For publication in 2019 ACC A flexible framework for building occupancy detection using spatiotemporal pattern networks

Tan, Sin Yong; Saha, Homagni; Florita, Anthony; Henze, Gregor P.; Sarkar, Soumik

doi:10.23919/acc.2019.8815089

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The WHISPER system collects three environmental data modalities: indoor air temperature ( • C), relative humidity (%), and illuminance (lux). In order to learn the relationship between these time series environmental data and the occupancy status, an occupancy detection spatiotemporal pattern network (Occ-STPN) [77] is implemented. In Occ-STPN, a discretization technique known as symbolic dynamic filtering (SDF) is applied to discretize time series data into bins, where each bin represents a range of data values [78] as shown in Figure 6.…”

Section: Modality Level Inferences Environmental Datamentioning

confidence: 99%

“…In Occ-STPN, a discretization technique known as symbolic dynamic filtering (SDF) is applied to discretize time series data into bins, where each bin represents a range of data values [78] as shown in Figure 6. Each bin is then assigned a designated symbol, which maps the time series data from the continuous domain into the symbolic (discrete) domain, forming symbol sequences [77,79]. Next, time embedding is performed on the symbol sequences in order to encode the historic symbol information into a single state.…”

Section: Modality Level Inferences Environmental Datamentioning

confidence: 99%

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WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Jacoby

Tan

Katanbaf

et al. 2021

JSAN

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Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

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Section: Modality Level Inferences Environmental Datamentioning

confidence: 99%

Section: Modality Level Inferences Environmental Datamentioning

confidence: 99%

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Jacoby

Tan

Katanbaf

et al. 2021

JSAN

Self Cite

View full text Add to dashboard Cite

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“…In most residential applications, a variety of passive sensing technologies have been used to infer occupancy state of the room. In this regard, image based occupancy sensing is generally avoided to ensure privacy of its occupants and avoid computationally expensive image/video processing algorithms that might be hardware intensive Tan et al (2019Tan et al ( , 2020; Tang and Mandal (2019). However, this modality of information is invaluable in providing instantaneous occupancy status of an indoor space with high confidence.…”

Section: Chapter 1 Overviewmentioning

confidence: 99%

Few Shot Clustering For Indoor Occupancy Detection With Extremely Low Quality Images From Battery Free Cameras

Saha

Tan

Saffari

et al.

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“…2007)), with applications in diagnostics and root-cause analysis of physical faults and cyber anomalies in CPSs( Liu et al (2017b),Saha et al (2018)), residential energy disaggregation(Liu et al (2018a)), building occupancy detection(Tan et al (2019b)) and wind energy prediction(Jiang et al (2017)). While the method has been shown to be effective in practice, so far there has been no rigorous analysis on whether it is able to capture causality among observations from the subsystems.…”

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confidence: 99%

Information fusion for decision making

Saha

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Making intelligent decisions in autonomous systems often requires the use of all available information. This information generally comes from multiple sources and may be expressed through multiple modalities. This thesis presents a collection of different approaches for processing and fusing sensor data that have been developed for systems that enjoy multiple sensors and/or sensing modalities. Within these approaches we focus on information fusion in the form of emerged communication for team tasks in multi-agent systems in the first part. After that we investigate information fusion in the context of active sensing by treatment of an agent as a hybrid switched system. Then we look at information fusion in a probabilistic graphical modeling framework. Here information from multiple sensors can be fused through joint probability distributions borrowing notions of causality. These notions of causality are used for deriving intelligent fusion rules that can either help in performance monitoring/anomaly detection or data abstraction by hierarchically combining information across multiple sensors. Finally we propose a novel modular approach for fusion of information from vision (RGB images) and language modalities (user input text) in order for a robot to follow and execute natural language instructions in a household environment. Our proposed technique achieves state of the art accuracy in the popular, realistic Ai2Thor environment based on a Unity based simulator.

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For publication in 2019 ACC A flexible framework for building occupancy detection using spatiotemporal pattern networks

Cited by 5 publications

References 16 publications

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Few Shot Clustering For Indoor Occupancy Detection With Extremely Low Quality Images From Battery Free Cameras

Information fusion for decision making

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