24,869 research outputs found
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
Enhancing the EAST-ADL error model with HiP-HOPS semantics
EAST-ADL is a domain-specific modelling language for the engineering of automotive embedded systems. The language has abstractions that enable engineers to capture a variety of information about design in the course of the lifecycle — from requirements to detailed design of hardware and software architectures. The specification of the EAST-ADL language includes an error model extension which documents language structures that allow potential failures of design elements to be specified locally. The effects of these failures are then later assessed in the context of the architecture design. To provide this type of useful assessment, a language and a specification are not enough; a compiler-like tool that can read and operate on a system specification together with its error model is needed. In this paper we integrate the error model of EAST-ADL with the precise semantics of HiP-HOPS — a state-of-the-art tool that enables dependability analysis and optimization of design models. We present the integration concept between EAST-ADL structure and HiP-HOPS error propagation logic and its transformation into the HiP-HOPS model. Source and destination models are represented using the corresponding XML formats. The connection of these two models at tool level enables practical EAST-ADL designs of embedded automotive systems to be analysed in terms of dependability, i.e. safety, reliability and availability. In addition, the information encoded in the error model can be re-used across different contexts of application with the associated benefits for cost reduction, simplification, and rationalisation of dependability assessments in complex engineering designs
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
A Pattern Language for High-Performance Computing Resilience
High-performance computing systems (HPC) provide powerful capabilities for
modeling, simulation, and data analytics for a broad class of computational
problems. They enable extreme performance of the order of quadrillion
floating-point arithmetic calculations per second by aggregating the power of
millions of compute, memory, networking and storage components. With the
rapidly growing scale and complexity of HPC systems for achieving even greater
performance, ensuring their reliable operation in the face of system
degradations and failures is a critical challenge. System fault events often
lead the scientific applications to produce incorrect results, or may even
cause their untimely termination. The sheer number of components in modern
extreme-scale HPC systems and the complex interactions and dependencies among
the hardware and software components, the applications, and the physical
environment makes the design of practical solutions that support fault
resilience a complex undertaking. To manage this complexity, we developed a
methodology for designing HPC resilience solutions using design patterns. We
codified the well-known techniques for handling faults, errors and failures
that have been devised, applied and improved upon over the past three decades
in the form of design patterns. In this paper, we present a pattern language to
enable a structured approach to the development of HPC resilience solutions.
The pattern language reveals the relations among the resilience patterns and
provides the means to explore alternative techniques for handling a specific
fault model that may have different efficiency and complexity characteristics.
Using the pattern language enables the design and implementation of
comprehensive resilience solutions as a set of interconnected resilience
patterns that can be instantiated across layers of the system stack.Comment: Proceedings of the 22nd European Conference on Pattern Languages of
Program
Principles of Antifragile Software
The goal of this paper is to study and define the concept of "antifragile
software". For this, I start from Taleb's statement that antifragile systems
love errors, and discuss whether traditional software dependability fits into
this class. The answer is somewhat negative, although adaptive fault tolerance
is antifragile: the system learns something when an error happens, and always
imrpoves. Automatic runtime bug fixing is changing the code in response to
errors, fault injection in production means injecting errors in business
critical software. I claim that both correspond to antifragility. Finally, I
hypothesize that antifragile development processes are better at producing
antifragile software systems.Comment: see https://refuses.github.io
A Verified Information-Flow Architecture
SAFE is a clean-slate design for a highly secure computer system, with
pervasive mechanisms for tracking and limiting information flows. At the lowest
level, the SAFE hardware supports fine-grained programmable tags, with
efficient and flexible propagation and combination of tags as instructions are
executed. The operating system virtualizes these generic facilities to present
an information-flow abstract machine that allows user programs to label
sensitive data with rich confidentiality policies. We present a formal,
machine-checked model of the key hardware and software mechanisms used to
dynamically control information flow in SAFE and an end-to-end proof of
noninterference for this model.
We use a refinement proof methodology to propagate the noninterference
property of the abstract machine down to the concrete machine level. We use an
intermediate layer in the refinement chain that factors out the details of the
information-flow control policy and devise a code generator for compiling such
information-flow policies into low-level monitor code. Finally, we verify the
correctness of this generator using a dedicated Hoare logic that abstracts from
low-level machine instructions into a reusable set of verified structured code
generators
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