Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

Multiple conduction paths in boron δ-doped diamond structures

Impedance spectroscopy has been used to investigate conductivity within boron-doped diamond in an intrinsic/delta-doped/intrinsic (i-d-i) multilayer structure. For a 5 nm thick delta layer, three conduction pathways are observed, which can be assigned to transport within the delta layer and to two differing conduction paths in the i-layers adjoining the delta layer. For transport in the i-layers, thermal trapping/detrapping processes can be observed, and only at the highest temperature investigated (673 K) can transport due to a single conduction process be seen. Impedance spectroscopy is an ideal nondestructive tool for investigating the electrical characteristics of complex diamond structures.

Size-dependent radiation tolerance in ion irradiated TiN/AlN nanolayer films

Interface effects on ion-irradiation tolerance properties are investigated in nanolayered TiN/AlN films with individual layer thickness varied from 5 nm to 50 nm, prepared by pulsed laser deposition. Evolution of the microstructure and hardness of the multilayer films are examined on the specimens before and after He ion-implantation to a fluence of 4 × 10 m at 50 keV. The suppression of amorphization in AlN layers and the reduction of radiation-induced softening are observed in all nanolayer films. A clear size-dependent radiation tolerance characteristic is observed in the nanolayer films, i.e., the samples with the optimum layer thickness from 10 nm to 20 nm show the best ion irradiation tolerance properties, and a critical layer thickness of more than 5 nm is necessary to prevent severe intermixing. This study suggests that both the interface characteristics and the critical length scale (layer thickness) contribute to the reduction of the radiation-induced damages in nitride-based ceramic materials. © 2013 Elsevier B.V. All rights reserved.

Nanostructured Cu/Nb multilayers subjected to helium ion-irradiation

Helium ion-irradiation experiments have been performed in single layer Cu films, Nb films and Cu/Nb multilayer films with layer thickness varying from 2.5 nm to 100 nm each layer. Peak helium concentration approaches a few atomic percent with 6-9 displacement-per-atom in Cu and Nb. He bubbles were observed in single layer Cu and Nb films, as well as in Cu 100 nm/Nb 100 nm multilayers with helium bubbles aligned along layer interfaces. Helium bubbles are not resolved via transmission electron microscopy in Cu 2.5 nm/Nb 2.5 nm multilayers. These studies indicate that layer interface may play an important role in annihilating ion-irradiation induced defects such as vacancies and interstitials and have implications in improving the radiation tolerance of metallic materials using nanostructured multilayers. © 2007 Elsevier B.V. All rights reserved.

Strengthening mechanisms in nanostructured copper/304 stainless steel multilayers

Nanostructured Cu/304 stainless steel (SS) multilayers were prepared by magnetron sputtering. 304SS has a face-centered-cubic (fcc) structure in bulk. However, in the Cu/304SS multilayers, the 304SS layers exhibit the fcc structure for layer thickness of =5 nm in epitaxy with the neighboring fcc Cu. For 304SS layer thickness larger than 5 nm, body-centered-cubic (bcc) 304SS grains grow on top of the initial 5 nm fcc SS with the Kurdjumov-Sachs orientation relationship between bcc and fcc SS grains. The maximum hardness of Cu/304SS multilayers is about 5.5 GPa (factor of two enhancement compared to rule-of-mixtures hardness) at a layer thickness of 5 nm. Below 5 nm, hardness decreases with decreasing layer thickness. The peak hardness of fcc/fcc Cu/304SS multilayer is greater than that of Cu/Ni, even though the lattice-parameter mismatch between Cu and Ni is five times greater than that between Cu and 304SS. This result may primarily be attributed to the higher interface barrier stress for single-dislocation transmission across the {111} twinned interfaces in Cu/304SS as compared to the {100} interfaces in Cu/Ni.

Microwave properties of an inhomogeneous optically illuminated plasma in a microstrip gap

The optical illumination of a microstrip gap on a thick semiconductor substrate creates an inhomogeneous electron-hole plasma in the gap region. This allows the study of the propagation mechanism through the plasma region. This paper uses a multilayer plasma model to explain the origin of high losses in such structures. Measured results are shown up to 50 GHz and show good agreement with the simulated multilayer model. The model also allows the estimation of certain key parameters of the plasma, such as carrier density and diffusion length, which are difficult to measure by direct means. The detailed model validation performed here will enable the design of more complex microwave structures based on this architecture. While this paper focuses on monocrystalline silicon as the substrate, the model is easily adaptable to other semiconductor materials such as GaAs.

Subject identification via ECG fiducial-based systems:influence of the type of QT interval correction

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.

Fourier Neural Networks: An Approach with Sinusoidal Activation Functions

* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

MPGN – An Approach for Discovering Class Association Rules

his article presents some of the results of the Ph.D. thesis Class Association Rule Mining Using MultiDimensional Numbered Information Spaces by Iliya Mitov (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt University, Faculty of Science on 15 November 2011 in Belgium

Automated Assessment Through Integration of Heterogeneous Systems with a Workflow Engine

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

Coke formation in the oxidative dehydrogenation of ethylbenzene to styrene by TEOM

A packed bed microbalance reactor setup (TEOM-GC) is used to investigate the formation of coke as a function of time-on-stream on γ-Al2O3 and 3P/SiO2 catalyst samples under different conditions for the ODH reaction of ethylbenzene to styrene. All samples show a linear correlation of the styrene selectivity and yield with the initial coverage of coke. The COX production increases with the coverage of coke. On the 3 wt% P/SiO2 sample, the initial coke build-up is slow and the coke deposition rate increases with time. On alumina-based catalyst samples, a fast initial coke build-up takes place, decreasing with time-on-stream, but the amount of coke does not stabilize. A higher O2 : EB feed ratio results in more coke, and a higher temperature results in less coke. This coking behaviour of Al2O3 can be described by existing "monolayer-multilayer" models. Further, the coverage of coke on the catalyst varies with the position in the bed. For maximal styrene selectivity, the optimal coverage of coke should be sufficient to convert all O2, but as low as possible to prevent selectivity loss by COX production. This is in favour of high temperature and low O2 : EB feed ratios. The optimal coke coverage depends in a complex way on all the parameters: temperature, the O2 : EB feed ratio, reactant concentrations, and the type of starting material. This journal is

Next generation high density three-dimensional magnetic recording systems

This work is the first work using patterned soft underlayers in multilevel three-dimensional vertical magnetic data storage systems. The motivation stems from an exponentially growing information stockpile, and a corresponding need for more efficient storage devices with higher density. The world information stockpile currently exceeds 150EB (ExaByte=1x1018Bytes); most of which is in analog form. Among the storage technologies (semiconductor, optical and magnetic), magnetic hard disk drives are posed to occupy a big role in personal, network as well as corporate storage. However; this mode suffers from a limit known as the Superparamagnetic limit; which limits achievable areal density due to fundamental quantum mechanical stability requirements. There are many viable techniques considered to defer superparamagnetism into the 100's of Gbit/in2 such as: patterned media, Heat-Assisted Magnetic Recording (HAMR), Self Organized Magnetic Arrays (SOMA), antiferromagnetically coupled structures (AFC), and perpendicular magnetic recording. Nonetheless, these techniques utilize a single magnetic layer; and can thusly be viewed as two-dimensional in nature. In this work a novel three-dimensional vertical magnetic recording approach is proposed. This approach utilizes the entire thickness of a magnetic multilayer structure to store information; with potential areal density well into the Tbit/in2 regime. ^ There are several possible implementations for 3D magnetic recording; each presenting its own set of requirements, merits and challenges. The issues and considerations pertaining to the development of such systems will be examined, and analyzed using empirical and numerical analysis techniques. Two novel key approaches are proposed and developed: (1) Patterned soft underlayer (SUL) which allows for enhanced recording of thicker media, (2) A combinatorial approach for 3D media development that facilitates concurrent investigation of various film parameters on a predefined performance metric. A case study is presented using combinatorial overcoats of Tantalum and Zirconium Oxides for corrosion protection in magnetic media. ^ Feasibility of 3D recording is demonstrated, and an emphasis on 3D media development is emphasized as a key prerequisite. Patterned SUL shows significant enhancement over conventional "un-patterned" SUL, and shows that geometry can be used as a design tool to achieve favorable field distribution where magnetic storage and magnetic phenomena are involved. ^

Focused ion beam manufacturing of magnetic nanostructures

A number of patterning methods including conventional photo-lithography and E-beam lithography have been employed to pattern devices with critical dimensions of submicrometer levels. The methods of device fabrication by lithography and multilevel processing are usually specific to the chemical and physical properties of the etchants and materials used, and require a number of processing steps. As an alternative, focused ion beam (FIB) lithography is a unique and straightforward tool to rapidly develop nanomagnetic prototyping devices. This feature of FIB is critical to conduct the basic study necessary to advance the state-of-the-art in magnetic recording. ^ The dissertation develops a specific design of nanodevices and demonstrates FIB-fabricated stable and reproducible magnetic nanostructures with a critical dimension of about 10 nm. The project included the fabrication of a patterned single and multilayer magnetic media with areal densities beyond 10 Terabit/in 2. Each block had perpendicular or longitudinal magnetic anisotropy and a single domain structure. The purpose was to demonstrate how the ability of FIB to directly etch nanoscale patterns allowed exploring (even in the academic environment) the true physics of various types of nanostructures. ^ Another goal of this study was the investigation of FIB patterned magnetic media with a set of characterization tools: e.g. Spinstand Guzik V2002, magnetic force microscopy, scanning electron microscopy with energy dispersive system and wavelength dispersive system. ^ In the course of this work, a unique prototype of a record high density patterned magnetic media device capable of 10 terabit/in 2 was built. The read/write testing was performed by a Guzik spinstand. The readback signals were recorded and analyzed by a digital oscilloscope. A number of different configurations for writing and reading information from a magnetic medium were explored. The prototype transducers for this work were fabricated via FIB trimming of different magnetic recording heads. ^