loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Dylan Molinié and Kurosh Madani

Affiliation: LISSI Laboratory EA 3956, Université Paris-Est Créteil, Senart-FB Institute of Technology, Campus de Sénart, 36-37 Rue Georges Charpak, F-77567 Lieusaint, France

Keyword(s): Space Partitioning, Sub-spaces, Cluster’s Density, Industry 4.0, Cognitive Systems.

Abstract: The new challenges Science is facing nowadays are legion; they mostly focus on high level technology, and more specifically Robotics, Internet of Things, Smart Automation (cities, houses, plants, buildings, etc.), and more recently Cyber-Physical Systems and Industry 4.0. For a long time, cognitive systems have been seen as a mere dream only worth of Science Fiction. Even though there is much to be done, the researches and progress made in Artificial Intelligence have let cognition-based systems make a great leap forward, which is now an actual great area of interest for many scientists and industrialists. Nonetheless, there are two main obstacles to system’s smartness: computational limitations and the infinite number of states to define; Machine Learning-based algorithms are perfectly suitable to Cognition and Automation, for they allow an automatic – and accurate – identification of the systems, usable as knowledge for later regulation. In this paper, we discuss the benefits of Ma chine Learning, and we present some new avenues of reflection for automatic behavior correctness identification through space partitioning, and density conceptualization and computation. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.215.79.68

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Molinié, D. and Madani, K. (2021). Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation. In Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL, ISBN 978-989-758-535-7, pages 13-24. DOI: 10.5220/0010657500003062

@conference{in4pl21,
author={Dylan Molinié. and Kurosh Madani.},
title={Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation},
booktitle={Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL,},
year={2021},
pages={13-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010657500003062},
isbn={978-989-758-535-7},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL,
TI - Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation
SN - 978-989-758-535-7
AU - Molinié, D.
AU - Madani, K.
PY - 2021
SP - 13
EP - 24
DO - 10.5220/0010657500003062