Academic literature on the topic 'Personal comfort model'
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Journal articles on the topic "Personal comfort model"
Zang, Miao, Zhiqiang Xing, and Yingqi Tan. "IoT-based personal thermal comfort control for livable environment." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771986550. http://dx.doi.org/10.1177/1550147719865506.
Full textXu, Zhaofei, Weidong Lu, Zhenyu Hu, Ta Zhou, Yi Zhou, Wei Yan, and Feifei Jiang. "Decision-Refillable-Based Two-Material-View Fuzzy Classification for Personal Thermal Comfort." Applied Sciences 12, no. 22 (November 17, 2022): 11700. http://dx.doi.org/10.3390/app122211700.
Full textAguilera, José Joaquín, Jørn Toftum, and Ongun Berk Kazanci. "Predicting personal thermal preferences based on data-driven methods." E3S Web of Conferences 111 (2019): 05015. http://dx.doi.org/10.1051/e3sconf/201911105015.
Full textCen, Lingkai, Joon-Ho Choi, Xiaomeng Yao, Yolanda Gil, Shrikanth Narayanan, and Maryann Pentz. "A personal visual comfort model: predict individual’s visual comfort using occupant eye pupil size and machine learning." IOP Conference Series: Materials Science and Engineering 609 (October 23, 2019): 042097. http://dx.doi.org/10.1088/1757-899x/609/4/042097.
Full textWang, Long, Hao Fan, Jianjie Chu, Dengkai Chen, and Suihuai Yu. "Effect of Personal Space Invasion on Passenger Comfort and Comfort Design of an Aircraft Cabin." Mathematical Problems in Engineering 2021 (June 25, 2021): 1–15. http://dx.doi.org/10.1155/2021/9968548.
Full textMerabet, Ghezlane Halhoul, Mohamed Essaaidi, and Driss Benhaddou. "A dynamic model for human thermal comfort for smart building applications." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 4 (July 28, 2019): 472–83. http://dx.doi.org/10.1177/0959651819865795.
Full textMashita, Tomohiro, Tetsuya Kanayama, and Photchara Ratsamee. "Personal Atmosphere: Estimation of Air Conditioner Parameters for Personalizing Thermal Comfort." Applied Sciences 10, no. 22 (November 13, 2020): 8067. http://dx.doi.org/10.3390/app10228067.
Full textMa, Liu, and Shang. "A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space." Sustainability 11, no. 18 (September 11, 2019): 4972. http://dx.doi.org/10.3390/su11184972.
Full textIzzati, Nurul, Sheikh Ahmad Zaki, Hom Bahadur Rijal, Jorge Alfredo Ardila Rey, Aya Hagishima, and Nurizzatul Atikha. "Investigation of Thermal Adaptation and Development of an Adaptive Model under Various Cooling Temperature Settings for Students’ Activity Rooms in a University Building in Malaysia." Buildings 13, no. 1 (December 23, 2022): 36. http://dx.doi.org/10.3390/buildings13010036.
Full textLee, Yein, Hyunjin Lee, Byung Ha Kang, and Jung Kyung Kim. "Machine learning-based personal thermal comfort model for electric vehicles with local infrared radiant warmers." Journal of Mechanical Science and Technology 35, no. 7 (June 29, 2021): 3239–47. http://dx.doi.org/10.1007/s12206-021-0644-7.
Full textDissertations / Theses on the topic "Personal comfort model"
Jung, Wooyoung. "Decentralized HVAC Operations: Novel Sensing Technologies and Control for Human-Aware HVAC Operations." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97600.
Full textDoctor of Philosophy
With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.
Arakawa, Martins Larissa. "Understanding thermal comfort and wellbeing of older South Australians using occupant-centric models." Thesis, 2022. https://hdl.handle.net/2440/135562.
Full textThesis (Ph.D.) -- University of Adelaide, School of Architecture and Built Environment, 2022
Dias, Sara Filipa Ferreira Costa. "Understanding personal perception of safety, security and comfort when using different transport modes." Master's thesis, 2021. https://hdl.handle.net/10216/135173.
Full textDias, Sara Filipa Ferreira Costa. "Understanding personal perception of safety, security and comfort when using different transport modes." Dissertação, 2021. https://hdl.handle.net/10216/135173.
Full textBook chapters on the topic "Personal comfort model"
Srivastava, Kavita. "Prediction Model for Personal Thermal Comfort for Naturally Ventilated Smart Buildings." In Proceedings of ICETIT 2019, 117–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_10.
Full textBishop, Jonathan. "Understanding and Facilitating the Development of Social Networks in Online Dating Communities." In Electronic Services, 1390–401. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-967-5.ch086.
Full textBishop, Jonathan. "Understanding and Facilitating the Development of Social Networks in Online Dating Communities." In Social Networking Communities and E-Dating Services, 266–77. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-104-9.ch015.
Full textBird, Jennifer Lynne, and Eric T. Wanner. "Enhancing Health Education with Collaborative Narratives." In Handbook of Research on Education and Technology in a Changing Society, 781–91. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6046-5.ch058.
Full textWatson, Lisa, and Anne M. Lavack. "Using Social Marketing to Encourage the Purchase of Fuel-Efficient Vehicles." In Dynamics of Competitive Advantage and Consumer Perception in Social Marketing, 253–77. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4430-4.ch010.
Full textMtshali, Thokozani Isaac, and Sylvia Manto Ramaligela. "Employability Skills for Civil Engineering." In New Models for Technical and Vocational Education and Training, 115–35. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2607-1.ch007.
Full textHojjati-Emami, Khashayar, Balbir S. Dhillon, and Kouroush Jenab. "The Integrative Time-Dependent Modeling of the Reliability and Failure of the Causes of Drivers' Error Leading to Road Accidents." In Transportation Systems and Engineering, 1279–94. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch065.
Full textHill, Juniper. "Overcoming Inhibitors of Creativity." In Becoming Creative, 171–220. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199365173.003.0005.
Full textHandel, Daniel L., and Stefani D. Madison. "Communication With Family." In Communication in Emergency Medicine, edited by Maria E. Moreira and Andrew J. French, 63–79. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190852917.003.0005.
Full textConference papers on the topic "Personal comfort model"
Chennapragada, Aniruddh, Divya Periyakoil, Hari Prasanna Das, and Costas J. Spanos. "Time series-based deep learning model for personal thermal comfort prediction." In e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3538637.3539617.
Full textLee, Jeehee, and Youngjib Ham. "Intra-Individual Differences in Predicting Personal Thermal Comfort Using Model-Based Recursive Partitioning (MOB)." In ASCE International Conference on Computing in Civil Engineering 2021. Reston, VA: American Society of Civil Engineers, 2022. http://dx.doi.org/10.1061/9780784483893.148.
Full textWang, Weiyu, Yuan Fang, Weizhen Wang, Qipeng He, and Nianyu Zou. "Study on Factors Correlation of Personal Lighting Comfort Model in Cyber-Physical Human Centric Systems." In 2020 Fifth Junior Conference on Lighting (Lighting). IEEE, 2020. http://dx.doi.org/10.1109/lighting47792.2020.9240565.
Full textOno, Eikichi, Yue Lei, Kuniaki Mihara, and Adrian Chong. "The impact of resolution of occupancy data on personal comfort model-based HVAC control performance." In BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3563357.3564061.
Full textLim, Yuto, Chenmian Zhou, Yasuo Tan, Yuan Fang, and Manmeet Mahinderjit Singh. "Personal Thermal Comfort Model for Cyber-Physical Human Centric Systems using Incomplete Supervised Learning Method." In 2022 International Conference on Information Networking (ICOIN). IEEE, 2022. http://dx.doi.org/10.1109/icoin53446.2022.9687181.
Full textAbou Jaoude, Rachelle, Roch El Khoury, Agnes Psikuta, and Maroun Nemer. "Individualization of Thermophysiological Models for Thermal Sensation Assessment in Complex Environments: A Preliminary Study." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71470.
Full textPeng, Bo, and Sheng-Jen Hsieh. "Data-Driven Thermal Comfort Prediction With Support Vector Machine." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-3003.
Full textHo, Son H., Luis Rosario, and Muhammad M. Rahman. "Analysis of Thermal Comfort and Contaminant Removal in an Office Room With Underfloor Air Distribution System." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72437.
Full textBolduc, Drew, Longxiang Guo, and Yunyi Jia. "Modeling and Characterization of Driving Styles for Adaptive Cruise Control in Personalized Autonomous Vehicles." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5277.
Full textOlawale, Opeoluwa Wonuola, Benjamin Gilbert, and Janet Reyna. "Demand Response Analysis for Different Residential Personas in a Comfort-Driven Behavioral Context." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-73143.
Full textReports on the topic "Personal comfort model"
DiGrande, Laura, Sue Pedrazzani, Elizabeth Kinyara, Melanie Hymes, Shawn Karns, Donna Rhodes, and Alanna Moshfegh. Field Interviewer– Administered Dietary Recalls in Participants’ Homes: A Feasibility Study Using the US Department of Agriculture’s Automated Multiple-Pass Method. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0045.2105.
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