Energy-based decision engine for household human activity recognition

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes

RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system

Utilizing machine learning on freight transportation and logistics applications: A review

This review article explores and locates the current state-of-the-art related to application areas from freight transportation, supply chain and logistics that focuses on arrival time, demand forecasting, industrial processes optimization, traffic flow and location prediction, the vehicle routing problem and anomaly detection on transportation data. This review categorizes the related works according to machine learning methodologies so as to present the methods’ evolution through time, their combinations and their connection with the various applications in the specified fields. Thus, a reader would effortlessly get insights about the current state-of-the-art related to machine learning in freight transportation and related application areas