Anomaly Detection Based on Aggregation of Indicators

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining a reasonable computational cost

Analyzing the benefits of communication channels between deep learning models

Comme les domaines d’application des systèmes d’intelligence artificielle ainsi que les tâches associées ne cessent de se diversifier, les algorithmes d’apprentissage automatique et en particulier les modèles d’apprentissage profond et les bases de données requises au fonctionnement de ces derniers grossissent continuellement. Certains algorithmes permettent de mettre à l’échelle les nombreux calculs en sollicitant la parallélisation des données. Par contre, ces algorithmes requièrent qu’une grande quantité de données soit échangée afin de s’assurer que les connaissances partagées entre les cellules de calculs soient précises. Dans les travaux suivants, différents niveaux de communication entre des modèles d’apprentissage profond sont étudiés, en particulier l’effet sur la performance de ceux-ci. La première approche présentée se concentre sur la décentralisation des multiples calculs faits en parallèle avec les algorithmes du gradient stochastique synchrone ou asynchrone. Il s’avère qu’une communication simplifiée qui consiste à permettre aux modèles d’échanger des sorties à petite bande passante peut se montrer bénéfique. Dans le chapitre suivant, le protocole de communication est modifié légèrement afin d’y communiquer des instructions pour l’entraînement. En effet, cela est étudié dans un environnement simplifié où un modèle préentraîné, tel un professeur, peut personnaliser l’entraînement d’un modèle initialisé aléatoirement afin d’accélérer l’apprentissage. Finalement, une voie de communication où deux modèles d’apprentissage profond peuvent s’échanger un langage spécifiquement fabriqué est analysée tout en lui permettant d’être optimisé de différentes manières.As artificial intelligence systems spread to more diverse and larger tasks in many domains, the machine learning algorithms, and in particular the deep learning models and the databases required to train them are getting bigger themselves. Some algorithms do allow for some scaling of large computations by leveraging data parallelism. However, they often require a large amount of data to be exchanged in order to ensure the shared knowledge throughout the compute nodes is accurate. In this work, the effect of different levels of communications between deep learning models is studied, in particular how it affects performance. The first approach studied looks at decentralizing the numerous computations that are done in parallel in training procedures such as synchronous and asynchronous stochastic gradient descent. In this setting, a simplified communication that consists of exchanging low bandwidth outputs between compute nodes can be beneficial. In the following chapter, the communication protocol is slightly modified to further include training instructions. Indeed, this is studied in a simplified setup where a pre-trained model, analogous to a teacher, can customize a randomly initialized model’s training procedure to accelerate learning. Finally, a communication channel where two deep learning models can exchange a purposefully crafted language is explored while allowing for different ways of optimizing that language

Anomaly Detection Based on Indicators Aggregation

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance. This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: International Joint Conference on Neural Networks (IJCNN 2014), Beijing : China (2014). arXiv admin note: substantial text overlap with arXiv:1407.088

Outcomes and Presurgical Correlates of Lumbar Interbody Cage Fusion

Rates of lumbar fusion surgery have been increasing with an estimated 192,000 procedures performed annually. However, satisfactory outcomes of lumbar fusion vary considerably and often emphasize technical success, such as arthrodesis, rather than Ill functional and quality of life outcomes. Interbody cage fusion was recently developed and touted as a superior alternative to existing lumbar fusion procedures. There is, however, a paucity of research to support these claims, particularly with regards to functional and quality of life outcomes. Moreover, predictive correlates of outcomes for interbody cage fusion have not been given adequate attention in the literature. The aims of this study were to characterize patients undergoing this new procedure, examine functional and multidimensional outcomes, and investigate the predictive efficacy of presurgical variables. A retrospective cohort research design was employed and entailed medical record reviews for presurgical data and telephone outcome surveys at least 18 months following surgery. Seventy-three patients who had undergone lumbar interbody cage fusion were identified from the private practice of an orthopedic surgeon and the Workers\u27 Compensation Fund of Utah. Presurgical variables coded for analysis included age at the time of surgery, severity rating of presurgical spinal pathology, smoking tobacco, depression, and pursuing litigation at the time of surgery. Of the total sample, 56 patients (76.7%) completed outcome surveys that assessed patient satisfaction, back-specific functioning, disability status, and physical and mental health functioning. While arthrodesis was achieved for most patients (84%), almost half were dissatisfied with their current back condition. Outcomes regarding disability and functioning were mixed. Arthrodesis was only moderately associated with better outcome and for a quite limited set of measure s. Three of the five presurgical variables (tobacco use, depression, and litigation) were consistently predictive of patient outcomes. Findings are discussed and compared to existing data on lumbar fusion procedures , and clinical implications for improved patient selection and possible interventions are highlighted. Consideration is given to the limitations of this study, such as retrospective design, no matched controls , and sample size. Directions for future research are suggested

The Effects of Cognitive Strategy and Exercise Setting on Running

The cognitive strategies of association and dissociation have been identified and studied in runners and other athletes. Association is said to involve thoughts that are task-oriented and may include a focus on pace, strategy, or physiological sensations. Conversely, dissociation involves task-irrelevant thoughts and may include thinking about such things as relationships, work, spiritual matters, or scenery. To date, studies have been largely descriptive, methodologically flawed, failed to use manipulation checks, and/or present unclear or differing conclusions. The emphasis with previous association and dissociation research has also been with elite and/or endurance athletes, such as marathon runners. Additionally, only a few studies have included more than one exercise setting, and these investigations seemed to indirectly suggest that the exercise environment may influence the use of cognitive strategies, performance, and perceived exertion. In an effort to clarify the effects of cognitive strategies and exercise setting on several dependent variables, the current study investigated a sample of experienced recreational runners in a 3 x 2 mixed experimental design. Exercise setting had three levels (treadmill, indoor track, and outdoor route) and was a within-groups independent variable and cognitive strategy had two levels (association vs. dissociation) as a between-groups factor. The dependent variables were the ratings of perceived exertion, course satisfaction, and performance time for a 5 km run. The results indicated strong effects for the influence of exercise setting. The treadmill setting was rated as least satisfying, while resulting in the highest perceived exertion and slowest performance time. Alternately, the outdoor route resulted in the highest level of course satisfaction, while also yielding the lowest level of perceived exertion. For the dissociation strategy, the outdoor setting garnered the lowest perceived exertion, followed by the indoor track and treadmill, respectively, while with the associative strategy perceived exertion did not significantly differ among the settings. There were no overall differences in perceived exertion or course satisfaction between the cognitive strategies; however, there was a medium effect size and trend for the association group to run faster. The implications and limitations of these data are discussed and suggestions for future research are provided

Visual Mining and Statistics for a Turbofan Engine Fleet

International audienceSnecma, as a turbofan manufacturer, needs to deal with a wide eet of more than thousands of engines. Every day, data from aircraft engines are broadcas- ted to the ground. Some airlines companies rely on their engine manufacturer to control the engines' behavior and help prepare for maintenance scheduling. The goal of the manufacturer is to detect abnormalities to help schedule main- tenance operations. The advantage of the manufacturer as MRO operator is the registered memory of all past events that appears on its eet of engines. If one opens the possibility to look in this huge amount of data for corresponding similar behaviors, which may have append in the past (for all engines of all customer companies), it becomes possible to make some targeted statistics of the future

Sudden change detection in turbofan engine behavior

International audienceSnecma, as a turbofan manufacturer, needs to deal with a wide eet of more than thousands of engines. Every day, data from aircraft engines are broadcas- ted to the ground. Some airlines companies rely on their engine manufacturer to control the engines' behavior and help prepare for maintenance scheduling. The goal of the manufacturer is to detect abnormalities to help schedule main- tenance operations. The advantage of the manufacturer as MRO operator is the registered memory of all past events that appears on its eet of engines. If one opens the possibility to look in this huge amount of data for corresponding similar behaviors, which may have append in the past (for all engines of all customer companies), it becomes possible to make some targeted statistics of the future

Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

International audienceDetecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a field in which anomaly detection is crucial. Manufacturers collect large amount of engine related data during flights which are used, among other applications, to detect anomalies. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that builds upon human expertise and that remains understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines

Étude des effets pathologiques du gène vpr du virus de l'immunodéficience humaine 1 dans un modèle de souris transgéniques

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal