The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …

The main challenges along with lessons learned from ongoing research in the application of
machine learning systems in practice are discussed, taking into account aspects of …

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of
application domains, including speech recognition, natural language processing, and image …

Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in
multiple application domains. However, some steps further are needed towards the …

This paper investigates the potential advantages and difficulties of integrating predictive
model equations in models of unit manufacturing processes. The method described uses …

This paper describes the development of semantic platforms to support the modeling,
architectural design, realization, and operation of cyber‐physical systems. Platform …

The use of Deep Learning (DL) algorithms is increasingly evolving in many application
domains. Despite the rapid growing of algorithm size and complexity, performing DL …

System simulation is a valuable tool to unveil inefficiencies and to test new strategies when
implementing and revising systems. Often, simulations are parameterized using offline data …

The impact of systems engineering on program cost has been recognized for over a decade.
From the very early stages, careful management of the relationships between the product …

Maintaining coherence between system functional, performance, production and operational
requirements is a key to the ability to optimize the design of large-scale systems. Different …