Abstract Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time‐series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one‐class and binary classification algorithms. This study aims to classify time‐series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time‐series data to train both one‐class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade‐off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one‐class and binary classification ultimately relies on dataset's features and task objectives.

Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China), and Stockholm (Sweden), as well as with controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task.

The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem.

Industrial Internet and Industrial Internet of Things are emerging concepts that concern the use of Internet technologies on industrial environments. The main objective of such architectural visions is allowing a tight and seamless integration between all the functional units and layers that compose industrial processes, from the lowest levels (e.g. field-level devices such as sensors and actuators) to the higher layers, including management, logistics and maintenance. This kind of architecture promises, among other advantages, improving efficiency and flexibility, reduce installation and maintenance costs and reduce unplanned downtime. However, industrial processes often encompass functionalities like closed-loop control of physical processes that are highly critical and have strict timeliness requirements. These requirements are not satisfied by normal Ethernet-based systems. Standards such as IEEE AVB and TSN are addressing this problem, enhancing the real-time properties of Ethernet. However, considering the information presently available, such standards still present some limitations and inefficiencies. This paper reports the extension of HaRTES, an Ethernet-based real-time architecture originally developed for use at the lower layers of industrial scenarios, with MAC Bridge standard functionalities, to make it capable of being integrated on Industrial Internet of Things frameworks. The paper also presents preliminary results obtained with a prototype realization of the extended HaRTES switch.