xiao hu

Mr. xiao hu


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 November 2020 in Sustainability
Verified
Journal Article
Open access
PDF file
JXJiao Xue
Jiao Xue
XHxiao hu
xiao hu
SSShu Nuke Sani
Shu Nuke Sani
YWYuanyuan Wu
Yuanyuan Wu
XLXinyu Li
Xinyu Li
Dayi LaiDayi Lai
Dayi Lai
Thermally comfortable outdoor spaces have contributed to high-quality urban living. In order to provide a further understanding of the influences of gender and long-term thermal history on outdoor thermal comfort, this study conducted field surveys at a university campus in Shanghai, China by carrying out microclimatic monitoring and subjective questionnaires from May to October, 2019. The analysis of collected data found that, during our survey, 57% of the occupants felt comfortable overall and 40–60% of them perceived the microclimate variables (air temperature, humidity, solar radiation, and wind speed) as “neutral”. The universal thermal climate index (UTCI) provided a better correlation with occupant thermal sensation than the physiologically equivalent temperature (PET). Females were more sensitive to the outdoor thermal environment than males. Older age led to lower thermal sensation, but the thermal sensitivities for age groups of 50 were similar. Occupants who had resided in Shanghai for a longer period showed higher overall comfort rating and lower thermal sensation. Interviewees who came from hot summer and cold winter climate regions were less effected by the change of UTCI than those from severe cold or cold climate regions.

JXJiao Xue
Jiao Xue
YFyige fan
yige fan
ZDZhanxun Dong
Zhanxun Dong
XHxiao hu
xiao hu
JYJiatong Yue
Jiatong Yue
As people develop higher standards for the beauty of urban architecture, more and more architectural designs are exploring sources of natural lighting, such as glass curtain walls and glass domes. However, the pursuit of architectural design aesthetics introduces glare into buildings, which can be uncomfortable and even dangerous for health. Scholars in related fields have carried out many theoretical studies and design activities based on this problem. In this study, we focus on issues of glare in public buildings, aiming to improve light comfort by solving this problem. First, we propose an improved design strategy based on IDEO design thinking by adding the step of diverging from the design scheme. Second, guided by the results of a questionnaire survey and light environment simulations, we avoided the subjective simplicity of the traditional design process. Third, taking the main library of Shanghai Jiao Tong University as an example, we designed a movable sunshade that could effectively reduce glare effects and improve visual comfort, which improved the light comfort in public buildings. The simulation results show that the proposed design can be useful in buildings to effectively control glare and improve visual comfort and health.

22 January 2026
Verified
Book Chapter
Open access
PDF file
XHxiao hu
xiao hu
Hao ZhengHao Zheng
Hao Zheng
Dayi LaiDayi Lai
Dayi Lai
The integration of artificial intelligence (AI) in architectural design, especially for generating floor plans, can greatly streamline the process. However, most AI-generated plans focus on form and spatial layout, often neglecting crucial performance evaluations because they are presented as images without the necessary geometric and physical properties for effective simulation. To address this limitation, we propose a novel approach that combines diffusion models with generative adversarial networks (GANs) for generating and evaluating floor plans. We fine-tuned a Low-Rank Adaptation (LoRA) model for creating residential floor plans, while a GAN quickly predicts daylighting performance. Our results show that the diffusion model generates a more varied set of floor plans compared to the training set. The GAN accurately assesses daylighting performance, with deviations from the ground truth not exceeding 5%, achieving a mean squared error (MSE) of 4.2 and a structural similarity index (SSIM) of 0.98. Additionally, it operates 267 times faster than traditional methods. This approach equips architects with a reliable tool for efficient early-stage design decisions, enhancing AI-driven workflows.