5 research outputs found
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
The superconducting LHC magnets are coupled with an electronic monitoring
system which records and analyses voltage time series reflecting their
performance. A currently used system is based on a range of preprogrammed
triggers which launches protection procedures when a misbehavior of the magnets
is detected. All the procedures used in the protection equipment were designed
and implemented according to known working scenarios of the system and are
updated and monitored by human operators.
This paper proposes a novel approach to monitoring and fault protection of
the Large Hadron Collider (LHC) superconducting magnets which employs
state-of-the-art Deep Learning algorithms. Consequently, the authors of the
paper decided to examine the performance of LSTM recurrent neural networks for
modeling of voltage time series of the magnets. In order to address this
challenging task different network architectures and hyper-parameters were used
to achieve the best possible performance of the solution. The regression
results were measured in terms of RMSE for different number of future steps and
history length taken into account for the prediction. The best result of
RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal
layer and 16 steps history buffer
Formalizing and safeguarding blockchain-based BlockVoke protocol as an ACME extension for fast certificate revocation
Certificates are integral to the security of today’s Internet. Protocols like BlockVoke allow secure, timely and efficient revocation of certificates that need to be invalidated. ACME, a scheme used by the non-profit Let’s Encrypt Certificate Authority to handle most parts of the certificate lifecycle, allows automatic and seamless certificate issuance. In this work, we bring together both protocols by describing and formalizing an extension of the ACME protocol to support BlockVoke, combining the benefits of ACME’s certificate lifecycle management and BlockVoke’s timely and secure revocations. We then formally verify this extension through formal methods such as Colored Petri Nets (CPNs) and conduct a risk and threat analysis of the ACME/BlockVoke extension using the ISSRM domain model. Identified risks and threats are mitigated to secure our novel extension. Furthermore, a proof-of-concept implementation of the ACME/BlockVoke extension is provided, bridging the gap towards deployment in the real world
Estudio de modelos de privacidad de datos
El presente trabajo surge como una investigación motivada por la necesidad de proteger la privacidad de los usuarios de sistemas en contextos de análisis estadÃstico, inteligencia artificial y publicación de datos. Para ello se ha llevado a cabo un estudio del estado del arte y se han explorado técnicas de privatización de datos basadas en Privacidad Diferencial.Agencia Nacional de Investigación e InnovaciónICT4
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083