The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional imag...The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional image segmentation methods often struggle to capture fine details such as edges and contours,limiting their effectiveness in identifying areas prone to energy loss.To address this challenge,we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model,Cascade U-Net.Object-wise processing isolates components of the facade,such as walls and windows,for independent analysis,while Cascade U-Net incorporates contour information to enhance segmentation accuracy.The methodology involves four steps:object isolation,which crops and adjusts the image based on bounding boxes;contour extraction,which derives contours;image segmentation,which modifies and reuses contours as guide data in Cascade U-Net to segment areas;and segmentation synthesis,which integrates the results obtained for each object to produce the final segmentation map.Applied to a dataset of Korean building images,the proposed method significantly outperformed traditional models,demonstrating improved accuracy and the ability to preserve critical structural details.Furthermore,we applied this approach to classify window thermal loss in real-world scenarios using infrared images,showing its potential to identify windows vulnerable to energy loss.Notably,our Cascade U-Net,which builds upon the relatively lightweight U-Net architecture,also exhibited strong performance,reinforcing the practical value of this method.Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.展开更多
Substantially glazed facades are extensively used in contemporary high-rise buildings to achieve attractive architectural aesthetics.Inherent conflicts exist among architectural aesthetics,building energy consumption,...Substantially glazed facades are extensively used in contemporary high-rise buildings to achieve attractive architectural aesthetics.Inherent conflicts exist among architectural aesthetics,building energy consumption,and solar energy harvesting for glazed facades.In this study,we addressed these conflicts by introducing a new dynamic and vertical photovoltaic integrated building envelope(dvPVBE)that offers extraordinary flexibility with weather-responsive slat angles and blind positions,superior architectural aesthetics,and notable energy-saving potential.Three hierarchical control strategies were proposed for different scenarios of the dvPVBE:power generation priority(PGP),natural daylight priority(NDP),and energy-saving priority(ESP).Moreover,the PGP and ESP strategies were further analyzed in the simulation of a dvPVBE.An office room integrated with a dvPVBE was modeled using EnergyPlus.The influence of the dvPVBE in improving the building energy efficiency and corresponding optimal slat angles was investigated under the PGP and ESP control strategies.The results indicate that the application of dvPVBEs in Beijing can provide up to 131%of the annual energy demand of office rooms and significantly increase the annual net energy output by at least 226%compared with static photovoltaic(PV)blinds.The concept of this novel dvPVBE offers a viable approach by which the thermal load,daylight penetration,and energy generation can be effectively regulated.展开更多
The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pu...The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.展开更多
To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-sca...To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.展开更多
群体感应是微生物之间相互交流的一种重要联络方式,它协助细菌能感应群体密度从而调节基因表达。20世纪80年代以来,多种群体感应信号及其传导机制得以阐明。DSF(diffusible signal factor)是近年来在野油菜黄单胞菌中首先鉴定的1个新型...群体感应是微生物之间相互交流的一种重要联络方式,它协助细菌能感应群体密度从而调节基因表达。20世纪80年代以来,多种群体感应信号及其传导机制得以阐明。DSF(diffusible signal factor)是近年来在野油菜黄单胞菌中首先鉴定的1个新型群体感应信号,化学结构为顺式11甲基2十二碳烯酸,其信号传导途径包括RpfC/RpfG双组分感应系统、二级信使环二鸟苷酸(c-di-GMP)、全局性转录因子Clp等。在野油菜黄单胞菌中,DSF群体感应信号调控3类生物学功能:一是促进致病相关基因的表达;二是抑制生物膜形成;三是促进黄单胞菌作出代谢调整,适应高群体密度环境。RpfF是DSF信号生物合成过程中的关键酶,黄单胞菌在高群体密度条件下通过一种新型自我诱导机制大量合成DSF。DSF信号不仅存在于所有黄单胞菌属细菌中,也广泛存在于苛养木杆菌、嗜麦芽寡养单胞菌、伯克氏菌、铜绿假单胞菌和许多海洋细菌中,其传导途径和生物学功能还有待进一步阐明。展开更多
基金supported by Korea Institute for Advancement of Technology(KIAT):P0017123,the Competency Development Program for Industry Specialist.
文摘The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional image segmentation methods often struggle to capture fine details such as edges and contours,limiting their effectiveness in identifying areas prone to energy loss.To address this challenge,we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model,Cascade U-Net.Object-wise processing isolates components of the facade,such as walls and windows,for independent analysis,while Cascade U-Net incorporates contour information to enhance segmentation accuracy.The methodology involves four steps:object isolation,which crops and adjusts the image based on bounding boxes;contour extraction,which derives contours;image segmentation,which modifies and reuses contours as guide data in Cascade U-Net to segment areas;and segmentation synthesis,which integrates the results obtained for each object to produce the final segmentation map.Applied to a dataset of Korean building images,the proposed method significantly outperformed traditional models,demonstrating improved accuracy and the ability to preserve critical structural details.Furthermore,we applied this approach to classify window thermal loss in real-world scenarios using infrared images,showing its potential to identify windows vulnerable to energy loss.Notably,our Cascade U-Net,which builds upon the relatively lightweight U-Net architecture,also exhibited strong performance,reinforcing the practical value of this method.Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.
基金supported by the National Natural Science Foundation of China(52078269 and 52325801).
文摘Substantially glazed facades are extensively used in contemporary high-rise buildings to achieve attractive architectural aesthetics.Inherent conflicts exist among architectural aesthetics,building energy consumption,and solar energy harvesting for glazed facades.In this study,we addressed these conflicts by introducing a new dynamic and vertical photovoltaic integrated building envelope(dvPVBE)that offers extraordinary flexibility with weather-responsive slat angles and blind positions,superior architectural aesthetics,and notable energy-saving potential.Three hierarchical control strategies were proposed for different scenarios of the dvPVBE:power generation priority(PGP),natural daylight priority(NDP),and energy-saving priority(ESP).Moreover,the PGP and ESP strategies were further analyzed in the simulation of a dvPVBE.An office room integrated with a dvPVBE was modeled using EnergyPlus.The influence of the dvPVBE in improving the building energy efficiency and corresponding optimal slat angles was investigated under the PGP and ESP control strategies.The results indicate that the application of dvPVBEs in Beijing can provide up to 131%of the annual energy demand of office rooms and significantly increase the annual net energy output by at least 226%compared with static photovoltaic(PV)blinds.The concept of this novel dvPVBE offers a viable approach by which the thermal load,daylight penetration,and energy generation can be effectively regulated.
基金National Natural Science of China(No.42201463)Guangxi Natural Science Foundation(No.2023GXNSFBA026350)+1 种基金Special Fund of Guangxi Science and Technology Base and Talent(Nos.Guike AD22035158,Guike AD23026167)Guangxi Young and Middle-aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0056).
文摘The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.
文摘To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.
文摘群体感应是微生物之间相互交流的一种重要联络方式,它协助细菌能感应群体密度从而调节基因表达。20世纪80年代以来,多种群体感应信号及其传导机制得以阐明。DSF(diffusible signal factor)是近年来在野油菜黄单胞菌中首先鉴定的1个新型群体感应信号,化学结构为顺式11甲基2十二碳烯酸,其信号传导途径包括RpfC/RpfG双组分感应系统、二级信使环二鸟苷酸(c-di-GMP)、全局性转录因子Clp等。在野油菜黄单胞菌中,DSF群体感应信号调控3类生物学功能:一是促进致病相关基因的表达;二是抑制生物膜形成;三是促进黄单胞菌作出代谢调整,适应高群体密度环境。RpfF是DSF信号生物合成过程中的关键酶,黄单胞菌在高群体密度条件下通过一种新型自我诱导机制大量合成DSF。DSF信号不仅存在于所有黄单胞菌属细菌中,也广泛存在于苛养木杆菌、嗜麦芽寡养单胞菌、伯克氏菌、铜绿假单胞菌和许多海洋细菌中,其传导途径和生物学功能还有待进一步阐明。
