José Mardones Fernandez | Universidad Austral de Chile (original) (raw)
I was born in Valdivia at October 27, 1956. I'm married and i have four childrens. I work at Universidad Austral de Chile, Valdivia, as lecturer and researcher. I'm PhD at Engineering Sciences and my research scope is IoT and Automation.
Address: Valdivia, Los Rios, Chile
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Graduate Center of the City University of New York
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Papers by José Mardones Fernandez
IEEE Transactions on Industrial Informatics, 2000
In this paper we assume that time synchronized measurements will be ubiquitously available at all... more In this paper we assume that time synchronized measurements will be ubiquitously available at all high-voltage substations at very high rates. We examine how this information can be utilized more effectively for real-time operation as well as for subsequent decision making. This new information available in real time is different, both in quality and in quantity, than the real-time measurements available today. The promise of new and improved applications to operate the power system more reliably and efficiently has been recognized but is still in conceptual stages. Also, the present system to handle this real-time data has been recognized to be inadequate but even conceptual designs of such infrastructure needed to store and communicate the data are in their infancy. In this paper, we first suggest the requirements for an information infrastructure to handle ubiquitous phasor measurements recognizing that the quantity and rate of data would make it impossible to store all the data centrally as done today. Then we discuss the new and improved applications, classified into two categories: one is the set of automatic wide-area controls and the other is the set of control center (EMS) functions with special attention to the state estimator. Finally, given that the availability of phasor measurements will grow over time, the path for smooth transition from present-day systems and applications to those discussed here is delineated.
The emerging compressed sensing (CS) theory can significantly reduce the number of sampling point... more The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.
IEEE Transactions on Industrial Informatics, 2000
In this paper we assume that time synchronized measurements will be ubiquitously available at all... more In this paper we assume that time synchronized measurements will be ubiquitously available at all high-voltage substations at very high rates. We examine how this information can be utilized more effectively for real-time operation as well as for subsequent decision making. This new information available in real time is different, both in quality and in quantity, than the real-time measurements available today. The promise of new and improved applications to operate the power system more reliably and efficiently has been recognized but is still in conceptual stages. Also, the present system to handle this real-time data has been recognized to be inadequate but even conceptual designs of such infrastructure needed to store and communicate the data are in their infancy. In this paper, we first suggest the requirements for an information infrastructure to handle ubiquitous phasor measurements recognizing that the quantity and rate of data would make it impossible to store all the data centrally as done today. Then we discuss the new and improved applications, classified into two categories: one is the set of automatic wide-area controls and the other is the set of control center (EMS) functions with special attention to the state estimator. Finally, given that the availability of phasor measurements will grow over time, the path for smooth transition from present-day systems and applications to those discussed here is delineated.
The emerging compressed sensing (CS) theory can significantly reduce the number of sampling point... more The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.