Industry 4.0 integrates a series of emerging technologies, such as the Internet of Things (IoT), cyber-physical systems (CPS), cloud computing, and big data, and aims to improve operational efficiency and accelerate productivity inside the industrial environment. This article provides a series of information about the required structure to adopt Industry 4.0 approaches and a brief review of related concepts to finally identify challenges and research opportunities to envision the adoption of so-called digital twins. We want to pay attention to upgrading older systems aiming to provide the well-known advantages of Industry 4.0 to such legacy systems as reducing production costs, increasing efficiency, acquiring better robustness of equipment, and reaching advanced process connectivity.
Neighborhood-based algorithms are some of the most promising memory-based collaborative filtering approaches for recommender systems. Many of these algorithms rely on a global similarity measure to select the most similar neighbors for rating prediction. However, these approaches may fail in capturing some meaningful relationships among users. In the real world, although users can show interest in a wide range of objects, they can express more interest in objects contained in a specific topic, which typically comprises a bulk of closely related objects. In this paper, we propose a local similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties. For this, we use a clustering method to find groups of similar objects. Then we create a user-based similarity model for each cluster, which we named Clusterbased Local Similarity (CBLS) model. Each similarity model relies on rating normalization and resource allocation techniques that are sensitive to the ratings assigned to objects contained in the cluster. We performed experiments using two clustering algorithms (affinity propagation and K-Means) and compared the results with other neighborhood-based collaborative filtering approaches. Our numerical results on three benchmark datasets (MovieLens 100k, MovieLens 1M, and Netflix) demonstrate that the proposed method is competitive and outperforms traditional and state-of-the-art collaborative filtering-based similarity models in terms of accuracy metrics like mean absolute error (MAE) and root-mean-square error (RMSE).
Um problema comum em sistemas de manufatura modernos é a recorrente necessidade de configuração das linhas montagem em função da mudança do produto, especialmente devido à chamada "costumização em massa". O desafio, portanto, é a rápida reconfiguração para permitir a produção de vários produtos customizados. Para isso, diversos paradigmas para a manufatura têm sido propostos, e, entre eles, estão os que trazem o conceito de sistemas evolutivos (EPS/EAS), os quais são capazes de inserir auto-organização no ambiente industrial. O presente trabalho é um estudo sobre uma arquitetura multiagente baseada no paradigma de Sistemas Evolutivos de Produção (EPS), a qual desenvolve o conceito de agente cyber-físico, permitindo a possibilidade de estudo de auto-organização e emergência. A arquitetura é denominada EPSCore e é utilizada como base para o estudo e descrição do sistema proposto.
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