Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.
Gestational diabetes mellitus (GDM) is today universally diagnosed during late pregnancy. Treating hyperglycaemia during pregnancy reduces the risk of complications, the effect of interventions is however limited due to the late diagnosis. It is thus important to identify biomarkers reaching a high precision for GDM development in early pregnancy. Here we aim to investigate soluble CD163 (sCD163) and soluble tumour necrosis factor-like weak inducer of apoptosis (sTWEAK) in early pregnancy GDM and their association to the development of later glucose intolerance. In this case-control study, women diagnosed with GDM in early pregnancy (n = 70) at Lund University Hospital, Lund, Sweden in 2011–2015 were age- and BMI matched to pregnant volunteers without diabetes (n = 70) recruited in early pregnancy from maternal health care centres in 2014–2015. Plasma levels of sCD163 and sTWEAK were analysed using commercial ELISA. Plasma levels of sCD163 did not differ between patients with and without GDM in early pregnancy (p = 0.86), plasma levels of sTWEAK however was decreased in women with GDM (0.71 [0.4–1.75] ng/ml) compared to controls (1.38 [0.63–4.86] ng/ml; p = 0.003). Women with sTWEAK levels in the lowest tertile had an increased risk of GDM in early pregnancy (p = 0.014). Neither sCD163 nor sTWEAK were associated with later glucose intolerance in women with GDM. This study reports decreased levels of sTWEAK in women with early pregnancy GDM, independent of age and BMI. Neither sCD163 nor sTWEAK were found to be associated to later glucose intolerance.
We formulate the challenging problem to establish information interoperability within a system of systems (SoS) as a machine-learning task, where autoencoder embeddings are aligned using message data and metadata to automate message translation. An SoS requires communication and collaboration between otherwise independently operating systems, which are subject to different standards, changing conditions, and hidden assumptions. Thus, interoperability approaches that are based on standardization and symbolic inference will have limited generalization and scalability in the SoS engineering domain. We present simulation experiments performed with message data generated using heating and ventilation system simulations. While the unsupervised learning approach proposed here remains unsolved in general, we obtained up to 75% translation accuracy with autoencoders aligned by back-translation after investigating seven different models with different training protocols and hyperparameters. For comparison, we obtain 100% translation accuracy on the same task with supervised learning, but the need for a labeled dataset makes that approach less interesting. We discuss possibilities to extend the proposed unsupervised learning approach to reach higher translation accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.