119 research outputs found
Integration of Building Information Modelling and Geographic Information System at Data Level Using Semantics and Geometry Conversion Approach Towards Smart Infrastructure Management
This study integrates Building Information Modelling (BIM)and Geographic Information System (GIS) at data level using an open source approach for geometry transformation and an automatic attribute searching algorithm for semantics transfer for the purpose of facilitating data transformation from BIM to GIS. Based on that, an infrastructure management system has been developed using Web GIS technology in conjunction with the models created by BIM and transformed into GIS using the proposed approach
A Semantics-Based Approach for Simplifying IFC Building Models to Facilitate the Use of BIM Models in GIS
Using solid building models, instead of the surface models in City Geography Markup Language (CityGML), can facilitate data integration between Building Information Modeling (BIM) and Geographic Information System (GIS). The use of solid models, however, introduces a problem of model simplification on the GIS side. The aim of this study is to solve this problem by developing a framework for generating simplified solid building models from BIM. In this framework, a set of Level of Details (LoDs) were first defined to suit solid building models—referred to as s-LoD, rang-ing from s-LoD1 to s-LoD4—and three unique problems in implementing s-LoDs were identified and solved by using a semantics-based approach, including identifying external objects for s-LoD2 and s-LoD3, distinguishing various slabs, and generating valid external walls for s-LoD2 and s-LoD3. The feasibility of the framework was validated by using BIM models, and the result shows that using semantics from BIM can make it easier to convert and simplify building models, which in turn makes BIM information more practical in GIS
CityGML in the Integration of BIM and the GIS: Challenges and Opportunities
CityGML (City Geography Markup Language) is the most investigated standard in the integration of building information modeling (BIM) and the geographic information system (GIS), and it is essential for digital twin and smart city applications. The new CityGML 3.0 has been released for a while, but it is still not clear whether its new features bring new challenges or opportunities to this research topic. Therefore, the aim of this study is to understand the state of the art of CityGML in BIM/GIS integration and to investigate the potential influence of CityGML3.0 on BIM/GIS integration. To achieve this aim, this study used a systematic literature review approach. In total, 136 papers from Web of Science (WoS) and Scopus were collected, reviewed, and analyzed. The main findings of this review are as follows: (1) There are several challenging problems in the IFC-to-CityGML conversion, including LoD (Level of Detail) mapping, solid-to-surface conversion, and semantic mapping. (2) The ‘space’ concept and the new LoD concept in CityGML 3.0 can bring new opportunities to LoD mapping and solid-to-surface conversion. (3) The Versioning module and the Dynamizer module can add dynamic semantics to the CityGML. (4) Graph techniques and scan-to-BIM offer new perspectives for facilitating the use of CityG
A Novel Scholar Embedding Model for Interdisciplinary Collaboration
Interdisciplinary collaboration has become a driving force for scientific
breakthroughs, and evaluating scholars' performance in interdisciplinary
researches is essential for promoting such collaborations.However, traditional
scholar evaluation methods based solely on individual achievements do not
consider interdisciplinary cooperation, creating a challenge for
interdisciplinary scholar evaluation and recommendation. To address this issue,
we propose a scholar embedding model that quantifies and represents scholars
based on global semantic information and social influence, enabling real-time
tracking of scholars' research trends. Our model incorporates semantic
information and social influence for interdisciplinary scholar evaluation,
laying the foundation for future interdisciplinary collaboration discovery and
recommendation projects. We demonstrate the effectiveness of our model on a
sample of scholars from the Beijing University of Posts and Telecommunications.Comment: 9 pages, 4 figures, 1 tabl
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
Cross-dataset emotion recognition as an extremely challenging task in the
field of EEG-based affective computing is influenced by many factors, which
makes the universal models yield unsatisfactory results. Facing the situation
that lacks EEG information decoding research, we first analyzed the impact of
different EEG information(individual, session, emotion and trial) for emotion
recognition by sample space visualization, sample aggregation phenomena
quantification, and energy pattern analysis on five public datasets. Based on
these phenomena and patterns, we provided the processing methods and
interpretable work of various EEG differences. Through the analysis of
emotional feature distribution patterns, the Individual Emotional Feature
Distribution Difference(IEFDD) was found, which was also considered as the main
factor of the stability for emotion recognition. After analyzing the
limitations of traditional modeling approach suffering from IEFDD, the
Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably
characterize emotional feature distribution patterns, four weight extraction
methods were designed, and the optimal was the correction T-test(CT) weight
extraction method. Finally, the performance of WCMF was validated on
cross-dataset tasks in two kinds of experiments that simulated different
practical scenarios, and the results showed that WCMF had more stable and
better emotion recognition ability.Comment: 18 pages, 12 figures, 8 table
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