DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Adaptive Software Fault Prediction Approach Using Object-Oriented Metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

L'activité tonique diaphragmatique chez les enfants avec et sans support respiratoire

Introduction : Les nourrissons, vu la grande compliance de leur cage thoracique, doivent maintenir activement leur volume pulmonaire de fin d’expiration (VPFE). Ceci se fait par interruption précoce de l’expiration, et par le freinage expiratoire au niveau laryngé et par la persistance de la contraction des muscles inspiratoires. Chez les nourrissons ventilés mécaniquement, notre équipe a montré que le diaphragme est activé jusqu’à la fin de l’expiration (activité tonique). Il n’est pas clair si cette activité tonique diaphragmatique compense pour l’absence de freinage laryngé liée à l’intubation endotrachéale. Objectif : Notre objectif est de déterminer si l’activité tonique diaphragmatique persiste après l’extubation chez les nourrissons et si elle peut être observée chez les enfants plus âgés. Méthode : Ceci est une étude observationnelle longitudinale prospective de patients âgés de 1 semaine à 18 ans admis aux soins intensifs pédiatriques (SIP), ventilés mécaniquement pour >24 heures et avec consentement parental. L’activité électrique du diaphragme (AEdi) a été enregistrée à l’aide d’une sonde nasogastrique spécifique à 4 moments durant le séjour aux SIP : en phase aigüe, pré et post-extubation et au congé. L’AEdi a été analysée de façon semi-automatique. L’AEdi tonique a été définie comme l’AEdi durant le dernier quartile de l’expiration. Résultats : 55 patients avec un âge médian de 10 mois (écart interquartile: 1-48) ont été étudiés. Chez les nourrissons (<1an, n=28), l’AEdi tonique en pourcentage de l’activité inspiratoire était de 48% (30-56) en phase aigüe, 38% (25-44) pré-extubation, 28% (17-42) post-extubation et 33% (22-43) au congé des SIP (p<0.05, ANOVA, avec différence significative entre enregistrements 1 et 3-4). Aucun changement significatif n’a été observé pré et post-extubation. L’AEdi tonique chez les patients plus âgés (>1an, n=27) était négligeable en phases de respiration normale (0.6mcv). Par contre, une AEdi tonique significative (>1mcv et >10%) a été observée à au moins un moment durant le séjour de 10 (37%) patients. La bronchiolite est le seul facteur indépendant associé à l’activité tonique diaphragmatique. Conclusion : Chez les nourrissons, l’AEdi tonique persiste après l’extubation et elle peut être réactivée dans certaines situations pathologiques chez les enfants plus âgés. Elle semble être un indicateur de l’effort du patient pour maintenir son VPFE. D’autres études devraient être menées afin de déterminer si la surveillance de l’AEdi tonique pourrait faciliter la détection de situations de ventilation inappropriée.

Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.