KoreaScience (Korea)

Full Text Available

Science.gov (United States)

Not Available

Energy Technology Data Exchange (ETDEWEB)

This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.

KoreaScience (Korea)

Full Text Available

International Nuclear Information System (INIS)

In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

Energy Technology Data Exchange (ETDEWEB)

The catalytic liquefaction of a Chinese bituminous coal was simulated by artificial neural network. Three liquefaction variables, catalyst loading, reaction temperature and reaction time were used as inputs and tetrohydrofuran (THF) conversion and toluene (T) conversion were used as outputs. The artificial neural network, trained by the experimental data, could represent the liquefaction process, with a mean squared deviation of less than 0.025. 7 refs.,1 fig., 3 tabs.

Energy Technology Data Exchange (ETDEWEB)

Supplementing the collection of artificial neural network methodologies devised for monitoring energy producing installations, a general regression artificial neural network is proposed for the identification of the two-phase flow that occurs in the coolant channels of boiling water reactors. The utilization of a limited number of image features derived from radiography images affords the proposed approach with efficiency and non-invasiveness. Additionally, the application of counter-clustering to the input patterns prior to training accomplishes an 80% reduction in network size as well as in training and test time. Cross-validation tests confirm accurate on-line flow regime identification.

Energy Technology Data Exchange (ETDEWEB)

Target recognition requires the ability to distinguish targets from non-targets, a capability called one-class generalization. Many neural network pattern classifiers fail as one-class classifiers because they use open decision boundaries. To function as one-class classifier, a neural network must have three types of generalization: within-class, between-class, and out-of-class. We discuss these three types of generalization and identify neural network architectures that meet these requirements. We have applied our one-class classifier ideas to the problem of automatic target recognition in synthetic aperture radar. We have compared three neural network algorithms: Carpenter and Grossberg`s algorithmic version of the Adaptive Resonance Theory (ART-2A), Kohonen`s Learning Vector Quantization (LVQ), and Reilly and Cooper`s ...

Energy Technology Data Exchange (ETDEWEB)

As an application of ART2 neural networks, computer aided monitoring of pump efficiency is successfully examined for an industrial waste-liquid treatment process with measured data of valve openness and liquid flow rates. By running the neural networks in parallel, we confirm that accuracy to detect system changes is good, and the adjustment of classifier parameters is relatively easy. Investigating the resulting classes carefully, frequency of each class is correlated with pump efficiency. The relative amount of variables are also related to the classes. (author)

Energy Technology Data Exchange (ETDEWEB)

The detectors used in the TS93 balloon flight produced a large volume of information for each cosmic ray trigger. Some of the data was visual in nature, other portions contained energy deposition and timing information. The data sets are amenable to conventional analysis techniques but there is no assurance that conventional techniques make full use of subtle correlations and relations amongst the detector responses. With the advent of neural network technologies, particularly adept at classification of complex phenomena, it would seem appropriate to explore the utility of neural network techniques to classify particles observed with the instruments. In this paper neural network based methodology for signal/background discrimination in a cosmic ray space experiment is discussed. Results are presented for electron and positron classification in the TS93 flight ...

Energy Technology Data Exchange (ETDEWEB)

A new recurrent neural network power system stabilizer (RNNPSS) based on genetic algorithm (GA) was presented. It shows faster convergence than the linear quadratic regulator (LQR) stabilizer in a multi-machine power system, because the proposed GA based neural network was first trained off-line to determine the optimal values of the learning rates. Otherwise, the RNNPSS consists of just two layers. As such, the time consumption of the damping oscillations is lower than with conventional methods. In addition, the operating range of the RNNPSS is greater than that of the LQR and conventional three layer neural networks, since the RNNPSS can greatly reduce system complexity and effectively damp system oscillations. 9 refs., 7 figs.

Energy Technology Data Exchange (ETDEWEB)

This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological ...

British Library Electronic Table of Contents (United Kingdom)

Because the state of a free-floating space robot model is uncertain and sudden changes in the model parameters might undermine the stability of the system, this paper proposes a control strategy based on a variable structure neural integrated controller. This scheme does not need a precise space robot model, making use of the radial basis function neural network ability approach to learn about an uncertain model. The network weights are adjusted online in real-time. During the early period of the control phase and parameter changes, the variable structure controller compensates for the uncertain model which the neural network could not learn well. It also creates global asymptotic stability for the whole closed-loop system. Simulation results show that the controller can handle bad changea...

Energy Technology Data Exchange (ETDEWEB)

A novel approach is presented to extract relevant parameters associated with the energy loss of ejectiles from nuclear reactions obtained by digitizing the signals of a Bragg curve spectrometer. New and more powerful computational paradigms allow a more thorough pulse-shape analysis. This is fulfilled using a back-propagation artificial neural network as a pattern identifier. The known problem of over-training is discussed.

CERN Document Server

Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian RBF kernel is proposed here for enterprise financial distress evaluation. BPN network is considered one of the simplest and are most general methods used for supervised training of multilayered neural network. The comparative results show that through the difference between the performance measures is marginal; SVM gives higher precision and lower error rates.

British Library Electronic Table of Contents (United Kingdom)

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of...

Energy Technology Data Exchange (ETDEWEB)

In this paper, a neural networks (NN) based adaptive sliding mode controller (SMC) is introduced. The selection of SMC feedback gains is normally based on one operating point and thus the performance of the controller away from the design operating point is, of necessity, a compromise. The adaptive SMC is proposed to overcome the limitations imposed on the effectiveness of the SMC under different operating conditions. Neural networks are used for online prediction of the optimal SMC gains when the operating point changes. The proposed method has been applied to a power system stabilizer (PSS) of a single machine power system. Simulation results are included to demonstrate the performance of the proposed control scheme.

CERN Document Server

We report the first experimental generation and characterization of a six-photon Dicke state and demonstrate its remarkable versatility by projecting out four- and five-photon Dicke states, in addition to four-photon GHZ- and W-states. These multipartite states are studied by developing experimentally favorable characterization tools. Furthermore, we show that Dicke states have interesting applications in multiparty quantum networking protocols such as open-destination teleportation, telecloning and quantum secret sharing.

British Library Electronic Table of Contents (United Kingdom)

Time delay neural networks trained with the backpropagation algorithm are derived for the gun fire control system to correct the miss distance between a target and the projectiles from the gun. Its performance is compared to optimum linear filter based on minimum mean square error [R.E. Kalman, A new approach to linear filtering and prediction problems, J. Basic Eng. 82D (1960) 35-44.]. The structure of the proposed neural controller is described and performance results are shown.

Science.gov (United States)

Using static Michelson interferometer to get the spectrum information of measurement targets for spectrum identification, under the condition that the interference length is constant, the system can be optimized by BP neural network algorithm for the mixed spectral separation process. Thereby it can realize improving the recognition probability of camouflage target. Collecting the spectrum information in field of view (FOV) by the interferometer and linear array CCD detector, composing the set of mixed spectrum data, with known absorption spectrum of the material as a hidden layer of rules, it used BP neural network to separate the mixed spectrum data. Experiment with different distances, different combinations of mixed background spectrum as the initial data, using steel target (size: 1.5 m x 1.5 m) made of four kinds, the recognition probability of non-camouflage target is about 90% by BP ...

UK PubMed Central (United Kingdom)

BackgroundWith the advent of increasingly efficient means to obtain genetic information, a great insurgence of data has resulted, leading to the need for methods for analyzing this...Full Text Available

Energy Technology Data Exchange (ETDEWEB)

The development of a computational intelligent tools based on neural network to identify commercial losses or fraud (theft energy), considering information from a database electric utility, is presented.

Energy Technology Data Exchange (ETDEWEB)

The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and ...

UK PubMed Central (United Kingdom)

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital...Full Text Available

Energy Technology Data Exchange (ETDEWEB)

Artificial Neural Networks (ANNs) are parallel distributed processing machines. The unique characteristics of ANNs are: Fault tolerance, robustness, plasticity and generalization. These offer great potential in many AI applications such as character recognition. Handwritten character recognition is an intrinsically interesting problem, but the difficulties of this task are the many variations in the characters. A robust new incremental learning method, which combines supervised and unsupervised learning paradigms implemented by the Functional Link Net, is illustrated with experimental results. Clustering, based on unsupervised learning, classifies the input data into several categories. The supervised learning paradigm then further classifies the data in the clustered categories.

Energy Technology Data Exchange (ETDEWEB)

A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

Energy Technology Data Exchange (ETDEWEB)

This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

Energy Technology Data Exchange (ETDEWEB)

Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted ...

Energy Technology Data Exchange (ETDEWEB)

This paper presents general considerations concerning the application of artificial neural networks algorithms, more specifically the back-propagation learning algorithm and feed-forward multi-layer networks, to several problems in power system. The main application in power systems is the load forecasting, and two solution methods are used to solve it. (author). 45 figs., 32 tabs., 144 refs.

CERN Document Server

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some ...

CERN Document Server

Very recently we have assisted to a new development of quantum information, the so-called continuous variable (CV) quantum information theory. Such a further development has been mainly due to the experimental and theoretical advantages offered by CV systems, i.e., quantum systems described by a set of observables, like position and momentum, which have a continuous spectrum of eigenvalues. According to this novel trend, quantum information protocols like quantum teleportation have been suitably extended to the CV framework. Here, we briefly review some mathematical tools relative to CV systems and we consequently develop the concepts of quantum entanglement and teleportation in the CV framework, by analogy with the qubit-based approach. Some connections between teleportation fidelity and entanglement properties of the underlying quantum ...

British Library Electronic Table of Contents (United Kingdom)

Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our kn...

Energy Technology Data Exchange (ETDEWEB)

Interactive seismic processing systems for editing noisy seismic traces and picking the first-break refraction events have been developed using a neural network learning algorithm. The authors employ a back propagation neural network (BNN) paradigm modified to improve the convergence rate of the BNN. The BNN is interactively trained'' to edit seismic data or pick first breaks by a human processor who judiciously selects and presents to the network examples of trace edits or refraction picks. The network then iteratively adjusts a set of internal weights until it can accurately duplicate the examples provided by the user. After the training session is completed, the BNN system an then process new data sets in a manner that mimics the human processor. Synthetic modeling studies indicated that the BNN uses many of the same subjective criteria that ...

Energy Technology Data Exchange (ETDEWEB)

Artificial neural network analysis is found to be far superior to multiple regression when applied to the evaluation of trap quality in the Northern Kuqa Depression, a gas-rich depression of Tarim Basin in western China. This is because this technique can correlate the complex and non-linear relationship between trap quality and related geological factors, whereas multiple regression can only describe a linear relationship. However, multiple regression can work as an auxiliary tool, as it is suited to high-speed calculations and can indicate the degree of dependence between the trap quality and its related geological factors which artificial neural network analysis cannot. For illustration, we have investigated 30 traps in the Northern Kuqa Depression. For each of the traps, the values of 14 selected geological factors were all known. While geologists were also able to assign individual trap quality ...

Science.gov (United States)

In this paper, we introduce a new recursive neural network model able to process directed acyclic graphs with labelled edges. The model uses a state transition function which considers the edge labels and is independent both from the number and the order of the children of each node. The computational capabilities of the new recursive architecture are assessed. Moreover, in order to test the proposed architecture on a practical challenging application, the problem of object detection in images is also addressed. In fact, the localization of target objects is a preliminary step in any recognition system. The proposed technique is general and can be applied in different detection systems, since it does not exploit any a priori knowledge on the particular problem. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results. PMID:16181770

International Nuclear Information System (INIS)

In order to build the safety culture for nuclear power industry, it is important to evaluate the safety culture scientifically. Considering the traits of safety culture in the nuclear power industry, 24 safety culture assessment indexes are established from 4 aspects such as Safety consciousness, Safety attitude, Safety action and Safety actuality by using the SMART criteria. Safety culture star-class assessment criterion is presented and safety culture star-class assessment system is developed by using Visual Basic 6.0 and BP neural network. The system has a better generalization ability, and it can show exactly which phase the safety culture is in. Experimental results show that safety culture star-class assessment is practical and easy to perform. (authors)

Energy Technology Data Exchange (ETDEWEB)

This study has been carried out in the framework of a collaboration between the laboratory of processes automation (LAP, Caen (France)), and Air Com, a monitoring network for the prevention of atmospheric pollution in Basse-Normandie. It aims at obtaining a medium and long term forecast of the ozone level above the Caen city. The expected goal is to foresee the pollution peaks exceeding the warning thresholds, but the rareness of such events make them more difficult to predict. In order to solve this kind of problem, a neural modeling method combined with a noise injection technique has been implemented in order to obtain a sufficiently performing model over the whole domain of operation. (J.S.)

British Library Electronic Table of Contents (United Kingdom)

The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective fault diagnosis method of locomotive roller bearings. First, the vibration signals captured from the locomotive roller bearings are preprocessed by EEMD method and intrinsic mode functions (IMFs) are produced. Second, a kurtosis based method is presented and used to select the sensitive IMF. Third, time- and frequency-domain features are extracted from the sensitive IMF, its frequency spectrum and its envelope spe...

Energy Technology Data Exchange (ETDEWEB)

Density is useful in deducing the spatial structure of coals. In this paper, nitrogen has been used instead of the commonly employed helium, for the gas displacement pycnometer based density determination of a number of coals of Indian origin. The results show that the nitrogen-based densities are always higher than the helium-based ones. Also, empirical relationships between the helium-based and nitrogen-based coal densities have been developed by two modeling methods, namely, multi-variable regression and artificial neural networks. Although the two models have fared well, the neural network model exhibits a relatively better prediction accuracy and generalization performance than the regression model. This study thus demonstrates that nitrogen, which is cheaper and easily available, can be used gainfully as the probe gas for estimating the true density of coals. 23 refs., 1 fig., 3 tabs.

Energy Technology Data Exchange (ETDEWEB)

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model

Energy Technology Data Exchange (ETDEWEB)

In the present scenario of market driven business, power supply has become more like a commodity. Reliable and quality power need to be ensured to meet customer requirements. In such a situation, it is extremely important that transmission line faults be identified accurately, reliably and in quick time. Advanced signal processing tools such as discrete wavelet transform (DWT) can be used very effectively for parameterisation and characterization of the fault signals. On the other hand, properly configured neural network (NN) can be utilized for classification of the faults based on the DWT signal. The present contribution uses electromagnetic transient program (EMTP) for modeling of a real transmission system and MATLAB for DWT and NN. Various types of faults have been simulated at different locations along the transmission line and an attempt has been made to correctly identify and locate the fault. (author)

Energy Technology Data Exchange (ETDEWEB)

A power system stabilizer based on GMV (Generalized Minimum Variance), one of the adaptive control techniques, is developed to enhance the dynamic performances of a power system using an Artificial Neural Network (ANN). The stabilizer consists of two parts. One part is Inverse Dynamics Neural Networks (IDNN), which is trained to identify the inverse dynamics of controlled plant and used as a one-step ahead controller, or inverse controller. The other part is Adaptive Reference Model (ARM), which prevents excessive controller output. The ARM produces the modified reference value by minimizing a cost function recursively on the assumption that the IDNN perfectly identifies the controlled plant. The IDNN is used in the minimization procedure to calculate the sensitivities. The proposed controller is simulated in a typical one-machine-infinite-bus power system to show its effectiveness to damp sustained low ...

Energy Technology Data Exchange (ETDEWEB)

The topic of supervised learning within the conceptual framework of artificial neural network (ANN) models is addressed. An ANN is a parallel distributed processing system that consists of many computationally simple processing elements interconnected through uni-directional weighted connections. Such networks, which are roughly patterned after biological nervous systems, have been proposed for use in areas in which the traditional von Neumann computer architecture has been relatively unsuccessful. Learning in these networks is accomplished through the use of algorithms that adjust the values of the connection weights. The work presented here addresses the issue of improving the rate at which ANNs can learn to achieve the mapping of an input pattern to a desired output pattern. The most successful learning algorithms for accomplishing this task are based on gradient descent error minimization ...

CERN Document Server

In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in MANET turns into dynamic optimization problem (DOP). Here the nodes ae made aware of the environmental condition, thereby making it intelligent, which goes as the input for GA. The implementation then uses GAs with immigrants and memory schemes to solve the dynamic SP routing problem (DSPRP) in MANETS. In our paper, once the network ...

Science.gov (United States)

University research group with research areas: * Land based and submersible autonomous robots, (UUVs: AUVs and ROVs); * Controllers, electronics, sensor design and fusion, motion control; * Guidance and navigation of underwater vehicles; * AI, neural networks, fuzzy logic, subsumption control, behaviour based control; * Optical fibre and ultrasonic sensors for proximal object detection; * Robot arm control, visual servoing; * Imaging sonar applications; * Simulator development: UUV simulator; imaging sonar simulator; Aircraft/flight simulator.

Science.gov (United States)

A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back ...

UK PubMed Central (United Kingdom)

BackgroundThermostable bacterial lipases occupy a place of prominence among biocatalysts owing to their novel, multifold applications and resistance to high temperature and other...Full Text Available

CERN Document Server

A recurrent idea in the study of complex systems is that optimal information processing is to be found near bifurcation points or phase transitions. However, this heuristic hypothesis has few (if any) concrete realizations where a standard and biologically relevant quantity is optimized at criticality. Here we give a clear example of such a phenomenon: a network of excitable elements has its sensitivity and dynamic range maximized at the critical point of a non-equilibrium phase transition. Our results are compatible with the essential role of gap junctions in olfactory glomeruli and retinal ganglionar cell output. Synchronization and global oscillations also appear in the network dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement of dynamic range, therefore allowing the coding of information spanning several orders of magnitude. The mechanism could provide a microscopic ...

Energy Technology Data Exchange (ETDEWEB)

We discuss strictly efficient models for measurement-based quantum computing using physical continuous variables, such as field modes of light. Such measurement-based quantum computing (MBQC) provides a promising paradigm for quantum computation as it does not require performing unitary gates during the computation, but rather appropriate readout. Here, we introduce novel schemes for which the resource state can be reasonably and efficiently prepared, and which notably do not require having infinite squeezing or mean energy available. What is more, error correction techniques are implementable, as the logical information is stored in finite-dimensional objects grasping correlations of the quantum states. Using the ideas of computational tensor networks we discuss how to sequentially prepare suitable physical resource states with cavity QED or with non-linear optics and how to ...

Energy Technology Data Exchange (ETDEWEB)

The objective of this study is to develop an artificial neural network (ANN) model to predict the thermal conductivity of ethylene glycol-water solutions based on experimentally measured variables. The thermal conductivity of solutions at different concentrations and various temperatures was measured using the cylindrical cell method that physical properties of the solution are being determined fills the annular space between two concentric cylinders. During the experiment, heat flows in the radial direction outwards through the test liquid filled in the annual gap to cooling water. In the steady state, conduction inside the cell was described by the Fourier equation in cylindrical coordinates, with boundary conditions corresponding to heat transfer between the solution and cooling water. The performance of ANN was evaluated by a regression analysis between the predicted and the experimental values. The ANN predictions yield R{sup 2} in the ...

Energy Technology Data Exchange (ETDEWEB)

Relationships of ultimate and proximate analysis of 4540 US coal samples from 25 states with gross calorific value (GCV) have been investigated by regression and artificial neural networks (ANNs) methods. Three set of inputs: (a) volatile matter, ash and moisture (b) C, H, N, O, S and ash (c) C, H{sub exclusive} {sub of} {sub moisture}, N, O{sub exclusive} {sub of} {sub moisture}, S, moisture and ash were used for the prediction of GCV by regression and ANNs. The multivariable regression studies have shown that the model (c) is the most suitable estimator of GCV. Running of the best arranged ANNs structures for the models (a) to (c) and assessment of errors have shown that the ANNs are not better or much different from regression, as a common and understood technique, in the prediction of uncomplicated relationships between proximate and ultimate analysis and coal GCV. (author)

Energy Technology Data Exchange (ETDEWEB)

Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150kV and 400kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be ...

Energy Technology Data Exchange (ETDEWEB)

The activities and results of a Small Business Innovation Research Phase II project entitled ''Rapid Tools for Joint Inversion and Imaging'' are presented. Research and development on three-dimensional methods to recover distributions of material property values from sparse data are reported. Innovations using artificial neural networks and extended Kalman filtering are described. The report also covers investigations on upscaling and downscaling, segmentation for data processing, and applications to ground penetrating radar and geohydraulic tomography.

Wastenet

... (restricted)] 406-419 E-auction in China: the case of Taobao by June Lu & Lu-Zhuang Wang & Chun-Sheng Yu [Downloadable! (restricted)] 420-441 The risks of business process outsourcing: a two-fold assessment in the German banking industry by Heiko Gewald & Jochen Franke [Downloadable! (restricted)] 442-459 Prediction of corporate financial health by Artificial Neural Network by Sumit Chakraborty & Sushil K. Sharma [Downloadable! (restricted)] 460-472 The development and performance evaluation of a Continuous Auditing Assistance System by ...

CERN Document Server

This thesis describes hadron reconstruction at the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN, Geneva. The focus is on the particle flow reconstruction of these objects. This thesis revisits the subject of the CMS calorimeters' non-linear response to hadrons. Data from testbeam experiments conducted in 2006 & 2007 is compared with simulations and substantial differences are found. A particle flow calibration to correct the energy response of the testbeam data is evaluated. The reconstructed jet response is found to change by ~ 5% when a data-driven calibration is used in place of the calibration derived from simulation. Collision data taken at the early stage of CMS' commissioning is also presented. The hadron response in data is determined to be compatible with testbeam results presented in this thesis. This thesis also details the use of neural networks to improve the energy measurement of hadrons at ...

Science.gov (United States)

High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a ...

CERN Document Server

Entanglement is the essential quantum resource for a potential speed-up of information processing, as well as for sophisticated quantum communication. Quantum information networks will be required to convey information from one place to another, by using entangled light beams. Many physical systems are under consideration as building blocks, with different merits and faults, so that hybrid systems are likely to be developed. Here we present an important tool for connecting systems that share no common resonance frequencies: we demonstrate the first direct generation of entanglement among more than two bright beams of light, all of different wavelengths (532.251 nm, 1062.102 nm, and 1066.915 nm). We also observe, for the first time, disentanglement for finite channel losses, the continuous variable counterpart to entanglement sudden death.

International Nuclear Information System (INIS)

A network of second-generation low-temperature gravitational radiation detectors is nearing completion. These detectors, sensitive to mechanical strains of order 10"-"1"8, are possible because of a variety of technical innovations that have been made in cryogenics, low-noise superconducting instrumentation, and vibration isolation techniques. Another five orders of magnitude improvement in energy sensitivity of resonant-mass detectors is possible before the linear amplifier quantum limit is encountered. 33 references, 3 figures, 1 table.

CERN Document Server

Because of the stochastic nature of traffic requirement matrix, it is very difficult to get the optimal traffic distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity already defined for each link. Hence there is a requirement to define such a method, which could generate the optimal solution very quickly and efficiently. This paper presenting a new concept to provide the adaptive optimal traffic distribution for dynamic condition of traffic matrix using nature based intelligence methods. With the defined load and fixed capacity of links, average delay for packet has minimized with various variations of evolutionary programming and particle swarm optimization. Comparative study has given over their performance in terms of converging speed. Universal approximation capability, the key feature of feed forward neural network has applied to predict the flow ...

Science.gov (United States)

Autism is a pervasive developmental condition, characterized by impairments in non-verbal communication, social relationships and stereotypical patterns of behavior. A large body of evidence suggests that several aspects of face processing are impaired in autism, including anomalies in gaze processing, memory for facial identity and recognition of facial expressions of emotion. In search of neural markers of anomalous face processing in autism, much interest has focused on a network of brain regions that are implicated in social cognition and face processing. In this review, we will focus on three such regions, namely the STS for its role in processing gaze and facial movements, the FFA in face detection and identification and the amygdala in processing facial expressions of emotion. Much evidence suggests that a better understanding of the normal development of these specialized regions is essential for discovering the ...

CERN Document Server

Quantum man

Science.gov (United States)

... A control design methodology enabling the adaptive neural augmentation. ... As an example, the problem of designing a neural augmentation system. ...

Energy Technology Data Exchange (ETDEWEB)

This study discusses and compares, from a practical point of view, three different approaches for permeability determination from logs. These are empirical, statistical, and the recently introduced virtual measurement methods. They respectively make use of empirically determined models, multiple variable regression, and artificial neural networks. All three methods are applied to well log data from a heterogeneous formation and the results are compared with core permeability, which is considered to be the standard. In this first part of the paper we present only the model development phase in which we are testing the capability of each method to match the presented data. Based on this, the best two methods are to be analyzed in terms of prediction performance in the second part of this paper.

International Nuclear Information System (INIS)

This paper is concerned with the real time automatic discriminating of flaws from two categories; i. cracks (planar defect) and ii. Non-cracks (volumetric defect such as cluster porosity and slag) using pulse-echo ultrasound. The raw ultrasonic flaws signal were collected from a computerized robotic plane scanning system over the whole of each reflector as the primary source of data. The signal is then filtered and the analysis in both time and frequency domain were executed to obtain the selected feature. The real time feature analysis techniques measured the number of peaks, maximum index, pulse duration, rise time and fall time. The obtained features could be used to distinguish between quantitatively classified flaws by using various tools in artificial intelligence such as neural networks. The proposed algorithm and complete system were implemented in a computer software developed using Microsoft Visual BASIC 6.0 (author)

Energy Technology Data Exchange (ETDEWEB)

The paper presents an alternative approach for the studies of high voltage transmission lines based on artificial intelligence and more specifically artificial neural networks (ANNs). In contrast to the existing conventional-analytical techniques and simulations which are using in the calculations empirical and/or approximating equations, this approach is based only on actual field data and actual measurements. The proposed approach is applied on high voltage transmission lines in order to calculate the lightning outages, on grounding systems in order to assess the grounding resistance and on high voltage transmission lines' polluted insulators in order to estimate the critical flashover voltage. The obtained results are very close to the actual ones for all three case studies, something which clearly implies that the ANN approach is well working and has an acceptable accuracy, constituting an additional tool of electric engineers. ...

Energy Technology Data Exchange (ETDEWEB)

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence and stability of periodic solution for shunting inhibitory cellular neural networks (SICNNs) with delays x-bar {sub ij}(t)=-a{sub ij}(t)x{sub ij}(t)--bar B{sup kl}-bar Nr(i,j)B{sub ij}{sup kl}(t)f{sub ij}(x{sub kl}(t))x{sub ij}(t)--bar C{sup kl}-bar Nr(i,j)C{sub ij}{sup kl}(t)g{sub ij}(x{sub kl}(t-{tau}{sub kl}))x{sub ij}(t)+L{sub ij}(t)

British Library Electronic Table of Contents (United Kingdom)

Mechanomyography (MMG) is the muscle surface oscillations that are generated by the dimensional change of the contracting muscle fibers. Because MMG reflects the number of recruited motor units and their firing rates, just as electromyography (EMG) is influenced by these two factors, it can be used to estimate the force exerted by skeletal muscles. The aim of this study was to demonstrate the feasibility of MMG for estimating the elbow flexion force at the wrist under an isometric contraction by using an artificial neural network in comparison with EMG. We performed experiments with five subjects, and the force at the wrist and the MMG from the contributing muscles were recorded. It was found that MMG could be utilized to accurately estimate the isometric elbow flexion force based on the v...

Energy Technology Data Exchange (ETDEWEB)

This paper describes the weekly load forecasting expert system (named WLoFy) which was developed and implemented for korea electric power corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial neural networks, rule-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results from WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remarkably. (author). 9 refs., 5 figs., 6 tabs.

Energy Technology Data Exchange (ETDEWEB)

This two-tomes book brings together the 108 presentations given at the first conference of the international institute of refrigeration (IIF/IIR) about air conditioning in high rise office buildings. The main themes are: general design and control systems, including split systems, radiant panels, fluctuating and gravity ventilation etc..; energy consumption, optimization and heat recovery; cold storage for peak shaving, including ice slurry circulation; indoor air quality; fire and smokes protection, protection against chimney effects and lighting spots; use of fuzzy logic and of neural networks. It includes also a description of the high rise building situation and works in progress in China, Japan and in some other countries. (J.S.)

British Library Electronic Table of Contents (United Kingdom)

Action potentials from the brain control the activity of spinal neural networks to produce, by as yet unknown mechanisms, a variety of motor behaviors. Particularly lacking are details on how identified descending neurons integrate diverse sensory inputs to generate specific locomotor patterns. We have examined the operations of the principal neurons in an intriguing midbrain nucleus, the nucleus of the medial longitudinal fasciculus (nMLF), in the larval zebrafish. The nMLF is the most rostral grouping of neurons that projects from the brain well into the spinal cord of teleost fishes, yet there is little direct physiological data available regarding its function. We report here that a distinct set of large, individually-identifiable neurons in nMLF (the MeL and MeM neurons) are activated...

Science.gov (United States)

Not Available

UK PubMed Central (United Kingdom)

Quantum computing is a quickly growing research field. This article introduces the basic concepts of quantum computing, recent developments in quantum searching, and decoherence in a possible quantum...Full Text Available

Energy Technology Data Exchange (ETDEWEB)

Previous work shows the presence of scale invariance and long-range correlations in ongoing and spontaneous activity of large scale brain responses (i.e. EEG), and such scaling behavior can also be modulated by simple sensory stimulus. However, little is known whether such alteration but not destruction in scaling properties also occurs during complex cognitive processing and if neuroplasticity plays any role in mediating such changes. In this study, we addressed these issues by investigating scaling properties of multivariate EEG signals obtained from two broad groups - artists and non-artists - while they performed complex tasks of perception and mental imagery of visual art objects. We found that brain regions showing increased correlation properties from rest were similar for both tasks, suggesting that brain networks responsible for visual perception are reactivated for mental imagery. Further, we observed that the two groups could be differentiated by scaling ...

International Nuclear Information System (INIS)

Growing of PV for electricity generation is one of the highest in the field of the renewable energies and this tendency is expected to continue in the next years. Due to the various seasonal, hourly and daily changes in climate, it is relatively difficult to find a suitable analytic model for predicting the performance of a grid-connected photovoltaic (GCPV) plant. In this paper, an artificial neural network is used for modelling and predicting the power produced by a 20 kWp GCPV plant installed on the roof top of the municipality of Trieste (latitude 45 deg. 40'N, longitude 13 deg. 46'E), Italy. An experimental database of climate (irradiance and air temperature) and electrical (power delivered to the grid) data from January 29th to May 25th 2009 has been used. Two ANN models have been developed and implemented on experimental climate and electrical data. The first one is a multivariate model based on the solar irradiance and the air ...

UK PubMed Central (United Kingdom)

The temporal synchrony of auditory and visual signals is known to affect the perception of an external event, yet it is unclear what neural mechanisms underlie the influence of temporal synchrony...Full Text Available

KoreaScience (Korea)

Full Text Available

CERN Document Server

Local access network technologies

Science.gov (United States)

Not Available

International Nuclear Information System (INIS)

The stunning, intricate interaction between the visual, vestibular and optomotor systems--each a miracle on its own--ensures maintenance of orientation in space as well as visual recognition and target selection despite a host of sensory conflicts and adversary disturbances. Their main goals are to keep a target of interest on the fovea by either maintaining or shifting the direction of gaze in order to produce an accurate internal representation of the visual surroundings, in particular the selected target, and to continuously mirror the spatial relationship between these various visual elements and the self. Not surprising, the implementation of this host of elaborate neural networks encompasses almost every part of the brain, including the brainstem, cerebellum, extrapyramidal system and many areas of the cerebral cortex. Thus far, these systems are among the best investigated in brain research; and enormous knowledge was amassed over the ...

Energy Technology Data Exchange (ETDEWEB)

Short communication.

Energy Technology Data Exchange (ETDEWEB)

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear ...

Energy Technology Data Exchange (ETDEWEB)

The use of electricity is indispensable to modern life. As Macao Special Administrative Region becomes a gaming and tourism center in Asia, modeling the consumption of electricity is critical to Macao's economic development. The purposes of this paper are to conduct an extensive literature review on modeling of electricity consumption, and to identify key climatic, demographic, economic and/or industrial factors that may affect the electricity consumption of a country/city. It was identified that the five factors, namely temperature, population, the number of tourists, hotel room occupancy and days per month, could be used to characterize Macao's monthly electricity consumption. Three selected approaches including multiple regression, artificial neural network (ANN) and wavelet ANN were used to derive mathematical models of the electricity consumption. The accuracy of these models was assessed by using the mean squared error ...

Energy Technology Data Exchange (ETDEWEB)

Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial ...

Science.gov (United States)

Cancer is a severe threat to human health. Early detection is considered the best way to increase the chance for survival. While the traditional cancer detection method, biopsy, is invasive, noninvasive optical diagnostic techniques are revolutionizing the way that cancer is diagnosed. Reflectance spectroscopy is one of these optical spectroscopy techniques showing promise as a diagnostic tool for pre-cancer detection. When a neoplasia occurs in tissue, morphologic and biochemical changes happen in the tissue, which in turn results in the change of optical properties and reflectance spectroscopy. Therefore, a pre-cancer can be detected by extracting optical properties from reflectance spectroscopy. This dissertation described the construction of a fiberoptic based reflectance system and the development of a series of modeling studies. This research is aimed at establishing an improved understanding of the optical properties of mucosal tissues by analyzing reflectance signals at ...

Energy Technology Data Exchange (ETDEWEB)

The growing concern of the population with regards to the problem of atmospheric pollution has induced a change in the role of air quality monitoring networks, especially those monitoring air pollution in large cities which suffer from summer smog. The population is no longer satisfied with real-time measurements but wants to be warmed of high pollutants concentrations in advance. Some countries have been forecasting air pollution, and especially ozone, for a large number of years. Although most of them use statistical models based on the analyses of past conditions which induced high pollution episodes, some predict ozone levels using only their knowledge of the meteorological situation. Nowadays two trends appear regarding ozone forecasting: either very basic statistical methods, such as regression, or more sophisticated ones, such as neural networks. The paper then reviews several behaviours common to most forecasting ...

Science.gov (United States)

An intemted approach to building a networking infrastructure is an absolute necessiry for meeting the multidisciplinary science networking requirements of ...

Science.gov (United States)

Not Available

Science.gov (United States)

Not Available

UK PubMed Central (United Kingdom)

The economics of medical computer networks are presented in context with the patient care and administrative goals of medical networks. Design alternatives and network topologies are discussed with...Full Text Available

UK PubMed Central (United Kingdom)

Failures in cortical control of fronto-striatal neural circuits may underpin impulsive and compulsive acts. In this narrative review, we explore these behaviors from the perspective of neural processes...Full Text Available

UK PubMed Central (United Kingdom)

Congenital deformities involving the coverings of the nervous system are called neural tube defects (NTDs). NTD can be classified as neurulation defects, which occur by stage 12, and postneurulation...Full Text Available

UK PubMed Central (United Kingdom)

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes...Full Text Available

UK PubMed Central (United Kingdom)

The tendency for some individuals to partake in high-risk behaviors (eg, substance abuse, gambling, risky sexual activities) is a matter of great public health concern, yet the characteristics and neural...Full Text Available

Energy Technology Data Exchange (ETDEWEB)

No abstract prepared

International Nuclear Information System (INIS)

A theoretical scheme for quantum secure direct communication (QSDC) is proposed, where a three-qubit symmetric W state functions as a quantum channel. Two legitimate communicators can transmit their secret information by using quantum teleportation and local measurements.

Energy Technology Data Exchange (ETDEWEB)

No abstract prepared.

Science.gov (United States)

... in artificial intelligence, human physiology and biomedical prosthesis. ... central and peripheral nerve systems [1 ... CMOS circuit interface for multiplexed ...

CERN Document Server

We discuss models of computing that are beyond classical. The primary motivation is to unearth the cause of nonclassical advantages in computation. Completeness results from computational complexity theory lead to the identification of very disparate problems, and offer a kaleidoscopic view into the realm of quantum enhancements in computation. Emphasis is placed on the `power of one qubit' model, and the boundary between quantum and classical correlations as delineated by quantum discord. A recent result by Eastin on the role of this boundary in the efficient classical simulation of quantum computation is discussed. Perceived drawbacks in the interpretation of quantum discord as a relevant certificate of quantum enhancements are addressed.

Energy Technology Data Exchange (ETDEWEB)

Quantum computers hold the promise of solving certain computational tasks much more efficiently than classical computers. We review recent experimental advances towards a quantum computer with trapped ions. In particular, various implementations of qubits, quantum gates and some key experiments are discussed. Furthermore, we review some implementations of quantum algorithms such as a deterministic teleportation of quantum information and an error correction scheme.

CERN Document Server

Networked Digital Library of Theses and Dissertations

British Library Electronic Table of Contents (United Kingdom)

Summary The effects of high-intensity pulsed electromagnetic stimulation (HIPEMS) on proliferation and differentiation of neonatal rat neural stem cells in vitro were investigated. Neural stem cells derived from neonatal rats were exposed to 0.1 Hz, 0.5-10 Tesla (T) [8 groups of B-I, respectively], 5 stimuli of HIPEMF. The sham exposure controls were correspondingly established. Inverted phase contrast microscope was used to observe the cultured cells, MTT assay to detect the viability of the cells as expressed by absorbance (A) value, and flow cytometry to measure differentiation of neural stem cells. The results showed that A values of neural stem cells in both 3.0 T and 4.0 T groups were significantly higher than the other groups 24 to 168 h post HPEMS, indicating a strong promotion of ...

British Library Electronic Table of Contents (United Kingdom)

This paper presents a supernetwork equilibrium model integrating supply chain networks with a transport network, namely, a supply chain-transport supernetwork equilibrium model. The model takes into account the behaviour of freight carriers and transport network users to endogenously determine the transport costs generated in the supply chain networks. The interaction between transport network and supply chain networks can also be examined. Results of the numerical tests reveal that the improvement of transport network could enhance the efficiency of supply chain networks. The paper makes contributions to modelling of supply chain networks as well as to that of transport networks.

CERN Document Server

When quantum gravity is used to discuss the big bang singularity, the most important, though rarely addressed, question is what role genuine quantum degrees of freedom play. Here, complete effective equations are derived for isotropic models with an interacting scalar to all orders in the expansions involved. The resulting coupling terms show that quantum fluctuations do not affect the bounce much. Quantum correlations, however, do have an important role and could even eliminate the bounce. How quantum gravity regularizes the big bang depends crucially on properties of the quantum state.

Energy Technology Data Exchange (ETDEWEB)

Two geochemical surveys were conducted in 1992 and 2000 respectively in the Yimeng Uplift of the Ordos Basin, China. The earlier survey grid had 1 x 5km spacing and the later survey grid had 0.5 x 0.5km spacing. The acid-extractable hydrocarbons of both surveys show similar geochemical trends. However, the anomalies obtained with traditional statistical methods do not correlate with existing oil/gas fields. This study reveals two problems in the data and their processing. The first one is interference caused by the variation of soil composition. We applied a wavelet-analysis-based method to eliminate this interference in the data of the later survey. The second is that micro-seepage anomalies did not identify existing oil/gas fields and seepage anomalies related with faults had not been previously recognized. We modified the logic multiplication cluster analysis and applied a multi-fractal model and a back propagation artificial neural network ...

Energy Technology Data Exchange (ETDEWEB)

Accurate lung tumor tracking in real time is a keystone to image-guided radiotherapy of lung cancers. Existing lung tumor tracking approaches can be roughly grouped into three categories: (1) deriving tumor position from external surrogates; (2) tracking implanted fiducial markers fluoroscopically or electromagnetically; (3) fluoroscopically tracking lung tumor without implanted fiducial markers. The first approach suffers from insufficient accuracy, while the second may not be widely accepted due to the risk of pneumothorax. Previous studies in fluoroscopic markerless tracking are mainly based on template matching methods, which may fail when the tumor boundary is unclear in fluoroscopic images. In this paper we propose a novel markerless tumor tracking algorithm, which employs the correlation between the tumor position and surrogate anatomic features in the image. The positions of the surrogate features are not directly tracked; instead, we use principal component analysis of regions ...

International Nuclear Information System (INIS)

Apart from conventional phase transitions driven by the thermal effects, quantum phase transitions generated by quantum fluctuations have their own mechanisms that are reflected in critical phenomena. Quantum phase transitions have an origin from spontaneous symmetry breaking commonly to thermal phase transitions. Even in this case, inherent quantum fluctuations substantially modify and yield new aspects. Quantum phase transitions have, however, another mechanism caused by topology changes, which gives completely new characters. Recently, a mechanism which connects these two has been found. Proimities from first-order transitions and phase separatins as well as from multiphase coexistence also generate characteristic and unconventional quantum criticalities. Understanding novel quantum criticalities offers a firm basis of recent active ...

Energy Technology Data Exchange (ETDEWEB)

Over the past two decades, quantum computing has become a popular and promising approach to trying to solve computationally difficult problems. Missing in many descriptions of quantum computing is just how probability enters into the process. Here, we discuss some simple examples of how uncertainty and probability enter, and how this and the ideas of quantum computing challenge our interpretations of quantum mechanics. It is found that this uncertainty can lead to intrinsic decoherence, and this raises challenges for error correction. (viewpoint)

International Nuclear Information System (INIS)

This paper discusses the concept of controllable subspace for open quantum dynamical systems. It is constructively demonstrated that combining structural features of decoherence-free subspaces with the ability to perform open-loop coherent control on open quantum systems will allow decoherence-free subspaces to be controllable. This is in contrast to the observation that open quantum dynamical systems are not open-loop controllable. To a certain extent, this paper gives an alternative control theoretical interpretation on why decoherence-free subspaces can be useful for quantum computation.

Science.gov (United States)

... MTR 93B0000095 Proposed Network Security Policy for Integrated Tactical Warning ... Proposed Network security Policy for Integrated Tactical ...

Science.gov (United States)

... AFIT/CI "OVERPRINT" Page 2. NETWORK SECURITY ISSUES by ... Page 4. Sinisi, Michael Jude (MS, Telecommunications) Network Security Issues ...

Science.gov (United States)

Page 1. Dynamic Virtual LANs for Adaptive Network Security ... Dynamic Virtual LANs for adaptive network security D. Merani, A. Berni, M. Leonard ...

Energy Technology Data Exchange (ETDEWEB)

This contribution is intended to introduce the principles of quantum computing to those who always wanted to know about quantum computing but never dared to ask. (copyright 2007 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

British Library Electronic Table of Contents (United Kingdom)

Transforming growth factor beta (TGF-@b) has a crucial role in the differentiation of ectodermal cells to neural or epidermal precursors. TGF-@b and bone morphogenetic protein molecules (BMPs) are involved in many developmental processes, including cell proliferation and differentiation, apoptosis, mitotic arrest and intercellular interactions during morphogenesis. Additionally, the failure of central thymic tolerance mechanisms, leading to T cells with a skewed autoreactive response, is being described as a contributor in inflammatory processes in autoimmune diseases such as multiple sclerosis. Since TGF-@b and BMP proteins are crucial for the development of the neural system and the thymus, as well as for the differentiation of T cells, it is essential to further investigate their role i...

International Nuclear Information System (INIS)

Werner states are paradigmatic examples of quantum states and play an innovative role in quantum information theory. In investigating the correlating capability of Werner states, we find the curious phenomenon that quantum correlations, as quantified by the entanglement of formation, may exceed the total correlations, as measured by the quantum mutual information. Consequently, though the entanglement of formation is so widely used in quantifying entanglement, it cannot be interpreted as a consistent measure of quantum correlations per se if we accept the folklore that total correlations are measured (or rather upper bounded) by the quantum mutual information.

International Science & Technology Center (ISTC)

Morphological, Electrophysiological and Behavioral Investigations of the Nervous Tissue Developed from the Embryonic Matrix Zone Cells of the Dorsolateral Walls of Lateral Ventricles, Implanted into the Lesioned Regions of the Adult Rat's Brain

UK PubMed Central (United Kingdom)

The potential of radiolabelled phenylpiperazines as agents for the detection and therapy of tumours of neural crest origin was evaluated by in vitro pharmacological studies with human neuroblastoma...Full Text Available

Science.gov (United States)

No abstract prepared.

Energy Technology Data Exchange (ETDEWEB)

Short communication.

UK PubMed Central (United Kingdom)

We study the statistical properties of a variety of diverse real-world networks. We present evidence of the occurrence of three classes of small-world networks: (a) scale-free networks,...Full Text Available

Science.gov (United States)

The results of this research centered on the experimental studies of a single superconducting persistent current qubit, the implementation of type-II algorithms using these qubits, and the proposal for adiabatic quantum computing using these qubits. The m...

Science.gov (United States)

For coupled quantum wires and dots, tunneling effects and coherent transport for quantum computing are being studied. In 2D systems, electron-hole bilayers for exciton...

Science.gov (United States)

... In this paper we review the recent results concerning physical aspects of QWlP and QDIP operation focusing primarily on the electron transport ...

Energy Technology Data Exchange (ETDEWEB)

There is considerable interest in the use of silicon devices as qubits for quantum computing. The existence of nuclear spin in a silicon isotope and the complex band structure of silicon are unfavourable for this application of silicon devices. (viewpoint)

CERN Document Server

A process has been proposed to increase the efficiency of an ideal Otto cycle via a quantum heat engine that has no cooler reservoir. We show that such a process is not feasible.

Science.gov (United States)

Feb 13, 2005 ... Part 8 of a non-mathematical historical review of elementary quantum theory, to help explain processes in the Sun and in stars; part of an ...

Science.gov (United States)

... analyze importance sampling schemes for stochastic networks ... an interesting mixed open/closed queueing network ... with a suitable terminal condition ...

Science.gov (United States)

... Accession Number : ADA060007. Title : Issues in Computer Network Security. Descriptive Note : Technical rept.,. Corporate ...

Science.gov (United States)

Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks

Science.gov (United States)

Page 1. Adaptive system and method for responding to computer network security attacks Abstract A dynamic network security ...

CERN Document Server

We define the Bloch spectrum of a quantum graph to be the collection of the spectra of a family of Schr\\"odinger operators parametrized by the cohomology of the quantum graph. We show that the Bloch spectrum determines the Albanese torus, the block structure and the planarity of the graph. It determines a geometric dual of a planar graph. This enables us to show that the Bloch spectrum completely determines planar 3-connected quantum graphs.

International Nuclear Information System (INIS)

We discuss the use of active control to reduce mirror position fluctuations at the quantum level. We have shown in a recent experiment that it is possible to reduce the thermal noise of a mirror by measuring and controlling its motion with an optomechanical sensor based on a high-finesse optical cavity. This approach can be extended to lock the mirror motion at the quantum level, and to suppress the quantum effects of radiation pressure in interferometric measurements such as gravitational-wave detectors. The sensitivity improvement is furthermore independent of losses in the interferometer.

Science.gov (United States)

DC Field, Value, Language.

International Nuclear Information System (INIS)

... Abstract only 1063-7869 v. 44(10) CLASSICAL AND QUANTUM MECHANICS,

British Library Electronic Table of Contents (United Kingdom)

[image omitted

CERN Document Server

Here we show that self-propulsion in quantum vacuum may be achieved by rotating or aggregating magneto-electric nano-particles. The back-action follows from changes in momentum of electro-magnetic zero-point fluctuations, generated in magneto-electric materials. This effect may provide new tools for investigation of the quantum nature of our world. It might also serve in the future as a "quantum wheel" to correct satellite orientation in space.

Energy Technology Data Exchange (ETDEWEB)

Recently it was demonstrated that long-lived quantum coherence exists during excitation energy transport in photosynthesis. It is a valid question up to which length, time and mass scales quantum coherence may extend, how one may detect this coherence and what, if any, role it plays in the dynamics of the system. Here we suggest that the selectivity filter of ion channels may exhibit quantum coherence, which might be relevant for the process of ion selectivity and conduction. We show that quantum resonances could provide an alternative approach to ultrafast two-dimensional (2D) spectroscopy to probe these quantum coherences. We demonstrate that the emergence of resonances in the conduction of ion channels that are modulated periodically by time-dependent external electric fields can serve as signatures of quantum coherence in such a system. Assessments of ...

Energy Technology Data Exchange (ETDEWEB)

A typical incident response pits technicians against networks that aren't prepared forensically. [1, 2] If practitioners do consider collecting network forensic data, they face a choice between expending extraordinary effort (time and money) collecting forensically sound data, or simply restoring the network as quickly as possible. In this context, the concept of organizational network forensic readiness has emerged. This paper proposes a methodology for "operationalizing" organizational network forensic readiness. The methodology, and the theoretical analysis that led to its development, are offered as a conceptual framework for thinking about more efficient, proactive approaches to digital forensics on networks.

CERN Document Server

We study quantum Darwinism -- the redundant recording of information about a decohering system by its environment -- in zero-temperature quantum Brownian motion. An initially nonlocal quantum state leaves a record whose redundancy increases rapidly with its spatial extent. Significant delocalization (e.g., a Schroedinger's Cat state) causes high redundancy: many observers can measure the system's position without perturbing it. This explains the objective (i.e. classical) existence of einselected, decoherence-resistant pointer states of macroscopic objects.

CERN Document Server

Big Bang nucleosynthesis requires a fine balance between equations of state for photons and relativistic fermions. Several corrections to equation of state parameters arise from classical and quantum physics, which are derived here from a canonical perspective. In particular, loop quantum gravity allows one to compute quantum gravity corrections for Maxwell and Dirac fields. Although the classical actions are very different, quantum corrections to the equation of state are remarkably similar. To lowest order, these corrections take the form of an overall expansion-dependent multiplicative factor in the total density. We use these results, along with the predictions of Big Bang nucleosynthesis, to place bounds on these corrections.

Science.gov (United States)

This is the homepage of "an Australian multi-university collaboration undertaking research on the fundamental physics and technology of building, at the atomic level, a solid state quantum computer in silicon together with other high potential implementations." Although attempts to develop a quantum computer have met with limited success, the centre has substantial resources invested in advancing toward practical uses of quantum computing technology. The site provides a very good introduction to the principles and implications of quantum computing, as well as details about various research projects underway at the Australian universities. Links to conference and journal papers produced by members of the centre, many from 2003, are also provided.

CERN Document Server

Integrated femtocell/macrocell networks, comprising a conventional cellular network overlaid with femtocells, offer an economically appealing way to improve coverage, quality of service, and access network capacity. The key element to successful femtocells/macrocell integration lies in its self-organizing capability. Provisioning of quality of service is the main technical challenge of the femtocell/macrocell integrated networks, while the main administrative challenge is the choice of the proper evolutionary path from the existing macrocellular networks to the integrated network. In this article, we introduce three integrated network architectures which, while increasing the access capacity, they also reduce the deployment and operational costs. Then, we discuss a number of technical issues, which are key to making such integration a ...

Science.gov (United States)

This manual is intended to provide a free resource on essential network security concepts for non-technical managers of small libraries. Managers of other small nonprofit or community organizations will also benefit from it. An introduction defines network security; outlines three goals of network security; discusses why a library should be concerned with network security; and describes limits of this work. The manual is divided into three main parts. Part One features the management issues related to network security: analyzing risk, developing a security plan and policy, the funding requirements libraries can expect in operating their networks, and implementing adequate security. Part Two describes the areas of computer networks that need to be secured, and provides a description of many of the security measures necessary for adequate ...

British Library Electronic Table of Contents (United Kingdom)

The precedence effect (PE) is thought to be beneficial for proper localization and perception of sounds. The majority of recent physiological studies focus on the neural discharges correlated with PE in the inferior colliculus (IC). Pentobarbital anesthesia is widely used in physiological studies. However, little is known of the effect of pentobarbital on the discharge of neurons in PE. Neuronal responses in the IC from 23 male SD rats were recorded by standard extracellular recording techniques following presentation of 4ms white noise bursts, presented from either or both of two loud speakers, at different interstimulus delays (ISDs). The neural responses were recorded for off-line analysis before or after intraperitoneal administration of pentobarbital at a loading or maintenance dose. ...

UK PubMed Central (United Kingdom)

This paper describes how a computerized MIS is used to assist HMO management to conduct utilization and quality of care review activities in a prepaid medical care network. The HMO is a ‘network’...Full Text Available

Science.gov (United States)

Materials World Network: Cooperative Activity in Materials Research between US Investigators and ... Program Title: Materials World Network: Cooperative Activity in Materials Research between US ...

Science.gov (United States)

Cyber attacks on a hospital's computer network is a new crime to be reckoned with. Should your hospital consider internet insurance? The author explains this new phenomenon and presents a risk assessment for determining network vulnerabilities. PMID:11951384

CERN Document Server

The Quantum Mechanics Conceptual Survey (QMCS) is a 12-question survey of students' conceptual understanding of quantum mechanics. It is intended to be used to measure the relative effectiveness of different instructional methods in modern physics courses. In this paper we describe the design and validation of the survey, a process that included observations of students, a review of previous literature and textbooks and syllabi, faculty and student interviews, and statistical analysis. We also discuss issues in the development of specific questions, which may be useful both for instructors who wish to use the QMCS in their classes and for researchers who wish to conduct further research of student understanding of quantum mechanics. The QMCS has been most thoroughly tested in, and is most appropriate for assessment of (as a posttest only), sophomore-level modern physics courses. We also describe testing with students in ...

CERN Document Server

A new mathematical framework is formulated to derive the effective equations of motion for the constrained quantum system which possesses an internal clock. In the realm close to classical behavior, the quantum evolution is approximated by a finite system of coupled but ordinary differential equations adhered to the weakly imposed Hamiltonian constraint. For the simplified version of loop quantum cosmology in the Bianchi I model with a free massless scalar filed, the resulting effective equations of motion affirm the bouncing scenario predicted by the previous studies: The big bang singularity is resolved and replaced by the big bounces, which take place up to three times, once in each diagonal direction, whenever the directional density approaches the critical value in the regime of Planckian density. It is also revealed that back-reaction arises from the quantum corrections and modifies the precise ...

CERN Document Server

The study of quantum walk process has been widely divided into the two standard variants, the discrete-time quantum walk (DTQW) and the continuous-time quantum walk (CTQW). The connection between the two variants has been established by considering limiting value of the coin operation parameter in the DTQW and the coin degree of freedom is show to be unnecessary [26]. But the coin degree of freedom is an additional resource which can be exploited to control the dynamics of the QW process. In this paper we present a generic quantum walk (QW) model using a quantum coin-embedded unitary shift operation U_{C}. The standard version of the DTQW and the CTQW can be conveniently retrieved from this generic model retaining the features of the coin degree of freedom in both the variants.

Energy Technology Data Exchange (ETDEWEB)

The role of computers has become increasingly important for oil and gas field research and operations support. Today, the computer network is an integral part of the increasingly complex computing environment that exists in many companies. Computer networks allow users to efficiently share information, software, and hardware to support critical global communication needs. Because users are able to share software and hardware, the use of computer networks can also result in significant cost savings. This paper describes typical network loading and demand levels for shared software applications on a computer network that has been used for several years.

Science.gov (United States)

'As part of the Computer Protection Plan, this Network Security Plan identifies the specific security measures used to protect Bechtel Hanford, Inc.'s (BHI's) enterprise network. The network consists of the communication infrastructure and information systems used by BHI to perform work related to the Environmental Restoration Contract (ERC) at the Hanford Site. It provides electronic communication between the ERC-leased facilities in Richland, Washington, and facilities located on the Hanford Site. Network gateways to other site and offsite networks provide electronic communication with the remainder of the Hanford community.'

Science.gov (United States)

5.2 Network Security. The features listed below can be used to help safeguard UNIX ...... The LaRCSCAN non-intrusive network security scanner package, ...

CERN Document Server

In this report, I surveyed the cognitive radio technique in wireless networks. Researched several kinds of cognitive techniques about their advantages and disadvantages.

Science.gov (United States)

NETWORK SECURITY, is revised to reflect, as item "g", incorporation ..... The Contractor shall provide support for the network security of the ...

Science.gov (United States)

... Accession Number : ADA298752. Title : Placement of Network Security Services for Secure Data Exchange. Descriptive Note : Final rept.,. ...

British Library Electronic Table of Contents (United Kingdom)

The tremendous growth of wireless technologies has introduced the potential of continuous service adaptation to the users' needs by giving them the ability to be able to select and access the proper network based on different criteria. Moreover, next generation wireless networks have been designed to provide support for multimedia services, with different traffic characteristics and different Quality of Service (QoS) guarantees. However, the expansion of Wireless Local Area Networks (WLANs) and Worldwide Interoperability for Microwave Access (WiMAX) networks poses new research era in the decision of the access network selection. In this paper, the existing access network selection schemes are classified into three categories: the network-centric, the user-centric and the collaborative sche...

Science.gov (United States)

... DTIC SELECTE z DEC 0 7, 1994 F THESIS NETWORK SECURITY AND THE NPS INTERNET FIREWALL by Jody L. Schivley September 1994 ...

Wastenet

...Air Quality Monitoring Networks Products and Equipment Find and compare a variety of air quality monitoring networks products and equipment on the world's largest environmental industry portal. ...

Science.gov (United States)

Jan 22, 2011 ... Title: Model checking for network security requirements via a flexible modeling framework. Authors: Powell, J. Gilliam, D. ...

Science.gov (United States)

... California where he is currently working on an AS in. Data Communication & Network Management as well as Microsoft. MCSE and Cisco CCNA certifications.

Science.gov (United States)

A Composite Architecture for Network Security at JPL. Robert B. Mead, Tom G. Dearmond, and Joseph S. Sherif. JPL, California Institute of Technology ...

UK PubMed Central (United Kingdom)

During development, multipotent neural precursors give rise to oligodendrocyte progenitor cells (OPCs), which migrate and divide to produce additional OPCs. Near the end of embryogenesis and...Full Text Available

UK PubMed Central (United Kingdom)

Hypercapnia is often used as vasodilatory challenge in clinical applications and basic research. In functional magnetic resonance imaging (fMRI), elevated CO₂ is applied to derive stimulus-induced...Full Text Available

UK PubMed Central (United Kingdom)

In utero electroporation is widely used to study neuronal development and function by introducing plasmid DNA into neural progenitors during embryogenesis. This is an effective and...Full Text Available

UK PubMed Central (United Kingdom)

Percutaneous radiofrequency ablation is the treatment of choice for osteoid osteoma of the appendicular skeleton. However, difficulties in localizing the lesion in the spine and its proximity to neural...Full Text Available

UK PubMed Central (United Kingdom)

1. Previous studies have shown that electrical stimulation (ES) of the guinea-pig cochlea causes a neurally mediated increase in cochlear blood flow (CBF). It is known that the centrifugal neuronal...Full Text Available

UK PubMed Central (United Kingdom)

The kinetic parameters of single bonds between neural cell adhesion molecules were determined from atomic force microscope measurements of the forced dissociation of the homophilic protein-protein bonds....Full Text Available

UK PubMed Central (United Kingdom)

An influential neural model of face perception suggests that the posterior superior temporal sulcus (STS) is sensitive to those aspects of faces that produce transient visual changes, including facial...Full Text Available

UK PubMed Central (United Kingdom)

The destiny of the mitotically active cells of the subventricular zone (SVZ) in adult rodents is to migrate to the olfactory bulb, where they contribute to the replacement of granular and periglomerular...Full Text Available

UK PubMed Central (United Kingdom)

During asymmetric mitosis, both in male Drosophila germline stem cells and in mouse embryo neural progenitors, the mother centrosome is retained by the self-renewed cell; hence suggesting...Full Text Available

UK PubMed Central (United Kingdom)

A century ago, Cajal noted striking similarities between the neural circuits that underlie vision in vertebrates and flies. Over the past few decades, structural and functional studies have...Full Text Available

UK PubMed Central (United Kingdom)

Stem cell therapies for neurodegenerative disorders require accurate delivery of the transplanted cells to the sites of damage. Numerous studies have established that fluid injections to the hippocampus...Full Text Available

UK PubMed Central (United Kingdom)

The neuromodulatory function of dopamine (DA) is an inherent feature of nervous systems of all animals. To learn more about the function of neural DA in Drosophila, we generated mutant...Full Text Available

UK PubMed Central (United Kingdom)

Even in healthy individuals, aging leads to deterioration in visual acuity, contrast sensitivity, visual field, and dark adaptation. Little is known about the neural mechanisms that drive the...Full Text Available

International Nuclear Information System (INIS)

Using a new approach to quaternion mechanics based on De Broglie waves, it is shown that such a theory describes tachyons and that the quantum theory of tachyons should be a quaternionic one. (U.K.).

International Nuclear Information System (INIS)

We obtain a symmetry algebra for any unitary minimal model by using the representation of conformal field theories. This symmetry algebra can be interpreted as a quantum group. The generalization to non-unitary minimal models is direct. (orig.).

Energy Technology Data Exchange (ETDEWEB)

A new model for computations is considered which combines the quantum computer with the chaotic dynamics amplifier, based on the logistic map. We discuss the satisfiability problem and argue that the problem can, in principle, be solved in polynomial time if one uses the new model for computations.

Energy Technology Data Exchange (ETDEWEB)

In this paper method of constructing quasi-exactly solvable models of quantum mechanics is proposed. This method is based on the use of infinite-dimensional representations of simple and semi-simple Lie algebras.

UK PubMed Central (United Kingdom)

A quantum computer (QC) can operate in parallel on all its possible inputs at once, but the amount of information that can be extracted from the result is limited by the phenomenon of wave function...Full Text Available

International Nuclear Information System (INIS)

Considered is a new type of generalized asymptotic functions, which are not functionals on some space of test functions as the Schwartz distributions. The definition of the generalized asymptotic functions is given. It is pointed out that in future the particular asymptotic functions will be used for solving some topics of quantum mechanics and quantum theory.