Occupancy Detection in Room Using Sensor Data
@article{Toutiaee2021OccupancyDI, title={Occupancy Detection in Room Using Sensor Data}, author={Mohammadhossein Toutiaee}, journal={ArXiv}, year={2021}, volume={abs/2101.03616} }
Purpose— With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments. Many researches have been implemented by scientists in different fields, to analyze such data for the purpose of security, energy efficiency, building reliability and smart environments. One study, that many researchers…
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References
SHOWING 1-10 OF 11 REFERENCES
Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models
- Environmental Science, Computer Science
- 2016
Development of an occupancy prediction model using indoor environmental data based on machine learning techniques
- Engineering, Computer Science
- 2016
A pyroelectric infrared sensor-based indoor location-aware system for the smart home
- EngineeringIEEE Transactions on Consumer Electronics
- 2006
A novel non-terminal-based approach using an array of pyroelectric infrared sensors (PIR sensors) that can detect residents that is evaluated experimentally on a test bed.
Occupancy measurement in commercial office buildings for demand-driven control applications : a survey and detection system evaluation
- Engineering
- 2015
Knowledge and Situation-Aware Vehicle Traffic Forecasting
- Computer Science2019 IEEE International Conference on Big Data (Big Data)
- 2019
This work aims to illustrate how knowledge and situational awareness can help data scientists to build more effective models in the field of vehicle traffic forecasting, and presents a novel modeling technique, Quadratic Extreme learning Machine, that generally improves upon the standard Extreme Learning Machine model while remaining relatively efficient.
Enhanced building thermal model by using CO2 based occupancy data
- EngineeringIECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society
- 2015
A novel method to predict thermal dynamics, including person count, in low energy buildings by formulated as a resistor-capacitor circuit (RC circuit) in the Modelica modeling language.
Video Contents Understanding using Deep Neural Networks
- Computer ScienceArXiv
- 2020
Experimental evaluation on video collections shows that the new proposed classifier achieves superior performance over existing solutions, and the classical approaches for video classification task is utilized.
Stereotype-Free Classification of Fictitious Faces
- Computer ScienceArXiv
- 2020
This work presents a novel approach through penalized regression to label stereotype-free GAN-generated synthetic unlabeled images by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.
Gaussian Function On Response Surface Estimation
- Computer Science2020 IEEE International Conference on Big Data (Big Data)
- 2020
We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the…