3,779 research outputs found

    Robust flow control in data-communication networks with multiple time-delays

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    Robust controller design for a flow control problem where uncertain multiple time-varying time-delays exist is considered. Although primarily data-communication networks are considered, the presented approach can also be applied to other flow control problems and can even be extended to other control problems where uncertain multiple time-varying time-delays exist. Besides robustness, tracking and fairness requirements are also considered. To solve this problem, an H∞ optimization problem is set up and solved. Unlike previous approaches, where only a suboptimal solution could be found, the present approach allows to design an optimal controller. Simulation studies are carried out in order to illustrate the time-domain performance of the designed controllers. The obtained results are also compared to the results of a suboptimal controller obtained by an earlier approach. Copyright © 2009 John Wiley & Sons, Ltd

    Information fusion architectures for security and resource management in cyber physical systems

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    Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

    Architecting Data Centers for High Efficiency and Low Latency

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    Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency. This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd

    FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

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    There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible

    Decentralized and Fault-Tolerant Control of Power Systems with High Levels of Renewables

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    Inter-area oscillations have been identified as a major problem faced by most power systems and stability of these oscillations are of vital concern due to the potential for equipment damage and resulting restrictions on available transmission capacity. In recent years, wide-area measurement systems (WAMSs) have been deployed that allow inter-area modes to be observed and identified.Power grids consist of interconnections of many subsystems which may interact with their neighbors and include several sensors and actuator arrays. Modern grids are spatially distributed and centralized strategies are computationally expensive and might be impractical in terms of hardware limitations such as communication speed. Hence, decentralized control strategies are more desirable.Recently, the use of HVDC links, FACTS devices and renewable sources for damping of inter-area oscillations have been discussed in the literature. However, very few such systems have been deployed in practice partly due to the high level of robustness and reliability requirements for any closed loop power system controls. For instance, weather dependent sources such as distributed winds have the ability to provide services only within a narrow range and might not always be available due to weather, maintenance or communication failures.Given this background, the motivation of this work is to ensure power grid resiliency and improve overall grid reliability. The first consideration is the design of optimal decentralized controllers where decisions are based on a subset of total information. The second consideration is to design controllers that incorporate actuator limitations to guarantee the stability and performance of the system. The third consideration is to build robust controllers to ensure resiliency to different actuator failures and availabilities. The fourth consideration is to design distributed, fault-tolerant and cooperative controllers to address above issues at the same time. Finally, stability problem of these controllers with intermittent information transmission is investigated.To validate the feasibility and demonstrate the design principles, a set of comprehensive case studies are conducted based on different power system models including 39-bus New England system and modified Western Electricity Coordinating Council (WECC) system with different operating points, renewable penetration and failures
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