Computational Imaging on the Electric Grid

Sheinin, Mark; Schechner, Yoav Y.; Kutulakos, Kiriakos N.

doi:10.1109/cvpr.2017.254

Cited by 35 publications

(13 citation statements)

References 44 publications

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“…In recent works, Kasper et al [8] tries to improve the computationally expensive technique of path-tracing for light estimation with the use of finite nonlinear parametrization while in [4] they introduce a learning based approach. Finally in [18] similarly to our work, light separation through a factorization technique is part of the computational imaging study on the electrical grid for bulb type and light phase estimation.…”

Section: Previous Workmentioning

confidence: 85%

LIT: A System and Benchmark for Light Understanding

Tsesmelis

Hasan

Cristani

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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A modern lighting system should automatically calibrate itself (light commissioning), assess its own status (which lights are on/off and how dimmed), and allow for the creation or preservation of lighting patterns (adjustability), e.g. after the sunset. Such a system does not exist today, nor (real) data, labels, or metrics are available to compare with and foster progress. In this paper we set the baselines to such a computational system, called LIT, and its applications. Using computational imaging we try to model and benchmark the light variations of indoor scenes with different illuminations (including natural light) and luminaire setups. We show that our lighting system can be easily trained with no manual intervention; after that, the benchmark allows to test automatic calibration (LIT-EST), status awareness (LIT-ID) and relighting (RE-LIT) as application.

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Section: Previous Workmentioning

confidence: 85%

LIT: A System and Benchmark for Light Understanding

Tsesmelis

Hasan

Cristani

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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“…An example of the technique applied to recently acquired imaging is shown in Figure 4. Subsequently, other targeted, single-night observations have similarly quantified the lighting profiles of urban environments using both multi-/hyperspectral data [117][118][119] as well as low [120,121] and high [122] frequency variability. By comparing the bulb type of individual sources over several years with persistent UO VNIR imaging, a longitudinal study demonstrating the effects of LED changeover is in progress.…”

Section: Lighting Technologies and End-usementioning

confidence: 99%

“…Further work by [96,[122][123][124] showed that an alternative to the oscillating shutter technique for creating an observable beat frequency is the serendipitous use of a camera's frame rate operating near a harmonic of the 60 Hz frequency. This type of grid monitoring via remote imaging is complimentary to more standard, modern approaches of large scale in situ phasor measurement unit (PMU) deployments [125,126], and planned future deployments of UO sites in developing cities will enable monitoring of the grid when such in situ devices are unavailable or deployment is unfeasible.…”

Section: Grid Stability and Phasementioning

confidence: 99%

The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems

et al. 2021

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We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.

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“…These methods are extremely accurate, but they are both manual methods that require extensive work by field personnel. Sheinin et al [29] describe a video-based approach. In this methodology, video footage of lighting from buildings is taken and then analysed for characteristics undetectable to the human eye.…”

Section: Related Workmentioning

confidence: 99%

“…Sheinin et al . [29] describe a video‐based approach. In this methodology, video footage of lighting from buildings is taken and then analysed for characteristics undetectable to the human eye.…”

Section: Related Workmentioning

confidence: 99%

Phase identification using co‐association matrix ensemble clustering

Blakely

Reno

2020

IET Smart Grid

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Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

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Computational Imaging on the Electric Grid

Cited by 35 publications

References 44 publications

LIT: A System and Benchmark for Light Understanding

LIT: A System and Benchmark for Light Understanding

The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems

Phase identification using co‐association matrix ensemble clustering

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