A K-Means Algorithm Approach for Classifying Wireless Signal Loss Using RTT and Bandwidth

Dasgupta, Bikramjit; Valles, Damian; McClellan, Stan

doi:10.1109/iemcon.2018.8615015

Cited by 2 publications

(1 citation statement)

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“…Approaches have been suggested which may provide a partial solution [27], but the inclusion of ML techniques in concert with the enhanced data-gathering approaches have not been clearly articulated. ML techniques have been shown to improve performance in various non-traditional applications such as network communication [28,29] where data collection can be ongoing, and the real-time leverage available via the volume of temporal data may exceed its utility for storage. As a result, the combination of ML techniques with an on-wire sensor system has the potential to create multiple disruptive application vectors in this space, including substantial promise in the implementation of DVO .…”

Section: Motivationmentioning

confidence: 99%

Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning

2019

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A feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.

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Section: Motivationmentioning

confidence: 99%