Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable, even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. An online version of the classic batch learning algorithm is also analyzed, showing very similar results (in an unsupervised context); which is essential even if multilevel structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood (ML) algorithms. The results are discussed with particular reference to a Latent Variable Model (LVM) structure.Comment: 20 pages, 8 figure

A study of generative adversarial networks to improve classification of microscopic foraminifera

Foraminifera are single-celled organisms with shells that live in the marine environment and can be found abundantly as fossils in e.g. sediment cores. The assemblages of different species and their numbers serves as an important source of data for marine, geological, climate and environmental research. Steps towards automatic classification of foraminifera using deep learning (DL) models have been made (Johansen and Sørensen, 2020), and this thesis sets out to improve the accuracy of their proposed model. The recent advances of DL models such as generative adversarial networks (GANs) (Goodfellow et al., 2014), and their ability to model high-dimensional distributions such as real-world images, are used to achieve this objective. GANs are studied and explored from a theoretical and empirical standpoint to uncover how they can be used to generate images of foraminifera. A multi-scale gradient GAN is implemented, tested and trained to learn the distributions of four high-level classes of a recent foraminifera dataset (Johansen and Sørensen, 2020), both conditionally and unconditionally. The conditional images are assessed by an expert and a deep learning classification model and is found to contain mostly valuable characteristics, although some artificial artifacts are introduced. The unconditional images measured a Fréchet Inception distance of 47.1. From the conditionally learned distributions a total of 10 000 images are sampled from the four distributions. These images are used to augment the original foraminifera training set in an attempt to improve the classification accuracy of (Johansen and Sørensen, 2020). Due to limitations of computational resources, the experiments were carried out with images of resolution 128 × 128. The synthetic image augmentation lead to an improvement in mean accuracy from 97.3 ± 0.4 % to 97.4 ± 0.7 % and an improvement in best achieved accuracy from 97.7 % to 98.5 %

Application of neural networks to model double tube heat exchangers

Treballs Finals de Grau d'Enginyeria Química, Facultat de Química, Universitat de Barcelona, Curs: 2022-2023, Tutor: David Curcó CantarellArtificial Intelligence is experiencing dramatic growth in recent times. AI models such as ChatGPT have become controversial topics as they continously transform our world. Nevertheless, the true nature of AI is still widely not yet understood by society. Nowadays, Artificial Intelligence is still seen by many as an obscure and foreign concept, even mysterious and threatening. However, this couldn’t be further from the truth. At their essence, they are just mathematical tools which rely on centuries-old knowledge: algebra and calculus. In this project, a neural network model has been created to solve a chemical engineering problem, the predictive model of a double tube heat exchanger. This model is a neural network that predicts future system outputs (inner stream output temperature) from the past values of the input variables of the system (inner and outer streams input temperatures and outer stream flow rate). The data used to train the model was obtained in a simulation written in the Python programming language. Afterwards, the optimal design parameters of the neural network were found experimentally by training different models and testing their performance. This was done in three stages: a proof of concept, a general design stage and a detailed design stage. The model has been successful in predicting the future state of the system with high exactitude while being circa. 3000 times faster than a conventional simulation