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  • 1
    Online Resource
    Online Resource
    Semantic Node-RED for rapid development of interoperable industrial IoT applications
    IOS Press ; 2020
    In:  Semantic Web Vol. 11, No. 6 ( 2020-10-29), p. 949-975
    Details
    In: Semantic Web, IOS Press, Vol. 11, No. 6 ( 2020-10-29), p. 949-975
    Abstract: The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated. We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-200405
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
    detail.hit.zdb_id: 2599324-0
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  • 2
    Online Resource
    Online Resource
    Combining chronicle mining and semantics for predictive maintenance in manufacturing processes
    IOS Press ; 2020
    In:  Semantic Web Vol. 11, No. 6 ( 2020-10-29), p. 927-948
    Details
    In: Semantic Web, IOS Press, Vol. 11, No. 6 ( 2020-10-29), p. 927-948
    Abstract: Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-200406
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
    detail.hit.zdb_id: 2599324-0
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  • 3
    Online Resource
    Online Resource
    SAREF4INMA: A SAREF extension for the industry and manufacturing domain
    IOS Press ; 2020
    In:  Semantic Web Vol. 11, No. 6 ( 2020-10-29), p. 911-926
    Details
    In: Semantic Web, IOS Press, Vol. 11, No. 6 ( 2020-10-29), p. 911-926
    Abstract: The IoT landscape is characterized by a fragmentation of standards, platforms and technologies, often scattered among different vertical domains. To prevent the market to continue to be fragmented and power-less, a protocol-independent semantic layer can serve as enabler of interoperability among the various smart devices from different manufacturers that co-exist in a specific industry domain, but also across different domains. To that end, the SAREF ontology was created in 2015 with the intention to interconnect data, enabling the communication between IoT devices that use different protocols and standards. A number of industrial sectors consequently expressed their interest to extend SAREF into their domains in order to fill the gaps of the semantics not yet covered by their communication protocols. Therefore, the SAREF4INMA ontology was recently created to extend SAREF for describing the Smart Industry & Manufacturing domain. SAREF4INMA is based on several standards and IoT initiatives, as well as on real use cases, and includes classes, properties and instances specifically created to cover the industry and manufacturing domain. This work describes the approach followed to develop this ontology, specifies its requirements and also includes a practical example of how to use it.
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-200402
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
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  • 4
    Online Resource
    Online Resource
    Estimating query rewriting quality over LOD
    IOS Press ; 2019
    In:  Semantic Web Vol. 10, No. 3 ( 2019-04-05), p. 529-554
    Details
    In: Semantic Web, IOS Press, Vol. 10, No. 3 ( 2019-04-05), p. 529-554
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-180311
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2019
    detail.hit.zdb_id: 2599324-0
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  • 5
    Online Resource
    Online Resource
    ExtruOnt: An ontology for describing a type of manufacturing machine for Industry 4.0 systems
    IOS Press ; 2020
    In:  Semantic Web Vol. 11, No. 6 ( 2020-10-29), p. 887-909
    Details
    In: Semantic Web, IOS Press, Vol. 11, No. 6 ( 2020-10-29), p. 887-909
    Abstract: Semantically rich descriptions of manufacturing machines, offered in a machine-interpretable code, can provide interesting benefits in Industry 4.0 scenarios. However, the lack of that type of descriptions is evident. In this paper we present the development effort made to build an ontology, called ExtruOnt, for describing a type of manufacturing machine, more precisely, a type that performs an extrusion process (extruder). Although the scope of the ontology is restricted to a concrete domain, it could be used as a model for the development of other ontologies for describing manufacturing machines in Industry 4.0 scenarios. The terms of the ExtruOnt ontology provide different types of information related with an extruder, which are reflected in distinct modules that constitute the ontology. Thus, it contains classes and properties for expressing descriptions about components of an extruder, spatial connections, features, and 3D representations of those components, and finally the sensors used to capture indicators about the performance of this type of machine. The ontology development process has been carried out in close collaboration with domain experts.
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-200376
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
    detail.hit.zdb_id: 2599324-0
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  • 6
    Online Resource
    Online Resource
    Quality metrics for RDF graph summarization
    IOS Press ; 2019
    In:  Semantic Web Vol. 10, No. 3 ( 2019-04-05), p. 555-584
    Details
    In: Semantic Web, IOS Press, Vol. 10, No. 3 ( 2019-04-05), p. 555-584
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-190346
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2019
    detail.hit.zdb_id: 2599324-0
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  • 7
    Online Resource
    Online Resource
    Semantic Web of Things for Industry 4.0
    IOS Press ; 2020
    In:  Semantic Web Vol. 11, No. 6 ( 2020-10-29), p. 885-886
    Details
    In: Semantic Web, IOS Press, Vol. 11, No. 6 ( 2020-10-29), p. 885-886
    Abstract: Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles four technical contributions that significantly advance the state-of-the-art in Semantic Web of Things for Industry 4.0 including the use of Semantic Web technologies and techniques in Industry 4.0 solutions.
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-200407
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2020
    detail.hit.zdb_id: 2599324-0
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  • 8
    Online Resource
    Online Resource
    A note on intelligent exploration of semantic data
    IOS Press ; 2019
    In:  Semantic Web Vol. 10, No. 3 ( 2019-04-05), p. 525-527
    Details
    In: Semantic Web, IOS Press, Vol. 10, No. 3 ( 2019-04-05), p. 525-527
    Type of Medium: Online Resource
    ISSN: 2210-4968 , 1570-0844
    URL: Article
    DOI: 10.3233/SW-190350
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2019
    detail.hit.zdb_id: 2599324-0
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  • 9
    Online Resource
    Online Resource
    Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 6 ( 2022-03-19), p. 2375-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 6 ( 2022-03-19), p. 2375-
    Abstract: Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22062375
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 10
    Online Resource
    Online Resource
    Tailored risk assessment and forecasting in intermittent claudication
    Oxford University Press (OUP) ; 2024
    In:  BJS Open Vol. 8, No. 1 ( 2024-01-03)
    Details
    In: BJS Open, Oxford University Press (OUP), Vol. 8, No. 1 ( 2024-01-03)
    Abstract: Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies. Methods Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset. Results The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression. Conclusion The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.
    Type of Medium: Online Resource
    ISSN: 2474-9842
    URL: Article
    DOI: 10.1093/bjsopen/zrad166
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 2902033-5
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