The phrase "automated environment" is correct and usable in written English.
It can be used to describe a setting or system that operates with minimal human intervention, often through technology or software.
Example: "The company has implemented an automated environment to streamline its production processes and reduce labor costs."
Alternatives: "automated setting" or "automated system".
Commonly, the developed maintenance strategies are implemented and managed within the computerized maintenance management system (CMMS), which is usually separate from the RCM automated environment.
A fully automated environment for development of virtual prototypes is presented here, offering maximal efficiency gains, and supporting both design and verification flows, from the algorithmic model to the virtual prototype.
BioMAJ provides a flexible, robust, fully automated environment for managing such massive amounts of data.
We compare it with the approach taken in the Bandera Environment Generator tool [Tkachuk, O., M. B. Dwyer and C. S. Pasareanu, Automated Environment Generation for Software Model Checking, 18th IEEE International Conference on Automated Software Engineering (ASE03), p. 116, 2003], designed for model checking of sets of Java classes.
In an automated environment however, these differences are significant.
An automated environment to sequentially execute the aforementioned processes is developed.
The flexibility of the process allows complex components to be manufactured in high volumes in fully automated environments.
In the pharmaceutical industry, a widely established approach for this is High-Throughput Screening (HTS), where large molecular libraries are screened against a bioassay in fully automated environments [1].
They exhibit the possibility for expression within automated environments by exploiting the complexity of the systems through random, yet intended, generation.
Fei, G. Modeling Human Attention and Performance in Automated Environments with Low Task Loading, Ph.D. Thesis (2016), Humans and Automation Laboratory, Engineering Systems Division, Massachusetts Institute of Technology.
This chapter attempts to develop a framework that evolves a better solution based on available spatial heuristics and human knowledge in a fully automated planning environment.
