This paper studies a secondary voltage control framework of the microgrids based on the consensus for a communication network of multiagent. The proposed control is designed by the communication network with one-way links. The communication network is modeled by a directed graph. At this time, the concept of sampling is considered as the communication constraint among each distributed generator...

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even ...

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus ...

Network frequency variations cause a dramatic performance decay in repetitive controller-based shunt active power filters. This problem may be solved by adapting the sampling period in order to keep the ratio between the network period and the sampling period at a constant value. However, these changes may yield closed-loop instability. The introduction of a precompensator that forces the plant...

This report provides the reader with an overview of the concept of social sampling. Social sampling is a class of methods where properties of a social network are used to obtain more accurate samplers than using classical polling algorithms. The main interest lies in the correlation between the sampling size and the sampling error and the systematic bias due to the network structure. I define t...

Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is i...

Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of graphical models where additional independence relations arise in the context of particular values of random variables (RVs). Identifying and exploiting CSI properties can simplify inference. Some generative network model...

We present Coordinated Sampling, a new technique for improved flow-level monitoring. Our approach derives from three key design decisions: flow sampling instead of uniform packet sampling; hash-based flow selection to achieve coordination between routers without needing explicit communication channels; and an approach for distributing responsibilities across routers to achieve network-wide moni...

Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional “classical computer”. In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sa...

Traffic measurement and monitoring is an important component of network QoS management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement are not only desirable, but increasingly becoming a necessity. In this paper, we propose and analyze an adaptive random packet sampling technique for traffic load measur...