响应面法是一种常见的多元统计优化方法,广泛应用于食品工业、工程技术、生物科学、电子信息等多个研究领域。基于中国知网和Web of Science数据库,收集国内外相关文献,采用CiteSpace软件可视化分析响应面法在国内外食用菌研究中的应用...响应面法是一种常见的多元统计优化方法,广泛应用于食品工业、工程技术、生物科学、电子信息等多个研究领域。基于中国知网和Web of Science数据库,收集国内外相关文献,采用CiteSpace软件可视化分析响应面法在国内外食用菌研究中的应用概况,并综述响应面法在食用菌各研究领域的应用成果,以期为响应面法在食用菌研究中进一步应用提供思路和参考。展开更多
Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely...Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.展开更多
文摘响应面法是一种常见的多元统计优化方法,广泛应用于食品工业、工程技术、生物科学、电子信息等多个研究领域。基于中国知网和Web of Science数据库,收集国内外相关文献,采用CiteSpace软件可视化分析响应面法在国内外食用菌研究中的应用概况,并综述响应面法在食用菌各研究领域的应用成果,以期为响应面法在食用菌研究中进一步应用提供思路和参考。
基金supported by the National Natural Science Foundation of China(52130801,U20A20312,52178271,and 52077213)the National Key Research and Development Program of China(2021YFF0500903)。
文摘Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.
