Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning

Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a general-purpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an $R^2$ score of 0.77 and a mean error of 24% using regressors, and with an F1 score above 0.6 for classifiers applying a six-months resolution

Annual Reports Of The Town Officers Of The Town Of Rutland, Massachusetts For The Fiscal Year Ending June 30, 2013

Modern high-energy particle accelerators and Free-Electron Lasers incorporate large quantities of sensitive RF and microwave frequency devices distributed over kilometer distances. Such devices require extreme stable phase and time synchronization by means of high frequency signal distributed along the accelerator facility. Coaxial cables are commonly used to distribute the reference signal over the large machine to synchronize electronic systems and they are the main source of undesirable phase drifts in the synchronization system. Signal phase drifts in cables are mainly caused by temperature and humidity variations and their values usually exceed required phase synchronization accuracy by more than order of magnitude. There are several approaches to reduce signal phase drifts in coaxial cables. This paper describes the realization of active phase stabilization system based on interference phenomenon. A phase-locked signal from the transmitter is reflected at the end of a coaxial cable link. Directional couplers placed along the cable pick up the forward and reflected signals and interfere them to cancel out the cable phase drifts. Distributed hardware including interferometer controller/transmitter and receiver modules were built to demonstrate system concept and performance. Link input and output devices used FPGA I/O boards with Ethernet interface to control system operation. Specialized firmware and software was developed to calibrate and control the system. This paper describes the concept of interferometer link, designed hardware, basic control algorithms and performance evaluation results. The link prototype was built to distribute 1.3 GHz signal through a coaxial cable. Measured phase drift suppression factor value exceeded level of 100

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.Comment: 17 pages, 8 figures, 2 table

High Precision Temperature Control of Normal-conducting RF GUN for a High Duty Cycle Free-Electron Laser

High precision temperature control of the RF GUN is necessary to optimally accelerate thousands of electrons within the injection part of the European X-ray free-electron laser XFEL and the Free Electron Laser FLASH. A difference of the RF GUN temperature from the reference value of only 0.01 K leads to detuning of the cavity and thus limits the performance of the whole facility. Especially in steady-state operation there are some undesired temperature oscillations when using classical standard control techniques like PID control. That is why a model based approach is applied here to design the RF GUN temperature controller for the free-electron lasers. A thermal model of the RF GUN and the cooling facility is derived based on heat balances, considering the heat dissipation of the Low-Level RF power. This results in a nonlinear model of the plant. The parameters are identified by fitting the model to data of temperature, pressure and control signal measurements of the FLASH facility, a pilot test facility for the European XFEL. The derived model is used for controller design. A linear model predictive controller was implemented in MATLAB/Simulink and tuned to stabilize the temperature of the RF GUN in steady-state operation. A test of the controller in simulation shows promising results

Design and Performance of the TESLA Test Facility Collimation System

To perform a proof of principle experiment of a SASE based Free Electron Laser operating at wavelength between 70-160 nanometer a 15 m long permanent magnet undulator has been installed in the TESLA Test Facility (TTF) linac phase 1. The type of magnets used (NdFeB) is known to be sensitive to radiation damages if exposed to high energy electrons. Already beam losses in the order of 10−6 of the nominal TTF beam current (64μA) are critical for the undulator and can cause an irreversible damage of its magnets after a few months of operation. To protect the undulator against radiation a two stage spoiler-absorber collimation system has been implemented to the beamline in front of the undulator. In this paper the design and the performance limits of the collimator system are presented

Simulation of Dark Current Transport Through the TESLA Test Facility Linac

The transported dark current in a high-duty cycle accelerator as the TESLA Test Facility linac (TTF) could significately contribute to radiation damages of components along the beamline. In the past high dark currents emitted from the laser driven rf gun have been observed duringsubstantial time of linac operation. For a better understanding of the dark current transported through and lost in the entire linac, particularly in undulator magnets, numerical simulations are compared with experimental observations. In order to identify possible locations for collimators to remove the dark current, the beam and its halo have been investigated. The operation of an absorber recently installed in the dispersive section of TTF magnetic bunch compressor and its impact on the downstream collimator section is discussed