37,845 research outputs found
Real-time predictive maintenance for wind turbines using Big Data frameworks
This work presents the evolution of a solution for predictive maintenance to
a Big Data environment. The proposed adaptation aims for predicting failures on
wind turbines using a data-driven solution deployed in the cloud and which is
composed by three main modules. (i) A predictive model generator which
generates predictive models for each monitored wind turbine by means of Random
Forest algorithm. (ii) A monitoring agent that makes predictions every 10
minutes about failures in wind turbines during the next hour. Finally, (iii) a
dashboard where given predictions can be visualized. To implement the solution
Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we
have improved the previous work in terms of data process speed, scalability and
automation. In addition, we have provided fault-tolerant functionality with a
centralized access point from where the status of all the wind turbines of a
company localized all over the world can be monitored, reducing O&M costs
Explicit Representation of Exception Handling in the Development of Dependable Component-Based Systems
Exception handling is a structuring technique that facilitates the design of systems by encapsulating the process of error recovery. In this paper, we present a systematic approach for incorporating exceptional behaviour in the development of component-based software. The premise of our approach is that components alone do not provide the appropriate means to deal with exceptional behaviour in an effective manner. Hence the need to consider the notion of collaborations for capturing the interactive behaviour between components, when error recovery involves more than one component. The feasibility of the approach is demonstrated in terms of the case study of the mining control system
What does fault tolerant Deep Learning need from MPI?
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML)
algorithm for large scale data analysis. DL algorithms are computationally
expensive - even distributed DL implementations which use MPI require days of
training (model learning) time on commonly studied datasets. Long running DL
applications become susceptible to faults - requiring development of a fault
tolerant system infrastructure, in addition to fault tolerant DL algorithms.
This raises an important question: What is needed from MPI for de- signing
fault tolerant DL implementations? In this paper, we address this problem for
permanent faults. We motivate the need for a fault tolerant MPI specification
by an in-depth consideration of recent innovations in DL algorithms and their
properties, which drive the need for specific fault tolerance features. We
present an in-depth discussion on the suitability of different parallelism
types (model, data and hybrid); a need (or lack thereof) for check-pointing of
any critical data structures; and most importantly, consideration for several
fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI
and their applicability to fault tolerant DL implementations. We leverage a
distributed memory implementation of Caffe, currently available under the
Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches
by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation
using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies
demonstrates the effectiveness of the proposed fault tolerant DL implementation
using OpenMPI based ULFM
Mining local staircase patterns in noisy data
Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising
Quantum attacks on Bitcoin, and how to protect against them
The key cryptographic protocols used to secure the internet and financial
transactions of today are all susceptible to attack by the development of a
sufficiently large quantum computer. One particular area at risk are
cryptocurrencies, a market currently worth over 150 billion USD. We investigate
the risk of Bitcoin, and other cryptocurrencies, to attacks by quantum
computers. We find that the proof-of-work used by Bitcoin is relatively
resistant to substantial speedup by quantum computers in the next 10 years,
mainly because specialized ASIC miners are extremely fast compared to the
estimated clock speed of near-term quantum computers. On the other hand, the
elliptic curve signature scheme used by Bitcoin is much more at risk, and could
be completely broken by a quantum computer as early as 2027, by the most
optimistic estimates. We analyze an alternative proof-of-work called Momentum,
based on finding collisions in a hash function, that is even more resistant to
speedup by a quantum computer. We also review the available post-quantum
signature schemes to see which one would best meet the security and efficiency
requirements of blockchain applications.Comment: 21 pages, 6 figures. For a rough update on the progress of Quantum
devices and prognostications on time from now to break Digital signatures,
see https://www.quantumcryptopocalypse.com/quantum-moores-law
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