5,895 research outputs found
Review of Health Prognostics and Condition Monitoring of Electronic Components
To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted
Soft fault detection using MIBs in computer networks
To improve network reliability and management in today\u27s high-speed communication system, a statistical anomaly network intrusion detection system (NIDS) has been investigated, for network soft faults using the Management Information Base (Mm) traffic parameters provided by Simple Network Management Protocol (SNMP), for both wired and wireless networks. The work done would be a contribution to a system to be designed MIB Anomaly Intrusion Detection, a hierarchical multi-tier and multiobservation-window Anomaly Intrusion Detection system. The data was derived from many experiments that had been carried out in the test bed that monitored 27 MIB traffic parameters simultaneously, focusing on the soft network faults. The work here has been focused on early detection, i.e., detection at low values of the ratio of fault to background traffic. The performance of this system would be measured using traffic intensity scenarios, as the fault traffic decreased from 10% to 0.5% of the background
Lateinisches und Romanisches in den Reichenauer Glossen
In today’s complex networks, timely identification and resolution of performance problems is extremely challenging. Current diagnostic practices to identify the root causes of such problems primarily rely on human intervention and investigation. Fully automated and scalable systems, which are capable of identifying complex problems are needed to provide rapid and accurate diagnosis. The study presented in this thesis creates the necessary scientific basis for the automatic diagnosis of network performance faults using novel intelligent inference techniques based on machine learning. We propose three new techniques for characterisation of network soft failures, and by using them, create the Intelligent Automated Network Diagnostic (IAND) system.
First, we propose Transmission Control Protocol (TCP) trace characterisation techniques that use aggregated TCP statistics. Faulty network components embed unique artefacts in TCP packet streams by altering the normal protocol behaviour. Our technique captures such artefacts and generates a set of unique fault signatures. We first introduce Normalised Statistical Signatures (NSSs) with 460 features, a novel representation of network soft failures to provide the basis for diagnosis. Since not all 460 features contribute equally to the identification of a particular fault, we then introduce improved forms of NSSs called EigenNSS and FisherNSS with reduced complexity and greater class separability. Evaluations show that we can achieve dimensionality reduction of over 95% and detection accuracies up to 95% while achieving micro-second diagnosis times with these signatures.
Second, given NSSs have features that are dependent on link properties, we introduce a technique called Link Adaptive Signature Estimation (LASE) using regression-based predictors to artificially generateNSSs for a large number of link parameter combinations. Using LASE, the system can be trained to suit the exact networking environment, however dynamic, with a minimal set of sample data. For extensive performance evaluation, we collected 1.2 million sample traces for 17 types of device failures on 8 TCP variants over various types of networks using a combination of fault injection and link emulation techniques.
Third, to automate fault identification, we propose a modular inference technique that learns from the patterns embedded in the signatures, and create Fault Classifier Modules (FCMs). FCMs use support vector machines to uniquely identify individual faults and are designed using soft class boundaries to provide generalised fault detection capability. The use of a modular design and generic algorithm that can be trained and tuned based on the specific faults, offers scalability and is a key differentiator from the existing systems that use specific algorithms to detect each fault. Experimental evaluations show that FCMs can achieve detection accuracies of between 90% – 98%.
The signatures and classifiers are used as the building blocks to create the IAND system with its two main sub-systems: IAND-k and IAND-h. The IANDk is a modular diagnostic system for automatic detection of previously known problems using FCMs. The IAND-k system is applied for accurately detecting faulty links and diagnosing problems in end-user devices in a wide range of network types (IAND-kUD, IAND-kCC). Extensive evaluation of the systems demonstrated high overall detection accuracies up to 96.6% with low false positives and over 90% accuracy even in the most difficult scenarios. Here, the FCMs use supervised machine learning methods and can only detect previously known problems. To extend the diagnostic capability to detect previously unknown problems, we propose IAND-h, a hybrid classifier system that uses a combination of unsupervised machine learning-based clustering and supervised machine learning-based classification. The evaluation of the system shows that previously unknown faults can be detected with over 92% accuracy. The IAND-h system also offers real-time detection capability with diagnosis times between 4 μs and 66 μs. The techniques and systems proposed during this research contribute to the state of the art of network diagnostics and focus on scalability, automation and modularity with evaluation results demonstrating a high degree of accuracy
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
GREEND: An Energy Consumption Dataset of Households in Italy and Austria
Home energy management systems can be used to monitor and optimize
consumption and local production from renewable energy. To assess solutions
before their deployment, researchers and designers of those systems demand for
energy consumption datasets. In this paper, we present the GREEND dataset,
containing detailed power usage information obtained through a measurement
campaign in households in Austria and Italy. We provide a description of
consumption scenarios and discuss design choices for the sensing
infrastructure. Finally, we benchmark the dataset with state-of-the-art
techniques in load disaggregation, occupancy detection and appliance usage
mining
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
Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces
Embedded devices are becoming more widespread, interconnected, and
web-enabled than ever. However, recent studies showed that these devices are
far from being secure. Moreover, many embedded systems rely on web interfaces
for user interaction or administration. Unfortunately, web security is known to
be difficult, and therefore the web interfaces of embedded systems represent a
considerable attack surface.
In this paper, we present the first fully automated framework that applies
dynamic firmware analysis techniques to achieve, in a scalable manner,
automated vulnerability discovery within embedded firmware images. We apply our
framework to study the security of embedded web interfaces running in
Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable
modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement
a scalable framework for discovery of vulnerabilities in embedded web
interfaces regardless of the vendor, device, or architecture. To achieve this
goal, our framework performs full system emulation to achieve the execution of
firmware images in a software-only environment, i.e., without involving any
physical embedded devices. Then, we analyze the web interfaces within the
firmware using both static and dynamic tools. We also present some interesting
case-studies, and discuss the main challenges associated with the dynamic
analysis of firmware images and their web interfaces and network services. The
observations we make in this paper shed light on an important aspect of
embedded devices which was not previously studied at a large scale.
We validate our framework by testing it on 1925 firmware images from 54
different vendors. We discover important vulnerabilities in 185 firmware
images, affecting nearly a quarter of vendors in our dataset. These
experimental results demonstrate the effectiveness of our approach
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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