Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
MULTIFILE
This video offers a concise exploration of the distinctions between Data Science, AI, Machine Learning, and Deep Learning. Starting with the foundational role of Data Science, it navigates through the various machine learning categories and touches upon the capabilities and constraints of Deep Learning. The discussion culminates in understanding the nuances of AI, differentiating between narrow and general AI. Through insightful examples, viewers are guided on selecting the right technique for specific projects, ensuring both clarity and cost-effectiveness in the realm of data science.
VIDEO
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.
The maritime transport industry is facing a series of challenges due to the phasing out of fossil fuels and the challenges from decarbonization. The proposal of proper alternatives is not a straightforward process. While the current generation of ship design software offers results, there is a clear missed potential in new software technologies like machine learning and data science. This leads to the question: how can we use modern computational technologies like data analysis and machine learning to enhance the ship design process, considering the tools from the wider industry and the industry’s readiness to embrace new technologies and solutions? The obbjective of this PD project is to bridge the critical gap between the maritime industry's pressing need for innovative solutions for a more agile Ship Design Process; and the current limitations in software tools and methodologies available via the implementation into Ship Design specific software of the new generation of computational technologies available, as big data science and machine learning.
