Amidst growing environmental concerns and the push for sustainability, the intricacies of energy management in diverse building settings demand innovative solutions. Traditional strategies fail to address the unique characteristics and needs of different building types and users leading to inefficiencies and a lack of optimization in energy use. This study presents a sophisticated building energy management system, assessed via pilot implementations, which utilizes a platform based on adaptive and intelligent edge computing. This comprehensive system integrates various elements such as smart meters, HVAC controls, electric vehicle charging stations, and smart plugs. Utilizing the ICT PSP framework, the methodology encompasses rigorous data collection and analysis, emphasizing usability, error rectification, and the detailed monitoring of impacts. Key findings reveal that tailored energy management solutions, augmented by real-time data and user-centric interfaces, significantly improve energy efficiency. The study also highlighted the impact of socio-economic factors and weather conditions on energy consumption, underscoring the necessity of incorporating these variables into energy management strategies. The integration of demand-side management (DSM) through energy retailers, distribution system operators (DSO), or other power grid stakeholders has yielded valuable insights into external factors affecting energy consumption patterns. Specifically, these insights pertain to electricity tariffs and consumer behavior.
In the intricate process of shipbuilding, digitalization plays a significant role in enhancing competitiveness and sustainability. This multifaceted process involves numerous minor pre-assemblies, where automation, specifically through robotic welding, proves instrumental in enhancing overall efficiency. This paper utilizes real data from a shipyard to implement an artificial vision system designed to identify the elements required for assembling a minor pre-assembly. Surface-based matching and 3D edge matching techniques were applied to a diverse dataset of point cloud images. By employing multiple computer-aided design models to generate corresponding surface models, we successfully identified all the pieces present on a tray. This identification process is particularly complex due to the multitude of minor pre-assembly types, the challenge of distinguishing between similar components, and the stacking of these parts. The distinction of these elements significantly contributes to ensuring traceability throughout the hole shipbuilding process.
Measurement in quantum theory is a key scientific case where one of many possible outcomes comes to pass, the other possible outcomes disappear, and the world continues from a newly-fixed state. In quantum theory, Lüder’s projection postulate constraints the way the state of a system has to change following measurement, an idea which has been multiply employed in applications of quantum theory outside physics, notably in cognition. Recently, Orrell presented an economic model based on quantum theory. We summarise this view, including evidence for its plausibility. Following from Orrell’s ideas, we consider how the notion of a system, measurement, and Lüder’s postulate might translate in a financial context. Specifically, quantum theory predicts a so-called quantum Zeno effect, that is, a reduction that some initial state changes, after receiving various pieces of information all pushing towards change, as the density of intermediate measurements increases. We aim to translate this idea to the financial world and we believe there is an application in the real estate market: Due to the individual nature of houses and the corresponding scarcity of comparable properties, we observe especially pronounced relationships between low liquidity and price uncertainty in the property market. We propose that this way of thinking essentially links low liquidity with volatility and we offer some preliminary analyses substantiating our point.
In a constantly expanding global market, where worldwide container traffic surged nearly fourfold between 2000 and 2019, predicting demand becomes crucial for a company’s optimal growth. Particularly, the fashion sector is highly unpredictable, characterized by: seasonality, cultural influences, and fashion trends. These factors make it challenging to forecast footwear demand between seasons. In recent years, various algorithms for demand prediction have been explored, generally classified into three main categories: statistical, artificial intelligence and hybrid algorithms, each with its unique characteristics. The goal of this work is to predict the sales of a specific shoe model for a company. To achieve this, some Key Performance Indicators (KPIs) have been established that provide a comprehensive description of the prediction made. Holt-Winters has been chosen as the prediction algorithm, which achieves an accuracy rate of 91.6
The emergence of Industry 4.0 as a de facto paradigm for the industries of the future still presents some needed innovation and problem solving. In a increasing interconnected industrial setup where all nodes in a industrial process are increasingly interconnected, new challenges arise from the need to gather and explain machine learning models obtained from data streams present in multiple locations. These problems may be tackled with the fields of explainable artificial intelligence (xAI) methods and federated learning. The data constraints in temporal dependencies as is the case with most industrial process, may also make these problems fall under the timeseries category. This article presents an initial approach to deal with these problems and provides a theoretical overview of a federated xAI system for timeseries applications in Industry 4.0. The main objective is to present and discuss the integration of previous efforts into a large scale application for machine learning algorithm within I4.0 that offer human understandable decisions for I4.0 decision makers.
