Terrain Mapping With MMW Radar: An Approach Using Sensor Fusion And Knowledge-Based Heuristics

1987 ◽  
Author(s):  
Miao-Li Pai ◽  
James C. Bezdek ◽  
Robert L. Cannon ◽  
William L. Cameron
Keyword(s):  
Sensor Fusion ◽  
Terrain Mapping ◽  
Knowledge Based

A Knowledge Based Sensor Fusion Editor

1993 ◽  
pp. 325-341
Author(s):  
L. F. Pau ◽  
X. Xiao ◽  
C. Westphal
Keyword(s):  
Sensor Fusion ◽  
Knowledge Based

Resource efficient sensor fusion by knowledge-based network pruning

2020 ◽  
Vol 11 ◽  
pp. 100231 ◽  
Author(s):  
Dieter Balemans ◽  
Wim Casteels ◽  
Simon Vanneste ◽  
Jens de Hoog ◽  
Siegfried Mercelis ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Pruning ◽  
Knowledge Based

A knowledge-based sensor fusion editor

1991 ◽  
Vol 21 (5) ◽  
pp. 1251-1259
Author(s):  
L.F. Pau ◽  
X. Xiao
Keyword(s):  
Sensor Fusion ◽  
Knowledge Based

scholarly journals An AutomationML Based Ontology for Sensor Fusion in Industrial Plants

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1311 ◽  
Author(s):  
Eder Gonçalves ◽  
Alvaro Freitas ◽  
Silvia Botelho
Keyword(s):  
Sensor Fusion ◽  
Production Systems ◽  
Data Format ◽  
Industrial Plants ◽  
Knowledge Based ◽  
Complete Knowledge ◽  
Engineering Data ◽  
Basic Functions ◽  
Sensors Fusion

AutomationML (AML) can be seen as a partial knowledge-based solution for manufacturing and automation domains since it permits integrating different engineering data format, and also contains information about physical and logical structures of production systems, using basic concepts as resources, process, and products, in semantic structures. However, it is not a complete knowledge-based solution because it does not have mechanisms for querying and reasoning procedures, which are basic functions for semantic inferences. Additionally, AutomationML does not deal with aspects of sensor fusion naturally. In this sense, we propose an ontology to describe those sensors’ fusion elements, including procedures for runtime processing, and also elements that can turn AutomationML into a complete knowledge-based solution. The approach was applied in a case study with two different industrial processes with some sensors under fusion. The results obtained demonstrate that the ontology allows describing sensors that are under fusion and deal with the occurrence of data divergence. In a broader view, the results show how to apply AutomationML description for runtime processing of data generated from different sensors of a manufacturing system using an ontology to complement the AML description, where AutomationML concentrates knowledge about a specific production system and the ontology describes a general and reusable knowledge about sensor fusion.

Is the Personal Always Political?

2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Negative Relationship ◽  
Openness To Experience ◽  
Political Sophistication ◽  
Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

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