As the climate changed in recent years, an increase in summer indoor temperatures in severe cold and cold regions of China has started to affect thermal comfort. However, the local design standard for energy efficiency does not recognize this phenomenon. This paper reports the potential overheating phenomenon in residential buildings and examines the rationale for the current thermal designs adopted in severe cold and cold regions of China. In this study, the two most commonly used building materials, reinforced concrete (RC) and cross laminated timber (CLT), are used separately in the design of an 18-story residential building envelope located in six different cities in the severe cold and cold regions. The energy consumption and indoor operative temperatures during the operation of these buildings are simulated using Integrated Environmental Solutions Virtual Environment (IES VE). The results demonstrate that both the RC and the CLT buildings experience varying degrees of overheating in any climate subregion. The CLT buildings have longer overheating hours compared to the RC buildings, especially in the cold regions. The results also indicate that for apartments on higher stories, the cooling energy consumption and indoor temperature also increase gradually. The research results suggest that the local design standard for energy efficiency needs to be adjusted by adding thermal design methods for summer to reduce the periods of overheating.

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.