scholarly journals Probability Density Function Analysis Based on Logistic Regression Model

2022 ◽  
Vol 16 ◽  
pp. 60-69
Author(s):  
Lingling Fang ◽  
Yunxia Zhang
Keyword(s):  
Logistic Regression ◽  
Probability Density Function ◽  
Regression Model ◽  
Probability Density ◽  
Density Function ◽  
Logistic Regression Model ◽  
Function Analysis ◽  
Great Influence ◽  
Data Fitting ◽  
Analysis Method

The data fitting level in probability density function analysis has great influence on the analysis results, so it is of great significance to improve the data fitting level. Therefore, a probability density function analysis method based on logistic regression model is proposed. The logistic regression model with kernel function is established, and the optimal window width and mean square integral error are selected to limit the solution accuracy of probability density function. Using the real probability density function, the probability density function with the smallest error is obtained. The estimated probability density function is analyzed from two aspects of consistency and convergence speed. The experimental results show that compared with the traditional probability density function analysis method, the probability density function analysis method based on logistics regression model has a higher fitting level, which is more suitable for practical research projects.

scholarly journals Assessment of the impacts of building materials on the reduction of CO2 emissions from high-rise commercial buildings in Hong Kong

2021 ◽  
Vol 261 ◽  
pp. 04026
Author(s):  
Min Qi Lin ◽  
Chi Kwan Chau ◽  
Meng Yi Xu ◽  
Cheng Ji ◽  
Xiao Hu Feng
Keyword(s):  
Hong Kong ◽  
Probability Density Function ◽  
Regression Model ◽  
Probability Density ◽  
Density Function ◽  
Carbon Emissions ◽  
Building Materials ◽  
Commercial Building ◽  
Function Model ◽  
High Rise

Based on data collected in 20 A-level high-rise commercial concrete buildings in Hong Kong, the research successfully established a probability density function model, which is used to describe the carbon emissions profile of a commercial building. Results indicate that the superstructure of a commercial building, on average, had a footprint of 226.65 kg CO2/m2 and 10.6 kg CO2/m2 separately in the material use stage and transportation stages. It also evaluates the carbon emissions of various building elements and divides them into three levels according to the magnitude of their contribution. The results show that upper floor construction and external wall in Tier 1 contribute nearly 80% of emissions and should be of great concern. In addition to the probability density function model, a regression model was also successfully established in the study to predict carbon emissions. Research has shown that building layers and gross floor area can predict carbon emissions per unit area, and there is a positive relationship between the independent variable and the dependent variable. The regression model can help building designers determine design options to reduce carbon emissions in the early stages of design.

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