Determinization of timed Petri nets behaviors

International audience

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.

Social-cognitive Antecedents of Ambidextrous Orientation in Family-owned Startups: The Role of Family Ties, Achievement Motivation, and Internal Locus of Control

Regulatory Focus Theory predicts that the motivation to self-regulate goal-directed thought and behavior depends on two distinct regulation strategies: a promotion focus based on attaining gains and a prevention focus based on avoiding losses. This study took a social-cognitive approach predicting that regulatory focus has an impact on how family startups (several family related founders) explore “new ideas”, exploit “old certainties” and achieve the balance of both (ambidexterity), compared to lone founder startups (only one founder present). It was proposed that the social context of family ties among founders leads them to a prevention focus concerned with avoiding the loss of the socio-emotional benefits of those ties. In order to avoid such a loss, family founders were expected to increase their risk perceptions and thus, explore less than lone founders, who lack such socio-emotional ties. It was also proposed that two commonly used psychological traits in entrepreneurship research --achievement motivation and internal locus of control, predispose entrepreneurs to a promotion focus. Founders with a promotion focus, in turn, were hypothesized to lead startups to more risk-seeking behaviors and to more explorative orientation. The previous argument was used as a springboard to derive hypotheses about ambidexterity (the ability to exploit and explore simultaneously) and survival hazards. Using Regulatory Focus Theory, exploitative orientation, conceptualized as the motivational strength to continue on previous paths of action, was hypothesized to be not significantly different from that of lone founder startups. Taking previous arguments together, lone founder startups were hypothesized to be more ambidextrous than family startups. Finally, ambidexterity and internal locus of control were hypothesized to reduce survival hazards in family startups. The findings suggested that family startups explore less than lone founder startups even after controlling for group effects. Interesting but contradictory findings revealed that internal locus of control have both a positive direct effect and a positive interaction that increases the explorative and ambidextrous orientation gap of family startups over lone founder startups. As expected, ambidexterity and internal locus of control reduced survival hazards on family startups. Implications for practitioners were derived based on a sample of 470 nascent entrepreneurs.

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.

A second order control-volume finite-element least-squares strategy for simulating diffusion in strongly anisotropic media

An unstructured mesh �nite volume discretisation method for simulating di�usion in anisotropic media in two-dimensional space is discussed. This technique is considered as an extension of the fully implicit hybrid control-volume �nite-element method and it retains the local continuity of the ux at the control volume faces. A least squares function recon- struction technique together with a new ux decomposition strategy is used to obtain an accurate ux approximation at the control volume face, ensuring that the overall accuracy of the spatial discretisation maintains second order. This paper highlights that the new technique coincides with the traditional shape function technique when the correction term is neglected and that it signi�cantly increases the accuracy of the previous linear scheme on coarse meshes when applied to media that exhibit very strong to extreme anisotropy ratios. It is concluded that the method can be used on both regular and irregular meshes, and appears independent of the mesh quality.