Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review

Amador-Domínguez, Elvira; Serrano, Emilio; Paz, Juan F. De

doi:10.3390/s19081774

Cited by 13 publications

(7 citation statements)

References 79 publications

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“…For example, ZAR [20] used the coe cient in biological studies. Additionally, Amador-Domínguez [21] have used it in the eld of arti cial intelligence. Kirch [22] described Pearson's correlation coe cient (Rho) as the measure of the linear similarity between two variables.…”

Section: Discussionmentioning

confidence: 99%

Validation of ALOS/PALSAR subsurface penetration depth in arid regions based on field ground-penetrating radar measurements

Mansour

Zaher

Deep

et al. 2022

Preprint

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In the last few years, ALOS/PALSAR (L-band) (HH, HV, VH, and VV) images have been widely used due to their ability to penetrate the surface in certain conditions, such as low moisture or dry friable sandy soil. Images from the ALOS-1 sensor were used to delineate subsurface structures, and optical images such as Landsat-7 ETM + data were used to discriminate between scatterings from the Earth’s surface and subsurface materials. Thus, the Farafra Desert is an optimal geological environment for L-band microwave penetration, as its geology is characterized by friable sand sheets covering limestone (Tarawan Formation). Speckle noise is found in radar images for many reasons, such as when an object strongly reflected between itself and the spacecraft causes noise. Refined LEE filter (RLF) is applied for speckle noise reduction; moreover, full polarimetric ALOS/PALSAR images (PLR) are transformed into circular polarization by changing both angles into orientation angle ψ = 0° and elliptical angle χ = 45°. The validation of ALOS/PALSAR outputs was carried out using ground penetrating radar (GPR) measurements. Three GPR long profiles using a 200 MHz antenna were scanned along with areas that were annotated according to ALOS/PALSAR results (high backscattering coefficient). The GPR system operated by a low-frequency antenna with a frequency of 200 MHz was capable of detecting the annotated geological structures beneath the sand sheets. Furthermore, statistical comparison of L-band SAR and GPR data illustrated a correlation that can reveal identical regions to delineate subsurface structures. These results prove that the integration of synthetic aperture radar SAR (L-band) and on-site low-frequency radar systems can be vital to detect soil structures down to several meters, ultimately innovating Earth observation systems for geological and hydrogeological mapping in arid regions.

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Section: Discussionmentioning

confidence: 99%

Validation of ALOS/PALSAR subsurface penetration depth in arid regions based on field ground-penetrating radar measurements

Mansour

Zaher

Deep

et al. 2022

Preprint

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“…Finally, exploring the interpretability and explainability of the machine learning models used is essential. As shown in a recent review [ 45 ], most prediction and decision-making methods in intelligent environments are interpretable. Therefore, the use of grey-box models, which combine the interpretability of a white-box model with the accuracy of a black-box model, will be considered [ 40 ].…”

Section: Conclusion and Future Workmentioning

confidence: 99%

Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning

Serrano

Bajo

2020

Sensors

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The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.

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“…While few existing studies [36]- [38] have demonstrated the promise of utilising KGs for systematically structuring conceptual information in the wind industry, they only serve as an end point [39] providing information for consultation without the ability to further reason over the data. Moreover, these ontologies need to be queried manually through specialised graph query languages to extract relevant and meaningful information, which may not be easily accessible to turbine engineers & technicians.…”

Section: Introductionmentioning

confidence: 99%

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee

Dethlefs

2022

IEEE Access

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Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine subcomponents, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy.

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Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review

Cited by 13 publications

References 79 publications

Validation of ALOS/PALSAR subsurface penetration depth in arid regions based on field ground-penetrating radar measurements

Validation of ALOS/PALSAR subsurface penetration depth in arid regions based on field ground-penetrating radar measurements

Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

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