Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295008890> ?p ?o ?g. }
Showing items 1 to 98 of
98
with 100 items per page.
- W4295008890 endingPage "57" @default.
- W4295008890 startingPage "53" @default.
- W4295008890 abstract "Self-healing for machine tools presents significant challenges. However, current research suggests a convergence of requisite self-healing capabilities for metals, electronics and networking. And advances in ultra-low-power machine learning referred to as TinyML, running locally on microcontrollers, is enabling new classes of machine learning applications that allow on-device and real-time inference needed for detecting, actuating and managing the self-healing process. Synchronization of the different time and length scales of failure and healing remains a key challenge. A complex event processing framework is described that builds on TinyML and complex event processing of damage and healing events to control the healing process(es)." @default.
- W4295008890 created "2022-09-09" @default.
- W4295008890 creator A5049697655 @default.
- W4295008890 date "2022-10-01" @default.
- W4295008890 modified "2023-10-09" @default.
- W4295008890 title "From self-aware to self-healing for perpetual manufacturing" @default.
- W4295008890 cites W1987950150 @default.
- W4295008890 cites W2123587053 @default.
- W4295008890 cites W2158039208 @default.
- W4295008890 cites W2163698383 @default.
- W4295008890 cites W2194250012 @default.
- W4295008890 cites W2269962542 @default.
- W4295008890 cites W2469953905 @default.
- W4295008890 cites W2617021151 @default.
- W4295008890 cites W2780610873 @default.
- W4295008890 cites W2796416796 @default.
- W4295008890 cites W2800509980 @default.
- W4295008890 cites W2804981481 @default.
- W4295008890 cites W2889471383 @default.
- W4295008890 cites W2897964503 @default.
- W4295008890 cites W2913529903 @default.
- W4295008890 cites W2915846571 @default.
- W4295008890 cites W2939155052 @default.
- W4295008890 cites W2963430975 @default.
- W4295008890 cites W2970225967 @default.
- W4295008890 cites W2977020002 @default.
- W4295008890 cites W3112984208 @default.
- W4295008890 cites W3162713913 @default.
- W4295008890 cites W3202905974 @default.
- W4295008890 cites W995505119 @default.
- W4295008890 doi "https://doi.org/10.1016/j.mfglet.2022.08.015" @default.
- W4295008890 hasPublicationYear "2022" @default.
- W4295008890 type Work @default.
- W4295008890 citedByCount "0" @default.
- W4295008890 crossrefType "journal-article" @default.
- W4295008890 hasAuthorship W4295008890A5049697655 @default.
- W4295008890 hasBestOaLocation W42950088901 @default.
- W4295008890 hasConcept C111919701 @default.
- W4295008890 hasConcept C120314980 @default.
- W4295008890 hasConcept C121332964 @default.
- W4295008890 hasConcept C127162648 @default.
- W4295008890 hasConcept C142724271 @default.
- W4295008890 hasConcept C149635348 @default.
- W4295008890 hasConcept C154945302 @default.
- W4295008890 hasConcept C173018170 @default.
- W4295008890 hasConcept C204787440 @default.
- W4295008890 hasConcept C26517878 @default.
- W4295008890 hasConcept C2776214188 @default.
- W4295008890 hasConcept C2778210392 @default.
- W4295008890 hasConcept C2778562939 @default.
- W4295008890 hasConcept C2779662365 @default.
- W4295008890 hasConcept C38652104 @default.
- W4295008890 hasConcept C41008148 @default.
- W4295008890 hasConcept C62520636 @default.
- W4295008890 hasConcept C71924100 @default.
- W4295008890 hasConcept C76155785 @default.
- W4295008890 hasConcept C98045186 @default.
- W4295008890 hasConceptScore W4295008890C111919701 @default.
- W4295008890 hasConceptScore W4295008890C120314980 @default.
- W4295008890 hasConceptScore W4295008890C121332964 @default.
- W4295008890 hasConceptScore W4295008890C127162648 @default.
- W4295008890 hasConceptScore W4295008890C142724271 @default.
- W4295008890 hasConceptScore W4295008890C149635348 @default.
- W4295008890 hasConceptScore W4295008890C154945302 @default.
- W4295008890 hasConceptScore W4295008890C173018170 @default.
- W4295008890 hasConceptScore W4295008890C204787440 @default.
- W4295008890 hasConceptScore W4295008890C26517878 @default.
- W4295008890 hasConceptScore W4295008890C2776214188 @default.
- W4295008890 hasConceptScore W4295008890C2778210392 @default.
- W4295008890 hasConceptScore W4295008890C2778562939 @default.
- W4295008890 hasConceptScore W4295008890C2779662365 @default.
- W4295008890 hasConceptScore W4295008890C38652104 @default.
- W4295008890 hasConceptScore W4295008890C41008148 @default.
- W4295008890 hasConceptScore W4295008890C62520636 @default.
- W4295008890 hasConceptScore W4295008890C71924100 @default.
- W4295008890 hasConceptScore W4295008890C76155785 @default.
- W4295008890 hasConceptScore W4295008890C98045186 @default.
- W4295008890 hasFunder F4320306076 @default.
- W4295008890 hasFunder F4320333028 @default.
- W4295008890 hasLocation W42950088901 @default.
- W4295008890 hasOpenAccess W4295008890 @default.
- W4295008890 hasPrimaryLocation W42950088901 @default.
- W4295008890 hasRelatedWork W1596201972 @default.
- W4295008890 hasRelatedWork W2108938726 @default.
- W4295008890 hasRelatedWork W2131630752 @default.
- W4295008890 hasRelatedWork W2160425906 @default.
- W4295008890 hasRelatedWork W2329452785 @default.
- W4295008890 hasRelatedWork W2356380379 @default.
- W4295008890 hasRelatedWork W2363925233 @default.
- W4295008890 hasRelatedWork W2386057428 @default.
- W4295008890 hasRelatedWork W2615259895 @default.
- W4295008890 hasRelatedWork W4231453522 @default.
- W4295008890 hasVolume "34" @default.
- W4295008890 isParatext "false" @default.
- W4295008890 isRetracted "false" @default.
- W4295008890 workType "article" @default.