AIMC Topic: Thermosensing

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A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation.

International journal of biometeorology
This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal comfort (OTC) across three key indices: Universal Thermal Climate Index (UTCI), Physiological Equiv...

Machine learning thermal comfort prediction models based on occupant demographic characteristics.

Journal of thermal biology
This study aims to investigate the predictive occupant demographic characteristics of thermal sensation (TS) and thermal satisfaction (TSa) as well as to find the most effective machine learning (ML) algorithms for predicting TS and TSa. To achieve t...

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Journal of thermal biology
Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majori...

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing.

International journal of biometeorology
The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effec...

Prediction of human thermal comfort preference based on supervised learning.

Journal of thermal biology
Human thermal comfort is relevant to human life comfort and plays a pivotal role in occupational health and thermal safety. To ensure that intelligent temperature-controlled equipment can deliver a sense of cosiness to people while improving its ener...

Machine learning and features for the prediction of thermal sensation and comfort using data from field surveys in Cyprus.

International journal of biometeorology
Perception can influence individuals' behaviour and attitude affecting responses and compliance to precautionary measures. This study aims to investigate the performance of methods for thermal sensation and comfort prediction. Four machine learning a...

Human thermal sensation over a mountainous area, revealed by the application of ANNs: the case of Ainos Mt., Kefalonia Island, Greece.

International journal of biometeorology
Mt. Ainos in Kefalonia Island, Greece, hosts a large variety of plant species, some of them endemic to the region. Because of its rich biodiversity, a large portion of the mountain area is designated as National Park and is protected from human activ...

Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior.

Applied ergonomics
Thermal comfort modeling has been of interest in built environment research for decades. Mostly the modeling approaches focused on an average response of a large group of building occupants. Recently, the focus has been shifted towards personal comfo...

Heat Flux Sensing for Machine-Learning-Based Personal Thermal Comfort Modeling.

Sensors (Basel, Switzerland)
In recent years, physiological features have gained more attention in developing models of personal thermal comfort for improved and accurate adaptive operation of Human-In-The-Loop (HITL) Heating, Ventilation, and Air-Conditioning (HVAC) systems. Pu...

Convergent Temperature Representations in Artificial and Biological Neural Networks.

Neuron
Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into...