Tesi di Dottorato

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Start date: 2020Subject: search.filters.subject.Machine learning

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    Arsenic Ore Mixture Froth Image Generation with Neural Networks and a Language for Declarative Data Validation
    (Università della Calabria, 2022-04-14) Zamayla, Arnel; Greco, Gianluigi; Alviano, Mario; Dodaro, Carmine
    Computer vision systems that measure froth flow velocities and stability designed for flotation froth image analysis are well established in industry, as they are used to control material recovery. However flotation systems that has limited data has not been explored in the same fashion bearing the fact that big data tools like deep convolutional neural networks require huge amounts of data. This lead to the motivation of the research reported in the first part of this thesis, which is to generate synthetic images from limited data in order to create a froth image dataset. The image synthesis is possible through the use of generative adversarial network. The performance of human experts in this domain in identifying the original and synthesized froth images were then compared with the performance of the models. The models exhibited better accuracy levels by average on the tests that were performed. The trained classifier was also compared with some of the established neural network models in the literature like the AlexNet, VGG16 ang ResNet34. Transfer learning was used as a method for this purpose. It also showed that these pretrained networks that are readily available have better accuracy by average comapared to trained experts. The second part of this thesis reports on a language designed for data validation in the context of knowledge representation and reasoning. Specifically, the target language is Answer Set Programming (ASP), a logic-based programming language widely adopted for combinatorial search and optimization, which however lacks constructs for data validation. The language presented in this thesis fulfills this gap by introducing specific constructs for common validation criteria, and also supports the integration of consolidated validation libraries written in Python. Moreover, the language is designed so to inject data validation in ordinary ASP programs, so to promote fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid.
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    A comparative study of reconstruction methods for neutron tomography
    (Università della Calabria, 2020-01-24) Micieli, Davide; Carbone, Vincenzo; Gorini, Giuseppe; Tassi, Enrico
    Neutron tomography is a well established technique to non-destructively investigate the inner structure of a wide range of objects. The main disadvantages of this technique are the time-consuming data acquisition, which generally requires several hours, and the low signal to noise ratio of the acquired images. One way for decreasing the total scan time is to reduce the number of radiographs. However, the Filtered Back-Projection, which is the most widely used reconstruction method in neutron tomography, generates low quality images affected by artifacts when the number of projections is limited or the signal to noise ratio of the radiographs is low. This doctoral thesis is focused on the comparative analysis of different reconstruction techniques, aimed at finding the data processing procedures suitable for neutron tomography that shorten the scan time without reduction of the reconstructed image quality. At first the performance of the algebraic reconstruction methods were tested using experimental neutron data and studied as a function of the number of projections and for different setups of the imaging system. The reconstructed images were quantitatively compared in terms of image quality indexes. Subsequently, the recently introduced Neural Network Filtered Back-Projection method was proposed in order to reduce the acquisition time during a neutron tomography experiment. This is the first study which proposes and tests a machine learning based reconstruction method for neutron tomography. The Neural Network Filtered Back-Projection method was quantitatively compared to conventional reconstruction algorithms used in neutron tomography. Finally, we present NeuTomPy, a new Python package for tomographic data processing and reconstruction. NeuTomPy is a cross-platform toolbox ready to work with neutron data. The first release of NeuTomPy includes pre-processing algorithms, a wide range of classical and state-of-the-art reconstruction methods and several image quality indexes, in order to evaluate the reconstruction quality. This software is free and open-source, hence researchers can freely use it and contribute to the project.
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    Technologies and IoT Protocols applied to Energy Management in Smart Home Environment
    (Università della Calabria, 2021-09-13) Serianni, Abdon; De Ranco, Floriano
    This thesis presents the studies during this period of my PhD course. In the first research period, I focused the activities principally on the study and analysis of the protocols and technologies used for the IoT solutions in Smart Home environment. It was analyzed the MQTT protocol and its possible applications. The MQTT protocol uses the event-driven publish/subscribe pattern. In our tests, MQTT usage was compared with a classic HTTP request/response paradigm, used in REST and CoAP approaches. A layered IoT communication architecture will be proposed and described. The usage of proposed IoT communication architecture was analyzed in Smart Home context and in other application contexts such as e-Health and Internet of Vehicles (IoV). After an analysis of Data Mining and Machine learning concepts, the focus of the activities was on Neural Networks. The use of LSTM networks was analyzed for time-series forecasting and prediction of consumption in two different environments (home and office). In the smart home environment, smart objects are characterized by limited resources. Our proposal to increase the computational capabilities of these smart devices is a hidden cognitive object that uses pre-trained NN and continuous learning for anomaly detection and suggested action prediction tasks. The Cognitive Smart Object is the joining of a smart device and a hidden cognitive object. The Cognitive Smart Object was used in thermal comfort control application and manage better energy consumption. The concepts introduced have been used for an assisted comfort solution and the neural network results were used to suggest to the user conventional management of the climatic comfort levels. A Continuous Learning mechanism was been implemented with the usage of user feedback to shape the neural network and obtain a neural network that follows user behaviours that diverge from behaviour compliant with the ASHRAE standard. From the analysis of the results obtained, it was possible to highlight how NN has given results closer to the user’s habits and at the same time the user has been educated to use the right levels of thermal comfort.