Soft human–machine interfaces: design, sensing and stimulation Tập 2 Số 3 - Trang 313-338 - 2018
Wentao Dong, Youhua Wang, Ying Zhou, Yunzhao Bai, Zhaojie Ju, Jiajie Guo, Guoying Gu, Kun Bai, Gaoxiang Ouyang, Shiming Chen, Qin Zhang, YongAn Huang
Digital twins: artificial intelligence and the IoT cyber-physical systems in Industry 4.0 Tập 6 Số 1 - Trang 171-185 - 2022
Petar Radanliev, David De Roure, Răzvan Nicolescu, Michael Huth, Omar Santos
AbstractThis paper presents a summary of mechanisms for the evolution of artificial intelligence in ‘internet of things’ networks. Firstly, the paper investigates how the use of new technologies in industrial systems improves organisational resilience supporting both a technical and human level. Secondly, the paper reports empirical results that correlate academic literature with Industry 4.0 interdependencies between edge components to both external and internal services and systems. The novelty of the paper is a new approach for creating a virtual representation operating as a real-time digital counterpart of a physical object or process (i.e., digital twin) outlined in a conceptual diagram. The methodology applied in this paper resembled a grounded theory analysis of complex interconnected and coupled systems. By connecting the human–computer interactions in different information knowledge management systems, this paper presents a summary of mechanisms for the evolution of artificial intelligence in internet of things networks.
A data assimilation framework for data-driven flow models enabled by motion tomography Tập 3 - Trang 158-177 - 2019
Dongsik Chang, Catherine R. Edwards, Fumin Zhang, Jing Sun
Autonomous underwater vehicles (AUVs) have become central to data collection for scientific and monitoring missions in the coastal and global oceans. To provide immediate navigational support for AUVs, computational data-driven flow models described as generic environmental models (GEMs) construct a map of the environment around AUVs. This paper proposes a data assimilation framework for the GEM to update the map using data collected by the AUVs. Unlike Eulerian data, Lagrangian data along the AUV trajectory carry time-integrated flow information. To facilitate assimilation of Lagrangian data into the GEM, the motion tomography method is employed to convert Lagrangian data of AUVs into an Eulerian spatial map of a flow field. This process allows assimilation of both Eulerian and Lagrangian data into the GEM to be incorporated in a unified framework, which introduces a nonlinear filtering problem. Considering potential complementarity of Eulerian and Lagrangian data in estimating spatial and temporal characteristics of flow, we develop a filtering method for estimation of the spatial and temporal parameters in the GEM. The observability is analyzed to verify the convergence of our filtering method. The proposed data assimilation framework for the GEM is demonstrated through simulations using two flow fields with different characteristics: (i) a double-gyre flow field and (ii) a flow field constructed by using real ocean surface flow observations from high-frequency radar.
