Elucidating families of ship designs using clustering algorithms

Ted Jaspers; Austin Kana

Elucidating families of ship designs using clustering algorithms

Ship Design, Production and Operations

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

This paper proposes a method to elucidate families of ship designs generated by the TU Delft packing approach using data clustering algorithms. The authors explore whether commonly used data science techniques can extract new information from the existing data. To test this hypothesis this paper applies data clustering algorithms to a test case of layouts of a Mine Counter Measures Vessel (MCMV) generated by the packing approach. Results look to improve the understanding of the multidimensional structure of the data, as well as to improve the comprehension and visualization of the complex interactions between the design and performance space.

Original language	English
Title of host publication	Proceedings of the 16th International Conference on Computer and IT Applications in the Maritime Industries COMPIT'17
Editors	V. Bertram
Place of Publication	Hamburg
Publisher	Technische Universität Hamburg-Harburg
Pages	474-485
ISBN (Print)	978-3-89220-701-6
Publication status	Published - 2017
Event	16th Conference on Computer and IT Applications in the Maritime Industries - Cardiff, United Kingdom Duration: 15 May 2017 → 17 May 2017 Conference number: 16

Conference

Conference	16th Conference on Computer and IT Applications in the Maritime Industries
Abbreviated title	COMPIT,17
Country/Territory	United Kingdom
City	Cardiff
Period	15/05/17 → 17/05/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Elucidating families of ship designs using clustering algorithms",

abstract = "This paper proposes a method to elucidate families of ship designs generated by the TU Delft packing approach using data clustering algorithms. The authors explore whether commonly used data science techniques can extract new information from the existing data. To test this hypothesis this paper applies data clustering algorithms to a test case of layouts of a Mine Counter Measures Vessel (MCMV) generated by the packing approach. Results look to improve the understanding of the multidimensional structure of the data, as well as to improve the comprehension and visualization of the complex interactions between the design and performance space. ",

author = "Ted Jaspers and Austin Kana",

year = "2017",

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booktitle = "Proceedings of the 16th International Conference on Computer and IT Applications in the Maritime Industries COMPIT'17",

publisher = "Technische Universit{\"a}t Hamburg-Harburg",

note = "16th Conference on Computer and IT Applications in the Maritime Industries, COMPIT,17 ; Conference date: 15-05-2017 Through 17-05-2017",

}

Jaspers, T & Kana, A 2017, Elucidating families of ship designs using clustering algorithms. in V Bertram (ed.), Proceedings of the 16th International Conference on Computer and IT Applications in the Maritime Industries COMPIT'17. Technische Universität Hamburg-Harburg, Hamburg, pp. 474-485, 16th Conference on Computer and IT Applications in the Maritime Industries, Cardiff, United Kingdom, 15/05/17.

Elucidating families of ship designs using clustering algorithms. / Jaspers, Ted; Kana, Austin.
Proceedings of the 16th International Conference on Computer and IT Applications in the Maritime Industries COMPIT'17. ed. / V. Bertram. Hamburg: Technische Universität Hamburg-Harburg, 2017. p. 474-485.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Elucidating families of ship designs using clustering algorithms

AU - Jaspers, Ted

AU - Kana, Austin

N1 - Conference code: 16

PY - 2017

Y1 - 2017

N2 - This paper proposes a method to elucidate families of ship designs generated by the TU Delft packing approach using data clustering algorithms. The authors explore whether commonly used data science techniques can extract new information from the existing data. To test this hypothesis this paper applies data clustering algorithms to a test case of layouts of a Mine Counter Measures Vessel (MCMV) generated by the packing approach. Results look to improve the understanding of the multidimensional structure of the data, as well as to improve the comprehension and visualization of the complex interactions between the design and performance space.

AB - This paper proposes a method to elucidate families of ship designs generated by the TU Delft packing approach using data clustering algorithms. The authors explore whether commonly used data science techniques can extract new information from the existing data. To test this hypothesis this paper applies data clustering algorithms to a test case of layouts of a Mine Counter Measures Vessel (MCMV) generated by the packing approach. Results look to improve the understanding of the multidimensional structure of the data, as well as to improve the comprehension and visualization of the complex interactions between the design and performance space.

M3 - Conference contribution

SN - 978-3-89220-701-6

SP - 474

EP - 485

BT - Proceedings of the 16th International Conference on Computer and IT Applications in the Maritime Industries COMPIT'17

A2 - Bertram, V.

PB - Technische Universität Hamburg-Harburg

CY - Hamburg

T2 - 16th Conference on Computer and IT Applications in the Maritime Industries

Y2 - 15 May 2017 through 17 May 2017

ER -

Elucidating families of ship designs using clustering algorithms

Abstract

Conference

UN SDGs

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