Industry foundation classes(IFC)is an open and neutral data format specification for building information modeling(BIM)that plays a crucial role in facilitating interoperability.With increases in web-based BIM applica...Industry foundation classes(IFC)is an open and neutral data format specification for building information modeling(BIM)that plays a crucial role in facilitating interoperability.With increases in web-based BIM applications,there is an urgent need for fast loading large IFC models on a web browser.However,the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser,and this significantly limits further BIM applications.In order to address the issue,a method is proposed for dynamically loading IFC models based on spatial semantic partitioning(SSP).First,the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server.Subsequently,based on user interaction,only the model data that a user is interested in is transmitted,loaded,and displayed on the client.The presented method is implemented via Web Graphics Library,and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins.When compared with conventional methods that load all IFC model data for display purposes,the proposed method significantly reduces memory consumption in a web browser,thereby allowing the loading of large IFC models.When compared with the existing method of spatial partitioning for 3D data,the proposed SSP entirely uses semantic information in the IFC file itself,and thereby provides a better interactive experience for users.展开更多
Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study pr...Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.展开更多
基金The study was supported by the National Key R&D Program of China(No.2018YFB0505400)the National Natural Science Foundation of China(No.61472202)+1 种基金the Special Scientific Research Fund of China Railway Corporation(No.J2017X010)the Research on Key Technologies of Virtual Engineering of Railway Engineering Unit Based on BIM Technology(No.K2018G055).
文摘Industry foundation classes(IFC)is an open and neutral data format specification for building information modeling(BIM)that plays a crucial role in facilitating interoperability.With increases in web-based BIM applications,there is an urgent need for fast loading large IFC models on a web browser.However,the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser,and this significantly limits further BIM applications.In order to address the issue,a method is proposed for dynamically loading IFC models based on spatial semantic partitioning(SSP).First,the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server.Subsequently,based on user interaction,only the model data that a user is interested in is transmitted,loaded,and displayed on the client.The presented method is implemented via Web Graphics Library,and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins.When compared with conventional methods that load all IFC model data for display purposes,the proposed method significantly reduces memory consumption in a web browser,thereby allowing the loading of large IFC models.When compared with the existing method of spatial partitioning for 3D data,the proposed SSP entirely uses semantic information in the IFC file itself,and thereby provides a better interactive experience for users.
文摘Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.
