La mise en place d’une politique de maintenance prévisionnelle est un défi majeur dans l’industrie qui tente de réduire le plus possible les frais relatifs à la maintenance. En effet, les systèmes sont de plus en plus complexes et demandent un suivi de plus en plus poussé afin de rester opérationnels et sécurisés. Une maintenance prévisionnelle nécessite d’une part d’évaluer l’état de dégradation des composants du système, et d’autre part de pronostiquer l’apparition future d’une panne. Plus précisément, il s’agit d’estimer le temps restant avant l’arrivée d’une défaillance, aussi appelé Remaining Useful Life ou RUL en anglais. L’estimation d’une RUL constitue un réel enjeu car la pertinence et l’efficacité des actions de maintenance dépendent de la justesse et de la précision des résultats obtenus. Il existe de nombreuses méthodes permettant de réaliser un pronostic de durée de vie résiduelle, chacune avec ses spécificités, ses avantages et ses inconvénients. Les travaux présentés dans ce manuscrit s’intéressent à une méthodologie générale pour estimer la RUL d’un composant. L’objectif est de proposer une méthode applicable à un grand nombre de cas et de situations différentes sans nécessiter de modification majeure. De plus, nous cherchons aussi à traiter plusieurs types d’incertitudes afin d’améliorer la justesse des résultats de pronostic. Au final, la méthodologie développée constitue une aide à la décision pour la planification des opérations de maintenance. La RUL estimée permet de décider de l’instant optimal des interventions nécessaires, et le traitement des incertitudes apporte un niveau de confiance supplémentaire dans les valeurs obtenues. / Predictive maintenance strategies can help reduce the ever-growing maintenance costs, but their implementation represents a major challenge. Indeed, it requires to evaluate the health state of the component of the system and to prognosticate the occurrence of a future failure. This second step consists in estimating the remaining useful life (RUL) of the components, in Other words, the time they will continue functioning properly. This RUL estimation holds a high stake because the precision and accuracy of the results will influence the relevance and effectiveness of the maintenance operations. Many methods have been developed to prognosticate the remaining useful life of a component. Each one has its own particularities, advantages and drawbacks. The present work proposes a general methodology for component RUL estimation. The objective i to develop a method that can be applied to many different cases and situations and does not require big modifications. Moreover, several types of uncertainties are being dealt With in order to improve the accuracy of the prognostic. The proposed methodology can help in the maintenance decision making process. Indeed, it is possible to select the optimal moment for a required intervention thanks to the estimated RUL. Furthermore, dealing With the uncertainties provides additional confidence into the prognostic results.

Dans cette thèse nous intéressons à la conception d'un robot mobile capable d’analyser le comportement et le mouvement d’une personne en environnement intérieur et encombré, par exemple le domicile d’une personne âgée. Plus précisément, notre objectif est de doter le robot des capacités de perception visuelle de la posture humaine de façon à mieux maîtriser certaines situations qui nécessitent de comprendre l’intention des personnes avec lesquelles le robot interagit, ou encore de détecter des situations à risques comme les chutes ou encore d’analyser les capacités motrices des personnes dont il a la garde. Le suivi de la posture dans un environnement dynamique et encombré relève plusieurs défis notamment l'apprentissage en continue du fond de la scène et l'extraction la silhouette qui peut être partiellement observable lorsque la personne est dans des endroits occultés. Ces difficultés rendent le suivi de la posture une tâche difficile. La majorité des méthodes existantes, supposent que la scène est statique et la personne est toujours visible en entier. Ces approches ne sont pas adaptées pour fonctionner dans des conditions réelles. Nous proposons, dans cette thèse, un nouveau système de suivi capable de suivre la posture de la personne dans ces conditions réelles. Notre approche utilise une grille d'occupation avec un modèle de Markov caché pour apprendre en continu l'évolution de la scène et d'extraire la silhouette, ensuite un algorithme de filtrage particulaire hiérarchique est utilisé pour reconstruire la posture. Nous proposons aussi un nouvel algorithme de gestion d'occlusion capable d'identifier et d'exclure les parties du corps cachées du processus de l'estimation de la pose. Finalement, nous avons proposé une base de données contenant des images RGB-D avec la vérité-terrain dans le but d'établir une nouvelle référence pour l'évaluation des systèmes de capture de mouvement dans un environnement réel avec occlusions. La vérité-terrain est obtenue à partir d'un système de capture de mouvement à base de marqueur de haute précision avec huit caméras infrarouges. L'ensemble des données est disponible en ligne. La deuxième contribution de cette thèse, est le développement d'une méthode de localisation visuelle à partir d'une caméra du type RGB-D montée sur un robot qui se déplace dans un environnement dynamique. En effet, le système de capture de mouvement que nous avons développé doit équiper un robot se déplaçant dans une scène. Ainsi, l'estimation de mouvement du robot est importante pour garantir une extraction de silhouette correcte pour le suivi. La difficulté majeure de la localisation d'une caméra dans un environnement dynamique, est que les objets mobiles de la scène induisent un mouvement supplémentaire qui génère des pixels aberrants. Ces pixels doivent être exclus du processus de l'estimation du mouvement de la caméra. Nous proposons ainsi une extension de la méthode de localisation dense basée sur le flux optique pour isoler les pixels aberrants en utilisant l'algorithme de RANSAC. / In this thesis we are interested in designing a mobile robot able to analyze the behavior and movement of a a person in indoor and cluttered environment. Our goal is to equip the robot by visual perception capabilities of the human posture to better analyze situations that require understanding of person with which the robot interacts, or detect risk situations such as falls or analyze motor skills of the person. Motion capture in a dynamic and crowded environment raises multiple challenges such as learning the background of the environment and extracting the silhouette that can be partially observable when the person is in hidden places. These difficulties make motion capture difficult. Most of existing methods assume that the scene is static and the person is always fully visible by the camera. These approaches are not able to work in such realistic conditions. In this thesis, We propose a new motion capture system capable of tracking a person in realistic world conditions. Our approach uses a 3D occupancy grid with a hidden Markov model to continuously learn the changing background of the scene and to extract silhouette of the person, then a hierarchical particle filtering algorithm is used to reconstruct the posture. We propose a novel occlusion management algorithm able to identify and discards hidden body parts of the person from process of the pose estimation. We also proposed a new database containing RGBD images with ground truth data in order to establish a new benchmark for the assessment of motion capture systems in a real environment with occlusions. The ground truth is obtained from a motion capture system based on high-precision marker with eight infrared cameras. All data is available online. The second contribution of this thesis is the development of a new visual odometry method to localize an RGB-D camera mounted on a robot moving in a dynamic environment. The major difficulty of the localization in a dynamic environment, is that mobile objects in the scene induce additional movement that generates outliers pixels. These pixels should be excluded from the camera motion estimation process in order to produce accurate and precise localization. We thus propose an extension of the dense localization method based on the optical flow method to remove outliers pixels using the RANSAC algorithm.

No description available.

Feature extraction is the main driving force behind the advancement of the image processing techniques infields suchas image quality assessment, objectdetection, and object recognition. In this work, we perform a comprehensive and in-depth study on feature extraction for the following applications: image macro-uniformity assessment, 2.5D printing quality assessment, streak defect detection, and pedestrian detection. Firstly, a set of multi-scale wavelet-based features is proposed, and a quality predictor is trained to predict the perceived macro-uniformity. Secondly, the 2.5D printing quality is characterized by a set of merits that focus on the surface structure.Thirdly, a set of features is proposed to describe the streaks, based on which two detectors are developed: the first one uses Support Vector Machine (SVM) to train a binary classifier to detect the streak; the second one adopts Hidden Markov Model (HMM) to incorporates the row dependency information within a single streak. Finally, a novel set of pixel-difference features is proposed to develop a computationally efficient feature extraction method for pedestrian detection.

<p>U doktorskoj disertaciji je prikazan postupak optimizacije podzemne mreže<br />za potrebe izgradnje beogradskog metroa. U postupku optimizacije kori&scaron;ćen<br />je metod prethodne ocene tačnosti. Na osnovu građevinskih standarda<br />izvr&scaron;en je proračun zahtevane tačnosti proboja tunela, kao osnovnog<br />kriterijuma tačnosti za razvijanje podzemne tunelske mreže. U postupku<br />optimizacije analizirani su različiti planovi opažanja, kao i dobijeni rezultati<br />prethodne analize za svaki plan pojedinačno. Na osnovu zadatog kriterijuma<br />maksimalne poprečene gre&scaron;ke proboja tunela usvojen je konačan plan<br />opažanja.</p> / <p>The docotoral thesis presents an optimization method of the underground<br />network for the construction of the Belgrade metro. In the process of<br />optimization, method of preanalysis has been used. Based on the<br />construction standards, the calculation of the required breakthrough<br />accuracy, as the fundamental criteria of accuracy for the development of the<br />underground tunnel network, has been made. In the process of optimization<br />different plans of observations have been analyzed, as well as the results<br />obtained from the preanalysis for each plan individually. Based on the<br />required criteria of maximal transverse error of the tunnel breakthrough, the<br />final plan of observations has been adopted.</p>

The hidden state Markov (HSM) model is introduced as a new conceptual framework for modelling long-term persistence in hydrological time series. Unlike the stochastic models currently used, the conceptual basis of the HSM model can be related to the physical processes that influence long-term hydrological time series in the Australian climatic regime. A Bayesian approach was used for model calibration. This enabled rigourous evaluation of parameter uncertainty, which proved crucial for the interpretation of the results. Applying the single site HSM model to rainfall data from selected Australian capital cities provided some revealing insights. In eastern Australia, where there is a significant influence from the tropical Pacific weather systems, the results showed a weak wet and medium dry state persistence was likely to exist. In southern Australia the results were inconclusive. However, they suggested a weak wet and strong dry persistence structure may exist, possibly due to the infrequent incursion of tropical weather systems in southern Australia. This led to the postulate that the tropical weather systems are the primary cause of two-state long-term persistence. The single and multi-site HSM model results for the Warragamba catchment rainfall data supported this hypothesis. A strong two-state persistence structure was likely to exist in the rainfall regime of this important water supply catchment. In contrast, the single and multi-site results for the Williams River catchment rainfall data were inconsistent. This illustrates further work is required to understand the application of the HSM model. Comparisons with the lag-one autoregressive [AR(1)] model showed that it was not able to reproduce the same long-term persistence as the HSM model. However, with record lengths typical of real data the difference between the two approaches was not statistically significant. Nevertheless, it was concluded that the HSM model provides a conceptually richer framework than the AR(1) model. / PhD Doctorate

In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.

Organohalide-respiring Bacteria (OHRB) play key roles in the reductive dehalogenation of natural organohalides and anthropogenic chlorinated contaminants. Reductive dehalogenases (RDases) catalyze the cleavage of carbon-halogen bonds, enabling respiratory energy conservation and growth. Large numbers of RDase genes, a majority lacking experimental characterization of function, are found on the genomes of OHRB. In silico genomics tools were employed to identify shared sequence features among RDase genes and proteins, predict RDase functionality, and elucidate RDase evolutionary history. These analyses showed that the RDase superfamily could be divided into proteins exported to the membrane and cytoplasmic proteins, indicating that not all RDases function in respiration. Further, Hidden Markov models (HMMs) and multiple sequence alignments (MSAs) based upon biochemically characterized RDases identified previously uncharacterized members of an RDase superfamily, delineated protein domains and amino acid motifs serving to distinguish RDases from unrelated iron-sulfur proteins. Such conserved and discriminatory features among RDases may facilitate monitoring of organohalide-degrading microbial communities or improve accuracy of genome annotation. Phylogenetic analyses of RDase superfamily sequences provided evidence of convergent evolution and horizontal gene transfer (HGT) across distinct OHRB genera. Yet, the low frequency of RDase transfer outside the genus level and the absence of RDase transfer between phyla indicate that RDases evolve primarily by vertical evolution or HGT is restricted among related OHRB strains. Polyphyletic evolutionary lineages within the RDase superfamily comprise distantly-related RDases, some exhibiting activities towards the same substrates, suggesting a longstanding history of OHRB adaptation to natural organohalides. Similar functional and phylogenetic analyses provided evidence that nitrous oxide (N₂O, a potent greenhouse gas) reductase (nosZ) genes from versatile OHRB members of the Anaeromyxobacter and Desulfomonile genera comprised a nosZ sub-family evolutionarily distinct from nosZ found in non-OHRB denitrifiers. Hence, elucidation of RDase and NosZ sequence diversity may enhance the mitigation of anthropogenic organohalides and greenhouse gases (i.e., N₂O), respectively. The tetrachloroethene-respiring bacterium Geobacter lovleyi strain SZ exhibited genomic features distinguishing it from non-organohalide-respiring members of the Geobacter genus, including a conjugative pilus transfer gene cluster, a chromosomal genomic island harboring two RDase genes, and a diminished set of c-type cytochrome genes. The G. lovleyi strain SZ genome also harbored a 77 kbp plasmid carrying 15 out of the 24 genes involved in biosynthesis of corrinoid, likely related to this strains ability to degrade PCE to cis-DCE in the absence of supplied corrinoid (i.e., vitamin B₁₂). Although corrinoids are essential cofactors to RDases, the strictly organohalide-respiring Dehalococcoides mccartyi strains are corrinoid auxotrophs and depend upon uptake of extracellular corrinoids via Archaeal and Bacterial salvage pathways. A key corrinoid salvage gene in D. mccartyi, cbiZ, occurs at duplicated loci adjacent to RDase genes and appears to have been horizontally-acquired from Archaea. These comparative genome analyses highlight RDase dependencies upon corrinoids and also suggest mobile genomic elements (e.g., plasmids) are associated with organohalide respiration and corrinoid acquisition among OHRB. In summary, analyses of OHRB genomes promise to enable more complete modeling of metabolic and evolutionary processes associated with the turnover of organohalides in anoxic environments. These efforts also expand knowledge of biomarkers for monitoring OHRB activity in anoxic environments, and will improve our understanding of the fate of chlorinated contaminants.

長久以來音樂於人們生活中扮演著極為重要角色，在大多數人的成長過程裡或多或少皆有令其印象深刻之旋律。然而這些旋律常由專業人士所創作，對於未接受過專業訓練的民眾而言，若欲創作自己專屬之音樂難度甚高，而此目標也變得遙不可及。 為解決上述問題，降低音樂自行創作門檻，本研究以行動裝置之使用環境不受限及直覺性觸控介面兩大特性為運行環境，設計Android系統上之音樂創作軟體，協助未受過音樂專業訓練的庶民透過音樂主旋律並搭配適合的和弦配樂達成自行創作音樂之目標。本創作音樂軟體利用行動裝置提供「繪畫旋律曲線」與「字詞輸入」兩種輸入方式，將使用者繪畫的旋律曲線轉換為一段音樂主旋律，進行調性判斷、修正主旋律組成音並利用音樂動機樣式變化加以使主旋律更為豐富，輔以隱藏式馬可夫模型產生適切之和弦序列。最後將主旋律聲波與其產生的和弦聲波以混音的結果呈現給予使用者。 為評估本創作軟體是否符合使用者需求，以實驗觀察法邀請38位受試者進行軟體操作與評估。分析結果顯示，近75%的受試者認為由音樂創作軟體所產生之主旋律與和弦彼此搭配良好且符合其音樂動機。在介面易用性評估方面，結果顯示有近90%受測者認為本研究所提出的音樂創作軟體具有簡單易用之特性且能夠協助其降低創作音樂之門檻。簡單且易用的音樂創作軟體在實務上之重要性不言可喻，不但可使非專業使用者達成自我創作音樂之夢想，更可讓其沉浸於音樂創作成就感之中。 / Music plays as an essential role in human life and it affects the listeners on a certain extent. However, a pleasing music is the production of musicians and is difficult to be created by novices without musical specialty. To lower the entry point of music creation, this thesis design and develop a music accompaniment system on Android with the characteristics of intuitive input and ubiquity for novices without professional music background. The developed system consists of the following modules, main melody preprocessing (key determination and melody modification), music similarity retrieval, main melody post processing (music motif variance), chord accompaniment (Hidden Markov Model and mixing main melody and chord melody) and text processing (tone determination and pitch finding) to automatically match the accordance between melodies and chords that are inputted by patting or word. Thirty-eight participants were invited for system evaluation using the observational experiment. Nearly 75% of participants perceived that the melody and chord matching fits their musical motivations, while 90% stated that they can rely on the system to easily produce desirable music. Our findings contribute to the essence of music creation that the system provides a simplified interface for novice being immersed in music accomplishments, similar to that of professional musicians.

Tracking and understanding human gait is an important step towards improving elderly mobility and safety. This thesis presents a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with cameras mounted on the moving walker. The tracker estimates 3D poses from images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. It employs a probabilistic approach based on particle filtering with three different camera setups: a monocular RGB camera, binocular RGB cameras, and a depth camera. For the RGB cameras, observation likelihoods are designed to compare the colors and gradients of each frame with initial templates that are manually extracted. Two strategies are also investigated for handling appearance change of tracking target: increasing number of templates and using different representations of colors. For the depth camera, two observation likelihoods are developed: the first one works directly in the 3D space, while the second one works in the projected image space. Experiments are conducted to evaluate the performance of the tracking system with different users for all three camera setups. It is demonstrated that the trackers with the RGB cameras produce results with higher error as compared to the depth camera, and the strategies for handling appearance change improve tracking accuracy in general. On the other hand, the tracker with the depth sensor successfully tracks the 3D poses of users over the entire video sequence and is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target.