Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems

Wickramarachchi, Ruwan; Henson, Cory; Sheth, Amit P.

doi:10.1109/mis.2022.3181015

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…To enhance collaboration, improved perception of all events is required which was tackled through knowledge infusion while utilizing KGs (Wickramarachchi et al, 2022). In that regard, other works also propose enhancing operator performances for better collaboration by providing valuable information about their performance and the state of the production system (Nagy et al, 2022).…”

Section: Collaborationmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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Section: Collaborationmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

Self Cite

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“…To meet this requirement, a feasible approach is to represent entities and their relationships in scenes as knowledge graphs (KGs) [5]. By constructing scene knowledge graphs, systems can better predict entity behaviors, thereby enhancing scene understanding capabilities [6,7]. For example, consider a scenario where an autonomous vehicle is navigating through a complex intersection.…”

Section: Introductionmentioning

confidence: 99%

Research on Driving Scenario Knowledge Graphs

Zhang,

Hong,

Wang

et al. 2024

Applied Sciences

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Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

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Flying Carpets: Assessing Artificial Intelligence as an Entertainment Service

Anagnostopoulos

2023

2023 27th International Conference on Circuits, Systems, Communications and Computers (CSCC)

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Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems

Cited by 5 publications

References 13 publications

Cognitive manufacturing: definition and current trends

Cognitive manufacturing: definition and current trends

Research on Driving Scenario Knowledge Graphs

Flying Carpets: Assessing Artificial Intelligence as an Entertainment Service

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