525 research outputs found
Data-Driven Anomaly Detection in Industrial Networks
Since the conception of the first Programmable Logic Controllers (PLCs) in the 1960s, Industrial Control Systems (ICSs) have evolved vastly. From the primitive isolated setups, ICSs have become increasingly interconnected, slowly forming the complex networked environments, collectively known as Industrial Networks (INs), that we know today. Since ICSs are responsible for a wide range of physical processes, including those belonging to Critical Infrastructures (CIs), securing INs is vital for the well-being of modern societies. Out of the many research advances on the field, Anomaly Detection Systems (ADSs) play a prominent role. These systems monitor IN and/or ICS behavior to detect abnormal events, known or unknown. However, as the complexity of INs has increased, monitoring them in the search of anomalous trends has effectively become a Big Data problem. In other words, IN data has become too complex to process it by traditional means, due to its large scale, diversity and generation speeds. Nevertheless, ADSs designed for INs have not evolved at the same pace, and recent proposals are not designed to handle this data complexity, as they do not scale well or do not leverage the majority of the data types created in INs.
This thesis aims to fill that gap, by presenting two main contributions: (i) a visual flow monitoring system and (ii) a multivariate ADS that is able to tackle data heterogeneity and to scale efficiently. For the flow monitor, we propose a system that, based on current flow data, builds security visualizations depicting network behavior while highlighting anomalies. For the multivariate ADS, we analyze the performance of Multivariate Statistical Process Control (MSPC) for detecting and diagnosing anomalies, and later we present a Big Data, MSPCinspired ADS that monitors field and network data to detect anomalies. The approaches are experimentally validated by building INs in test environments and analyzing the data created by them. Based on this necessity for conducting IN security research in a rigorous and reproducible environment, we also propose the design of a testbed that serves this purpose
A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors
located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This
new SBN Program will deliver a rich and compelling physics opportunity,
including the ability to resolve a class of experimental anomalies in neutrino
physics and to perform the most sensitive search to date for sterile neutrinos
at the eV mass-scale through both appearance and disappearance oscillation
channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND
and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we
estimate that a search for muon neutrino to electron neutrino appearance can be
performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter
region. In this proposal for the SBN Program, we describe the physics analysis,
the conceptual design of the LAr1-ND detector, the design and refurbishment of
the T600 detector, the necessary infrastructure required to execute the
program, and a possible reconfiguration of the BNB target and horn system to
improve its performance for oscillation searches.Comment: 209 pages, 129 figure
Earth Resources: A continuing bibliography with indexes, issue 6, December 1975
This bibliography lists 484 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1975 and June 1975. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis
Maintenance Management of Wind Turbines
“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements
ON-BOARD ARTIFICIAL INTELLIGENCE FOR FAILURE DETECTION AND SAFE TRAJECTORY GENERATION
The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. Adequate commanded trajectory generation and modification, as well as high-performance trajectory tracking control laws have been an essential focus of researchers given that integration into the National Air Space (NAS) is becoming a primary need. However, the operational safety of these systems can be easily affected if abnormal flight conditions are present, thereby compromising the nominal bounds of design of the system\u27s flight envelop and trajectory following. This thesis focuses on investigating methodologies for modeling, prediction, and protection of autonomous vehicle trajectories under normal and abnormal flight conditions. An Artificial Immune System (AIS) framework is implemented for fault detection and identification in combination with the multi-goal Rapidly-Exploring Random Tree (RRT*) path planning algorithm to generate safe trajectories based on a reduced flight envelope. A high-fidelity model of a fixed-wing unmanned aerial vehicle is used to demonstrate the capabilities of the approach by timely generating safe trajectories as an alternative to original paths, while integrating 3D occupancy maps to simulate obstacle avoidance within an urban environment
Semantic Spaces for Video Analysis of Behaviour
PhDThere are ever growing interests from the computer vision community into human behaviour
analysis based on visual sensors. These interests generally include: (1) behaviour recognition -
given a video clip or specific spatio-temporal volume of interest discriminate it into one or more
of a set of pre-defined categories; (2) behaviour retrieval - given a video or textual description
as query, search for video clips with related behaviour; (3) behaviour summarisation - given a
number of video clips, summarise out representative and distinct behaviours. Although countless
efforts have been dedicated into problems mentioned above, few works have attempted to
analyse human behaviours in a semantic space. In this thesis, we define semantic spaces as a
collection of high-dimensional Euclidean space in which semantic meaningful events, e.g. individual
word, phrase and visual event, can be represented as vectors or distributions which are
referred to as semantic representations. With the semantic space, semantic texts, visual events
can be quantitatively compared by inner product, distance and divergence. The introduction of
semantic spaces can bring lots of benefits for visual analysis. For example, discovering semantic
representations for visual data can facilitate semantic meaningful video summarisation, retrieval
and anomaly detection. Semantic space can also seamlessly bridge categories and datasets which
are conventionally treated independent. This has encouraged the sharing of data and knowledge
across categories and even datasets to improve recognition performance and reduce labelling effort.
Moreover, semantic space has the ability to generalise learned model beyond known classes
which is usually referred to as zero-shot learning. Nevertheless, discovering such a semantic
space is non-trivial due to (1) semantic space is hard to define manually. Humans always have
a good sense of specifying the semantic relatedness between visual and textual instances. But a
measurable and finite semantic space can be difficult to construct with limited manual supervision.
As a result, constructing semantic space from data is adopted to learn in an unsupervised
manner; (2) It is hard to build a universal semantic space, i.e. this space is always contextual
dependent. So it is important to build semantic space upon selected data such that it is always
meaningful within the context. Even with a well constructed semantic space, challenges are still
present including; (3) how to represent visual instances in the semantic space; and (4) how to mitigate
the misalignment of visual feature and semantic spaces across categories and even datasets
when knowledge/data are generalised. This thesis tackles the above challenges by exploiting data
from different sources and building contextual semantic space with which data and knowledge
can be transferred and shared to facilitate the general video behaviour analysis.
To demonstrate the efficacy of semantic space for behaviour analysis, we focus on studying
real world problems including surveillance behaviour analysis, zero-shot human action recognition
and zero-shot crowd behaviour recognition with techniques specifically tailored for the
nature of each problem.
Firstly, for video surveillances scenes, we propose to discover semantic representations from
the visual data in an unsupervised manner. This is due to the largely availability of unlabelled
visual data in surveillance systems. By representing visual instances in the semantic space, data
and annotations can be generalised to new events and even new surveillance scenes. Specifically,
to detect abnormal events this thesis studies a geometrical alignment between semantic representation
of events across scenes. Semantic actions can be thus transferred to new scenes and
abnormal events can be detected in an unsupervised way. To model multiple surveillance scenes
simultaneously, we show how to learn a shared semantic representation across a group of semantic
related scenes through a multi-layer clustering of scenes. With multi-scene modelling we
show how to improve surveillance tasks including scene activity profiling/understanding, crossscene
query-by-example, behaviour classification, and video summarisation.
Secondly, to avoid extremely costly and ambiguous video annotating, we investigate how
to generalise recognition models learned from known categories to novel ones, which is often
termed as zero-shot learning. To exploit the limited human supervision, e.g. category names,
we construct the semantic space via a word-vector representation trained on large textual corpus
in an unsupervised manner. Representation of visual instance in semantic space is obtained by
learning a visual-to-semantic mapping. We notice that blindly applying the mapping learned
from known categories to novel categories can cause bias and deteriorating the performance
which is termed as domain shift. To solve this problem we employed techniques including semisupervised
learning, self-training, hubness correction, multi-task learning and domain adaptation.
All these methods in combine achieve state-of-the-art performance in zero-shot human action
task.
In the last, we study the possibility to re-use known and manually labelled semantic crowd
attributes to recognise rare and unknown crowd behaviours. This task is termed as zero-shot
crowd behaviours recognition. Crucially we point out that given the multi-labelled nature of
semantic crowd attributes, zero-shot recognition can be improved by exploiting the co-occurrence
between attributes.
To summarise, this thesis studies methods for analysing video behaviours and demonstrates
that exploring semantic spaces for video analysis is advantageous and more importantly enables
multi-scene analysis and zero-shot learning beyond conventional learning strategies
Contrast
This thesis engages the contrast phenomenon in its various manifestations across the different sense modalities in order to assess the plausibility of contrast as a general perceptual principle. There is some question as to whether contrast may spill over into modalities it is not commonly associated with. In particular, a number of researchers have argued that contrast occurs in audition and has similarities to the brightness contrast illusion in visual perception. For example, studies of noise pitch have noted similar psychophysical properties to brightness contrast and invoked the same underlying neural mechanism. Furthermore, there is some suggestion that contrast may come in a non-simultaneous form characterised by perceptual exaggerations arising from the contrasting spectral content of successive auditory stimuli. The speech perception literature on context effects are engaged as it is in this domain that there is some evidence for non-simultaneous contrast. A study was conducted in which 34 subjects were presented synthesized tokens of "da" and "ga" diotically and dichotically following the precursors "al" and "ar". Significantly more "da" identifications were observed (F=62.85, p=.000) following "ar" precursors when stimuli were presented diotically, whereas no significant effects were observed in identifications of the target for different precursors (F=.553, p=.457) when targets were presented to the ear contralateral to that of the precursor. Results fail to support an explanation of the context effect in the form of a causal mechanism with a central locus. Rather, the locus of the context effect can only be hypothesized to occur at the periphery of the nervous system. This suggests that auditory contrast may be a plausible explanation for the context effect, and which if correct, may be a universal phenomenon common to all people regardless of environment and acquired knowledge. Given these results, contrast is explored more broadly. As generic use of the term is often encountered in musical contexts, aspects of music perception relating to contrast are examined. Some theoretical ideas are put forward in this capacity, focusing on an interactive neural network approach to understanding hypothetical structural contrasts, including tonal contrasts. The thesis closes with a consideration of contrast-like effects that may be of some relevance towards understanding contrast more generally
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