G. de Arcas

Implementation of the Disruption Predictor APODIS in JET’s Real-Time Network Using the MARTe Framework

This paper describes the implementation of a real-time disruption predictor that is based on support vector machine (SVM) classifiers. The implementation was performed under the MARTe framework on a six-core x86 architecture. The system is connected via JETs Real-time Data Network (RTDN). The online results show a high degree of successful predictions and a low rate of false alarms, thus confirming the usefulness of this approach in a disruption mitigation scheme. The implementation shows a low computational load, which will be exploited in the immediate future to increase the predictions temporal resolution.

Design of an Intelligent Front-End Signal Conditioning Circuit for IR Sensors

This paper presents the design of an intelligent front-end signal conditioning system for IR sensors. The system has been developed as an interface between a PbSe IR sensor matrix and a TMS320C67x digital signal processor. The system architecture ensures its scalability so it can be used for sensors with different matrix sizes. It includes an integrator based signal conditioning circuit, a data acquisition converter block, and a FPGA based advanced control block that permits including high level image preprocessing routines such as faulty pixel detection and sensor calibration in the signal conditioning front-end. During the design phase virtual instrumentation technologies proved to be a very valuable tool for prototyping when choosing the best A/D converter type for the application. Development time was significantly reduced due to the use of this technology.

Self-adaptive sampling rate data acquisition in JET's correlation reflectometer.

Data acquisition systems with self-adaptive sampling rate capabilities have been proposed as a solution to reduce the shear amount of data collected in every discharge of present fusion devices. This paper discusses the design of such a system for its use in the KG8B correlation reflectometer at JET. The system, which is based on the ITMS platform, continuously adapts the sample rate during the acquisition depending on the signal bandwidth. Data are acquired continuously at the expected maximum sample rate and transferred to a memory buffer in the host processor. Thereafter the rest of the process is based on software. Data are read from the memory buffer in blocks and for each block an intelligent decimation algorithm is applied. The decimation algorithm determines the signal bandwidth for each block in order to choose the optimum sample rate for that block, and from there the decimation factor to be used. Memory buffers are used to adapt the throughput of the three main software modules (data acquisition, processing, and storage) following a typical producer-consumer architecture. The system optimizes the amount of data collected while maintaining the same information. Design issues are discussed and results of performance evaluation are presented.

Implementation of local area network extension for instrumentation standard trigger capabilities in advanced data acquisition platforms

Synchronization mechanisms are an essential part of the real-time distributed data acquisition systems (DASs) used in fusion experiments. Traditionally, they have been based on the use of digital signals. The approach known as local area network extension for instrumentation (LXI) provides a set of very powerful synchronization and trigger mechanisms. The Intelligent Test Measurement System (ITMS) is a new platform designed to implement distributed data acquisition and fast data processing for fusion experiments. It is based on COMPATPCI technology and its extension to instrumentation (PXI). Hardware and software elements have been developed to include LXI trigger and synchronization mechanisms in this platform in order to obtain a class A LXI instrument. This paper describes the implementation of such a system, involving the following components: commercial hardware running a Linux operating system; a real-time extension to an operating system and network (RTAI and RTNET), which implements a software precision time protocol (PTP) using IEEE1588; an ad hoc PXI module to support hardware implementation of PTP-IEEE 1588; and the multipoint, low-voltage differential signaling hardware LXI trigger bus.

Exploiting graphic processing units parallelism to improve intelligent data acquisition system performance in JET's correlation reflectometer

The performance of intelligent data acquisition systems relies heavily on their processing capabilities and local bus bandwidth, especially in applications with high sample rates or high number of channels. This is the case of the self adaptive sampling rate data acquisition system installed as a pilot experiment in KG8 B correlation reflectometer at JET. The system, which is based on the ITMS platform, continuously adapts the sample rate during the acquisition depending on the signal bandwidth. In order to do so it must transfer acquired data to a memory buffer in the host processor and run heavy computational algorithms for each data block. The processing capabilities of the host CPU and the bandwidth of the PXI bus limit the maximum sample rate that can be achieved, therefore limiting the maximum bandwidth of the phenomena that can be studied. Graphic processing units (GPU) are becoming an alternative for speeding up compute intensive kernels of scientific, imaging and simulation applications. However, integrating this technology into data acquisition systems is not a straight forward step, not to mention exploiting their parallelism efficiently. This paper discusses the use of GPUs with new high speed data bus interfaces to improve the performance of the self adaptive sampling rate data acquisition system installed on JET. Integration is sues are discussed and performance evaluations are presented.

New information processing methods for control in magnetic confinement nuclear fusion

Thermonuclear plasmas are complex and highly nonlinear physical objects and, therefore, are the most advanced present-day devices for the study of magnetic confinement fusion. Thousands of signals have to be acquired for each experiment in order to progress the understanding that is indispensable for the final reactor. On the other hand, the resulting massive databases, more than 40 Tbytes in the case of the Joint European Torus (JET) Joint Undertaking, pose significant problems. In this paper, solutions to reduce the sheer amount of data by different compression techniques and adaptive sampling frequency architectures are presented. As an example of methods capable of providing significant help in data analysis and real-time control, a Classification and Regression Tree (CART) software is applied to the problem of regime identification to discriminate in an automatic way whether the plasma is in the Low (L) or High (H) confinement mode.

Practical considerations in the verification of personal sound exposure meters

This paper analyses the problems that appear when calibration or verification of personal sound exposure meters is done following the recommendations of IEC 61252. The tests recommended in the standard to verify the characteristics of such instruments are discussed and alternative procedures are proposed to solve the problems detected. The two main problems detected are related to the duration of the process, which has a direct effect on the verification cost, and the measurement uncertainty. The use of procedures based on measurement modes that are not dependent on integration times, such as when measuring sound pressure level, is recommended whenever possible.

A Review of Non-Acoustic Measures to Handle Community Response to Noise around Airports

It seems obvious that the noise levels in local communities surrounding airports influences the level of acceptance of an airport. What is not so evident is the effect of non-acoustic factors that increase the societal rejection, like the lack of sensitivity and empathy from the authorities and airport managers, the lack of trust in them, the lack of information and transparency, the perception of being excluded from the decision making and so on. Complementary to the traditional strategies based on the reduction of noise exposure, a community engagement and involvement approach brings new possibilities to manage noise around airports, trying to exploit the non-acoustic factors that have negatively affected the community response. Building trust among the stakeholders is a key factor in this strategy, and it must be based on a long-term, honest, and transparent two-way communication. In the last decade, the huge growth of the information and communication technologies has opened new opportunities that the aviation organizations and stakeholders are starting to explore in depth trying to reduce the degree of rejection of the airport, which may compromise the utilization of existing and future infrastructure. In this review, we make a short introduction on aircraft noise health effects, to focus annoyance and the influence that non-acoustic factors on it. Then, we describe the basis of community engagement as a parallel approach to mitigate noise issues around airports, setting the focus on the noise metrics and the involvement techniques that must be implemented to engage the community.

Integration and Validation of a Disruption Predictor Simulator in JET

Abstract Disruptions in tokamak devices are inevitable and can severely damage a tokamak device’s wall. For this reason, different protection mechanisms have to be implemented. In the Joint European Torus (JET), these protection systems are structured in different levels. At the lowest level are those systems that are responsible for protecting the machine’s integrity, which must be highly reliable. More complex systems are located at higher levels; these higher-level systems have been designed to take action before low-level systems. Since the installation of the new metallic wall in JET, new protection systems have been being developed to improve the overall protection of the device. This work focuses on a software application – a disruption predictor – that detects an incoming disruption. This software application simulates the behavior of a real-time implementation. In recent years, efforts have been devoted to developing and optimizing a reliable system that is capable of predicting disruptions. This has been accomplished by the novel combination of machine-learning techniques based on supervised learning methods. Disruptions must be predicted early enough so that the protection systems can mitigate the effects of disruptions. This paper summarizes the software development of the JET disruption predictor. This software simulates the real-time data acquisition and data processing. It has been an essential software tool to both optimize the disruption prediction model and implement a simulator of the real-time predictor.

Design of an advanced intelligent instrument with waveform recognition based on the ITMS platform

Searching for similar behaviours in previous data plays a key role in fusion research, but it can be quite challenging to implement from a practical point of view. This paper describes the design of an intelligent measurement instrument that uses Similar Waveform Recognition Systems (SWRS) to extract knowledge from the signals it acquires. The system is perceived as an ethernet measurement instrument that permits acquisition of several waveforms simultaneously and identification of similar behaviours by searching in previous data using distributed SWRS. The implementation is another example of the advantages that local processing capabilities can provide in Data Acquisition (DAQ) applications.