The air conditioning (A/C) of cabins allows for customized control, but manual adjustments may distract drivers, as well as result in energy inefficiency. Several existing thermal sensation models require complex inputs, which are challenging to gather whilst driving. To address this issue, this study developed a non-contact thermal sensation model for cabin occupants based on thermal imaging sensor. To collect actual data used for modeling, an outdoor subject experiment was conducted. In this study, initial training was conducted to compare the performance of six algorithms in building the model, with random forests algorithm showing the best performance. Besides, this study employed the recursive feature elimination (RFE) method with cross-validation algorithm for identifying the key features. In the end, the model was retrained using the selected features. The model that incorporated both environmental parameters and facial-temperature features demonstrated the best performance, with an R² of 0.659 on the test set. Eliminating the hard-to-measure windshield surface temperature resulted in a slight reduction in accuracy, yielding an R² of 0.651. To verify the generalizability of the model, this study further conducted independent validation experiments. The selected model, which exhibited a mean absolute error (MAE) of less than 0.4 in thermal sensation units, was proven to be highly applicable. The results can offer new solutions for automatic control of cabin A/C.

To date, dynamic sleep environment has been attracted the focus of researchers. Owing to the individual difference on sleep phase and thermal comfort, changes in sleep environment should be occupant-centered, and precise regulation of the environment required current sleep stages. However, few studies connected occupants and the environment through physiological signal-based model of sleep staging. Therefore, this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information. Raw database was processed and selected efficiently according to the characteristics of physiological signals. Finally, the sleep staging model with an average accuracy of 93.9% was built, and other mean indicators (precision: 82.5%, recall: 83.1%, F1 score: 82.8%) performed well. The features adopted by model were found to come from different brain regions, and the global brain signals were suggested to play an important role in the construction of sleep staging model. Moreover, the computational processing of physiology signals should consider their characteristics, i.e., time domain, frequency domain, time-frequency domain and nonlinear characteristics. The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.

Determining required sample size is one of the critical pathways to reproducible, reliable and robust results in human-related studies. This paper aims to answer a fundamental but often overlooked question: what sample size is required in surveys of occupant responses to indoor environmental quality (IEQ). The statistical models are introduced in order to promote determining required sample size for various types of data analysis methods commonly used in IEQ field studies. The Monte Carlo simulations are performed to verify the statistical methods and to illustrate the impact of sample size on the study accuracy and reliability. Several examples are presented to illustrate how to determine the value of the parameters in the statistical models based on previous similar research or existing databases. The required sample size including "worst" and "optimal" cases in each condition is obtained by this method and references. It is indicated that 385 is a "worst case" sample size to be adequate for a subgroup analysis, while if the researcher has an estimate of the study design and outcome, the "optimal case" sample size can potentially be reduced. When the required sample size is not achievable, the uncertainty in the result can properly interpret via a confidence interval. It is hoped that this paper would fill in the gap between statistical analysis of sample size and IEQ field research, and it can provide a useful reference for researchers when planning their own studies.

Noise exposure is becoming extremely common in urban area, but its specific impact on sleep remains controversial. Considering the limitations of previous researches, a field study which can conduct both horizontal and longitudinal analysis was designed. Urban participants were tested during two weeks in their homes, and the noise level of bedroom was artificially regulated by changing the status of window and door. During the 1050 test nights in 75 households, noise exposure was reflected from both instrument monitoring at night and perception questionnaire in the morning, and sleep quality was accessed from actigraphy and questionnaire. The analysis results showed that, 92.3% of the bedroom acoustic environment did not meet the minimum requirements of Chinese standards, and 87.9% of subjects had ever experienced harmful noise during the test period. Furthermore, sleep quality was affected by noise exposure from the perspective of both physiological and psychological; the duration of rapid eye movement (REM) sleep was significantly (p < 0.05) shortened with the increase of sound intensity, the duration of deep sleep shortened and subjective sleep quality worsened significantly (p < 0.05) with the increase of acoustic sensation vote. In addition, females were more sensitive to noise exposure and their subjective sleep quality was more likely to be influenced by emotions. This study has important implications for acoustic environment design of bedrooms in cities, and suggested more attention should be paid to the anxiety caused by noise exposure.

Sleep thermal environment has a great effect on occupants' sleep quality. The purpose of this study was to explore the relationships between sleep thermal environment parameters and sleep quality in winter and summer. The whole experiments involved indoor ambient temperatures (winter: 17 ℃, 20 ℃, 23 ℃; summer: 23 ℃, 26 ℃, 29 ℃) and relative humidity (RH; winter: 40%, 55%, 70%; summer: 40%, 60%, 80%). A total of 18 young subjects (23±2 years old) were divided into the two different season groups to experience the corresponding environmental conditions. Their subjective environmental perception and objective physiological signals were monitored. The results showed that thermal sensations at 20 ℃ in winter, as well as 26 ℃ in summer, were considered as the thermal neutral states. Humid sensation in winter before sleep was susceptible to SET^*, while the middle value of SET^* (32 ℃) related to the middle indoor air temperature (26 ℃) and air humidity (60%) before and after sleep could be the summer combination of neutral thermal environment. Furthermore, cold and wet environment in winter was found to seriously affect the deep sleep. In summer, mild temperature contributing to good sleep quality might weaken the effect of humidity on sleep. Through combining the sleep environmental perception result with contour line and regression analysis on objective sleep quality, the suggested values of thermal environment parameters for young adults (21–25 years old) were as follows: 20.3 ℃ and 56%RH in winter, as well as 26.1 ℃ and 52%RH in summer.

Courtyard vernacular underground dwellings most commonly called "dig pit houses" , have been for centuries a shelter for the human kind. Despite their noticeable energy saving properties and security enhancing design features, they present several disadvantages related to indoor environment quality such as poor indoor natural daylighting. The dig pit in these typologies is the main design feature that allows natural light into the rooms. In this paper the effect of changing the courtyard’s geometry on the daylighting performance inside the rooms is investigated by the mean of computer simulations using Ecotect-RADIANCE software pack. The findings of this study show that dimensions corresponding to a WI (Well Index) = 0.5 result in better daylighting performance inside the rooms. Moreover, it shows that increasing the number of wall surfaces on the courtyard surface tend to engender a noticeable improvement of the room’s daylighting.