Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

Automatic disruption classification at JET: comparison of different pattern recognition techniques

In this paper, different pattern recognition techniques have been tested in order to implement an automatic tool for disruption classification in a tokamak experiment. The methods considered refer to clustering and classification techniques. In particular, the investigated clustering techniques are self-organizing maps and K-means, while the classification techniques are multi-layer perceptrons, support vector machines, and k- nearest neighbours. Training and testing data have been collected selecting suitable diagnostic signals recorded over 4 years of EFDA-JET experiments. Multi-layer perceptron classifiers exhibited the best performance in classifying mode lock, density limit/high radiated power, H-mode/L-mode transition and internal transport barrier plasma disruptions. This classification performance can be increased using multiple classifiers. In particular the outputs of five multi-layer perceptron classifiers have been combined using multiple classifier techniques in order to obtain a more robust and reliable classification tool, that is presently implemented at JET

Dynamic Neural Networks for Prediction of Disruptions in Fusion Reactors (Tokamaks)

In this paper, dynamic neural networks are proposed to predict the plasma disruptions in a nuclear fusion device. Disruptions are critical events where the plasma, which is magnetically confined in a vacuum vessel, becomes unstable, cools down and the confinement is suddenly destroyed. These events may damage to the vessel, so they have to be foreseen well in advance in order to take mitigating action. Dynamic neural networks act as filters, which predict one step ahead the value of diagnostic signals acquired during a plasma pulse. The prediction error of the neural network depends on the regularity of signals. For this reason, an increasing prediction error reveals that the plasma operative conditions are changing, hence a disruption could be imminent. In this work, different diagnostic approaches, network adapting parameters, and diagnosis thresholds have been tested in order to determine the best performance in terms of prediction capability

Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines2007 IEEE Workshop on Machine Learning for Signal Processing

This paper presents a so called Geometrical Kernel Machine used to predict disruptive events in nuclear fusion reactors. Here, the prediction problem is modeled as a two classes classification problem, and the predictor is built by using a new constructive algorithm that allows us to automatically determine both the number of neurons and the synaptic weights of a Multilayer Perceptron network with a single hidden layer. It has been demonstrated that the resulting network is able to classify any set of patterns defined in a real domain. The geometrical interpretation of the network equations allows us both to develop the predictor and to manage the so called ageing of the kernel machine. In fact, using the same kernel machine, a novelty detection system has been integrated in the predictor, increasing the overall system performance

DYNAMIC BEHAVIOUR OF TYPE I EDGE LOCALIZED MODES IN THE JET

Understanding and control of ELMs are crucial issues for the operation of ITER where the type-I ELMy H-mode has been chosen as the standard operation scenario. Determine whether ELM dynamic is chaotic or random is crucial to correctly describe the ELM cycle. In this paper, the dynamic characteristics of 8 ELM time-series from the JET tokamak are investigated. Characteristic parameters, such as the Hurst exponent and the Maximal Lyapunov Exponent, have been evaluated. The obtained results suggest the presence of deterministic chaos in some of the analysed time series

SUPPORT VECTOR MACHINES FOR DISRUPTION PREDICTION AND NOVELTY DETECTION AT JET

In the last years there has been a growing interest on black box approaches to disruption prediction. The drawback of these approaches is that the system could deteriorate its performance once it does not get updated. This could be the case of a disruption predictor for JET, where new plasma configurations might present features completely different from those observed in the experiments used during the training phase. This 'novelty' can be incorrectly classified by the system. A novelty detection method, which determines the novelty of the input of the prediction system, can be used to assess the system reliability. This paper presents a support vector machines disruption predictor for JET, wherein multiple plasma diagnostic signals are combined to provide a composite impending disruption warning indicator. In a support vector machine the analysis of the decision function value gives useful information about the novelty of an input and, on the reliability of the predictor output, during on-line applications. Results show the suitability of support vector machines both for prediction and novelty detection tasks at JET