Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings |
| Herausgeber/-innen | Van-Nam Huynh, Katsuhiro Honda, Bac Le, Masahiro Inuiguchi, Hieu T. Huynh |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 76-84 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 978-981-96-4603-6 |
| ISBN (Print) | 9789819646029 |
| Publikationsstatus | Veröffentlicht - 24 März 2025 |
| Veranstaltung | 11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025 - Ho Chi Minh City, Vietnam Dauer: 17 März 2025 → 19 März 2025 |
Publikationsreihe
| Name | Lecture Notes in Computer Science |
|---|---|
| Band | 15586 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
For complex engineering systems, the usual way to estimate their reliability is to run simulations. If the resulting estimate does not satisfy the desired reliability level, we must replace some components with more reliable and again run simulations. This can take several iterations, so the required computation time often becomes unrealistically long. It is known that it is possible to speed up computations if components belong to a few types, and components of each type are identical. So, a natural idea to deal with the general case is to use the general granularity idea, i.e., to group components with similar reliability characteristics into a single cluster, and for each component from the cluster, use the same average reliability instead of the original (somewhat different) reliability characteristics. The accuracy of the resulting approximate estimate of the system’s reliability depends on how exactly we divide the components into clusters. It is therefore desirable to select the clustering that leads to the most accurate reliability estimate. In this paper, we describe an algorithm for such optimal clustering.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings. Hrsg. / Van-Nam Huynh; Katsuhiro Honda; Bac Le; Masahiro Inuiguchi; Hieu T. Huynh. Springer Science and Business Media Deutschland GmbH, 2025. S. 76-84 (Lecture Notes in Computer Science; Band 15586 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - What Is Optimal Granularity When Estimating Reliability of a Complex Engineering Systems
AU - Winnewisser, Niklas
AU - Beer, Michael
AU - Kosheleva, Olga
AU - Kreinovich, Vladik
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/3/24
Y1 - 2025/3/24
N2 - For complex engineering systems, the usual way to estimate their reliability is to run simulations. If the resulting estimate does not satisfy the desired reliability level, we must replace some components with more reliable and again run simulations. This can take several iterations, so the required computation time often becomes unrealistically long. It is known that it is possible to speed up computations if components belong to a few types, and components of each type are identical. So, a natural idea to deal with the general case is to use the general granularity idea, i.e., to group components with similar reliability characteristics into a single cluster, and for each component from the cluster, use the same average reliability instead of the original (somewhat different) reliability characteristics. The accuracy of the resulting approximate estimate of the system’s reliability depends on how exactly we divide the components into clusters. It is therefore desirable to select the clustering that leads to the most accurate reliability estimate. In this paper, we describe an algorithm for such optimal clustering.
AB - For complex engineering systems, the usual way to estimate their reliability is to run simulations. If the resulting estimate does not satisfy the desired reliability level, we must replace some components with more reliable and again run simulations. This can take several iterations, so the required computation time often becomes unrealistically long. It is known that it is possible to speed up computations if components belong to a few types, and components of each type are identical. So, a natural idea to deal with the general case is to use the general granularity idea, i.e., to group components with similar reliability characteristics into a single cluster, and for each component from the cluster, use the same average reliability instead of the original (somewhat different) reliability characteristics. The accuracy of the resulting approximate estimate of the system’s reliability depends on how exactly we divide the components into clusters. It is therefore desirable to select the clustering that leads to the most accurate reliability estimate. In this paper, we describe an algorithm for such optimal clustering.
KW - Clustering
KW - Complex engineering systems
KW - Optimal granules
KW - Reliability
UR - http://www.scopus.com/inward/record.url?scp=105002717700&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-4603-6_7
DO - 10.1007/978-981-96-4603-6_7
M3 - Conference contribution
AN - SCOPUS:105002717700
SN - 9789819646029
T3 - Lecture Notes in Computer Science
SP - 76
EP - 84
BT - Integrated Uncertainty in Knowledge Modelling and Decision Making - 11th International Symposium, IUKM 2025, Proceedings
A2 - Huynh, Van-Nam
A2 - Honda, Katsuhiro
A2 - Le, Bac
A2 - Inuiguchi, Masahiro
A2 - Huynh, Hieu T.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025
Y2 - 17 March 2025 through 19 March 2025
ER -