堆石混凝土作为我国自主研发的新一代大体积混凝土筑坝技术,其智能化质量控制与自动化(无人或少人)施工技术的研发,是推动其高质量快速建设乃至发展为下一代筑坝技术的必然条件。基于此,引入物联网、大数据、人工智能、云计算等新一代...堆石混凝土作为我国自主研发的新一代大体积混凝土筑坝技术,其智能化质量控制与自动化(无人或少人)施工技术的研发,是推动其高质量快速建设乃至发展为下一代筑坝技术的必然条件。基于此,引入物联网、大数据、人工智能、云计算等新一代信息技术,研发了面向参建各方的堆石混凝土智能信息化施工技术与系统(Construction Information Modeling for RFC,CIM4R),重点解决堆石混凝土坝堆石入仓、高自密实性能混凝土浇筑、温控防裂以及层面处理等四条施工主线的实时监控、快速评价、报警预警和反馈控制等问题,以期实现相关工程的“提质-降本-增效”,为堆石混凝土坝智能建造技术的发展打下基础,推动我国下一代筑坝技术与新质生产力的发展。展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
在智慧城市建设快速发展的背景下,建筑信息模型(Building Information Modeling,BIM)作为城市信息模型(City Information Modeling,CIM)平台的核心组成部分,其应用效果直接影响城市信息化管理的水平。本文系统分析了BIM模型在CIM平台应...在智慧城市建设快速发展的背景下,建筑信息模型(Building Information Modeling,BIM)作为城市信息模型(City Information Modeling,CIM)平台的核心组成部分,其应用效果直接影响城市信息化管理的水平。本文系统分析了BIM模型在CIM平台应用中存在的三大问题:模型精度与性能失衡、数据互通性与完整性不足、平台承载能力受限。针对这些问题,提出了一套包含建模标准优化、几何数据处理、格式转换机制和云端协同架构的完整解决方案。通过云服务器实现数据同步与共享,结合5G与AI技术,最终实现BIM模型在CIM平台的高效应用,为智慧城市的信息化管理提供技术支撑。展开更多
Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address ...Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.展开更多
文摘堆石混凝土作为我国自主研发的新一代大体积混凝土筑坝技术,其智能化质量控制与自动化(无人或少人)施工技术的研发,是推动其高质量快速建设乃至发展为下一代筑坝技术的必然条件。基于此,引入物联网、大数据、人工智能、云计算等新一代信息技术,研发了面向参建各方的堆石混凝土智能信息化施工技术与系统(Construction Information Modeling for RFC,CIM4R),重点解决堆石混凝土坝堆石入仓、高自密实性能混凝土浇筑、温控防裂以及层面处理等四条施工主线的实时监控、快速评价、报警预警和反馈控制等问题,以期实现相关工程的“提质-降本-增效”,为堆石混凝土坝智能建造技术的发展打下基础,推动我国下一代筑坝技术与新质生产力的发展。
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
文摘在智慧城市建设快速发展的背景下,建筑信息模型(Building Information Modeling,BIM)作为城市信息模型(City Information Modeling,CIM)平台的核心组成部分,其应用效果直接影响城市信息化管理的水平。本文系统分析了BIM模型在CIM平台应用中存在的三大问题:模型精度与性能失衡、数据互通性与完整性不足、平台承载能力受限。针对这些问题,提出了一套包含建模标准优化、几何数据处理、格式转换机制和云端协同架构的完整解决方案。通过云服务器实现数据同步与共享,结合5G与AI技术,最终实现BIM模型在CIM平台的高效应用,为智慧城市的信息化管理提供技术支撑。
文摘Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.
