Semantic Decision Support for Information Fusion Applications

La thèse s'inscrit dans le domaine de la représentation des connaissances et la modélisation de l'incertitude dans un contexte de fusion d'informations. L'idée majeure est d'utiliser les outils sémantiques que sont les ontologies, non seulement pour représenter les connaissances générales du domaine et les observations, mais aussi pour représenter les incertitudes que les sources introduisent dans leurs observations. Nous proposons de représenter ces incertitudes au travers d'une méta-ontologie (DS-ontology) fondée sur la théorie des fonctions de croyance. La contribution de ce travail porte sur la définition d'opérateurs d'inclusion et d'intersection sémantique et sur lesquels s'appuie la mise en œuvre de la théorie des fonctions de croyance, et sur le développement d'un outil appelé FusionLab permettant la fusion d'informations sémantiques à partir du développement théorique précédent. Une application de ces travaux a été réalisée dans le cadre d'un projet de surveillance maritime.This thesis is part of the knowledge representation domain and modeling of uncertainty in a context of information fusion. The main idea is to use semantic tools and more specifically ontologies, not only to represent the general domain knowledge and observations, but also to represent the uncertainty that sources may introduce in their own observations. We propose to represent these uncertainties and semantic imprecision trough a metaontology (called DS-Ontology) based on the theory of belief functions. The contribution of this work focuses first on the definition of semantic inclusion and intersection operators for ontologies and on which relies the implementation of the theory of belief functions, and secondly on the development of a tool called FusionLab for merging semantic information within ontologies from the previous theorical development. These works have been applied within a European maritime surveillance project.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster–Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces

Distributed Detection and Fusion in Parallel Sensor Architectures

Parallel distributed detection system consists of several separate sensor-detector nodes (separated spatially or by their principles of operation), each with some processing capabilities. These local sensor-detectors send some information on an observed phenomenon to a centrally located Data Fusion Center for aggregation and decision making. Often, the local sensors use electro-mechanical, optical or RF modalities and are known as ``hard'' sensors. For such data sources, the sensor observations have structure and often some tractable statistical distributions which help in weighing their contribution to an integrated global decision. In a distributed detection environment, we often also have ``humans in the loop.''. Humans provide their subjective opinions on these phenomena. These opinions are labeled ``soft'' data. It is of interest to integrate "soft'' decisions, mostly assessments provided by humans, with data from the "hard" sensors, in order to improve global decision reliability. Several techniques were developed to combine data from traditional hard sensors, and a body of work was also created about integration of "soft'' data. However relatively little work was done on combining hard and soft data and decisions in an integrated environment. Our work investigates both "hard'' and "hard/soft'' fusion schemes, and proposes data integration architectures to facilitate heterogeneous sensor data fusion. In the context of "hard'' fusion, one of the contributions of this thesis is an algorithm that provides a globally optimum solution for local detector (hard sensor) design that satisfies a Neyman-Pearson criterion (maximal probability of detection under a fixed upper bound on the global false alarm rate) at the fusion center. Furthermore, the thesis also delves into application of distributed detection techniques in both parallel and sequential frameworks. Specifically, we apply parallel detection and fusion schemes to the problem of real time computer user authentication and sequential Kalman filtering for real time hypoxia detection. In the context of "hard/soft'' fusion, we propose a new Dempster-Shafer evidence theory based approach to facilitate heterogeneous sensor data fusion. Application of the framework to a number of simulated example scenarios showcases the wide range of applicability of the developed approach. We also propose and develop a hierarchical evidence tree based architecture for representing nested human opinions. The proposed framework is versatile enough to deal with both hard and soft source data using the evidence theory framework, it can handle uncertainty as well as data aggregation.Ph.D., Electrical Engineering -- Drexel University, 201

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Scientific uncertainty and decision making

It is important to have an adequate model of uncertainty, since decisions must be made before the uncertainty can be resolved. For instance, flood defenses must be designed before we know the future distribution of flood events. It is standardly assumed that probability theory offers the best model of uncertain information. I think there are reasons to be sceptical of this claim. I criticise some arguments for the claim that probability theory is the only adequate model of uncertainty. In particular I critique Dutch book arguments, representation theorems, and accuracy based arguments. Then I put forward my preferred model: imprecise probabilities. These are sets of probability measures. I offer several motivations for this model of uncertain belief, and suggest a number of interpretations of the framework. I also defend the model against some criticisms, including the so-called problem of dilation. I apply this framework to decision problems in the abstract. I discuss some decision rules from the literature including Levi’s E-admissibility and the more permissive rule favoured by Walley, among others. I then point towards some applications to climate decisions. My conclusions are largely negative: decision making under such severe uncertainty is inevitably difficult. I finish with a case study of scientific uncertainty. Climate modellers attempt to offer probabilistic forecasts of future climate change. There is reason to be sceptical that the model probabilities offered really do reflect the chances of future climate change, at least at regional scales and long lead times. Indeed, scientific uncertainty is multi-dimensional, and difficult to quantify. I argue that probability theory is not an adequate representation of the kinds of severe uncertainty that arise in some areas in science. I claim that this requires that we look for a better framework for modelling uncertaint

Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Assessing the sustainability performance of inter-urban intelligent transport

The implementation of ITS to increase the efficiency of saturated highways has become increasingly prevalent. It is a high level objective for many international governments and operators that highways should be managed in a way that is both sustainable i.e. environmental, social and economically sound and supportive of a Low-Carbon-Energy Future. Some clarity is therefore needed to understand how Intelligent Transport Systems perform within the constraints of that objective. This thesis describes the development of performance criteria that reflect the contributions of Information Communication Technology (ICT) emissions, vehicle emissions and the embedded carbon within the physical transport infrastructure that typically comprises three types of Intelligent Transport System. Active Traffic Management, Intelligent Speed Adaptation and the Automated Highway System are a collection of systems designed to transform the road network into a highly efficient and congestion free transport solution and all possess varying levels of uncertainty in terms of sustainability performance. The performance criteria form part of a new framework methodology ‘EnvFUSION’ (Environmental Fusion for ITS) outlined here. An attributional LCA and c-LCA (consequential lifecycle assessment) are both undertaken which forms part of a data fusion process using data from various sources. The models forecast improvements for the three ITS technologies in-line with social acceptability, economic profitability and major carbon reduction scenarios up to 2050 on one of the UK's most congested highways. Analytical Hierarchy Process and Dempster-Shafer theory are used to weight criteria which form part of an Intelligent Transport Sustainability Index. Overall performance is then synthesized. Results indicate that there will be a substantial increase in socio-economic and emissions benefits, provided that the policies are in place and targets are reached which would otherwise delay their realisation. To conclude, an integrated strategic performance management framework is proposed which performs socio-technical comparisons of four key performance areas between ITS schemes in order to identify energy and emission hotspots

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems