Open Access
Issue |
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
|
|
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Article Number | 02007 | |
Number of page(s) | 6 | |
Section | Health | |
DOI | https://doi.org/10.1051/itmconf/20246902007 | |
Published online | 13 December 2024 |
- Milana C., Ashta A. Artificial intelligence techniques in finance and financial markets: A survey of the literature, Strateg. Chang., 30 (3), pp. 189–209 (2021). [CrossRef] [Google Scholar]
- Yang, Y. Application of wearable devices based on artificial intelligence sensors in sports human health monitoring. Measurement: Sensors, 33(January), 101086 (2024). [CrossRef] [Google Scholar]
- Gao, Y. Application of sensor recognition based on artificial intelligence image algorithms in sports and human health. Measurement: Sensors, 33, 101127 (2024). [CrossRef] [Google Scholar]
- Bakht, A., Sharma, S., Park, D., Lee, H.: Deep LearningBased Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms. Toxics, 10(10) (2022). [Google Scholar]
- Candanedo, L. M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings, 112, 28–39 (2016). [CrossRef] [Google Scholar]
- Candanedo, L. M., Feldheim, V., Deramaix, D.: Reconstruction of the indoor temperature dataset of a house using data driven models for performance evaluation. Building and Environment, 138, pp. 250–261 (2018). [CrossRef] [Google Scholar]
- Gavidia-Calderón, M., Schuch, D., Vara-Vela, A., Inoue, R., Freitas, E. D., de Albuquerque, T. T. A., Zhang, Y., de Andrade, M. F., Bell, M. L.: Air quality modeling in the metropolitan area of São Paulo, Brazil: A review. Atmospheric Environment, 319 (2024) 120301, December 2023. [CrossRef] [Google Scholar]
- Qabbal, L., Younsi, Z., Hassane, N.: Indoor air quality (IAQ) measurements in a tertiary building via a smart sensor connected to a Raspberry Pi card: application to a demonstrator building. Advances in Smart Systems Research, 7(1), pp. 10–19 (2012). [Google Scholar]
- Qabbal, L., Younsi, Z., Naji, H.: An indoor air quality and thermal comfort appraisal in a retrofitted university building via low-cost smart sensor. Indoor and Built Environment, 31(3), 586–606 (2022). [CrossRef] [Google Scholar]
- Shokrollahi, A., Persson, J. A., Malekian, R., Sarkheyli-Hägele, A., Karlsson, F.: Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodol-ogies and Machine Learning Approaches (2024). [Google Scholar]
- Dai, X., Shang, W., Liu, J., Xue, M., Wang, C.: Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges (2023). [Google Scholar]
- Aldakheel, J., Bahrar, M., El Mankibi, M.: Indoor environmental quality evaluation of smart/artificial intelligence techniques in buildings - a review (2023). [Google Scholar]
- Wiryasaputra, R., Huang, C. Y., Kristiani, E., Liu, P. Y., Yeh, T. K., Yang, C. T.: Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation (2023). [Google Scholar]
- Richardson, I., Thomson, M., Infield, D.: A highresolution domestic building occupancy model for energy demand simulations, Energy Build. 40 (8), 1560–1566 (2008). [CrossRef] [Google Scholar]
- Meyn, S., Surana, A., Lin Y., Oggianu, S. M., Narayanan, S., Frewen, T.A.: A sensor-utility-network method for estimation of occupancy in buildings, in: Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on, IEEE, Shanghai, P.R. China, pp. 1494–1500 (2009). [Google Scholar]
- Erickson, V.L., Lin, Y., Kamthe, A., Brahme, R., Surana, A., Cerpa, A.E., Sohn, M.D., Narayanan, S.: Energy efficient building environment control strategies using real-time occupancy measurements, in: Proceedings of the first ACM workshop on embedded sensing systems for energy-efficiency in buildings, ACM, Berkeley, California, pp. 19–24 (2009). [CrossRef] [Google Scholar]
- Liao, C., Barooah, P.: An integrated approach to occupancy modeling and estimation in commercial buildings, in: American Control Conference (ACC), IEEE, Baltimore, MD, pp. 3130–3135 (2010). [Google Scholar]
- Liao, C., Lin, Y., Barooah, P.: Agent-based and graphical modelling of building occupancy, J. Build. Perform. Simulat. 5 (1), 5–25 (2011). [Google Scholar]
- Ebadat, A., Bottegal, G., Varagnolo, D., Wahlberg, B., Johansson, K.H.: Estimation of building occupancy levels through environmental signals deconvolution, in: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, ACM, Rome, Italy, pp. 1–8 (2013). [Google Scholar]
- Lam, K.P., Höynck, M., Dong, B., Andrews, B., Chiou, Y.S., Zhang, R., Benitez, D., Choi, J.: Occupancy detection through an extensive environmental sensor network in an open-plan office building, IBPSA Build. Simulat. 145, 1452–1459 (2009). [Google Scholar]
- Castanedo, F., López-de-Ipina, D., Aghajan, H.K., Kleihorst, R.P.: Building an occupancy model from sensor networks in office environments, ICDSC 3, 1–6 (2011). [Google Scholar]
- Hailemariam, E., Goldstein, R., Attar, R., Khan, A.: Realtime occupancy detection using decision trees with multiple sensor types, in: Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design, Society for Computer Simulation International, San Diego, CA, pp. 141–148 (2011). [Google Scholar]
- Yang, Z., Li, N., Becerik-Gerber, B., Orosz, M.: A multisensor-based occupancy estimation model for supporting demand driven HVAC operations, in: Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, Society for Computer Simulation International, San Diego, CA, USA, pp. 49–56 (2012). [Google Scholar]
- Ekwevugbe, T., Brown, N., Pakka, V.: Real-time building occupancy sensing for supporting demand driven HVAC operations. 13th International Conference for Enhanced Building Operations, Montreal, Quebec (2013). [Google Scholar]
- Mark, H., Eibe, F., Geoffrey, H., Bernhard, P., Peter, R., Witten, I.H.: The WEKA data mining software: an update, SIGKDD Explor. 11 (1) (2009). [Google Scholar]
- Ekwevugbe, T., Brown, N., Pakka, V., Fan, D.: Real-time building occupancy sensing using neural-network based sensor network, in: 7th IEEE International Conference on IEEE, Digital Ecosystems and Technologies (DEST), Menlo Park, California, pp. 114–119 (2013). [Google Scholar]
- Beltran, A., Erickson, V.L., Cerpa, A.E.: Thermosense: occupancy thermal based sensing for hvac control, in: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, ACM, Rome, Italy, pp. 11:11–11:18 (2013). [Google Scholar]
- Kleiminger, W., Beckel, C., Staake, T., Santini, S.: Occupancy detection from electricity consumption data, in: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, ACM, Rome, Italy, pp. 1–8 (2013). [Google Scholar]
- Yang, Z., Li, N., Becerik-Gerber, B., Orosz, M.: A systematic approach to occupancy modeling in ambient sensor-rich buildings, Simulation 90 (8), 960–977 (2014). [CrossRef] [Google Scholar]
- Emery, C., Liu, Z., Russell, A.G., Odman, M.T., Yarwood, G., Kumar, N.: Recommendations on statistics and benchmarks to assess photochemical model performance. J. Air Waste Manag. Assoc. 67 (5), 582–598 (2017). [CrossRef] [Google Scholar]
- Peralta, A.H.D., Gavidia-Calder’on, M., de Andrade, M. F.: Future ozone levels responses to changes in meteorological conditions under RCP 4.5 and RCP 8.5 scenarios over São Paulo, Brazil. Atmosphere 14 (4) (2023). [Google Scholar]
- Martins, L.D., Andrade, M.D.F.: Ozone formation potentials of volatile organic compounds and ozone sensitivity to their emission in the megacity of São Paulo, Brazil. Water Air Soil Pollut. 195 (1-4), 201–213 (2008b). [CrossRef] [Google Scholar]
- Vara-Vela, A., de Fátima Andrade, M., Zhang, Y., Kumar, P., Ynoue, R.Y., Souto-Oliveira, C.E., et al.: Modeling of atmospheric aerosol properties in the Sao Paulo metropolitan area: impact of biomass burning. J. Geophys. Res. Atmos. 123 (17), 9935–9956 (2018). [CrossRef] [Google Scholar]
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