Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
Project BAMBAM, BAby Motor development monitored By A Multisensor wearable, richt zich op het begin, namelijk bij de zorg voor kinderen van 0-2 jaar. In het bijzonder op het optimaliseren van de ontwikkeling van de motoriek wanneer dit niet vanzelf gaat. Kinderfysiotherapeuten begeleiden veel baby’s waarbij er zorgen zijn over de motorische ontwikkeling. Een goed ontwikkelde motoriek is de basis voor andere ontwikkelingsdomeinen,en een voorwaarde voor een fysiek actieve leefstijl op latere leeftijd. Het inzetten van technologie bij het analyseren van bewegingsproblemen bij het jonge kind kan een waardevolle aanvulling zijn voor de kinderfysiotherapeut, die nu eigen observaties gebruikt. Op dit moment is er nog geen geschikt systeem voor het observeren van de motorische ontwikkeling voor kinderfysiotherapeuten. Daarom werken we in project BAMBAM aan een meetinstrument voor het objectiveren van bewegingsgedrag van baby’s, dat verantwoord ingezet kan worden in de kinderfysiotherapeutische praktijk en interventiestudies. Uitgangspunt is een bestaande smartsuit, een ‘slimme' romper, met sensortechnologie en Artificiële Intelligentie die doorontwikkeld wordt in co creatie met kinderfysiotherapeuten, ouders en experts. Ook onderzoeken we hoe de uitkomsten van het systeem waarde toevoegen als beslissingsondersteuning voor de kinderfysiotherapeut. Hierbij richten we ons vooral op de bewegingsparameters die belangrijk zijn voor het kinderfysiotherapeutisch onderzoek en behandeling en hoe we die duidelijk kunnen weergeven. Het systeem moet valide en betrouwbare metingen verzorgen in de thuissituatie voor de kinderfysiotherapeut in praktijk en ziekenhuis. De impact van deze toepassing op ouders en kinderfysiotherapeuten is een belangrijk onderdeel bij het ontwikkelen van deze technologie, zodat het op een verantwoorde manier gebruikt kan worden. De gezondheidszorg vraagt om evidence-based diagnostiek en interventies. Met de schaarste van zorg, wordt het zorgvuldig signaleren van de baby’s die de zorg echt nodig hebben steeds belangrijker, net als de inzet van effectieve interventies. Technologie kan bijdragen aan toegankelijkheid en duurzame borging hiervan.
Promoting entrepreneurship is an enabler of smart, sustainable and inclusive growth and it is one objective EU regions have pursued since the EC included it into 2020 Strategy. Entrepreneurship development has economic and social benefits, since it is not only a driving force for job creation, competitiveness and growth; it also contributes to personal fulfillment and to achieve social objectives. That is why the EU encourages entrepreneurial initiatives and to unlock the growth potential of businesses and citizens. However, only a 37% of Europeans (Eurobarometer 2012) would like to be self-employed. The Entrepreneurship Action Plan adopted by the EC in 2013 to reignite Europe’s entrepreneurial spirit includes initiatives for educating young people on entrepreneurship. To ensure that EU economy remains globally competitive, young generations of Europeans need to be inspired to develop their entrepreneurial mindset. EU 2020 Action Plan argues that young people benefitting of a specialised entrepreneurial education are more likely to start-up a business and to better tackle challenges in their professional career and life in general. Hence, there is good reason to ensure better quality of entrepreneurial education. Most approaches in recent years have focused on improving the skills or competences youngsters should obtain only within the education system. However, an integrated approach is needed, where the school, their friends, family and the social environment, shall play each one a relevant role, contributing to generate a more adequate atmosphere to boost their entrepreneurial mindsets, intrapreneurial attitudes and innovation capacities. This project will identify and exchange – through a quadruple helix approach- good practices for creating friendlier entrepreneurial ecosystems and actions to boost entrepreneurship in young people mindsets. The good practices and lessons learnt will be transferred into Action Plans to be included in regional policies.
