deposited by Cathodic arc deposition and detailed microstructural studies were carried out employing Energy Dispersive Spectroscopy, Electron Backscattered Diffraction, Focused Ion Beam and Cross sectional Transmission Electron Microscopy in order to identify the different phases and to study microstructural features of the various layers formed as a result of the deposition process. Modulus mapping was also performed to study the effect of varying composition on the moduli of the nano-multilayers within the triple layer coating by using a Scanning Probe Microscopy based technique. To the best of our knowledge, this is the first attempt on modulus mapping of cathodic arc deposited nitride multilayer coatings. This work demonstrates the application of Scanning Probe Microscopy based modulus mapping and electron microscopy for the study of coating properties and their relation to composition and microstructure. - Highlights: • Microstructure of a triple layer nitride coating studied at multiple length scales. • Phases identified by EDS, EBSD and SAED (TEM). • Nanolayered, nanocomposite structure of the coating studied using FIB and TEM. • Modulus mapping identified moduli variation even in a nani-multilayer architecture

Ring-dot-shaped multilayer piezoelectric step-down transformers using PZT-based ceramics

International Nuclear Information System (INIS)

Kim, Insung; Joo, Hyeonkyu; Song, Jaesung; Jeong, Soonjong; Kim, Minsoo

2010-01-01

In this study, multilayer piezo stack transformers for switching mode power supply (SMPS) application were manufactured using 0.01Pb(Ni 1/3 Nb 2/3 )O 3 - 0.08Pb(Mn 1/3 Nb 2/3 )O 3 - 0.91Pb(Zr 0.505 Ti 0.495 )O 3 (PNN-PMN-PZT) ceramics. The voltage ratio of a multilayer piezo stack transformer showed a maximum at the resonance frequency of the input and then increased with increasing load resistance. The efficiency of the multilayer piezo stack transformer showed its highest value at around the matching load. The output power increased with increasing input voltage. The temperature of the multilayer piezo stack transformer increased with increasing output power and load resistance. The manufactured multilayer piezo stack transformer could be used up to 5 W at a resonance frequency of 70.25 kHz for SMPS application because the temperature rise from room temperature is believed to about 20 .deg. C and because the transformer is electrically stable. The newly-developed ring-dot-type step-down multilayer piezo stack transformer shows possible applications as SMPS for electronic power sources with excellent input-to-output properties.

High-reflectance La/B-based multilayer mirror for 6.x  nm wavelength

NARCIS (Netherlands)

Kuznetsov, Dmitry; Yakshin, Andrey; Sturm, Jacobus Marinus; van de Kruijs, Robbert Wilhelmus Elisabeth; Louis, Eric; Bijkerk, Frederik

2015-01-01

We report a hybrid thin-film deposition procedure to significantly enhance the reflectivity of La/B-based multilayer structures. This is of relevance for applications of multilayer optics at 6.7-nm wavelength and beyond. Such multilayers showed a reflectance of 64.1% at 6.65 nm measured at

Fabrication of anticorrosive multilayer onto magnesium alloy substrates via spin-assisted layer-by-layer technique

Energy Technology Data Exchange (ETDEWEB)

Cai Kaiyong, E-mail: Kaiyong_cai@cqu.edu.cn [Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044 (China); Sui Xiaojing; Hu Yan [Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044 (China); Zhao Li [China National Centre for Biotechnology Development, No. 16, Xi Si Huan Zhong Lu, Haidian District, Beijing 100036 (China); Lai Min; Luo Zhong; Liu Peng; Yang Weihu [Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044 (China)

2011-12-01

To improve the corrosion resistance of magnesium alloy, we reported a novel approach for the fabrication of anticorrosive multilayers onto AZ91D substrates. The multilayers were composed of poly(ethylene imine) (PEI), poly(styrene sulfonate) (PSS) and 8-hydroxyquinoline (8HQ). They were deposited onto AZ91D substrates via a spin-assisted layer-by-layer (LbL) technique. The multilayered structure was stabilized with glutaraldehyde (GA) as crossing linker. It was confirmed by Fourier transform infrared spectroscopy (FT-IR). Surface morphologies and elemental compositions of the formed anticorrosive multilayers were characterized with scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), respectively. The corrosion performance of the multilayer coated AZ91D substrates was characterized by hydrogen evolution. The results of electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization measurements suggested that the multilayered coating improved the corrosion resistance of AZ91D substrates. In vitro study revealed that the multilayered coating was cytocompatible. The study provides a potential alternative for the fabrication of corrosion resistant magnesium alloy-based implants. Highlights: {yields} Corrosion protective multilayers have been constructed onto AZ91D substrates via layer by layer technique. {yields} The multilayered structured containing 8-hydroxyquinoline highly improves the corrosion resistance of AZ91D substrates. {yields} The novel multilayered coating is potentially important for developing corrosion resistant magnesium alloy-based implants.

Fabrication of anticorrosive multilayer onto magnesium alloy substrates via spin-assisted layer-by-layer technique

International Nuclear Information System (INIS)

Cai Kaiyong; Sui Xiaojing; Hu Yan; Zhao Li; Lai Min; Luo Zhong; Liu Peng; Yang Weihu

2011-01-01

To improve the corrosion resistance of magnesium alloy, we reported a novel approach for the fabrication of anticorrosive multilayers onto AZ91D substrates. The multilayers were composed of poly(ethylene imine) (PEI), poly(styrene sulfonate) (PSS) and 8-hydroxyquinoline (8HQ). They were deposited onto AZ91D substrates via a spin-assisted layer-by-layer (LbL) technique. The multilayered structure was stabilized with glutaraldehyde (GA) as crossing linker. It was confirmed by Fourier transform infrared spectroscopy (FT-IR). Surface morphologies and elemental compositions of the formed anticorrosive multilayers were characterized with scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), respectively. The corrosion performance of the multilayer coated AZ91D substrates was characterized by hydrogen evolution. The results of electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization measurements suggested that the multilayered coating improved the corrosion resistance of AZ91D substrates. In vitro study revealed that the multilayered coating was cytocompatible. The study provides a potential alternative for the fabrication of corrosion resistant magnesium alloy-based implants. Highlights: → Corrosion protective multilayers have been constructed onto AZ91D substrates via layer by layer technique. → The multilayered structured containing 8-hydroxyquinoline highly improves the corrosion resistance of AZ91D substrates. → The novel multilayered coating is potentially important for developing corrosion resistant magnesium alloy-based implants.

Investigation of CoFeV/TiZr multilayer by polarized neutron reflectometry

International Nuclear Information System (INIS)

Chen Bo; Li Xinxi; Huang Chaoqiang

2007-06-01

The interracial structures of CoFeV/TiZr multilayer play an important role in performance of polarizing supermirrors. Aiming to requirement, CoFeV/ TiZr layered samples with different structures were prepared. Specular reflection of polarized neutrons was employed to study the depth profile of scattering length, density, thickness and roughness of CoFeV/TiZr multilayer and magnetically dead layers. The result shows that the roughness in CoFeV/ TiZr multilayer can be described with roughness increase law and the thickness of magnetically dead layers is about 0.5 nm. The producing technology of the multilayer reaches the requirements. (authors)

Recurrent Intracerebral Hemorrhage

DEFF Research Database (Denmark)

Schmidt, Linnea Boegeskov; Goertz, Sanne; Wohlfahrt, Jan

2016-01-01

BACKGROUND: Intracerebral hemorrhage (ICH) is a disease with high mortality and a substantial risk of recurrence. However, the recurrence risk is poorly documented and the knowledge of potential predictors for recurrence among co-morbidities and medicine with antithrombotic effect is limited....... OBJECTIVES: 1) To estimate the short- and long-term cumulative risks of recurrent intracerebral hemorrhage (ICH). 2) To investigate associations between typical comorbid diseases, surgical treatment, use of medicine with antithrombotic effects, including antithrombotic treatment (ATT), selective serotonin...

Degradation of periodic multilayers as seen by small-angle x-ray scattering and x-ray diffraction

CERN Document Server

Rafaja, D; Simek, D; Zdeborova, L; Valvoda, V

2002-01-01

The capabilities of small-angle x-ray scattering (SAXS) and wide-angle x-ray diffraction (XRD) to recognize structural changes in periodic multilayers were compared on Fe/Au multilayers with different degrees of structural degradation. Experimental results have shown that both methods are equally sensitive to the multilayer degradation, i.e., to the occurrence of non-continuous interfaces, to short-circuits in the multilayer structure and to the multilayer precipitation. XRD yielded additional information on the multilayer crystallinity, whilst SAXS could better recognize fragments of a long-range periodicity (remnants of the original multilayer structure). Changes in the multilayer structure were initiated by successive annealing at 200 and 300 deg. C. Experimental data were complemented by numerical simulations performed using a combination of optical theory and the distorted wave Born approximation for SAXS or the kinematical Born approximation for XRD.

Multilayer DNA Origami Packed on Hexagonal and Hybrid Lattices

DEFF Research Database (Denmark)

Ke, Yonggang; Voigt, Niels Vinther; Shih, William M.

2012-01-01

“Scaffolded DNA origami” has been proven to be a powerful and efficient approach to construct two-dimensional or three-dimensional objects with great complexity. Multilayer DNA origami has been demonstrated with helices packing along either honeycomb-lattice geometry or square-lattice geometry....... Here we report successful folding of multilayer DNA origami with helices arranged on a close-packed hexagonal lattice. This arrangement yields a higher density of helical packing and therefore higher resolution of spatial addressing than has been shown previously. We also demonstrate hybrid multilayer...... DNA origami with honeycomb-lattice, square-lattice, and hexagonal-lattice packing of helices all in one design. The availability of hexagonal close-packing of helices extends our ability to build complex structures using DNA nanotechnology....

Multilayer DNA origami packed on hexagonal and hybrid lattices.

Science.gov (United States)

Ke, Yonggang; Voigt, Niels V; Gothelf, Kurt V; Shih, William M

2012-01-25

"Scaffolded DNA origami" has been proven to be a powerful and efficient approach to construct two-dimensional or three-dimensional objects with great complexity. Multilayer DNA origami has been demonstrated with helices packing along either honeycomb-lattice geometry or square-lattice geometry. Here we report successful folding of multilayer DNA origami with helices arranged on a close-packed hexagonal lattice. This arrangement yields a higher density of helical packing and therefore higher resolution of spatial addressing than has been shown previously. We also demonstrate hybrid multilayer DNA origami with honeycomb-lattice, square-lattice, and hexagonal-lattice packing of helices all in one design. The availability of hexagonal close-packing of helices extends our ability to build complex structures using DNA nanotechnology. © 2011 American Chemical Society

Nanolaminated TiN/Mo2N hard multilayer coatings

International Nuclear Information System (INIS)

Martev, I N; Dechev, D A; Ivanov, N P; Uzunov, T S D; Kashchieva, E P

2010-01-01

The paper presents results on the synthesis of hard multilayer coatings consisting of titanium nitride and molybdenum nitride thin films with thickness of several nm. The TiN and Mo 2 N films were successively deposited by reactive DC magnetron sputtering. These multilayer structures were investigated by Auger electron spectroscopy (AES), transmission electron microscopy (TEM), selected area electron diffraction (SAED), X-ray diffraction (XRD), cross-section scanning electron microscopy (CSSEM) and cross-section electron probe microanalysis (CSEPMA). The mechanical properties of the multilayer coatings, namely, hardness, Young's modulus and the coefficient of plastic deformation were measured. The adhesion was evaluated by the Rockwell-C-impact test. Coatings with different total thickness were examined with respect to adhesion to substrates of tool materials.

Multilayer modal actuator-based piezoelectric transformers.

Science.gov (United States)

Huang, Yao-Tien; Wu, Wen-Jong; Wang, Yen-Chieh; Lee, Chih-Kung

2007-02-01

An innovative, multilayer piezoelectric transformer equipped with a full modal filtering input electrode is reported herein. This modal-shaped electrode, based on the orthogonal property of structural vibration modes, is characterized by full modal filtering to ensure that only the desired vibration mode is excited during operation. The newly developed piezoelectric transformer is comprised of three layers: a multilayered input layer, an insulation layer, and a single output layer. The electrode shape of the input layer is derived from its structural vibration modal shape, which takes advantage of the orthogonal property of the vibration modes to achieve a full modal filtering effect. The insulation layer possesses two functions: first, to couple the mechanical vibration energy between the input and output, and second, to provide electrical insulation between the two layers. To meet the two functions, a low temperature, co-fired ceramic (LTCC) was used to provide the high mechanical rigidity and high electrical insulation. It can be shown that this newly developed piezoelectric transformer has the advantage of possessing a more efficient energy transfer and a wider optimal working frequency range when compared to traditional piezoelectric transformers. A multilayer piezoelectric, transformer-based inverter applicable for use in LCD monitors or portable displays is presented as well.

Multilayer Relaxation and Surface Energies of Metallic Surfaces

Science.gov (United States)

Bozzolo, Guillermo; Rodriguez, Agustin M.; Ferrante, John

1994-01-01

The perpendicular and parallel multilayer relaxations of fcc (210) surfaces are studied using equivalent crystal theory (ECT). A comparison with experimental and theoretical results is made for AI(210). The effect of uncertainties in the input parameters on the magnitudes and ordering of surface relaxations for this semiempirical method is estimated. A new measure of surface roughness is proposed. Predictions for the multilayer relaxations and surface energies of the (210) face of Cu and Ni are also included.

Seminal quality prediction using data mining methods.

Science.gov (United States)

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of

Anisotropic in-plane thermal conductivity in multilayer silicene

Science.gov (United States)

Zhou, Yang; Guo, Zhi-Xin; Chen, Shi-You; Xiang, Hong-Jun; Gong, Xin-Gao

2018-06-01

We systematically study thermal conductivity of multilayer silicene by means of Boltzmann Transportation Equation (BTE) method. We find that their thermal conductivity strongly depends on the surface structures. Thermal conductivity of bilayer silicene varies from 3.31 W/mK to 57.9 W/mK with different surface structures. Also, the 2 × 1 surface reconstruction induces unusual large thermal conductivity anisotropy, which reaches 70% in a four-layer silicene. We also find that the anisotropy decreases with silicene thickness increasing, owing to the significant reduction of thermal conductivity in the zigzag direction and its slight increment in the armchair direction. Finally, we find that both the phonon-lifetime anisotropy and the phonon-group-velocity anisotropy contribute to the thermal conductivity anisotropy of multilayer silicene. These findings could be helpful in the field of heat management, thermoelectric applications involving silicene and other multilayer nanomaterials with surface reconstructions in the future.

Optimisation of multi-layer rotationally moulded foamed structures

Science.gov (United States)

Pritchard, A. J.; McCourt, M. P.; Kearns, M. P.; Martin, P. J.; Cunningham, E.

2018-05-01

Multi-layer skin-foam and skin-foam-skin sandwich constructions are of increasing interest in the rotational moulding process for two reasons. Firstly, multi-layer constructions can improve the thermal insulation properties of a part. Secondly, foamed polyethylene sandwiched between solid polyethylene skins can increase the mechanical properties of rotationally moulded structural components, in particular increasing flexural properties and impact strength (IS). The processing of multiple layers of polyethylene and polyethylene foam presents unique challenges such as the control of chemical blowing agent decomposition temperature, and the optimisation of cooling rates to prevent destruction of the foam core; therefore, precise temperature control is paramount to success. Long cooling cycle times are associated with the creation of multi-layer foam parts due to their insulative nature; consequently, often making the costs of production prohibitive. Devices such as Rotocooler®, a rapid internal mould water spray cooling system, have been shown to have the potential to significantly decrease cooling times in rotational moulding. It is essential to monitor and control such devices to minimise the warpage associated with the rapid cooling of a moulding from only one side. The work presented here demonstrates the use of threaded thermocouples to monitor the polymer melt in multi-layer sandwich constructions, in order to analyse the cooling cycle of multi-layer foamed structures. A series of polyethylene skin-foam test mouldings were produced, and the effect of cooling medium on foam characteristics, mechanical properties, and process cycle time were investigated. Cooling cycle time reductions of 45%, 26%, and 29% were found for increasing (1%, 2%, and 3%) chemical blowing agent (CBA) amount when using internal water cooling technology from ˜123°C compared with forced air cooling (FAC). Subsequently, a reduction of IS for the same skin-foam parts was found to be 1%, 4

High proton conductivity in the molecular interlayer of a polymer nanosheet multilayer film.

Science.gov (United States)

Sato, Takuma; Hayasaka, Yuta; Mitsuishi, Masaya; Miyashita, Tokuji; Nagano, Shusaku; Matsui, Jun

2015-05-12

High proton conductivity was achieved in a polymer multilayer film with a well-defined two-dimensional lamella structure. The multilayer film was prepared by deposition of poly(N-dodecylacryamide-co-acrylic acid) (p(DDA/AA)) monolayers onto a solid substrate using the Langmuir-Blodgett technique. Grazing-angle incidence X-ray diffraction measurement of a 30-layer film of p(DDA/AA) showed strong diffraction peaks in the out-of-plane direction at 2θ = 2.26° and 4.50°, revealing that the multilayer film had a highly uniform layered structure with a monolayer thickness of 2.0 nm. The proton conductivity of the p(DDA/AA) multilayer film parallel to the layer plane direction was 0.051 S/cm at 60 °C and 98% relative humidity with a low activation energy of 0.35 eV, which is comparable to perfluorosulfonic acid membranes. The high conductivity and low activation energy resulted from the formation of uniform two-dimensional proton-conductive nanochannels in the hydrophilic regions of the multilayer film. The proton conductivity of the multilayer film perpendicular to the layer plane was determined to be 2.1 × 10(-13) S/cm. Therefore, the multilayer film showed large anisotropic conductivity with an anisotropic ratio of 2.4 × 10(11).

Handling magnetic anisotropy and magnetoimpedance effect in flexible multilayers under external stress

Energy Technology Data Exchange (ETDEWEB)

Agra, K.; Bohn, F. [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59078-900 Natal, RN (Brazil); Mori, T.J.A. [Laboratório Nacional de Luz Síncrotron, Rua Giuseppe Máximo Scolfaro, 1000, Guará, 13083-100 Campinas, SP (Brazil); Callegari, G.L.; Dorneles, L.S. [Departamento de Física, Universidade Federal de Santa Maria, 97105-900 Santa Maria, RS (Brazil); Correa, M.A., E-mail: marciocorrea@dfte.ufrn.br [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59078-900 Natal, RN (Brazil)

2016-12-15

We investigate the dynamic magnetic response though magnetoimpedance effect of ferromagnetic flexible NiFe/Ta and FeCuNbSiB/Ta multilayers under external stress. We explore the possibility of handling magnetic anisotropy, and consequently the magnetoimpedance effect, of magnetostrictive multilayers deposited onto flexible substrates. We quantify the sensitivity of the multilayers under external stress by calculating the ratio between impedance variations and external stress changes, and show that considerable values can be reached by tuning the magnetic field, frequency, magnetostriction constant, and external stress. The results extend possibilities of application of magnetostrictive multilayers deposited onto flexible substrates when under external stress and place them as very attractive candidates as element sensor for the development of sensitive smart touch sensors. - Highlights: • We investigate the magnetoimpedance effect in magnetostrictive flexible multilayers grown on flexible substrates. • The external applied stress enables to tuning the samples anisotropies, and consequently the MI performance. • The flexible substrate becomes promising candidate for RF-frequency devices.

Electromechanical field effect transistors based on multilayer phosphorene nanoribbons

Energy Technology Data Exchange (ETDEWEB)

Jiang, Z.T., E-mail: jiangzhaotan@hotmail.com; Lv, Z.T.; Zhang, X.D.

2017-06-21

Based on the tight-binding Hamiltonian approach, we demonstrate that the electromechanical field effect transistors (FETs) can be realized by using the multilayer phosphorene nanoribbons (PNRs). The synergistic combination of the electric field and the external strains can establish the on–off switching since the electric field can shift or split the energy band, and the mechanical strains can widen or narrow the band widths. This kind of multilayer PNR FETs, much solider than the monolayer PNR one and more easily biased by different electric fields, has more transport channels consequently leading to the higher on–off current ratio or the higher sensitivity to the electric fields. Meanwhile, the strain-induced band-flattening will be beneficial for improving the flexibility in designing the electromechanical FETs. In addition, such electromechanical FETs can act as strain-controlled FETs or mechanical detectors for detecting the strains, indicating their potential applications in nano- and micro-electromechanical fields. - Highlights: • Electromechanical transistors are designed with multilayer phosphorene nanoribbons. • Electromechanical synergistic effect can establish the on–off switching more flexibly. • Multilayer transistors, solider and more easily biased, has more transport channels. • Electromechanical transistors can act as strain-controlled transistors or mechanical detectors.

Recurrent Childhood Animal Cruelty and Its Link to Recurrent Adult Interpersonal Violence.

Science.gov (United States)

Trentham, Caleb E; Hensley, Christopher; Policastro, Christina

2018-06-01

In the early 1960s, researchers began to examine the potential link between childhood animal cruelty and future interpersonal violence. Findings since then have been inconsistent in establishing a relationship between the two. This may be due to researchers failing to measure the recurrency of childhood animal abuse and the recurrency of later violent acts committed in adulthood. The current study, using data from 257 inmates at a medium-security prison in a Southern state, is a replication of research conducted by Tallichet and Hensley, and Hensley, Tallichet, and Dutkiewicz, which examined this recurrency issue. The only statistically significant predictor of recurrent adult interpersonal violence in this study was recurrent childhood animal cruelty. Inmates who engaged in recurrent childhood animal cruelty were more likely to commit recurrent adult interpersonal violence. Respondents' race, education, and childhood residence were not significant predictors of the outcome variable.

X-ray grazing incidence diffraction from multilayers

Energy Technology Data Exchange (ETDEWEB)

Tixier, S.; Boeni, P.; Swygenhoven, H. van; Horisberger, M. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

1997-09-01

Grazing incidence scattering geometries using synchrotron radiation have been applied in order to characterise the roughness profiles and the structural coherence of multilayers. The lateral correlation length of the roughness profiles was evaluated using diffuse reflectivity in the `out of plane` geometry. This type of measurement is the only diffuse reflectivity technique allowing large lateral momentum transfer. It is typically suitable for correlation lengths smaller than 1000 A. The lateral structural coherence length of Ni{sub 3}Al/Ni multilayers as a function of the layer thickness was obtained by grazing incidence diffraction (GID). 3 figs., 1 ref.

Preparation of multilayered nanocrystalline thin films with composition-modulated interfaces

International Nuclear Information System (INIS)

Biro, D.; Barna, P.B.; Szekely, L.; Geszti, O.; Hattori, T.; Devenyi, A.

2008-01-01

The properties of multilayer thin film structures depend on the morphology and structure of interfaces. A broad interface, in which the composition is varying, can enhance, e.g., the hardness of multilayer thin films. In the present experiments multilayers of TiAlN and CrN as well as TiAlN, CrN and MoS 2 were studied by using unbalanced magnetron sputter sources. The sputter sources were arranged side by side on an arc. This arrangement permits development of a transition zone between the layers, where the composition changes continuously. The multilayer system was deposited by one-fold oscillating movement of substrates in front of sputter sources. Thicknesses of layers could be changed both by oscillation frequency and by the power applied to sputter sources. Ti/Al: 50/50 at%, pure chromium and MoS 2 targets were used in the sputter sources. The depositions were performed in an Ar-N 2 mixture at 0.22 Pa working pressure. The sputtering power of the TiAl source was feed-back adjusted in fuzzy-logic mode in order to avoid fluctuation of the TiAl target sputter rate due to poisoning of the target surface. Structure characterization of films deposited on Si wafers covered by thermally grown SiO 2 was performed by cross-sectional transmission electron microscopy. At first a 100 nm thick Cr base layer was deposited on the substrate to improve adhesion, which was followed by a CrN transition layer. The CrN transition layer was followed by a 100 nm thick TiAlN/CrN multilayer system. The TiAlN/CrN/MoS 2 multilayer system was deposited on the surface of this underlayer system. The underlayer systems Cr, CrN and TiAlN/CrN were crystalline with columnar structure according to the morphology of zone T of the structure zone models. The column boundaries contained segregated phases showing up in the under-focused TEM images. The surface of the underlayer system was wavy due to dome-shaped columns. The nanometer-scaled TiAlN/CrN/MoS 2 multilayer system followed this waviness

Ultra-short-period WC/SiC multilayer coatings for x-ray applications

International Nuclear Information System (INIS)

Fernández-Perea, Mónica; Pivovaroff, Mike J.; Soufli, Regina; Alameda, Jennifer; Mirkarimi, Paul; Descalle, Marie-Anne; Baker, Sherry L.; McCarville, Tom; Ziock, Klaus; Hornback, Donald; Romaine, Suzanne; Bruni, Ric; Zhong, Zhong; Honkimäki, Veijo; Ziegler, Eric; Christensen, Finn E.; Jakobsen, Anders C.

2013-01-01

Multilayer coatings enhance x-ray mirror performance at incidence angles steeper than the critical angle, allowing for improved flux, design flexibility and facilitating alignment. In an attempt to extend the use of multilayer coatings to photon energies higher than previously achieved, we have developed multilayers with ultra-short periods between 1 and 2 nm based on the material system WC/SiC. This material system was selected because it possesses very sharp and stable interfaces. In this article, we show highlights from a series of experiments performed in order to characterize the stress, microstructure and morphology of the multilayer films, as well as their reflective performance at photon energies from 8 to 384 keV

Synchronous determination of mercury (II) and copper (II) based on quantum dots-multilayer film

International Nuclear Information System (INIS)

Ma Qiang; Ha Enna; Yang Fengping; Su Xingguang

2011-01-01

Graphical abstract: We developed a sensitive sensor for synchronous detection of Hg (II) and Cu (II) based on the quenchedand recovered PL intensity of QDs-multilayer films. Solutions containing Hg (II) or Cu (II) were used to quench the fluorescence of the QDs-multilayer films firstly. Then, glutathione (GSH) was used to remove Hg (II) or Cu (II) from the QDs-multilayer films due to stronger affinity of GSH-metal ions than that of QDs metal ions. Thus, the fluorescence of QDs-multilayer films was recovered. Highlights: → QDs-multilayer films were developed for synchronous detection of Hg (II) and Cu (II). → Hg (II) and Cu (II) could quench the photoluminescence of the QDs-multilayer films. → Glutathione was used to remove metal ions and recovery photoluminescence of QDs-multilayer films. - Abstract: A sensitive sensor for mercury (II) and copper (II) synchronous detection was established via the changed photoluminescence of CdTe quantum dots (QDs) multilayer films in this work. QDs were deposited on the quartz slides to form QDs-multilayer films by electrostatic interactions with poly(dimethyldiallyl ammonium chloride) (PDDA). Hg 2+ or Cu 2+ could quench the photoluminescence of the QDs-multilayer films, and glutathione (GSH) was used to remove Hg 2+ or Cu 2+ from QDs-multilayer films due to strong affinity of GSH-metal ions, which resulted in the recovered photoluminescence of QDs-multilayer films. There are good linear relationships between the metal ions concentration and the photoluminescence intensity of QDs in the quenched and recovered process. It was found that the Stern-Volmer constants for Hg 2+ are higher than that for Cu 2+ . Based on different quenching and recovery constant between Hg 2+ and Cu 2+ , the synchronous detection of Hg 2+ and Cu 2+ can be achieved. The linear ranges of this assay were obtained from 0.005 to 0.5 μM for Hg 2+ and from 0.01 to 1 μM for Cu 2+ , respectively. And the artificial water samples were determined by this

Near-field heat transfer between graphene/hBN multilayers

Science.gov (United States)

Zhao, Bo; Guizal, Brahim; Zhang, Zhuomin M.; Fan, Shanhui; Antezza, Mauro

2017-06-01

We study the radiative heat transfer between multilayer structures made by a periodic repetition of a graphene sheet and a hexagonal boron nitride (hBN) slab. Surface plasmons in a monolayer graphene can couple with hyperbolic phonon polaritons in a single hBN film to form hybrid polaritons that can assist photon tunneling. For periodic multilayer graphene/hBN structures, the stacked metallic/dielectric array can give rise to a further effective hyperbolic behavior, in addition to the intrinsic natural hyperbolic behavior of hBN. The effective hyperbolicity can enable more hyperbolic polaritons that enhance the photon tunneling and hence the near-field heat transfer. However, the hybrid polaritons on the surface, i.e., surface plasmon-phonon polaritons, dominate the near-field heat transfer between multilayer structures when the topmost layer is graphene. The effective hyperbolic regions can be well predicted by the effective medium theory (EMT), thought EMT fails to capture the hybrid surface polaritons and results in a heat transfer rate much lower compared to the exact calculation. The chemical potential of the graphene sheets can be tuned through electrical gating and results in an additional modulation of the heat transfer. We found that the near-field heat transfer between multilayer structures does not increase monotonously with the number of layers in the stack, which provides a way to control the heat transfer rate by the number of graphene layers in the multilayer structure. The results may benefit the applications of near-field energy harvesting and radiative cooling based on hybrid polaritons in two-dimensional materials.

A multilayer approach for turbidity currents

Science.gov (United States)

Fernandez-Nieto, Enrique; Castro Díaz, Manuel J.; Morales de Luna, Tomás

2017-04-01

When a river that carries sediment in suspension enters into a lake or the ocean it can form a plume that can be classified as hyperpycnal or hypopycnal. Hypopycnal plumes occurs if the combined density of the sediment and interstitial fluid is lower than that of the ambient. Hyperpycnal plumes are a class of sediment-laden gravity current commonly referred to as turbidity currents [7,9]. Some layer-averaged models have been previously developed (see [3, 4, 8] among others). Although this layer-averaged approach gives a fast and valuable information, it has the disadvantage that the vertical distribution of the sediment in suspension is lost. A recent technique based on a multilayer approach [1, 2, 6] has shown to be specially useful to generalize shallow water type models in order to keep track of the vertical components of the averaged variables in the classical shallow water equations. In [5] multilayer model is obtained using a vertical discontinuous Galerkin approach for which the vertical velocity is supposed to be piecewise linear and the horizontal velocity is supposed to be piecewise constant. In this work the technique introduced in [5] is generalized to derive a model for turbidity currents. This model allows to simulate hyperpycnal as well as hypopycnal plumes. Several numerical tests will be presented. References [1] E. Audusse, M. Bristeau, B. Perthame, and J. Sainte-Marie. A multilayer Saint-Venant system with mass exchanges for shallow water flows. derivation and numerical validation. ESAIM: Mathematical Modelling and Numerical Analysis, 45(1):169-200, (2010). [2] E. Audusse, M.-O. Bristeau, M. Pelanti, and J. Sainte-Marie. Approximation of the hydrostatic Navier–Stokes system for density stratified flows by a multilayer model: Kinetic interpretation and numerical solution. Journal of Computational Physics, 230(9):3453-3478, (2011). [3] S. F. Bradford and N. D. Katopodes. Hydrodynamics of turbid underflows. i: Formulation and numerical

Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.

Science.gov (United States)

Mandke, Kanad; Meier, Jil; Brookes, Matthew J; O'Dea, Reuben D; Van Mieghem, Piet; Stam, Cornelis J; Hillebrand, Arjan; Tewarie, Prejaas

2018-02-01

There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons. Copyright © 2017 Elsevier

"{Deposition and characterization of multilayers on thin foil x-ray

DEFF Research Database (Denmark)

Hussain, A.M.; Joensen, K.D.; Hoeghoej, P.

1996-01-01

W/Si and Co/C multilayers have been deposited on epoxy- replicated Au mirrors from the ASTRO-E telescope project, SPectrum Roentgen Gamma (SRG) flight mirrors, DURAN glass substrates and Si witness wafers. A characterization of the multilayers with both hard x-rays and soft x-rays is presented. T...

Designing multilayered nanoplatforms for SERS-based detection of genetically modified organisms

Science.gov (United States)

Uluok, Saadet; Guven, Burcu; Eksi, Haslet; Ustundag, Zafer; Tamer, Ugur; Boyaci, Ismail Hakki

2015-01-01

In this study, the multilayered surface-enhanced Raman spectroscopy (SERS) platforms were developed for the analysis of genetically modified organisms (GMOs). For this purpose, two molecules [11-mercaptoundecanoic acid (11-MUA) and 2-mercaptoethylamine (2-MEA)] were attached with Aurod and Auspherical nanoparticles to form multilayered constructions on the gold (Au)slide surface. The best multilayered platform structure was chosen depending on SERS enhancement, and this surface was characterised with atomic force microscopy (AFM) and attenuated total reflectance Fourier transform infrared spectroscopy. After the optimum multilayered SERS platform and nanoparticle interaction was identified, the oligonucleotides on the Aurod nanoparticles and Auslide were combined to determine target concentrations from the 5,5'-dithiobis (2-nitrobenzoic acid) (DTNB) signals using SERS. The correlation between the SERS intensities for DTNB and target concentrations was found to be linear within a range of 10 pM to 1 µM, and with a detection limit of 34 fM. The selectivity and specificity of the developed sandwich assay were tested using negative and positive controls, and nonsense and real sample studies. The obtained results showed that the multilayered SERS sandwich method allows for sensitive, selective, and specific detection of oligonucleotide sequences.

Realistic absorption coefficient of each individual film in a multilayer architecture

Science.gov (United States)

Cesaria, M.; Caricato, A. P.; Martino, M.

2015-02-01

A spectrophotometric strategy, termed multilayer-method (ML-method), is presented and discussed to realistically calculate the absorption coefficient of each individual layer embedded in multilayer architectures without reverse engineering, numerical refinements and assumptions about the layer homogeneity and thickness. The strategy extends in a non-straightforward way a consolidated route, already published by the authors and here termed basic-method, able to accurately characterize an absorbing film covering transparent substrates. The ML-method inherently accounts for non-measurable contribution of the interfaces (including multiple reflections), describes the specific film structure as determined by the multilayer architecture and used deposition approach and parameters, exploits simple mathematics, and has wide range of applicability (high-to-weak absorption regions, thick-to-ultrathin films). Reliability tests are performed on films and multilayers based on a well-known material (indium tin oxide) by deliberately changing the film structural quality through doping, thickness-tuning and underlying supporting-film. Results are found consistent with information obtained by standard (optical and structural) analysis, the basic-method and band gap values reported in the literature. The discussed example-applications demonstrate the ability of the ML-method to overcome the drawbacks commonly limiting an accurate description of multilayer architectures.

Ring-dot-shaped multilayer piezoelectric step-down transformers using PZT-based ceramics

Energy Technology Data Exchange (ETDEWEB)

Kim, Insung; Joo, Hyeonkyu; Song, Jaesung; Jeong, Soonjong; Kim, Minsoo [Korea Electrotechnology Research Institute, Changwon (Korea, Republic of)

2010-10-15

In this study, multilayer piezo stack transformers for switching mode power supply (SMPS) application were manufactured using 0.01Pb(Ni{sub 1/3}Nb{sub 2/3})O{sub 3} - 0.08Pb(Mn{sub 1/3}Nb{sub 2/3})O{sub 3} - 0.91Pb(Zr{sub 0.505}Ti{sub 0.495})O{sub 3} (PNN-PMN-PZT) ceramics. The voltage ratio of a multilayer piezo stack transformer showed a maximum at the resonance frequency of the input and then increased with increasing load resistance. The efficiency of the multilayer piezo stack transformer showed its highest value at around the matching load. The output power increased with increasing input voltage. The temperature of the multilayer piezo stack transformer increased with increasing output power and load resistance. The manufactured multilayer piezo stack transformer could be used up to 5 W at a resonance frequency of 70.25 kHz for SMPS application because the temperature rise from room temperature is believed to about 20 .deg. C and because the transformer is electrically stable. The newly-developed ring-dot-type step-down multilayer piezo stack transformer shows possible applications as SMPS for electronic power sources with excellent input-to-output properties.

Mo/Si multilayers with enhanced TiO II- and RuO II-capping layers

Science.gov (United States)

Yulin, Sergiy; Benoit, Nicolas; Feigl, Torsten; Kaiser, Norbert; Fang, Ming; Chandhok, Manish

2008-03-01

The lifetime of Mo/Si multilayer-coated projection optics is one of the outstanding issues on the road of commercialization of extreme-ultraviolet lithography (EUVL). The application of Mo/Si multilayer optics in EUVL requires both sufficient radiation stability and also the highest possible normal-incidence reflectivity. A serious problem of conventional high-reflective Mo/Si multilayers capped by silicon is the considerable degradation of reflective properties due to carbonization and oxidation of the silicon surface layer under exposure by EUV radiation. In this study, we focus on titanium dioxide (TiO II) and ruthenium dioxide (RuO II) as promising capping layer materials for EUVL multilayer coatings. The multilayer designs as well as the deposition parameters of the Mo/Si systems with different capping layers were optimized in terms of maximum peak reflectivity at the wavelength of 13.5 nm and longterm stability under high-intensive irradiation. Optimized TiO II-capped Mo/Si multilayer mirrors with an initial reflectivity of 67.0% presented a reflectivity drop of 0.6% after an irradiation dose of 760 J/mm2. The reflectivity drop was explained by the partial oxidation of the silicon sub-layer. No reflectivity loss after similar irradiation dose was found for RuO II-capped Mo/Si multilayer mirrors having initial peak reflectivity of 66%. In this paper we present data on improved reflectivity of interface-engineered TiO II- and RuO II-capped Mo/Si multilayer mirrors due to the minimization of both interdiffusion processes inside the multilayer stack and absorption loss in the oxide layer. Reflectivities of 68.5% at the wavelength of 13.4 nm were achieved for both TiO II- and RuO II-capped Mo/Si multilayer mirrors.

Spin-resolved unpolarized neutron off-specular scattering for magnetic multilayer studies

CERN Document Server

Lauter, H J; Toperverg, B P; Romashev, L; Ustinov, V; Kravtsov, E; Vorobiev, A; Major, J; Nikonov, O A

2002-01-01

The capabilities of the method of using unpolarized neutron off-specular scattering for investigation of magnetic structures in exchange-coupled magnetic multilayers are thoroughly examined. It is demonstrated that strong anomalies in spin-flip selective scattering processes originating from magnetic fluctuations enables a straightforward determination of the coupling angle between the magnetization direction of successive Fe layers in Fe/Cr multilayers. A complete quantitative 2-dimensional data analysis of specular and off-specular scattering has been employed to provide detailed information on the lateral and transverse magnetization arrangement in the multilayer. (orig.)

Application of Statistical, Fuzzy and Perceptron Neural Networks in Drought Forecasting (Case Study: Gonbad-e Kavous Station

Directory of Open Access Journals (Sweden)

S.M. Hosseini-Moghari

2016-10-01

Full Text Available Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11. The current research employed multi-layer perceptron (MLP, adaptive neuro-fuzzy inference system (ANFIS, radial basis function (RBF and general regression neural network (GRNN. It is interesting to note that, there has not been any record of applying GRNN in drought forecasting. Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2, Root Mean Square Error (RMSE, Mean Absolute Error (MAE. Results Discussion: According to statistical distribution

Anomalous magnetoresistance in Fibonacci multilayers.

Energy Technology Data Exchange (ETDEWEB)

Machado, L. D.; Bezerra, C. G.; Correa, M. A.; Chesman, C.; Pearson, J. E.; Hoffmann, A. (Materials Science Division); (Universidade Federal do Rio Grande do Norte)

2012-01-01

We theoretically investigated magnetoresistance curves in quasiperiodic magnetic multilayers for two different growth directions, namely, [110] and [100]. We considered identical ferromagnetic layers separated by nonmagnetic layers with two different thicknesses chosen based on the Fibonacci sequence. Using parameters for Fe/Cr multilayers, four terms were included in our description of the magnetic energy: Zeeman, cubic anisotropy, bilinear coupling, and biquadratic coupling. The minimum energy was determined by the gradient method and the equilibrium magnetization directions found were used to calculate magnetoresistance curves. By choosing spacers with a thickness such that biquadratic coupling is stronger than bilinear coupling, unusual behaviors for the magnetoresistance were observed: (i) for the [110] case, there is a different behavior for structures based on even and odd Fibonacci generations, and, more interesting, (ii) for the [100] case, we found magnetic field ranges for which the magnetoresistance increases with magnetic field.

Surface modification of multilayer graphene using Ga ion irradiation

Energy Technology Data Exchange (ETDEWEB)

Wang, Quan, E-mail: wangq@mail.ujs.edu.cn [School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013 (China); State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Shanghai 200050 (China); Shao, Ying; Ge, Daohan; Ren, Naifei [School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013 (China); Yang, Qizhi [School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013 (China); State key laboratory of Robotics, Chinese Academy of Sciences, Shengyang 110000 (China)

2015-04-28

The effect of Ga ion irradiation intensity on the surface of multilayer graphene was examined. Using Raman spectroscopy, we determined that the irradiation caused defects in the crystal structure of graphene. The density of defects increased with the increase in dwell times. Furthermore, the strain induced by the irradiation changed the crystallite size and the distance between defects. These defects had the effect of doping the multilayer graphene and increasing its work function. The increase in work function was determined using contact potential difference measurements. The surface morphology of the multilayer graphene changed following irradiation as determined by atomic force microscopy. Additionally, the adhesion between the atomic force microscopy tip and sample increased further indicating that the irradiation had caused surface modification, important for devices that incorporate graphene.

Design considerations for energy efficient, resilient, multi-layer networks

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Hansen, Line Pyndt; Ruepp, Sarah Renée

2016-01-01

measures. In this complex problem, considerations such as client traffic granularity, applied grooming policies and multi-layer resiliency add even more complexity. A commercially available network planning tool is used to investigate the interplay between different methods for resilient capacity planning......This work investigates different network design considerations with respect to energy-efficiency, under green-field resilient multi-layer network deployment. The problem of energy efficient, reliable multi-layer network design is known to result in different trade-offs between key performance....... Switching off low-utilized transport links has been investigated via a pro-active re-routing applied during the network planning. Our analysis shows that design factors such as the applied survivability strategy and the applied planning method have higher impact on the key performance indicators compared...

Multilayer Photonic Crystal for Spectral Narrowing of Emission

Directory of Open Access Journals (Sweden)

Zhanfang LIU

2017-08-01

Full Text Available Multilayer colloidal crystal has been prepared by the layer-by-layer deposition of silica microspheres on a glass slide. Each layer is a slab consisting of a fcc close-packed colloidal arrays. By properly choosing the sizes of spheres, the whole spectral feature of multilayer colloidal crystal can be tuned. Here, we engineered a multilayer superlattice structure with an effective passband between two stop bands. This gives a strong narrowing effect on emission spectrum. With the stop bands at the shortwave and longwave edges of emission spectrum, the passband in the central wavelength region can be regarded as a strong decrease of suppression effect and enhancement of a narrow wavelength region of emission. The spectral narrowing modification effect of suitably engineered colloidal crystals shows up their importance in potential application as optical filters and lasing devices.DOI: http://dx.doi.org/10.5755/j01.ms.23.3.16320

Research on the Multilayer Free Damping Structure Design

Directory of Open Access Journals (Sweden)

Jie Meng

2018-01-01

Full Text Available The aim of this paper is to put forward a design model for multilayer free damping structures. It sets up a mathematical model and deduces the formula for its structural loss factor η and analyzes the change rules of η along with the change rate of the elastic modulus ratio q1, the change rate of the loss factors of damping materials q2, and the change rate of the layer thickness ratio q3 under the condition with the layer thickness ratio h2=1,3,5,10 by software MATLAB. Based on three specific damping structures, the mathematical model is verified through ABAQUS. With the given structural loss factor (η≥2 and the layer number (n=3,4,5,6, 34 kinds of multilayer free damping structures are then presented. The study is meant to provide a more flexible and more diverse design solution for multilayer free damping structures.

Three dimensional multilayer solenoid microcoils inside silica glass

Science.gov (United States)

Meng, Xiangwei; Yang, Qing; Chen, Feng; Shan, Chao; Liu, Keyin; Li, Yanyang; Bian, Hao; Si, Jinhai; Hou, Xun

2016-01-01

Three dimensional (3D) solenoid microcoils could generate uniform magnetic field. Multilayer solenoid microcoils are highly pursued for strong magnetic field and high inductance in advanced magnetic microsystems. However, the fabrication of the 3D multilayer solenoid microcoils is still a challenging task. In this paper, 3D multilayer solenoid microcoils with uniform diameters and high aspect ratio were fabricated in silica glass. An alloy (Bi/In/Sn/Pb) with high melting point was chosen as the conductive metal to overcome the limitation of working temperature and improve the electrical property. The inductance of the three layers microcoils was measured, and the value is 77.71 nH at 100 kHz and 17.39 nH at 120 MHz. The quality factor was calculated, and it has a value of 5.02 at 120 MHz. This approach shows an improvement method to achieve complex 3D metal microstructures and electronic components, which could be widely integrated in advanced magnetic microsystems.

Unsupervised process monitoring and fault diagnosis with machine learning methods

CERN Document Server

Aldrich, Chris

2013-01-01

This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

Prediction of heart abnormality using MLP network

Science.gov (United States)

Hashim, Fakroul Ridzuan; Januar, Yulni; Mat, Muhammad Hadzren; Rizman, Zairi Ismael; Awang, Mat Kamil

2018-02-01

Heart abnormality does not choose gender, age and races when it strikes. With no warning signs or symptoms, it can result to a sudden death of the patient. Generally, heart's irregular electrical activity is defined as heart abnormality. Via implementation of Multilayer Perceptron (MLP) network, this paper tries to develop a program that allows the detection of heart abnormality activity. Utilizing several training algorithms with Purelin activation function, an amount of heartbeat signals received through the electrocardiogram (ECG) will be employed to condition the MLP network.

NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

Directory of Open Access Journals (Sweden)

В. Єременко

2011-02-01

Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

A neural method for determining electromagnetic shower positions in laterally segmented calorimeters

International Nuclear Information System (INIS)

Roy, A.; Ray, A.; Mitra, T.; Roy, A.

1995-01-01

A method based on a neural network technique is proposed to calculate the coordinates of an incident photon striking a laterally segmented calorimeter and depositing shower energies in different segments. The technique uses a multilayer perceptron trained by back-propagation implemented through standard gradient descent followed by conjugate gradient algorithms and has been demonstrated with GEANT simulations of a BAF2 detector array. The position resolution results obtained by using this method are found to be substantially better than the first moment method with logarithmic weighting. (orig.)

Identification of discrete chaotic maps with singular points

Directory of Open Access Journals (Sweden)

P. G. Akishin

2001-01-01

Full Text Available We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. We use as a simple test model the Cusp map. We compare the traditional Multilayer Perceptron, the Chebyshev Neural Network and the Wavelet Neural Network. The numerical scheme for the accurate determination of a singular point is also developed. We show that combining a neural network with the numerical algorithm for the determination of the singular point we are able to accurately approximate discrete chaotic maps with singularities.

Application of neuro-fuzzy model for neutron activation analysis (NAA)

International Nuclear Information System (INIS)

Khalafi, H.; Terman, M.S.; Rahmani, F.

2011-01-01

Neutron activation analysis (NAA) is a precise chemical multielemental method of analysis which is satisfactorily used for qualitative and quantitative analyses. Repeated irradiation is needed because of mal-determination of some elements due to peak overlap in qualitative analysis. In this study, NAA procedure has been modified using a neuro-fuzzy model to avoid repeated irradiation based on multilayer perceptrons network trained by the Levenberg Marquardt algorithm. This method increases the precision of spectrum analysis in the case of strong background and peak overlap. (authors)

Recurrent Takotsubo Cardiomyopathy Related to Recurrent Thyrotoxicosis.

Science.gov (United States)

Patel, Keval; Griffing, George T; Hauptman, Paul J; Stolker, Joshua M

2016-04-01

Takotsubo cardiomyopathy, or transient left ventricular apical ballooning syndrome, is characterized by acute left ventricular dysfunction caused by transient wall-motion abnormalities of the left ventricular apex and mid ventricle in the absence of obstructive coronary artery disease. Recurrent episodes are rare but have been reported, and several cases of takotsubo cardiomyopathy have been described in the presence of hyperthyroidism. We report the case of a 55-year-old woman who had recurrent takotsubo cardiomyopathy, documented by repeat coronary angiography and evaluations of left ventricular function, in the presence of recurrent hyperthyroidism related to Graves disease. After both episodes, the patient's left ventricular function returned to normal when her thyroid function normalized. These findings suggest a possible role of thyroid-hormone excess in the pathophysiology of some patients who have takotsubo cardiomyopathy.

Fabrication and Characteristics of Al/PTFE Multilayers and Application in Micro-initiator

Science.gov (United States)

Zhang, Yuxin; Jiang, Hongchuan; Zhao, Xiaohui; Zhang, Wanli; Li, Yanrong

2017-12-01

In this paper, a micro-initiator was designed and fabricated by integrating Al/PTFE multilayers with a Cu film bridge. The regularity layer structure and interface composition of Al/PTFE multilayers was analysed by transmission electron microscope and X-ray photoelectron spectroscopy, respectively. The heat release reaction in Al/PTFE multilayers can be triggered with reaction temperature of 430 °C, and the overall heat of reaction is 3192 J/g. Al/PTFE multilayers with bilayer thickness of 200 nm was alternately deposited on a Cu film bridge to improve the electric explosion performances. Compared to Cu film bridge, the Al/PTFE/Cu integrated film bridge exhibits improved performances with longer explosion duration time, more violent explosion phenomenon and larger quantities of ejected product particles.

Structural evolution of Ti/TiC multilayers

International Nuclear Information System (INIS)

Dahan, I.; Frage, N.; Dariel, M.P.

2004-01-01

Hard coatings based on metal/ceramic multilayers with periods in the nanometer range have been shown to possess some potential for improved tribological and mechanical properties. The present work is concerned with the structural evolution of (Ti/TiC) multilayers. Two kinds of multilayers consisting of 30 equithick (40 nm)TiC layers and 20 and 60 nm thick Ti layers, respectively, were sputter deposited on Mo substrates. The structural and the compositional evolution of these multilayers were examined by x-ray diffraction, transition electron microscopy (TEM), high-resolution TEM, Auger electron microscopy spectroscopy and differential thermal analysis (DTA), in the as-deposited state and after various heat treatments up to 500 deg. C. Initially, the Ti layers had a crystalline columnar grain structure displaying a (002) texture. The TiC layers displayed weak crystallinity with a pronounced (111) texture. In the course of the heat treatments, carbon diffused from the carbide layer into the adjacent Ti layers transforming the latter into off-stoichiometric TiC x with x≅0.5 and simultaneously depleting the carbon content of the initial carbide layer. The formed TiC x layers maintained the textural relationship with the neighboring TiC layers, consistent with a transformation that involved only a ABAB to ABC stacking change of the Ti sublattice. Increased mobility of the Ti atoms in carbon-depleted original TiC layers led to their full or partial recrystallization. The thermal effects associated both with the transformation of Ti layers into TiC, due to the influx of carbon atoms, and with the recrystallization of the original TiC layers were clearly revealed by the DTA measurements

Magnetic studies of spin wave excitations in Fe/Mn multilayers

Energy Technology Data Exchange (ETDEWEB)

Salhi, H. [LPMMAT, Faculté des Sciences Ain Chock, Université Hassan II de Casablanca, B.P. 5366 Mâarif, Casablanca (Morocco); LMPG, Ecole supérieure de technologie, Université Hassan de Casablanca, Casablanca (Morocco); Moubah, R.; El Bahoui, A.; Lassri, H. [LPMMAT, Faculté des Sciences Ain Chock, Université Hassan II de Casablanca, B.P. 5366 Mâarif, Casablanca (Morocco)

2017-04-15

The structural and magnetic properties of Fe/Mn multilayers grown by thermal evaporation technique were investigated by transmission electron microscopy, vibrating sample magnetometer and spin wave theory. Transmission electron microscopy shows that the Fe and Mn layers are continuous with a significant interfacial roughness. The magnetic properties of Fe/Mn multilayers were studied for various Fe thicknesses (t{sub Fe}). The change of magnetization as a function of temperature is well depicted by a T{sup 3/2} law. The Fe spin-wave constant was extracted and found to be larger than that reported for bulk Fe, which we attribute to the fluctuation of magnetic moments at the interface, due to the interfacial roughness. The experimental M (T) data were satisfactory fitted for multilayers with different Fe thicknesses; and several exchange interactions were extracted. - Highlights: • The structural and magnetic properties of Fe/Mn multilayers were studied. • Fe and Mn layers are continuous with an important interfacial roughness. • The Fe spin-wave constant is larger than that reported for bulk Fe due to the fluctuation of the interfacial magnetic moments.

Magnetic studies of spin wave excitations in Fe/Mn multilayers

International Nuclear Information System (INIS)

Salhi, H.; Moubah, R.; El Bahoui, A.; Lassri, H.

2017-01-01

The structural and magnetic properties of Fe/Mn multilayers grown by thermal evaporation technique were investigated by transmission electron microscopy, vibrating sample magnetometer and spin wave theory. Transmission electron microscopy shows that the Fe and Mn layers are continuous with a significant interfacial roughness. The magnetic properties of Fe/Mn multilayers were studied for various Fe thicknesses (t Fe ). The change of magnetization as a function of temperature is well depicted by a T 3/2 law. The Fe spin-wave constant was extracted and found to be larger than that reported for bulk Fe, which we attribute to the fluctuation of magnetic moments at the interface, due to the interfacial roughness. The experimental M (T) data were satisfactory fitted for multilayers with different Fe thicknesses; and several exchange interactions were extracted. - Highlights: • The structural and magnetic properties of Fe/Mn multilayers were studied. • Fe and Mn layers are continuous with an important interfacial roughness. • The Fe spin-wave constant is larger than that reported for bulk Fe due to the fluctuation of the interfacial magnetic moments.

Construction of pegylated multilayer architectures via (strept)avidin/biotin interactions

International Nuclear Information System (INIS)

Dai Zhifei; Wilson, John T.; Chaikof, Elliot L.

2007-01-01

Pegylated multilayer architectures were fabricated as films on planar substrates, as shells on colloidal particles, or as free-standing hollow capsules using layer-by-layer (LbL) self-assembly of biotinylated poly-L-lysine (PLL) and (strept)avidin. Poly(ethylene glycol) (PEG) was incorporated into the multilayer architectures by assembly with biotin-derivatized poly(L-lysine)-g-poly(ethylene glycol)(PPB). Stepwise growth of multilayers was followed by UV-vis spectroscopy and the formation of core-shells and hollow capsules characterized by means of confocal laser scanning microscopy (CLSM) and transmission electron microscopy (TEM). Both absorbance and TEM data suggest that approximately two layers of FITC-avidin were adsorbed with each surface deposition. In contrast, use of unmodified PLL did not lead to formation of multilayer coatings, confirming that (strept)avidin-biotin interactions were responsible for film growth even in the presence of electrostatic repulsive forces between PLL and avidin and the steric hindrance of associated PEG chains. This technique provides new opportunities for the generation of robust films with tailored interfacial binding and transport properties

Falling in the elderly: Do statistical models matter for performance criteria of fall prediction? Results from two large population-based studies.

Science.gov (United States)

Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Fantino, Bruno; Levinoff, Elise J; Allali, Gilles; Beauchet, Olivier

2016-01-01

To compare performance criteria (i.e., sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic curve and accuracy) of linear and non-linear statistical models for fall risk in older community-dwellers. Participants were recruited in two large population-based studies, "Prévention des Chutes, Réseau 4" (PCR4, n=1760, cross-sectional design, retrospective collection of falls) and "Prévention des Chutes Personnes Agées" (PCPA, n=1765, cohort design, prospective collection of falls). Six linear statistical models (i.e., logistic regression, discriminant analysis, Bayes network algorithm, decision tree, random forest, boosted trees), three non-linear statistical models corresponding to artificial neural networks (multilayer perceptron, genetic algorithm and neuroevolution of augmenting topologies [NEAT]) and the adaptive neuro fuzzy interference system (ANFIS) were used. Falls ≥1 characterizing fallers and falls ≥2 characterizing recurrent fallers were used as outcomes. Data of studies were analyzed separately and together. NEAT and ANFIS had better performance criteria compared to other models. The highest performance criteria were reported with NEAT when using PCR4 database and falls ≥1, and with both NEAT and ANFIS when pooling data together and using falls ≥2. However, sensitivity and specificity were unbalanced. Sensitivity was higher than specificity when identifying fallers, whereas the converse was found when predicting recurrent fallers. Our results showed that NEAT and ANFIS were non-linear statistical models with the best performance criteria for the prediction of falls but their sensitivity and specificity were unbalanced, underscoring that models should be used respectively for the screening of fallers and the diagnosis of recurrent fallers. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Performance of multilayer coated silicon pore optics

Science.gov (United States)

Ackermann, M. D.; Collon, M. J.; Jensen, C. P.; Christensen, F. E.; Krumrey, M.; Cibik, L.; Marggraf, S.; Bavdaz, M.; Lumb, D.; Shortt, B.

2010-07-01

The requirements for the IXO (International X-ray Observatory) telescope are very challenging in respect of angular resolution and effective area. Within a clear aperture with 1.7 m > R > 0.25 m that is dictated by the spacecraft envelope, the optics technology must be developed to satisfy simultaneously requirements for effective area of 2.5 m2 at 1.25 keV, 0.65 m2 at 6 keV and 150 cm2 at 30 keV. The reflectivity of the bare mirror substrate materials does not allow these requirements to be met. As such the IXO baseline design contains a coating layout that varies as a function of mirror radius and in accordance with the variation in grazing incidence angle. The higher energy photon response is enhanced through the use of depth-graded multilayer coatings on the inner radii mirror modules. In this paper we report on the first reflectivity measurements of wedged ribbed silicon pore optics mirror plates coated with a depth graded W/Si multilayer. The measurements demonstrate that the deposition and performance of the multilayer coatings is compatible with the SPO production process.

SnO2/PPy Screen-Printed Multilayer CO2 Gas Sensor

Directory of Open Access Journals (Sweden)

S.A. WAGHULEY

2007-05-01

Full Text Available Tin dioxide (SnO2 plays a dominant role in solid state gas sensors and exhibit sensitivity towards oxidizing and reducing gases by a variation of its electrical properties. The electrical conducting polymer-polypyrrole (PPy has high anisotropy of electrical conduction and used as a gas sensor. SnO2/PPy multilayer, pure SnO2, pure PPy sensors were prepared by screen-printing method on Al2O3 layer followed by glass substrate. The sensors were used for different concentration (ppm of CO2 gas investigation at room temperature (303 K. The sensitivity of SnO2/PPy multilayer sensor was found to be higher, compared with pure SnO2 and pure PPy sensors. The multilayer sensor exhibited improved stability. The response and recovery time of multilayer sensor were found to be ~2 min and ~10 min respectively.

Stronger multilayer acrylic dielectric elastomer actuators with silicone gel coatings

Science.gov (United States)

Lau, Gih-Keong; La, Thanh-Giang; Sheng-Wei Foong, Ervin; Shrestha, Milan

2016-12-01

Multilayer dielectric elastomer actuators (DEA) perform worst off than single-layer DEAs due to higher susceptibility to electro-thermal breakdown. This paper presents a hot-spot model to predict the electro-thermal breakdown field of DEAs and its dependence on thermal insulation. To inhibit the electrothermal breakdown, silicone gel coating was applied as barrier coating to multilayer acrylic DEA. The gel coating helps suppress the electro-thermally induced puncturing of DEA membrane at the hot spot. As a result, the gel-coated DEAs, in either a single layer or a multilayer stack, can produce 30% more isometric stress change as compared to those none-coated. These gel-coated acrylic DEAs show great potential to make stronger artificial muscles.

Recurrent hamburger thyrotoxicosis

Science.gov (United States)

Parmar, Malvinder S.; Sturge, Cecil

2003-01-01

RECURRENT EPISODES OF SPONTANEOUSLY RESOLVING HYPERTHYROIDISM may be caused by release of preformed hormone from the thyroid gland after it has been damaged by inflammation (recurrent silent thyroiditis) or by exogenous administration of thyroid hormone, which might be intentional or surreptitious (thyrotoxicosis factitia). Community-wide outbreaks of “hamburger thyrotoxicosis” resulting from inadvertent consumption of beef contaminated with bovine thyroid gland have been previously reported. Here we describe a single patient who experienced recurrent episodes of this phenomenon over an 11-year period and present an approach to systematically evaluating patients with recurrent hyperthyroidism. PMID:12952802

Substrate and coating defect planarization strategies for high-laser-fluence multilayer mirrors

International Nuclear Information System (INIS)

Stolz, Christopher J.; Wolfe, Justin E.; Mirkarimi, Paul B.; Folta, James A.; Adams, John J.; Menor, Marlon G.; Teslich, Nick E.; Soufli, Regina; Menoni, Carmen S.; Patel, Dinesh

2015-01-01

Planarizing or smoothing over nodular defects in multilayer mirrors can be accomplished by a discrete deposit-and-etch process that exploits the angle-dependent etching rate of optical materials. Typically, nodular defects limit the fluence on mirrors irradiated at 1064 nm with 10 ns pulse lengths due to geometrically- and interference-induced light intensification. Planarized hafina/silica multilayer mirrors have demonstrated > 125 J/cm 2 laser resistance for single-shot testing and 50 J/cm 2 for multi-shot testing for nodular defects originating on the substrate surface. Two planarization methods were explored: thick planarization layers on the substrate surface and planarized silica layers throughout the multilayer in which only the silica layers that are below one half of the incoming electric field value are etched. This paper also describes the impact of planarized defects that are buried within the multilayer structure compared to planarized substrate particulate defects. - Highlights: • Defect planarization significantly improves multilayer mirror laser resistance • Substrate and coating defects have both been effectively planarized • Single and multishot laser resistance improvement was demonstrated

A Magnetron Sputter Deposition System for the Development of X-Ray Multilayer Optics

Science.gov (United States)

Broadway, David

2015-01-01

The project objective is to establish the capability to deposit multilayer structures for x-ray, neutron, and extreme ultraviolet (EUV) optic applications through the development of a magnetron sputtering deposition system. A specific goal of this endeavor is to combine multilayer deposition technology with the replication process in order to enhance NASA Marshall Space Flight Center's (MSFC's) position as a world leader in the design of innovative x-ray instrumentation through the development of full shell replicated multilayer optics. The development of multilayer structures are absolutely necessary in order to advance the field of x-ray astronomy by pushing the limit for observing the universe to ever-increasing photon energies (i.e., up to 200 keV or higher), well beyond Chandra's (approx.10 keV) and NuStar's (approx.75 keV) capability. The addition of multilayer technology would significantly enhance the x-ray optics capability at MSFC and allow NASA to maintain its world leadership position in the development, fabrication, and design of innovative x-ray instrumentation, which would be the first of its kind by combining multilayer technology with the mirror replication process. This marriage of these technologies would allow astronomers to see the universe in a new light by pushing to higher energies that are out of reach with today's instruments. To this aim, a magnetron vacuum sputter deposition system for the deposition of novel multilayer thin film x-ray optics is proposed. A significant secondary use of the vacuum deposition system includes the capability to fabricate multilayers for applications in the field of EUV optics for solar physics, neutron optics, and x-ray optics for a broad range of applications including medical imaging.

A Magnetron Sputter Deposition System for the Development of Multilayer X-Ray Optics

Science.gov (United States)

Broadway, David; Ramsey, Brian; Gubarev, Mikhail

2014-01-01

The proposal objective is to establish the capability to deposit multilayer structures for x-ray, neutron, and EUV optic applications through the development of a magnetron sputtering deposition system. A specific goal of this endeavor is to combine multilayer deposition technology with the replication process in order to enhance the MSFC's position as a world leader in the design of innovative X-ray instrumentation through the development of full shell replicated multilayer optics. The development of multilayer structures is absolutely necessary in order to advance the field of X-ray astronomy by pushing the limit for observing the universe to ever increasing photon energies (i. e. up to 200 keV or higher); well beyond Chandra (approx. 10 keV) and NuStar's (approx. 75 keV) capability. The addition of multilayer technology would significantly enhance the X-ray optics capability at MSFC and allow NASA to maintain its world leadership position in the development, fabrication and design of innovative X-ray instrumentation which would be the first of its kind by combining multilayer technology with the mirror replication process. This marriage of these technologies would allow astronomers to see the universe in a new light by pushing to higher energies that are out of reach with today's instruments.To this aim, a magnetron vacum sputter deposition system for the deposition of novel multilayer thin film X-ray optics is proposed. A significant secondary use of the vacuum deposition system includes the capability to fabricate multilayers for applications in the field of EUV optics for solar physics, neutron optics, and X-ray optics for a broad range of applications including medical imaging.

Influence of Fe underlayers on stress evolution of Ti in Ti/Fe multilayers

Energy Technology Data Exchange (ETDEWEB)

Wan, Li; Thompson, Gregory, E-mail: gthompson@eng.ua.edu [Department of Metallurgical Engineering, The University of Alabama, Tuscaloosa, Alabama 35487-0202 (United States)

2016-11-15

A series of 40–2 nm bilayer spacing Ti/Fe multilayers were sputter-deposited. As the length scale of individual Ti layers equaled to 2 nm, Ti phase transforms from a hexagonal close packed (hcp)-to-body centered cubic (bcc) crystal structures for equal layer thicknesses in Ti/Fe multilayers. Further equal reductions in bilayer spacing to less than 1 nm resulted in an additional transformation from a crystalline to amorphous structure. Atom probe tomography reveals significant intermixing between layers which contributes to the observed phase transformations. Real-time, intrinsic growth stress measurements were also performed to relate the adatom mobility to these phase transformations. For the hcp Ti/bcc Fe multilayers of equivalent volume fractions, the multilayers undergo an overall tensile stress state to a compressive stress state with decreasing bilayer thickness for the multilayers. When the above phase transformations occurred, a modest reduction in the overall compressive stress of the multilayer was noted. Depending on the Fe thickness, the Ti growth was observed to be a tensile to compressive growth change to a purely compressive growth for thinner bilayer spacing. Fe retained a tensile growth stress regardless of the bilayer spacing studied.

A chemically stable PVD multilayer encapsulation for lithium microbatteries

International Nuclear Information System (INIS)

Ribeiro, J F; Sousa, R; Cunha, D J; Vieira, E M F; Goncalves, L M; Silva, M M; Dupont, L

2015-01-01

A multilayer physical vapour deposition (PVD) thin-film encapsulation method for lithium microbatteries is presented. Lithium microbatteries with a lithium cobalt oxide (LiCoO 2 ) cathode, a lithium phosphorous oxynitride (LiPON) electrolyte and a metallic lithium anode are under development, using PVD deposition techniques. Metallic lithium film is still the most common anode on this battery technology; however, it presents a huge challenge in terms of material encapsulation (lithium reacts with almost any materials deposited on top and almost instantly begins oxidizing in contact with atmosphere). To prove the encapsulation concept and perform all the experiments, lithium films were deposited by thermal evaporation technique on top of a glass substrate, with previously patterned Al/Ti contacts. Three distinct materials, in a multilayer combination, were tested to prevent lithium from reacting with protection materials and atmosphere. These multilayer films were deposited by RF sputtering and were composed of lithium phosphorous oxide (LiPO), LiPON and silicon nitride (Si 3 N 4 ). To complete the long-term encapsulation after breaking the vacuum, an epoxy was applied on top of the PVD multilayer. In order to evaluate oxidation state of lithium films, the lithium resistance was measured in a four probe setup (cancelling wires/contact resistances) and resistivity calculated, considering physical dimensions. A lithium resistivity of 0.16 Ω μm was maintained for more than a week. This PVD multilayer exonerates the use of chemical vapour deposition (CVD), glove-box chambers and sample manipulation between them, significantly reducing the fabrication cost, since battery and its encapsulation are fabricated in the same PVD chamber. (paper)

A chemically stable PVD multilayer encapsulation for lithium microbatteries

Science.gov (United States)

Ribeiro, J. F.; Sousa, R.; Cunha, D. J.; Vieira, E. M. F.; Silva, M. M.; Dupont, L.; Goncalves, L. M.

2015-10-01

A multilayer physical vapour deposition (PVD) thin-film encapsulation method for lithium microbatteries is presented. Lithium microbatteries with a lithium cobalt oxide (LiCoO2) cathode, a lithium phosphorous oxynitride (LiPON) electrolyte and a metallic lithium anode are under development, using PVD deposition techniques. Metallic lithium film is still the most common anode on this battery technology; however, it presents a huge challenge in terms of material encapsulation (lithium reacts with almost any materials deposited on top and almost instantly begins oxidizing in contact with atmosphere). To prove the encapsulation concept and perform all the experiments, lithium films were deposited by thermal evaporation technique on top of a glass substrate, with previously patterned Al/Ti contacts. Three distinct materials, in a multilayer combination, were tested to prevent lithium from reacting with protection materials and atmosphere. These multilayer films were deposited by RF sputtering and were composed of lithium phosphorous oxide (LiPO), LiPON and silicon nitride (Si3N4). To complete the long-term encapsulation after breaking the vacuum, an epoxy was applied on top of the PVD multilayer. In order to evaluate oxidation state of lithium films, the lithium resistance was measured in a four probe setup (cancelling wires/contact resistances) and resistivity calculated, considering physical dimensions. A lithium resistivity of 0.16 Ω μm was maintained for more than a week. This PVD multilayer exonerates the use of chemical vapour deposition (CVD), glove-box chambers and sample manipulation between them, significantly reducing the fabrication cost, since battery and its encapsulation are fabricated in the same PVD chamber.

Design and application of multilayer monolithic microwave integrated circuit transformers

Energy Technology Data Exchange (ETDEWEB)

Economides, S.B

1999-07-01

The design and performance of planar spiral transformers, using multilayer GaAs and silicon MMIC technology, are presented. This multilayer technology gives new opportunities for improving the performance of planar transformers, couplers and baluns. Planar transformers have high parasitic resistance and capacitance and low levels of coupling. Using multilayer technology these problems are overcome by applying a multilayer structure of three metal layers separated by two polyimide dielectric layers. The improvements gained by placing the conductors on different metal layers, and using conductors raised on polyimide layers for low capacitance, have been investigated. The circuits were fabricated using a novel experimental fabrication process, which uses entirely standard materials and techniques and is compatible with BJT's and silicon-germanium HBT's. The transformers were all characterised up to 20 GHz using RF-on-wafer measurements. They demonstrated good performance, considering the experimental nature of in-house multilayer technology and the difficulties in simulating these three-dimensional new geometries. With high resistivity substrates, the silicon components achieved virtually the same performance as their gallium arsenide counterparts. The transformers were then used in simulations of transformer-coupled HBT amplifier circuits, to demonstrate their capabilities. It was shown that these circuits present good performance compared to standard off-the shelf component circuits and are very promising for use in most multilayer MMIC applications. The structures were further used in coupling configurations, and applied in balun circuits and pushpull amplifiers. The spiral transformer coupler can operate at low frequencies without using up much chip area. In a balun configuration, the balun can compensate for coupling and phase imbalance and operates over 5 to 15 GHz. The spiral coupler does not always need multilayer processing, so the balun may be

Origin of perpendicular magnetic anisotropy in Co/Ni multilayers

Science.gov (United States)

Arora, M.; Hübner, R.; Suess, D.; Heinrich, B.; Girt, E.

2017-07-01

We studied the variation in perpendicular magnetic anisotropy of (111) textured Au /N ×[Co /Ni ]/Au films as a function of the number of bilayer repeats N . The ferromagnetic resonance and superconducting quantum interference device magnetometer measurements show that the perpendicular magnetic anisotropy of Co/Ni multilayers first increases with N for N ≤10 and then moderately decreases for N >10 . The model we propose reveals that the decrease of the anisotropy for N reduction in the magnetoelastic and magnetocrystalline anisotropies. A moderate decrease in the perpendicular magnetic anisotropy for N >10 is due to the reduction in the magnetocrystalline and the surface anisotropies. To calculate the contribution of magnetoelastic anisotropy in the Co/Ni multilayers, in-plane and out-of-plane x-ray diffraction measurements are performed to determine the spacing between Co/Ni (111) and (220) planes. The magnetocrystalline bulk anisotropy is estimated from the difference in the perpendicular and parallel g factors of Co/Ni multilayers that are measured using the in-plane and out-of-plane ferromagnetic resonance measurements. Transmission electron microscopy has been used to estimate the multilayer film roughness. These values are used to calculate the roughness-induced surface and magnetocrystalline anisotropy coefficients as a function of N .

Multilayer Controller for Outdoor Vehicle

DEFF Research Database (Denmark)

Reske-Nielsen, Anders; Mejnertsen, Asbjørn; Andersen, Nils Axel

2006-01-01

A full software and hardware solution has been designed, implemented and tested for control of a small agricultural automatic tractor. The objective was to realise a user-friendly, multi-layer controller architecture for an outdoor platform. The collaborative research work was done as a part of a...

Modeling Macroscopic Shape Distortions during Sintering of Multi-layers

DEFF Research Database (Denmark)

Tadesse Molla, Tesfaye

as to help achieve defect free multi-layer components. The initial thickness ratio between the layers making the multi-layer has also significant effect on the extent of camber evolution depending on the material systems. During sintering of tubular bi-layer structures, tangential (hoop) stresses are very...... large compared to radial stresses. The maximum value of hoop stress, which can generate processing defects such as cracks and coating peel-offs, occurs at the beginning of the sintering cycle. Unlike most of the models defining material properties based on porosity and grain size only, the multi...... (firing). However, unintended features like shape instabilities of samples, cracks or delamination of layers may arise during sintering of multi-layer composites. Among these defects, macroscopic shape distortions in the samples can cause problems in the assembly or performance of the final component...

Wrapped Multilayer Insulation

Science.gov (United States)

Dye, Scott A.

2015-01-01

New NASA vehicles, such as Earth Departure Stage (EDS), Orion, landers, and orbiting fuel depots, need improved cryogenic propellant transfer and storage for long-duration missions. Current cryogen feed line multilayer insulation (MLI) performance is 10 times worse per area than tank MLI insulation. During each launch, cryogenic piping loses approximately 150,000 gallons (equivalent to $300,000) in boil-off during transfer, chill down, and ground hold. Quest Product Development Corp., teaming with Ball Aerospace, developed an innovative advanced insulation system, Wrapped MLI (wMLI), to provide improved thermal insulation for cryogenic feed lines. wMLI is high-performance multilayer insulation designed for cryogenic piping. It uses Quest's innovative discrete-spacer technology to control layer spacing/ density and reduce heat leak. The Phase I project successfully designed, built, and tested a wMLI prototype with a measured heat leak 3.6X lower than spiral-wrapped conventional MLI widely used for piping insulation. A wMLI prototype had a heat leak of 7.3 W/m2, or 27 percent of the heat leak of conventional MLI (26.7 W/m2). The Phase II project is further developing wMLI technology with custom, molded polymer spacers and advancing the product toward commercialization via a rigorous testing program, including developing advanced vacuuminsulated pipe for ground support equipment.

An ultra-broadband multilayered graphene absorber

KAUST Repository

Amin, Muhammad; Farhat, Mohamed; Bagci, Hakan

2013-01-01

An ultra-broadband multilayered graphene absorber operating at terahertz (THz) frequencies is proposed. The absorber design makes use of three mechanisms: (i) The graphene layers are asymmetrically patterned to support higher order surface plasmon

The stress-strain relationship for multilayers of the high Tc superconducting oxides

International Nuclear Information System (INIS)

Hidaka, H.; Yamamura, H.

1988-01-01

This paper reports the calculation of the stress-strain relationship for multilayers of the high Tc superconducting oxides. The elucidation of this relationship is expected quite helpful for the preparation of high-quality multilayers of these materials. This calculation is possible to do in the same way of Timoshenko's bi-metal treatment. The authors did computation of the residual stress and strain, and the state of stress and strain for these multilayers has been acquired in detail by this calculation

Thermal performance measurement and application of a multilayer insulator for emergency architecture

International Nuclear Information System (INIS)

Salvalai, Graziano; Imperadori, Marco; Scaccabarozzi, Diego; Pusceddu, Cristina

2015-01-01

Lightness coupled with a quick assembly method is crucial for emergency architecture in post-disaster area where accessibility and action time play a huge barer to rescue people. In this prospective, the following work analyses the potentiality (technological and thermal performances) of multilayer insulator for a new shelter envelope able to provide superior thermal comfort for the users. The thermal characteristics are derived experimentally by means of a guard ring apparatus under different working temperatures. Tests are performed on the multilayer insulator itself and on a composite structure, made of the multilayer insulator and two air gaps wrapped by a polyester cover, which is the core of a new lightweight emergency architecture. Experimental results show good agreement with literature data, providing a thermal conductivity and transmittance of about 0.04 W/(m °C) and 1.6 W/(m 2  °C) for the tested multilayer. The composite structure called Thermo Reflective Multilayer System (TRMS) shows better insulation performances, providing a thermal transmittance set to 0.85 W/(m 2  °C). A thermal model of an emergency tent based on the new insulating structure (TRMS) has been developed and its thermal performances have been compared with those of a UNHCR traditional emergency shelter. The shelter model was simulated (Trnsys v.17 environment) in the winter season considering the climate of Belgrade and using only the casual gains from occupant and solar radiation through opaque wall. Numerical simulations evidenced that the new insulating composite envelope reduces required heating load of about two and four times with respect to the traditional insulation. The study sets a starting point to develop a lightweight emergency architecture made with a combination between multilayer, air, polyester and vulcanized rubber. - Highlights: • Multilayer insulator tested by means of a guard ring apparatus. • Thermo reflective multilayer system (TRMS) development

Self organized formation of Ge nanocrystals in multilayers

OpenAIRE

Zschintzsch-Dias, Manuel

2012-01-01

The aim of this work is to create a process which allows the tailored growth of Ge nanocrystals for use in photovoltic applications. The multilayer systems used here provide a reliable method to control the Ge nanocrystal size after phase separation. In this thesis, the deposition of GeOx/SiO2 and Ge:SiOx~ 2/SiO2 multilayers via reactive dc magnetron sputtering and the self-ordered Ge nanocrystal formation within the GeOx and Ge:SiOx~ 2 sublayers during subsequent annealing is investigated...

Soft Magnetic Multilayered Thin Films for HF Applications

Science.gov (United States)

Loizos, George; Giannopoulos, George; Serletis, Christos; Maity, Tuhin; Roy, Saibal; Lupu, Nicoleta; Kijima, Hanae; Yamaguchi, Masahiro; Niarchos, Dimitris

Multilayered thin films from various soft magnetic materials were successfully prepared by magnetron sputtering in Ar atmosphere. The magnetic properties and microstructure were investigated. It is found that the films show good soft magnetic properties: magnetic coercivity of 1-10 Oe and saturation magnetization higher than 1T. The initial permeability of the films is greater than 300 and flattens up to 600 MHz. The multilayer thin film properties in combination with their easy, fast and reproducible fabrication indicate that they are potential candidates for high frequency applications.

Negative Refraction Using Frequency-Tuned Oxide Multilayer Structure

Directory of Open Access Journals (Sweden)

Yalin Lu

2008-01-01

Full Text Available An oxide-based multilayer structure was proposed to realize negative refraction. The multilayer composes of alternative layers having negative permittivity and negative permeability, respectively. In order to realize negative refraction, their dielectric and magnetic resonances of layers will be tuned to the frequency as close as possibly via changing their temperature, composition, structure, and so forth. Such oxide-based NIMs are attractive for their potential applications as optical super lenses, imagers, optical cloaking, sensors, and so forth, those are required with low-loss, low-cost, and good fabrication flexibility.

Multilayer DNA Origami Packed on Hexagonal and Hybrid Lattices

OpenAIRE

Ke, Yonggang; Voigt, Niels V.; Gothelf, Kurt V.; Shih, William M.

2012-01-01

“Scaffolded DNA origami” has been proven to be a powerful and efficient approach to construct two-dimensional or three-dimensional objects with great complexity. Multilayer DNA origami has been demonstrated with helices packing along either honeycomb-lattice geometry or square-lattice geometry. Here we report successful folding of multilayer DNA origami with helices arranged on a close-packed hexagonal lattice. This arrangement yields a higher density of helical packing and therefore higher r...

76 FR 92 - Multilayered Wood Flooring From the People's Republic of China: Postponement of Preliminary...

Science.gov (United States)

2011-01-03

... DEPARTMENT OF COMMERCE International Trade Administration [C-570-971] Multilayered Wood Flooring... Department'') initiated an investigation of multilayered wood flooring from the People's Republic of China (``PRC''). See Multilayered Wood Flooring From the People's Republic of China: Initiation of...

Thermoelasticity and interdiffusion in CuNi multilayers

International Nuclear Information System (INIS)

Benoudia, M.C.; Gao, F.; Roussel, J.M.; Labat, S.; Gailhanou, M.; Thomas, O.; Beke, D.L.; Erdelyi, Z.; Langer, G.A.; Csik, A.; Kis-Varga, M.

2012-01-01

Complete text of publication follows. The idea of observing artificial metallic multilayers with x-ray diffraction techniques to study interdiffusion phenomena dates back to the work of DuMond and Youtz. Interestingly, these pioneering contributions even suggested that the approach could be used to measure the concentration dependence of the diffusion coefficient. This remark is precisely the subject of the present work: we aim to revisit this issue in light of recent atomistic simulation results obtained for coherent CuNi multilayers. More generally, CuNi multilayers have been extensively studied for their magnetic, mechanical, and optical properties. These physical properties depend critically on interfaces and require a good control on the evolution of composition and strain fields under heat treatment. Understanding of how interdiffusion proceeds in these nanosystems should therefore improve these practical aspects. From a theoretical viewpoint these synthetic modulated structures have been also used as valuable model systems to test the various diffusion theories accounting in particular for the influence of the alloying energy, the coherency strain, and the local concentration. Nowadays, this field remains active and has been extended with the development of atomic simulations and many microscopy techniques like atom probe tomography which give details on the intermixing mechanisms. We have performed x-ray diffraction experiments on coherent CuNi multilayers to probe thermoelasticity and interdiffusion in these samples. Kinetic mean-field simulations combined with the modeling of the x-ray spectra were also achieved to rationalize the experimental results. We have shown that classical thermoelastic arguments combined with bulk data can be used to model the x-ray scattered intensity of annealed coherent CuNi multilayers. This result provides a valuable framework to analyze the evolution of the concentration profiles at higher temperature. The typical coherent

Design guidelines for advanced LSI microcircuit packaging using thick film multilayer technology

Science.gov (United States)

Peckinpaugh, C. J.

1974-01-01

Ceramic multilayer circuitry results from the sequential build-up of two or more layers of pre-determined conductive interconnections separated by dielectric layers and fired at an elevated temperature to form a solidly fused structure. The resultant ceramic interconnect matrix is used as a base to mount active and passive devices and provide the necessary electrical interconnection to accomplish the desired electrical circuit. Many methods are known for developing multilevel conductor mechanisms such as multilayer printed circuits, welded wire matrices, flexible copper tape conductors, and thin and thick-film ceramic multilayers. Each method can be considered as a specialized field with each possessing its own particular set of benefits and problems. This design guide restricts itself to the art of design, fabrication and assembly of ceramic multilayer circuitry and the reliability of the end product.

Kossel interferences of proton-induced X-ray emission lines in periodic multilayers

Energy Technology Data Exchange (ETDEWEB)

Wu, Meiyi; Le Guen, Karine; André, Jean-Michel [Sorbonne Universités, UPMC Univ Paris 06, Laboratoire de Chimie Physique-Matière et Rayonnement, 11 rue Pierre et Marie Curie, F-75231 Paris cedex 05 (France); CNRS UMR 7614, Laboratoire de Chimie Physique-Matière et Rayonnement, 11 rue Pierre et Marie Curie, F-75231 Paris cedex 05 (France); Ilakovac, Vita [Sorbonne Universités, UPMC Univ Paris 06, Laboratoire de Chimie Physique-Matière et Rayonnement, 11 rue Pierre et Marie Curie, F-75231 Paris cedex 05 (France); CNRS UMR 7614, Laboratoire de Chimie Physique-Matière et Rayonnement, 11 rue Pierre et Marie Curie, F-75231 Paris cedex 05 (France); Université de Cergy-Pontoise, F-95031 Cergy-Pontoise (France); Vickridge, Ian [Sorbonne Universités, UPMC Univ Paris 06, Institut des NanoSciences de Paris, 4 place Jussieu, boîte courrier 840, F-75252 Paris cedex 05 (France); CNRS UMR 7588, Institut des NanoSciences de Paris, 4 place Jussieu, boîte courrier 840, F-75252 Paris cedex 05 (France); Schmaus, Didier [Sorbonne Universités, UPMC Univ Paris 06, Institut des NanoSciences de Paris, 4 place Jussieu, boîte courrier 840, F-75252 Paris cedex 05 (France); CNRS UMR 7588, Institut des NanoSciences de Paris, 4 place Jussieu, boîte courrier 840, F-75252 Paris cedex 05 (France); Université Paris Diderot-P7, F-75205 Paris cedex 13 (France); and others

2016-11-01

The Kossel interferences generated by characteristic X-ray lines produced inside a periodic multilayer have been observed upon proton irradiation, by submitting a Cr/B{sub 4}C/Sc multilayer stack to 2 MeV protons and observing the intensity of the Sc and Cr Kα characteristic emissions as a function of the detection angle. When this angle is close to the Bragg angle corresponding to the emission wavelength and period of the multilayer, an oscillation of the measured intensity is detected. The results are in good agreement with a model based on the reciprocity theorem. The combination of the Kossel measurements and their simulation, will be a useful tool to obtain a good description of the multilayer stack and thus to study nanometer-thick layers and their interfaces.

Electrical resistivity of thin metal films and multilayers

International Nuclear Information System (INIS)

Fenn, M.

1999-01-01

The electrical resistivity and temperature coefficient of resistivity (TCR) of thin films and multilayers of Cu, Nb and Zr have been measured over a wide range of layer thicknesses. The structure of the films has been characterised using transmission electron microscopy (TEM) and x-ray reflectivity. The experimental results have been compared with the semiclassical theory due to Dimmich. The values of the grain boundary reflectivity, R, in the single films has been found to be approximately 0.35 for Cu in agreement with the literature. The value of R for Nb and Zr has been found to vary with grain size, although it is approximately 0.55 for Nb and 0.925 for Zr over a wide range of grain sizes, and this is believed to be presented for the first time. The value of the interfacial specularity parameter, p, is not found to have a significant effect compared to R in the single films. Dimmich's theoretical expression for the TCR does not match experiment, but by adapting the resistivity expression of the theory to different temperatures a satisfactory fit has been obtained. It has been concluded that the assumption of the free electron model in the presence of grain boundary scattering is in error. The adapted theory predicts negative TCR in sufficiently thin films with experimentally plausible values of the input parameters, and this is believed to be demonstrated for the first time. The experimental resistivity of the multilayers was much lower than expected from the resistivity of the single films. A theoretical fit to the experimental resistivity and TCR of the multilayers was obtained by adjusting the parameter values obtained from single films, and the value of p was found to be significant. This procedure leads to a contradiction in the value of R for Nb. With a view to extending the above work to magnetic multilayers, an AC susceptometer has been designed, built and tested. The results indicate that this instrument would be suitable for work on magnetic

Predictors of recurrence in pheochromocytoma.

Science.gov (United States)

Press, Danielle; Akyuz, Muhammet; Dural, Cem; Aliyev, Shamil; Monteiro, Rosebel; Mino, Jeff; Mitchell, Jamie; Hamrahian, Amir; Siperstein, Allan; Berber, Eren

2014-12-01

The recurrence rate of pheochromocytoma after adrenalectomy is 6.5-16.5%. This study aims to identify predictors of recurrence and optimal biochemical testing and imaging for detecting the recurrence of pheochromocytoma. In this retrospective study we reviewed all patients who underwent adrenalectomy for pheochromocytoma during a 14-year period at a single institution. One hundred thirty-five patients had adrenalectomy for pheochromocytoma. Eight patients (6%) developed recurrent disease. The median time from initial operation to diagnosis of recurrence was 35 months. On multivariate analysis, tumor size >5 cm was an independent predictor of recurrence. One patient with recurrence died, 4 had stable disease, 2 had progression of disease, and 1 was cured. Recurrence was diagnosed by increases in plasma and/or urinary metanephrines and positive imaging in 6 patients (75%), and by positive imaging and normal biochemical levels in 2 patients (25%). Patients with large tumors (>5 cm) should be followed vigilantly for recurrence. Because 25% of patients with recurrence had normal biochemical levels, we recommend routine imaging and testing of plasma or urinary metanephrines for prompt diagnosis of recurrence. Copyright © 2014 Elsevier Inc. All rights reserved.

Interface characterization in B-based multilayer mirrors for next generation lithography

International Nuclear Information System (INIS)

Naujok, Philipp; Yulin, Sergiy; Müller, Robert; Kaiser, Norbert; Tünnermann, Andreas

2016-01-01

The interfaces in La/B_4C and LaN/B_4C multilayer mirrors designed for near normal incidence reflection of 6.x nm EUV light were investigated by grazing incidence X-ray reflectometry, high-resolution transmission electron microscopy and EUV reflectometry. The thickness and roughness asymmetries of the different interfaces in both studied systems have been identified. A development of interface roughness with an increasing number of bilayers was found by different investigation methods. For near normal incidence, R = 51.1% @ λ = 6.65 nm could be reached with our La/B_4C multilayer mirrors, whereas R = 58.1% was achieved with LaN/B_4C multilayers at the same wavelength. - Highlights: • Interface structure in B-based multilayer mirrors investigated. • Combining X-ray reflection, EUV reflection and transmission electron microscopy • Interface thickness and roughness asymmetry identified • Interface roughness increases with higher number of bilayers.

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.

Performance Test of the Microwave Ion Source with the Multi-layer DC Break

International Nuclear Information System (INIS)

Kim, Dae Il; Kwon, Hyeok Jung; Kim, Han Sung; Seol, Kyung Tae; Cho, Yong Sub

2012-01-01

A microwave proton source has been developed as a proton injector for the 100-MeV proton linac of the PEFP (Proton Engineering Frontier Project). On microwave ion source, the high voltage for the beam extraction is applied to the plasma chamber, also to the microwave components such as a 2.45GHz magnetron, a 3-stub tuner, waveguides. If microwave components can be installed on ground side, the microwave ion source can be operated and maintained easily. For the purpose, the multi-layer DC break has been developed. A multi-layer insulation has the arrangement of conductors and insulators as shown in the Fig. 1. For the purpose of stable operation as the multi-layer DC break, we checked the radiation of the insulator depending on materials and high voltage test of a fabricated multi-layer insulation. In this report, the details of performance test of the multi-layer DC break will be presented

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

Science.gov (United States)

Florios, Kostas; Kontogiannis, Ioannis; Park, Sung-Hong; Guerra, Jordan A.; Benvenuto, Federico; Bloomfield, D. Shaun; Georgoulis, Manolis K.

2018-02-01

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 - 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude {>} M1 and {>} C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02), and Heidke skill score HSS=0.49(0.01) for {>} M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01), and HSS=0.59(0.01) for {>} C1 flare prediction with probability threshold 35%.

The calculation of electron depth-dose distributions in multilayer medium

International Nuclear Information System (INIS)

Wang Chuanshan; Xu Mengjie; Li Zhiliang; Feng Yongxiang; Li Panlin

1989-01-01

Energy deposition in multilayer medium and the depth dose distribution in the layers are studied. Based on semi-empirical calculation of electron energy absorption in matter with EDMULT program of Tabata and Ito, further work has been carried out to extend the computation to multilayer composite material. New program developed in this paper makes IBM-PC compatible with complicated electron dose calculations

Processing and characterization of multilayers for energy device fabrication (invited)

DEFF Research Database (Denmark)

Kaiser, Andreas; Kiebach, Wolff-Ragnar; Gurauskis, Jonas

SOFC and tubular OTM, we present selected challenges in ceramic processing such asymmetric multilayer structures. By optimizing different steps in the ceramic processing, we improved the mechanical properties and gas permeability of porous supports and the (electrochemical) performance of electrodes......The performance of asymmetric multilayer structures in solid oxide fuel cells (SOFC)/solid oxide electrolysis cells (SOEC), tubular oxygen transport membranes (OTM) and similar high temperature energy devices is often determined by the ceramic fabrication (for given materials and design). A good...... understanding and control of different processing steps (from powder/materials selection, through shaping and sintering) is of crucial importance to achieve a defect-free multilayer microstructure with the desired properties and performance. Based on the experiences at DTU Energy with the fabrication of planar...

Interface alloying in multilayer thin films using polarized neutron reflectometry

International Nuclear Information System (INIS)

Basu, Saibal

2013-01-01

Polarized Neutron Reflectometry (PNR) is an excellent tool to probe magnetic depth profile in multilayer thin film samples. In case of multilayer films with alternating magnetic and non-magnetic layers, PNR can provide magnetic depth profile at the interfaces with better than nanometer resolution. Using PNR and Xray Reflectometry (XRR) together one can obtain chemical composition and magnetic structure, viz. magnetic moment density at interfaces in multilayer films. We have used these two techniques to obtain kinetics of alloy formation at the interfaces and the magnetic nature of the alloy at the interfaces in several important thin films with magnetic/non-magnetic bilayers. These include Ni/Ti, Ni/Al and Si/Ni pairs. Results obtained from these studies will be presented in this talk. (author)

Lamellar multilayer hexadecylaniline-modified gold nanoparticle ...

Indian Academy of Sciences (India)

standard Wilhelmy plate was used for surface pressure sensing. Multilayer ... carried out on a JEOL model 1200EX instrument operated at an accelerating voltage of ... the gold nanoparticles within domains (and reorganization of the domains ...

Tuning the electronic properties of gated multilayer phosphorene: A self-consistent tight-binding study

Science.gov (United States)

Li, L. L.; Partoens, B.; Peeters, F. M.

2018-04-01

By taking account of the electric-field-induced charge screening, a self-consistent calculation within the framework of the tight-binding approach is employed to obtain the electronic band structure of gated multilayer phosphorene and the charge densities on the different phosphorene layers. We find charge density and screening anomalies in single-gated multilayer phosphorene and electron-hole bilayers in dual-gated multilayer phosphorene. Due to the unique puckered lattice structure, both intralayer and interlayer charge screenings are important in gated multilayer phosphorene. We find that the electric-field tuning of the band structure of multilayer phosphorene is distinctively different in the presence and absence of charge screening. For instance, it is shown that the unscreened band gap of multilayer phosphorene decreases dramatically with increasing electric-field strength. However, in the presence of charge screening, the magnitude of this band-gap decrease is significantly reduced and the reduction depends strongly on the number of phosphorene layers. Our theoretical results of the band-gap tuning are compared with recent experiments and good agreement is found.

76 FR 76693 - Multilayered Wood Flooring From the People's Republic of China: Countervailing Duty Order

Science.gov (United States)

2011-12-08

..., tongue-and-groove construction or locking joints). All multilayered wood flooring is included within the... DEPARTMENT OF COMMERCE International Trade Administration [C-570-971] Multilayered Wood Flooring...''), the Department is issuing a countervailing duty (``CVD'') order on multilayered wood flooring from the...

Grazing incidence Fe-line telescopes using W/B4C multilayers

DEFF Research Database (Denmark)

Joensen, K. D.; Gorenstein, P.; Christensen, Finn Erland

1995-01-01

The loss of throughput observed at higher energies for traditional grazing-incidence X-ray telescopes coated with high-Z elements can be partly countered by employing multilayers on the outermost reflectors. Using 8-keV reflectivity data from a periodic W/B4C multilayer, the expected performance...

Writing nanopatterns with electrochemical oxidation on redox responsive organometallic multilayers by AFM

NARCIS (Netherlands)

Song, Jing; Hempenius, Mark A.; Chung, H.J.; Vancso, Gyula J.

2015-01-01

Nanoelectrochemical patterning of redox responsive organometallic poly(ferrocenylsilane) (PFS) multilayers is demonstrated by electrochemical dip pen lithography (EDPN). Local electrochemical oxidation and Joule heating of PFS multilayers from the tip are considered as relevant mechanisms related to

Comparison of Mg-based multilayers for solar He II radiation at 30.4 nm wavelength

Energy Technology Data Exchange (ETDEWEB)

Zhu Jingtao; Zhou Sika; Li Haochuan; Huang Qiushi; Wang Zhanshan; Le Guen, Karine; Hu, Min-Hui; Andre, Jean-Michel; Jonnard, Philippe

2010-07-10

Mg-based multilayers, including SiC/Mg, Co/Mg, B4C/Mg, and Si/Mg, are investigated for solar imaging and a He II calibration lamp at a 30.4 nm wavelength. These multilayers were fabricated by a magnetron sputtering method and characterized by x-ray reflection. The reflectivities of these multilayers were measured by synchrotron radiation. Near-normal-incidence reflectivities of Co/Mg and SiC/Mg multilayer mirrors are as high as 40.3% and 44.6%, respectively, while those of B4C/Mg and Si/Mg mirrors are too low for application. The measured results suggest that SiC/Mg, Co/Mg multilayers are promising for a 30.4 nm wavelength.

Surface patterning of multilayer graphene by ultraviolet laser irradiation in biomolecule sensing devices

Energy Technology Data Exchange (ETDEWEB)

Chang, Tien-Li, E-mail: tlchang@ntnu.edu.tw; Chen, Zhao-Chi

2015-12-30

Graphical abstract: - Highlights: • Direct UV laser irradiation on multilayer graphene was discussed. • Multilayer graphene with screen-printed process was presented. • Surface patterning of multilayer graphene at fluence threshold was investigated. • Electrical response of glucose in sensing devices can be studied. - Abstract: The study presents a direct process for surface patterning of multilayer graphene on the glass substrate as a biosensing device. In contrast to lithography with etching, the proposed process provides simultaneous surface patterning of multilayer graphene through nanosecond laser irradiation. In this study, the multilayer graphene was prepared by a screen printing process. Additionally, the wavelength of the laser beam was 355 nm. To perform the effective laser process with the small heat affected zone, the surface patterns on the sensing devices could be directly fabricated using the laser with optimal control of the pulse overlap at a fluence threshold of 0.63 J/cm{sup 2}. The unique patterning of the laser-ablated surface exhibits their electrical and hydrophilic characteristics. The hydrophilic surface of graphene-based sensing devices was achieved in the process with the pulse overlap of 90%. Furthermore, the sensing devices for controlling the electrical response of glucose by using glucose oxidase can be used in sensors in commercial medical applications.

Simple electrodepositing of CoFe/Cu multilayers: Effect of ferromagnetic layer thicknesses

Energy Technology Data Exchange (ETDEWEB)

Tekgül, Atakan, E-mail: atakantekgul@gmail.com [Akdeniz University, Physics Department, Science Faculty, TR-07058 Antalya (Turkey); Uludag University, Physics Department, Science and Literature Faculty, TR-16059 Bursa (Turkey); Alper, Mürsel [Uludag University, Physics Department, Science and Literature Faculty, TR-16059 Bursa (Turkey); Kockar, Hakan [Balikesir University, Physics Department, Science and Literature Faculty, TR-10145 Balikesir (Turkey)

2017-01-01

The CoFe/Cu magnetic multilayers were produced by changing CoFe ferromagnetic layers from 3 nm to 10 nm using electrodeposition. By now, the thinnest Cu (0.5 nm) layer thicknesses were used to see whether the GMR effect in the multilayers can be obtained or not since the pinning of non-magnetic layer between the ferromagnetic layers is required. For the proper depositions, the cyclic voltammograms was used, and the current–time transients were obtained. The Cu and CoFe layers were deposited at a cathode potential of −0.3 and −1.5 V with respect to saturated calomel electrode, respectively. From the XRD patterns, the multilayers were shown to be fcc crystal structures. For the magnetization measurements, saturation magnetization increases from 160 to 600 kA/m from 3 to 8 nm ferromagnetic layer thicknesses. And, the coercivity values increase until the 8 nm of the CoFe layer thickness. It is seen that the thin Cu layer (fixed at 0.5 nm) and pinholes support the random magnetization orientation and thus all multilayers exhibited the giant magnetoresistance (GMR) effect, and the highest GMR value was observed about 5.5%. And, the variation of GMR field sensitivity was calculated. The results show that the GMR and GMR sensitivity are compatible among the multilayers. The CoFe/Cu magnetic multilayers having GMR properties are used in GMR sensors and hard disk drive of the nano-technological devices. - Highlights: • The much thinner (0.5 nm) Cu layer was used to obtain the GMR effect on the electrodeposited CoFe/Cu multilayers. • All samples exhibited GMR and the maximum GMR value was 5.5%. • The M{sub s} and the H{sub c} changed with increasing magnetic layer thickness.

77 FR 5484 - Multilayered Wood Flooring From the People's Republic of China: Amended Antidumping and...

Science.gov (United States)

2012-02-03

..., tongue-and-groove construction or locking joints). All multilayered wood flooring is included within the... DEPARTMENT OF COMMERCE International Trade Administration [A-570-970, C-570-971] Multilayered Wood... (``CVD'') orders on multilayered wood flooring from the People's Republic of China (``PRC'') to remove an...

Overview of the CERES Edition-4 Multilayer Cloud Property Datasets

Science.gov (United States)

Chang, F. L.; Minnis, P.; Sun-Mack, S.; Chen, Y.; Smith, R. A.; Brown, R. R.

2014-12-01

Knowledge of the cloud vertical distribution is important for understanding the role of clouds on earth's radiation budget and climate change. Since high-level cirrus clouds with low emission temperatures and small optical depths can provide a positive feedback to a climate system and low-level stratus clouds with high emission temperatures and large optical depths can provide a negative feedback effect, the retrieval of multilayer cloud properties using satellite observations, like Terra and Aqua MODIS, is critically important for a variety of cloud and climate applications. For the objective of the Clouds and the Earth's Radiant Energy System (CERES), new algorithms have been developed using Terra and Aqua MODIS data to allow separate retrievals of cirrus and stratus cloud properties when the two dominant cloud types are simultaneously present in a multilayer system. In this paper, we will present an overview of the new CERES Edition-4 multilayer cloud property datasets derived from Terra as well as Aqua. Assessment of the new CERES multilayer cloud datasets will include high-level cirrus and low-level stratus cloud heights, pressures, and temperatures as well as their optical depths, emissivities, and microphysical properties.

Photon wavelength dependent valley photocurrent in multilayer MoS2

Science.gov (United States)

Guan, Hongming; Tang, Ning; Xu, Xiaolong; Shang, LiangLiang; Huang, Wei; Fu, Lei; Fang, Xianfa; Yu, Jiachen; Zhang, Caifeng; Zhang, Xiaoyue; Dai, Lun; Chen, Yonghai; Ge, Weikun; Shen, Bo

2017-12-01

The degree of freedom (DOF) of the K (K') valley in transition-metal dichalcogenides, especially molybdenum disulfide (MoS2), offers an opportunity for next-generation valleytronics devices. In this work, the K (K') valley DOF of multilayer MoS2 is studied by means of the photon wavelength dependent circular photogalvanic effect (CPGE) at room temperature upon a strong external out-of-plane electric field induced by an ionic liquid (IL) gate, which breaks the spatial-inversion symmetry. It is demonstrated that only on resonant excitations in the K (K') valley can the valley-related CPGE signals in multilayer MoS2 with an IL gate be detected, indicating that the valley contrast is indeed regenerated between the K and K' valleys when the electric field is applied. As expected, it can also be seen that the K (K') valley DOF in multilayer MoS2 can be modulated by the external electric field. The observation of photon wavelength dependent valley photocurrent in multilayer MoS2, with the help of better Ohmic contacts, may pave a way for optoelectronic applications of valleytronics in the future.

Interlayer growth in Mo/B4C multilayered structures upon thermal annealing

International Nuclear Information System (INIS)

Nyabero, S. L.; Kruijs, R. W. E. van de; Yakshin, A. E.; Zoethout, E.; Bosgra, J.; Loch, R. A.; Blanckenhagen, G. von; Bijkerk, F.

2013-01-01

Both multilayer period thickness expansion and compaction were observed in Mo/B 4 C multilayers upon annealing, and the physical causes for this were explored in detail. Using in situ time-dependent grazing incidence X-ray reflectometry, period changes down to picometer-scale were resolved. It was shown that the changes depend on the thickness of the B 4 C layers, annealing temperature, and annealing time. Although strong stress relaxation during annealing was observed, it was excluded as a cause for period expansion. Auger electron spectroscopy and wide angle X-ray diffraction measurements revealed the growth of interlayers, with associated period changes influenced by the supply of B and C atoms to the growing compound interlayers. For multilayers with a Mo thickness of 3 nm, two regimes were recognized, depending on the deposited B 4 C thickness: in multilayers with B 4 C ≤ 1.5 nm, the supply of additional Mo into the already formed MoB x C y interlayer was dominant and led to densification, resulting in period compaction. For multilayers with B 4 C ≥ 2 nm, the B and C enrichment of interlayers formed low density compounds and yielded period expansion.

[The role of BCP in electroluminescence of multilayer organic light-emitting devices].

Science.gov (United States)

Deng, Zhao-Ru; Yang, Sheng-Yi; Lou, Zhi-Dong; Meng, Ling-Chuan

2009-03-01

As a hole-blocking layer, 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline (BCP) is usually used in blue and white light electroluminescent devices. The ability of blocking holes of BCP layer depends on its thickness, and basically holes can tunnel through thin BCP layer. In order to know the role of BCP layer in electroluminescence (EL) of multilayer organic light-emitting diodes (OLEDs), in the present paper, the authors designed a multilayer OLED ITO/NPB/BCP/Alq3 : DCJTB/Alq3/Al and investigated the influence of thickness of BCP on the EL spectra of multilayer OLEDs at different applied voltages. The experimental data show that thin BCP layer can block holes partially and tune the energy transfer between different emissive layers, and in this way, it is easy to obtain white emission, but its EL spectra will change with the applied voltages. The EL spectra of multilayer device will remain relatively stable when BCP layer is thick enough, and the holes can hardly tunnel through when the thickness of BCP layer is more than 15 nm. Furthermore, the stability of EL spectra of the multilayer OLED at different applied voltages was discussed.

Direct Magnetic Relief Recording Using As40S60: Mn-Se Nanocomposite Multilayer Structures.

Science.gov (United States)

Stronski, A; Achimova, E; Paiuk, O; Meshalkin, A; Prisacar, A; Triduh, G; Oleksenko, P; Lytvyn, P

2017-12-01

Processes of holographic recording of surface relief structures using As 2 S 3 :Mn-Se multilayer nanostructures as registering media were studied in this paper. Optical properties of As 2 S 3 :Mn, Se layers, and As 2 S 3 :Mn-Se multilayer nanostructures were investigated. Values of optical bandgaps were obtained from Tauc dependencies. Surface relief diffraction gratings were recorded. Direct one-stage formation of surface relief using multilayer nanostructures is considered. For the first time, possibility of direct formation of magnetic relief simultaneous with surface relief formation under optical recording using As 2 S 3 :Mn-Se multilayer nanostructures is shown.

Structural color of a lycaenid butterfly: analysis of an aperiodic multilayer structure

International Nuclear Information System (INIS)

Yoshioka, S; Shimizu, Y; Kinoshita, S; Matsuhana, B

2013-01-01

We investigated the structural color of the green wing of the lycaenid butterfly Chrysozephyrus brillantinus. Electron microscopy revealed that the bottom plate of the cover scale on the wing consists of an alternating air–cuticle multilayer structure. However, the thicknesses of the layers were not constant but greatly differed depending on the layer, unlike the periodic multilayer designs often adopted for artificial laser-reflecting mirrors. The agreement between the experimentally determined and theoretically calculated reflectance spectra led us to conclude that the multilayer interference in the aperiodic system is the primary origin of the structural color. We analyzed optical interference in this aperiodic system using a simple analytical model and found that two spectral peaks arise from constructive interference among different parts of the multilayer structure. We discuss the advantages and disadvantages of the aperiodic system over a periodic one. (paper)

Automation Enhancement of Multilayer Laue Lenses

Energy Technology Data Exchange (ETDEWEB)

Lauer K. R.; Conley R.

2010-12-01

X-ray optics fabrication at Brookhaven National Laboratory has been facilitated by a new, state of the art magnetron sputtering physical deposition system. With its nine magnetron sputtering cathodes and substrate carrier that moves on a linear rail via a UHV brushless linear servo motor, the system is capable of accurately depositing the many thousands of layers necessary for multilayer Laue lenses. I have engineered a versatile and automated control program from scratch for the base system and many subsystems. Its main features include a custom scripting language, a fully customizable graphical user interface, wireless and remote control, and a terminal-based interface. This control system has already been successfully used in the creation of many types of x-ray optics, including several thousand layer multilayer Laue lenses.Before reaching the point at which a deposition can be run, stencil-like masks for the sputtering cathodes must be created to ensure the proper distribution of sputtered atoms. Quality of multilayer Laue lenses can also be difficult to measure, given the size of the thin film layers. I employ my knowledge of software and algorithms to further ease these previously painstaking processes with custom programs. Additionally, I will give an overview of an x-ray optic simulator package I helped develop during the summer of 2010. In the interest of keeping my software free and open, I have worked mostly with the multiplatform Python and the PyQt application framework, utilizing C and C++ where necessary.