Yasmin Adriane de Paula Campos , Paulo Haron da Silva Pereira , Robson Aparecido Duarte , José Manuel Gonzalez Túbio Perez , Gustavo Pessin , Thomas Vargas Barsante Pinto
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引用次数: 0
Abstract
We present a converter software program that automatically translates Python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models in a programming language widely used in industrial controllers. It supports the conversion of decision tree and multilayer perceptron models built using scikit-learn library. Moreover, the generated Structure Text code is compatible with ABB’s Industrial IT 800xA DCS syntax. A practical example demonstrates the effectiveness of this converter software program and its potential to enhance the integration of machine learning models into industrial automation systems.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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