Using role play to develop an empathetic mindset in executive education

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub...

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Main Authors: RAVI, Anuradha, GAMLATH, Kasun, HU, Siyan, MISRA, Archan
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6267
https://ink.library.smu.edu.sg/context/sis_research/article/7270/viewcontent/3._CS_Light_Camera_Sensing_Based_Occupancy_Aware_Robust__BuidlingSys_21_.pdf
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spelling sg-smu-ink.sis_research-72702021-11-19T08:11:28Z Using role play to develop an empathetic mindset in executive education RAVI, Anuradha GAMLATH, Kasun HU, Siyan MISRA, Archan We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) of image frames into accurate estimates of the illuminance (LUX) of the various sub-regions, under variations in ambient lighting and layouts. To overcome this challenge, we develop a novel technique that (a) classifies image pixels as corresponding to light vs. dark-colored surfaces, and (b) uses unsupervised ML-based color-specific, pixel-to-LUX classifiers and statistical aggregation to provide robust LUX estimates. Experimental studies, conducted over a collaborative work area in an operational ZEB, demonstrate CS-Light's efficacy: it supports accurate pixel-to-LUX estimation (median error= 8.5%), and its real-time multi-LED adaptation results in appreciable energy savings (63.5% in low occupancy situations), while ensuring negligible perceptual discomfort to human occupants. 2021-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6267 info:doi/10.1145/3486611.3486657 https://ink.library.smu.edu.sg/context/sis_research/article/7270/viewcontent/3._CS_Light_Camera_Sensing_Based_Occupancy_Aware_Robust__BuidlingSys_21_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University smart building smart lighting LED lighting cyber physical system Computer Sciences
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic smart building
smart lighting
LED lighting
cyber physical system
Computer Sciences
spellingShingle smart building
smart lighting
LED lighting
cyber physical system
Computer Sciences
RAVI, Anuradha
GAMLATH, Kasun
HU, Siyan
MISRA, Archan
Using role play to develop an empathetic mindset in executive education
description We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) of image frames into accurate estimates of the illuminance (LUX) of the various sub-regions, under variations in ambient lighting and layouts. To overcome this challenge, we develop a novel technique that (a) classifies image pixels as corresponding to light vs. dark-colored surfaces, and (b) uses unsupervised ML-based color-specific, pixel-to-LUX classifiers and statistical aggregation to provide robust LUX estimates. Experimental studies, conducted over a collaborative work area in an operational ZEB, demonstrate CS-Light's efficacy: it supports accurate pixel-to-LUX estimation (median error= 8.5%), and its real-time multi-LED adaptation results in appreciable energy savings (63.5% in low occupancy situations), while ensuring negligible perceptual discomfort to human occupants.
format text
author RAVI, Anuradha
GAMLATH, Kasun
HU, Siyan
MISRA, Archan
author_facet RAVI, Anuradha
GAMLATH, Kasun
HU, Siyan
MISRA, Archan
author_sort RAVI, Anuradha
title Using role play to develop an empathetic mindset in executive education
title_short Using role play to develop an empathetic mindset in executive education
title_full Using role play to develop an empathetic mindset in executive education
title_fullStr Using role play to develop an empathetic mindset in executive education
title_full_unstemmed Using role play to develop an empathetic mindset in executive education
title_sort using role play to develop an empathetic mindset in executive education
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6267
https://ink.library.smu.edu.sg/context/sis_research/article/7270/viewcontent/3._CS_Light_Camera_Sensing_Based_Occupancy_Aware_Robust__BuidlingSys_21_.pdf
_version_ 1770575913009807360