This paper analyzes the demands of national tourism in China. They are respectively the upraise of national tourism, the rapid increase of tourist scale, the strength with the leadership of government, the combination...This paper analyzes the demands of national tourism in China. They are respectively the upraise of national tourism, the rapid increase of tourist scale, the strength with the leadership of government, the combination of marketing, the variety of behavior, regional consumption and the relationship with economic income as well as the guide of leisure time.展开更多
After the success of Texworld USA in July,the important global sourcing show returned to the Javits Convention Center January 22-24,2018.Texworld USA is a dynamic industry event and business platform bringing together...After the success of Texworld USA in July,the important global sourcing show returned to the Javits Convention Center January 22-24,2018.Texworld USA is a dynamic industry event and business platform bringing together the best apparel fabric,trims and accessory suppliers展开更多
Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-qui...Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.展开更多
基金Under the auspices of the National Natural Science Foundation of China (No.49771031)
文摘This paper analyzes the demands of national tourism in China. They are respectively the upraise of national tourism, the rapid increase of tourist scale, the strength with the leadership of government, the combination of marketing, the variety of behavior, regional consumption and the relationship with economic income as well as the guide of leisure time.
文摘After the success of Texworld USA in July,the important global sourcing show returned to the Javits Convention Center January 22-24,2018.Texworld USA is a dynamic industry event and business platform bringing together the best apparel fabric,trims and accessory suppliers
文摘Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.
