Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling.

<p>During the last decades, great steps have been taken to decrease passenger fatality in cars. Systems such as ABS and airbags have been developed for this purpose alone. But not much effort has been put into pedestrian safety. In traffic today, pedestrians are one of the most endangered participants and in recent years, there has been an increased demand for pedestrian safety from the European Enhanced Vehicle safety Committee and the European New Car Assessment Programme has thereby developed tests where pedestrian safety is rated. With this, detection of pedestrians has arised as a part in the automotive safety research.</p><p>This thesis provides some of this research available in the area and a brief introduction to some of the sensors readily available. The objective of this work is to detect pedestrians in front of a vehicle by using thermoelectric infrared sensors fused with short range radar sensors and also to minimize any missed detections or false alarms. There has already been extensive work performed with the thermoelectric infrared sensors for this sole purpose and this thesis is based on that work.</p><p>Information is provided about the sensors used and an explanation of how they are set up during this work. Methods used for classifying objects are given and the assumptions made about pedestrians in this system. A basic tracking algorithm is used to track radar detected objects in order to provide the fusion system with better data. The approach chosen for the sensor fusion is a central-level fusion where the probabilities for a pedestrian from the radars and the thermoelectric infrared sensors are combined using Dempster-Shafer Theory and accumulated over time in the Occupancy Grid framework. Theories that are extensively used in this thesis are explained in detail and discussed accordingly in different chapters.</p><p>Finally the experiments undertaken and the results attained from the presented system are shown. A comparison is made with the previous detection system, which only uses thermoelectric infrared sensors and of which this work continues on. Conclusions regarding what this system is capable of are drawn with its inherent strengths and weaknesses.</p>

Thèse (Ph.D.)--Université de Sherbrooke (Canada), 2006. / Titre de l'écran-titre (visionné le 27 févr. 2008). In ProQuest dissertations and theses. Publié aussi en version papier.

Foresters are faced with difficulties to obtain sub-polygon information with the mapping methods available nowadays. The main objective of this work consisted in the development of new methods able to improve the map accuracy of regenerating forest stands and mature forest stands in the South of Québec, Canada. The Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) showed their ability to integrate multiple heterogenous data sources to go further than the classical classification procedures like the maximum likelihood or the spectral unmixing, in terms of map accuracy. Improvement on the ability to map regenerating stands, passed from 82.7% with the maximum likelihood method to 91.1% with the Free DSm model with a total transfer of the mass of the"Union" class to the"Intersection" class (+ 8.4%). For the mature stands, the improvement passed from 63.8% with the K nearest neighbour to 79.5% with the DST according to a classical belief structuration and the hybrid decision rule for which the conflict threshold was fixed at 10% (+ 15.7%). Our results with DST and a bayesian belief structuration showed the difficulty to model the uncertainty in the fusion process. This is probably due to the lack of scientific knowledge about the influence of the biophysical and climatic parameters on the mapped forest stands and to the necessity to model specifically the uncertainty for each source. Our work showed concrete improvement when mapping forest stands with DST which is encouraging to continue explorating the fundamental principle of the proposed hybrid decision rule. This means a particular focus on the difference between the fused masses of each potential class after the fusion, to choose the best hypothesis.

Cette thèse constitue une nouvelle approche pour la prédiction des maladies des plantes dans une plantation par combinaison de fusion de données et modélisation spatio-temporelle. La maladie des plantes est un problème majeur dans le monde de l'agriculture. Par exemple en Malaisie, la maladie de la pourriture de basal de la tige (BSR) causée par le champignon Ganoderma Boninense est la maladie la plus grave pour les plantations de palmiers à huile. Le champignon infecte les palmiers à huile,causant des pertes de rendement et détruisant au final les arbres. Divers facteurs ont été précédemment signalés, qui influencent l'incidence de la BSR, tels que les cultures précédentes, les techniques de replantation, les types de sols et l'âge des arbres. Une gestion efficace et durable des stratégies pour contrôler le BSR se heurte principalement à un manque de compréhension des mécanismes d'établissement de la maladie, de son développement et de sa propagation. La présente recherche est une tentative d'appliquer la technique de fusion de données et la modélisation temporelle en système d'Information géographique (SIG) pour étudier le comportement des maladies des plantes dans un domaine particulier (zone artisanale). Cette recherche portera sur comment les SIG peuvent aider à évaluer la distribution des maladies des plantes dans une plantation de petite échelle. Avec les progrès simultanés dans les systèmes de positionnement global (GPS) et l'utilisation des systèmes d'Information géographique, ces techniques ont fourni de puissants outils d'analyse pour l'agriculture de précision. Les données pour l'analyse proviennent de palmiers à huile des expériences de densité de plantation aux stations de recherche MPOB à Teluk Intan, Perak, Malaisie.Dans le cas de la maladie de la BSR, les résultats de l'émission de modélisation prédictive ont observé une corrélation entre les maladies BSR prédites avec celles visuellement données par le BSR. Il a été constaté que la modélisation prédictive proposée a bien prédit la présence de la maladie de la BSR. Même si au début d'infection des maladies BSR, le modèle n'a pas fixé exactement la distribution de la maladie, la performance du modèle sera améliorée avec la sélection de la source de données. Dans l'ensemble, le modèle a bien prédit la présence de maladies avec une précision allant jusqu'à 98,9 %. / This thesis represents a new approach for predicting plant disease in a plantation through combination of data fusion and spatio-temporal modelling. Plant disease is a major problem in the world of agriculture. Example in Malaysia, basalstem rot disease (BSR) caused by Ganoderma Boinense is the most serious disease for oil palm plantation in Malaysia. The fungus infects oil palm trees, initially causing yield loss and finally killing the trees. Various factors were previously reported to influence incidence of BSR, such as previous crops, techniques for replanting, types of soils and the age of trees. At present effective and sustainable management strategies to control BSR are hampered mainly by a lack of understanding of mechanisms of disease establishment, development and spread. The present research is an attempt to apply data fusion technique and temporal modelling in Geographical Information System (GIS) to investigate the behaviour of plant disease in a specific area (small skill area). This research will focus on how GIS can help to assess the distribution plant disease in a small scale plantation. With concurrent advances in global positioning systems (GPS) and the use of geographical Information Systems(GIS) techniques have provided powerful analysis tools for precision agriculture. Data for analysis were obtained from oil palm planting density experiments at MPOB research stations at Teluk Intan, Perak, Malaysia. In the case of BSR disease, the results of the predictive modelling show a significance correlation between predicted BSR diseases with visually observed BSR data. It found that the proposed predictive modelling has well predicted the presence of BSR disease. Although at the beginning stage of BSR diseases infection, the model has not fitted exactly the distribution of the disease, we believe that with the proper selection of the source of data, the performance of the model will be improved.Overall, the model has well predicted the presence of diseases with accuracy up to 98.9%.

La fusion d'informations issues de plusieurs sources cherche à améliorer la prise de décision. Pour réaliser cette fusion, la théorie des fonctions de croyance utilise des règles de combinaison faisant bien souvent l'hypothèse de l'indépendance des sources. Cette forte hypothèse n'est, cependant, ni formalisée ni vérifiée. Elle est supposée pour justifier le choix du type de règles à utiliser sans avoir, pour autant, un moyen de la vérifier. Nous proposons dans ce rapport de thèse un apprentissage de l'indépendance cognitive de sources d'information. Nous détaillons également une approche d'apprentissage de la dépendance positive et négative des sources. Les degrés d'indépendance, de dépendance positive et négative des sources ont principalement trois utilités. Premièrement, ces degrés serviront à choisir le type de règles de combinaison à utiliser lors de la combinaison. Deuxièmement, ces degrés exprimés par une fonction de masse sont intégrés par une approche d'affaiblissement avant de réaliser la combinaison d'information. Une troisième utilisation de cette mesure d'indépendance consiste à l'intégrer dans une nouvelle règle de combinaison. La règle que nous proposons est une moyenne pondérée avec ce degré d'indépendance. / The theory of belief functions manages uncertainty and proposes a set of combination rules to aggregate beliefs of several sources. Some combination rules mix evidential information where sources are independent; other rules are suited to combine evidential information held by dependent sources. Information on sources ' independence is required to justify the choice of the adequate type of combination rules. In this thesis, we suggest a method to quantify sources' degrees of independence that may guide the choice of the appropriate type of combination rules. In fact, we propose a statistical approach to learn sources' degrees of independence from all provided evidential information. There are three main uses of estimating sources' degrees of independence: First, we use sources' degree of independence to guide the choice of combination rules to use when aggregating beliefs of several sources. Second, we propose to integrate sources' degrees of independence into sources' beliefs leading to an operator similar to the discounting. Finally, we define a new combination rule weighted with sources' degree of independence.

Supply chain systems have become a vital component of successful networked business firms/organisations. Over the last three decades, there has been a dramatic growth globally in the formation of supply chain networks. Research, however, indicates that there has been an increase in reported supply chains failures, and the incompatibility issues between participated organisations. Yet, these incompatibility issues are not just technical, but encompass wider cultural, organisational, and economical factors. Whilst research has shown the effect of such factors on supply chain performance, the influence of achieving corporate culture compatibility to the success of supply chains remains poorly understood. This is because it is widely accepted that organisations that operate in the same region possess a similar culture. In contrast, this research will examine the existence of corporate culture diversity between organisations in the same region, rather than diversity of national culture across different regions. Specifically, the study described the development of corporate culture compatibility between supply chains’ organisations and its influences on supply chain performance. Therefore, the thesis focus is the complex interrelationships between corporate culture compatibility of member organisations and supply chain performance. This research identifies cultural norms and beliefs of supply chain members within key organisational factors, rather than national or multi-national organisations factors, as in Hofstede (1983). A multi-method research design (combining case study, simulation, and neuro-fuzzy methods) was used to provide a rounded perspective on the phenomena studied. The multiple case studies helped to explore how corporate culture compatibility influences supply chain performance and develop a conceptual model for this association. The simulation experiments were conducted to verify the obtained conceptual framework from the multiple case studies, and investigate the effects of changing the corporate culture compatibility level on supply chain performance. The simulation is designed based on a Multi-Agent System (MAS) approach, in which each organisation in a supply chain is represented as an intelligent agent. Finally, a neuro-fuzzy approach is presented to assess corporate culture on supply chains context using real data. The analysis of the quantitative neuro-fuzzy study confirmed and validated the theoretical findings and adds depth to our understanding of the influences of corporate culture compatibility on supply chain performance. The study confirmed that organisations within the same supply chain in the same region possess different corporate cultures that consequently need the achievement of corporate culture compatibility as it is indicated by the literature. Moreover, the study revealed two types of corporate culture in supply chains’ context: individual culture and common culture. Individual culture refers to the internal beliefs within the organisation’s boundary, while common culture refers to beliefs when trading with partners across the organisation’s boundary. However, the study shows that common culture has more influences on supply chain performance than individual culture. In addition, the study highlighted bi-directional association between individual culture and common culture that helps the supply chain’s organisations developing their corporate culture compatibility. The results from the current study also showed that supply chain performance was shown to arise dramatically in response to corporate culture compatibility level increases. Yet, this increase in performance is diminished at a higher level of corporate culture compatibility, because more corporate culture compatibility increases are not cost effective for the organisations. In addition, organisations at a higher level of compatibility have more preferences to preserve their individual culture because it represents their identity. Furthermore, the study complements the gap in the literature related to the assessment of corporate culture of individual organisations in supply chains for sustaining a higher supply chain performance. While current culture assessment models observe individual organisations’ culture, the proposed approach describes a single concentrated model that integrates both individual and common culture in measuring influences of culture compatibility on supply chain performance. The findings from this study provide scholars, consultants, managers, and supply chain systems vendors with valuable information. This research thesis contributes to supply chain configuration and partnership formation theory, along with corporate culture theory, and is the first of its kind to establish the use of intelligent methods to model corporate culture compatibility. It is also one of the first empirical studies to compare corporate culture compatibility of supply chains’ organisations from organisational perspectives, rather than national perspectives.

La couverture hivernale des sols a un impact avéré sur la qualité de l'eau, en influant sur le transfert des flux de polluants d'origine agricole au sein de bassins versants. sa prédiction, si elle peut constituer une aide à la programmation d'actions dans le cadre de programmes de restauration de la qualité de l'eau, pose cependant un certain nombre de problèmes d'ordre méthodologique. L'objectif de ce travail est de développer une méthode reproductible qui permette d'effectuer une prédiction à court terme de la couverture hivernale des sols. Une approche approximative reposant sur un modèle expert utilisant la règle des évidences de dempster-Shafer a été retenue, car elle permet de prendre en compte l'imprécision et les lacunes de connaissances liées à cette problématique et aux données disponibles, et d'associer un degré de confiance aux résultats produits. Des prédictions de la couverture hivernale des sols sont d'abord effectuées sur un site expérimental localisé en Bretagne selon les deux hypothèses "sols couverts" et "sols nus à peu couverts". Après avoir déterminé les dynamiques spatio-temporelles des changements passés, notamment à partir d'une série multitemporelle d'images de télédétection, et avoir défini les facteurs motivant les changements passés et à venir, la fusion des sources d'informations qui représentent ces facteurs est réalisée avec la règle de Dempster-Shafer. Les résultats sont mitigés: les prédictions sont cohérentes à l'échelle du bassin versant mais l'affectation spatiale des prédictions à l'échelle parcellaire, qui est bonne pour la classe "sols couverts" reste problématique pour la classe "sols nus à peu couverts" en raison du niveau de conflit élévé entre les sources d'information pour cette hypothèse. L'utilisation de la théorie récente de Dezert-Smarandache, qui autorise la prise en compte d'informations paradoxales, permet d'améliorer les scores de prédictions pour la classe "sols nus à peu couverts". Une évaluation de la reproductibilité de l'approche prédictive développée est alors effectuée à travers l'application du modèle sur un bassin versant plus étendu et où les données sont moins nombreuses. Si la classe "sols couverts" reste bien évaluée avec les deux règles de fusion, l'affectation spatiale de la classe "sols nus à peu couverts" est en revanche toujours affectée d'un faible taux de prédiction correcte, illustrant la nécessité de l'intégration de nouvelles sources d'information dans le modèle pour cette hypothèse aux fortes variabilités spatiotemporelles.

Cette thèse se situe dans la cadre de l'organisation semi-automatique d'albums photo et s'intègre dans un cadre applicatif particulier d'un prototype de service développé par la division recherche et développement de France Telecom. Dans ce cadre applicatif, les photos peuvent être partagées entre plusieurs personnes et peuvent êtres commentées vocalement et/ou textuellement par ces déférents utilisateurs. Le processus d'indexation développé dans cette thèse ne se limite pas seulement à l'indexation d'une collection d'images mais traite également leurs commentaires associés ce qui rend ces contenus multimédia. D'autres informations peuvent également être associées aux photos comme les dates et les lieux d'acquisition de l'image (qui sont connus avec une grande précision grâce notamment au développement de moyens de géo-localisation des appareils multimédia) et peuvent êtres exploitées pour l'organisation de la base. Ainsi, le travail envisagé dans le cadre de cette thèse se focalise sur des documents multimédias avec déférentes modalités : image, texte, son et données. L'un des objectifs à atteindre concerne la fusion des informations issues de ces déférentes modalités dans le but d'identifier les personnages figurant dans les images qui permettent ainsi d'indexer les documents. Chacun des documents de la collection est représenté par ses contenus relatifs aux déférents médias mais est aussi considéré dans son contexte. Pour analyser chaque contenu d'un document, nous utilisons des outils d'indexation qui leur sont spécifiques. Le contexte d'une image est exploité à partir de descripteurs déjà calculés sur des documents de la base en exploitant les dates et lieux d'acquisition des images associées. La contribution essentielle de ce travail concerne donc l'indexation de documents multimédia par leur contenu et leur contexte.

The aim of this thesis is to explore the adaptability of the Evidential Reasoning (ER) Rule as a method to provide a useful supporting tool for helping investors make decisions on financial investments. Decision making in financial investment often involves conflicting information and subjective judgment of the investors. Accordingly, the ER Rule, extended from the original popular Evidential Reasoning algorithm and developed for MCDM (Multiple Criteria Decision Making), is particularly suited for handling conflicts in information and to allow for judgmental weighting on the sources of evidence. In order to do so, a specific EIA (Efficient Information Assessment) process modeled by the mass function of Dempster-Shafer Theory has been constructed such that the underlying architecture of the model satisfies the requirement of the ER rule. The fundamental concern is to define and assess “efficient information”. For this purpose, a process denoted the Efficient Information Assessment (EIA) is defined which applies the mass function of Dempster-Shafer theory. Any relevant information selected from an expert’s knowledge database is “efficient” if the data is fully in compliance with the requirement of the ER rule. The logical process of the EIA model proceeds with a set of portfolio strategies from the information recommended by top financial analysts. Then, as a result, the model enables the ER rule to make an evaluation of all strategies for helping investors make decisions. Experiments were carried out to back-test the investment strategy using data from the China Stock Market & Accounting Research (CSMAR) Database for the four-year period between 2009 and 2012. The data contained more than 270,000 reports from more than 4,600 financial analysts. The risk-adjusted average annual return of the strategy outperformed that of the CSI300 index by as much as 10.69% for an investment horizon of six months, with the p value from Student’s t-test as low as 0.02%. The EIA model serves as the first successful application adapting the ER Rule for a new and effective decision-making process in financial investment, and this work is the only empirical study applying the ER Rule to the opinions of financial analysts, to the best of my knowledge.