4,626 research outputs found
Sensor data fusion for the industrial artificial intelligence of things
The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio
From BIM towards digital twin: Strategy and future development for smart asset management
With the rising adoption of Building Information Model (BIM) for as-set management within architecture, engineering, construction and owner-operated (AECO) sector, BIM-enabled asset management has been increasingly attracting more attentions in both research and practice. This study provides a comprehensive review and analysis of the state-of-the-art latest research and industry standards development that impact upon BIM and asset management within the operations and maintenance (O&M) phase. However, BIM is not always enough in whole-life cycle asset management, especially in the O&M phase. Therefore, a framework for future development of smart asset management are proposed, integrating the concept of Digital Twin (DT). DT integrates artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. The findings will contribute to inspiring novel research ideas and promote wide-spread adoption of smart DT-enabled asset management within the O&M phaseCentre for Digital Built Britain, Innovate U
From BIM towards digital twin: Strategy and future development for smart asset management
With the rising adoption of Building Information Model (BIM) for asset management within architecture, engineering, construction and owner-operated (AECO) sector, BIM-enabled asset management has been increasingly attracting more attentions in both research and practice. This study provides a comprehensive review and analysis of the state-of-the-art latest research and industry standards development that impact upon BIM and asset management within the operations and maintenance (O&M) phase. However, BIM is not always enough in whole-life cycle asset management, especially in the O&M phase. Therefore, a framework for future development of smart asset management is proposed, integrating the concept of Digital Twin (DT). DT integrates artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. The findings will contribute to inspiring novel research ideas and promote widespread adoption of smart DT-enabled asset management within the O&M phase
Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies
This work aims at investigating and quantifying the Urban Transport System
(UTS) resilience enhancement enabled by the adoption of emerging technology
such as Internet of Everything (IoE) and the new trend of the Connected
Community (CC). A conceptual extension of Functional Resonance Analysis Method
(FRAM) and its formalization have been proposed and used to model UTS
complexity. The scope is to identify the system functions and their
interdependencies with a particular focus on those that have a relation and
impact on people and communities. Network analysis techniques have been applied
to the FRAM model to identify and estimate the most critical community-related
functions. The notion of Variability Rate (VR) has been defined as the amount
of output variability generated by an upstream function that can be
tolerated/absorbed by a downstream function, without significantly increasing
of its subsequent output variability. A fuzzy based quantification of the VR on
expert judgment has been developed when quantitative data are not available.
Our approach has been applied to a critical scenario (water bomb/flash
flooding) considering two cases: when UTS has CC and IoE implemented or not.
The results show a remarkable VR enhancement if CC and IoE are deploye
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
In the rapidly advancing information era, various human behaviors are being
precisely recorded in the form of data, including identity information,
criminal records, and communication data. Law enforcement agencies can
effectively maintain social security and precisely combat criminal activities
by analyzing the aforementioned data. In comparison to traditional data
analysis methods, deep learning models, relying on the robust computational
power in cloud centers, exhibit higher accuracy in extracting data features and
inferring data. However, within the architecture of cloud centers, the
transmission of data from end devices introduces significant latency, hindering
real-time inference of data. Furthermore, low-latency edge computing
architectures face limitations in direct deployment due to relatively weak
computing and storage capacities of nodes. To address these challenges, a
lightweight distributed knowledge graph completion architecture is proposed.
Firstly, we introduce a lightweight distributed knowledge graph completion
architecture that utilizes knowledge graph embedding for data analysis.
Subsequently, to filter out substandard data, a personnel data quality
assessment method named PDQA is proposed. Lastly, we present a model pruning
algorithm that significantly reduces the model size while maximizing
performance, enabling lightweight deployment. In experiments, we compare the
effects of 11 advanced models on completing the knowledge graph of public
security personnel information. The results indicate that the RotatE model
outperforms other models significantly in knowledge graph completion, with the
pruned model size reduced by 70\%, and hits@10 reaching 86.97\%.}Comment: 20 pages, 6 figures, Has been accepted by Wireless Networ
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
Semantically intelligent semi-automated ontology integration
An ontology is a way of information categorization and storage. Web Ontologies provide help in retrieving the required and precise information over the web. However, the problem of heterogeneity between ontologies may occur in the use of multiple ontologies of the same domain. The integration of ontologies provides a solution for the heterogeneity problem. Ontology integration is a solution to problem of interoperability in the knowledge based systems. Ontology integration provides a mechanism to find the semantic association between a pair of reference ontologies based on their concepts. Many researchers have been working on the problem of ontology integration; however, multiple issues related to ontology integration are still not addressed. This dissertation involves the investigation of the ontology integration problem and proposes a layer based enhanced framework as a solution to the problem. The comparison between concepts of reference ontologies is based on their semantics along with their syntax in the concept matching process of ontology integration. The semantic relationship of a concept with other concepts between ontologies and the provision of user confirmation (only for the problematic cases) are also taken into account in this process. The proposed framework is implemented and validated by providing a comparison of the proposed concept matching technique with the existing techniques. The test case scenarios are provided in order to compare and analyse the proposed framework in the analysis phase. The results of the experiments completed demonstrate the efficacy and success of the proposed framework
- …