English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Generating sequential decision process from huge amounts of measured processdata is a future research direction for collaborative factory automation,making full use of those online or offline process data to directly designflexible make decisions policy, and evaluate performance. The key challengesfor the sequential decision process is to online generate sequentialdecision-making policy directly, and transferring knowledge across tasksdomain. Most multi-task policy generating algorithms often suffer frominsufficient generating cross-task sharing structure at discrete-time nonlinearsystems with applications. This paper proposes the multi-task generativeadversarial nets with shared memory for cross-domain coordination control,which can generate sequential decision policy directly from raw sensory inputof all of tasks, and online evaluate performance of system actions indiscrete-time nonlinear systems. Experiments have been undertaken using aprofessional flexible manufacturing testbed deployed within a smart factory ofWeichai Power in China. Results on three groups of discrete-time nonlinearcontrol tasks show that our proposed model can availably improve theperformance of task with the help of other related tasks.

Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control