WEC-Sim Application for the Assessment of Wave Energy Generation in the Tyrrhenian Sea and the North Sea Through the 2-Body Point Absorber (2BPA) and the Oscillating Surge Wave Energy Converter (OSWEC)

In recent years, developed countries have turned their attention to clean and renewable energy, such as wind energy and wave energy that can be converted to electrical power. Companies and academic groups worldwide are investigating several wave energy ideas today. Accordingly, this thesis studies the numerical simulation of the dynamic response of the wave energy converters (WECs) subjected to the ocean waves. This study considers a two-body point absorber (2BPA) and an oscillating surge wave energy converter (OSWEC). The first aim is to mesh the bodies of the earlier mentioned WECs to calculate their hydrostatic properties using axiMesh.m and Mesh.m functions provided by NEMOH. The second aim is to calculate the first-order hydrodynamic coefficients of the WECs using the NEMOH BEM solver and to study the ability of this method to eliminate irregular frequencies. The third is to generate a *.h5 file for 2BPA and OSWEC devices, in which all the hydrodynamic data are included. The BEMIO, a pre-and post-processing tool developed by WEC-Sim, is used in this study to create *.h5 files. The primary and final goal is to run the wave energy converter Simulator (WEC-Sim) to simulate the dynamic responses of WECs studied in this thesis and estimate their power performance at different sites located in the Mediterranean Sea and the North Sea. The hydrodynamic data obtained by the NEMOH BEM solver for the 2BPA and OSWEC devices studied in this thesis is imported to WEC-Sim using BEMIO. Lastly, the power matrices and annual energy production (AEP) of WECs are estimated for different sites located in the Sea of Sicily, Sea of Sardinia, Adriatic Sea, Tyrrhenian Sea, and the North Sea. To this end, the NEMOH and WEC-Sim are still the most practical tools to estimate the power generation of WECs numerically.

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Tree regularized neural networks for key point analysis

This thesis contributes to the ArgMining 2021 shared task on Key Point Analysis. Key Point Analysis entails extracting and calculating the prevalence of a concise list of the most prominent talking points, from an input corpus. These talking points are usually referred to as key points. Key point analysis is divided into two subtasks: Key Point Matching, which involves assigning a matching score to each key point/argument pair, and Key Point Generation, which consists of the generation of key points. The task of Key Point Matching was approached using different models: a pretrained Sentence Transformers model and a tree-constrained Graph Neural Network were tested. The best model was the fine-tuned Sentence Transformers, which achieved a mean Average Precision score of 0.75, ranking 12 compared to other participating teams. The model was then used for the subtask of Key Point Generation using the extractive method in the selection of key point candidates and the model developed for the previous subtask to evaluate them.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.