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Showing 1 to 8 of 8 for “"Network Policies"”.

  1. Verified compilation of abstract network policies

    Configuring large networks can be very complex. A network administrator typically has a set of high-level policies in mind when creating a network configuration, but implementing the configuration onto existing hardware often requires specifying many low-level details. As a result, configuring a …

    mit Repository record for Verified compilation of abstract network policies (opens in a new tab)

  2. Implementation and Validation of Network Policy Services

    … today's campus, enterprise, and service-provider networks. Network administrators need to protect and guarantee QoS elements such as bandwidth, delay and jitter to mission critical applications. At the networking level, QoS can be provided using Differentiated services, integrated services or some …

    ncsu Repository record for Implementation and Validation of Network Policy Services (opens in a new tab)

  3. Evaluation of Policies for the Maintenance of Bridges Using Discrete Event Simulation

    … format and simulate user defined element level policies. The model testing was performed using the interstate bridges of the Salem district in Virginia. All the relevant information was extracted from their PONTIS databases. Several scenarios with varying network policies were simulated. The …

    vt Repository record for Evaluation of Policies for the Maintenance of Bridges Using Discrete Event Simulation (opens in a new tab)

  4. Retrospective Policy Gradient

    … techniques adjust the parameters of parametric policies through stochastic gradient ascent, typically utilizing on-policy trajectory samples to estimate the policy gradient. Nevertheless, this reliance on newly collected data makes them sample-inefficient. Specifically, standard PG algorithms …

    uic

  5. Efficient Imitation Learning for Robust, Adaptive, Vision-based Agile Flight Under Uncertainty

    … can train computationally efficient deep neural network policies from those algorithms have limited robustness and/or are impractical (large number of demonstrations, training time), limiting rapid policy learning once new mission specifications or flight data become available. This thesis …

    mit Repository record for Efficient Imitation Learning for Robust, Adaptive, Vision-based Agile Flight Under Uncertainty (opens in a new tab)

  6. Development of a scaled doubly-fed induction generator for assessment of wind power integration issues

    … before the wind energy share increases in the network. Therefore, the wind energy integration issues serve as an interesting topic for authors to improve the perception of integration, distribution, variability and power flow issues. Several simulation models have been introduced in order to …

    cape-town Repository record for Development of a scaled doubly-fed induction generator for assessment of wind power integration issues (opens in a new tab)

  7. Novel approaches to performance evaluation and benchmarking for energy-efficient multicast: empirical study of coded packet wireless networks

    With the advancement of communication networks, a great number of multicast applications such as multimedia, video and audio communications have emerged. As a result, energy efficient multicast in wireless networks is becoming increasingly important in the field of Information and Communications …

    cape-town Repository record for Novel approaches to performance evaluation and benchmarking for energy-efficient multicast: empirical study of coded packet wireless networks (opens in a new tab)

  8. Learning-based optimal and robust control: A policy optimization perspective

    … policy optimization (PO), a subclass of RL where policies—parameterized mappings from observations to actions—are iteratively optimized to enhance system performance. PO’s flexibility, scalability, and data-driven nature make it effective for tackling challenging problems involving nonlinear …

    uiuc Repository record for Learning-based optimal and robust control: A policy optimization perspective (opens in a new tab)