Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon

Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, vis-NIR spectrophotometer (305-2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R-2) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R-2 and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5_{2.5} concentration

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from characterizing ambient air pollution exposure solely using measurements. In the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at finer spatial and temporal scales (daily, usually) with complete coverage. Some of these methods include: geostatistical techniques, such as kriging; spatial statistical models that use the information contained in air quality model outputs (statistical downscaling models); linear regression modeling approaches that leverage the information in GIS covariates (land use regression); or machine learning methods that mine the information contained in relevant variables (neural network and deep learning approaches). Although some of these exposure modeling approaches have been used in several air pollution epidemiological studies, it is not clear how much the predicted exposures generated by these methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance and computational difficulty. Using PM$_{2.5}$ in year 2011 over the continental U.S. as case study, we examine the methods' performances across seasons, rural vs urban settings, and levels of PM$_{2.5}$ concentrations (low, medium, high)

Negative Ion Beam profile estimation on STRIKE calorimeter by means of Machine Learning method

openQuesta tesi affronta la sfida della stima dei parametri dei fasci di particelle termici dalle immagini termiche nel calorimetro diagnostico STRIKE, un componente cruciale per la fonte di fasci neutri SPIDER. Quest'ultimo è un clone della parte principale dell'Iniettore di Fasci Neutri MITICA, che è il prototipo in scala reale del sistema di iniezione aggiuntiva di potenza del tokamak ITER. SPIDER produce fino a 1280 fasci di particelle H-/D-, che vengono raccolti da STRIKE durante scatti di 10 secondi. STRIKE è osservato tramite telecamere termiche, e il pattern termico fornito da ogni singolo fascio di particelle è stato sperimentalmente dimostrato essere approssimabile come una curva Gaussiana 2D. I metodi tradizionali per stimare i fasci di particelle sono insufficienti a causa di richiedere un lungo tempo di predizione, e i metodi rapidi sviluppati precedentemente non sono fattibili per le nuove condizioni operative di SPIDER. Questo studio testa due tecniche di apprendimento automatico, applicando sia l'apprendimento non supervisionato che quello supervisionato per una stima rapida ed efficiente dei parametri dei fasci di particelle. Un Modello di Misture di Gaussiane non supervisionato (GMM) e un modello di apprendimento profondo supervisionato, YOLO (You Only Look Once), sono stati addestrati su immagini sintetiche per rilevare e localizzare approssimazioni Gaussiane dei fasci di particelle. Il modello YOLO, in particolare, ha dimostrato prestazioni superiori, identificando accuratamente i fasci di particelle con riquadri di delimitazione precisi anche in casi di sovrapposizione significativa. Sono state esplorate tecniche di perfezionamento per YOLO come PX modifier ed Ensemble, ma non hanno portato a risultati migliori. Le sfide rimangono nell'estimare correttamente l'altezza delle Gaussiane sovrapposte. Pertanto, la tesi sottolinea la necessità di lavori futuri per districare le Gaussiane sovrapposte ed estendere l'addestramento del modello ai dati sperimentali di STRIKE. Inoltre, i metodi sviluppati in questa tesi offrono un approccio promettente per caratterizzare i profili dei fasci di SPIDER: YOLO per rilevare e caratterizzare i fasci di particelle con predizioni veloci di solito inferiori a un secondo, e GMM come metodo di supporto per etichettare futuri dati sperimentali.This thesis addresses the challenge of estimating the parameters of beamlets from thermal images in the STRIKE diagnostic calorimeter, a crucial component for the neutral beam source SPIDER. The latter is a clone of the main part of the MITICA Neutral Beam Injector, which is the full-scale prototype of the ITER tokamak additional injection power system. SPIDER produces up to 1280 beamlets of H-/D-, which are collected by STRIKE during 10-second shots. STRIKE is observed via thermal cameras, and the thermal pattern given by every single beamlet has been experimentally proven to be approximated as a 2D Gaussian curve. Traditional methods for fitting beamlets are insufficient due to their time-consuming nature, and previously developed rapid methods are not feasible for the new operation conditions of SPIDER. This study tests two machine learning techniques, applying both unsupervised and supervised learning for fast and efficient beamlet parameter estimation. An unsupervised Gaussian Mixture Model (GMM) and a supervised deep learning model, YOLO (You Only Look Once), were trained on synthetic images to detect and localize Gaussian approximations of the beamlets. The YOLO model, in particular, demonstrated superior performance, accurately identifying beamlets with tight bounding boxes even in cases of significant overlap. Refinement techniques for YOLO as PX modifier and Ensemble were explored but didn't yield better results. Challenges remain in correctly estimating the amplitude of overlapping Gaussians. Therefore, the thesis emphasizes the need for future work in disentangling overlapped Gaussians and extending model training to experimental STRIKE data. Besides, the methods developed in this thesis offer a promising approach for characterizing SPIDER beam profiles: YOLO for detecting and characterizing the beamlets with fast predictions of less than one second usually, and GMM as a support method to label future experimental data

Quantitative Mapping of Lung Ventilation Using Hyperpolarized Gas Magnetic Resonance Imaging

The main objective of this project was to develop and implement techniques for high-resolution quantitative imaging of ventilation in lungs using hyperpolarized gas magnetic resonance imaging (MRI). Pulmonary ventilation is an important aspect of lung function and is frequently compromised through several different mechanisms and at varying degrees in presence of certain lung conditions, such as chronic obstructive pulmonary diseases. The primary focus of this development is on large mammalian species as a steppingstone towards translation to human subjects. The key deliverables of this project are a device for real-time mixing and delivery of hyperpolarized gases such as 3He and 129Xe in combination with O2, an MRI acquisition scheme for practical imaging of ventilation signal build-up in the lungs, and a robust mathematical model for estimation of regional fractional ventilation values at a high resolution. A theoretical framework for fractional gas replacement in the lungs is presented to describe MRI signal dynamics during continuous breathing of a mixture of hyperpolarized gases in presence of several depolarization mechanisms. A hybrid ventilation and imaging acquisition scheme is proposed to acquire a series of images during short end-inspiratory breath-holds over several breaths. The sensitivity of the estimation algorithm is assessed with respect to noise, model uncertainty and acquisition parameters, and subsequently an optimal set of acquisition parameters is proposed to minimize the fractional ventilation estimation error. This framework is then augmented by an undersampled parallel MRI scheme to accelerate image acquisition to enable fractional ventilation imaging over the entire lung volume in a single pass. The image undersampling was also leveraged to minimize the coupling associated with signal buildup in the airways and the irreversible effect of RF pulses. The proposed technique was successfully implemented in pigs under mechanical ventilation, and preliminary measurements were performed in an adult human subject under voluntary breathing

Exploring the grand-canonical phase diagram of interacting bosons in optical lattices by trap squeezing

In this paper we theoretically discuss how quantum simulators based on trapped cold bosons in optical lattices can explore the grand-canonical phase diagram of homogeneous lattice boson models, via control of the trapping potential independently of all other experimental parameters (trap squeezing). Based on quantum Monte Carlo, we establish the general scaling relation linking the global chemical potential to the Hamiltonian parameters of the Bose-Hubbard model in a parabolic trap, describing cold bosons in optical lattices; we find that this scaling relation is well captured by a modified Thomas-Fermi scaling behavior - corrected for quantum fluctuations - in the case of high enough density and/or weak enough interactions, and by a mean-field Gutzwiller Ansatz over a much larger parameter range. The above scaling relation allows to control experimentally the chemical potential, independently of all other Hamiltonian parameters, via trap squeezing; given that the global chemical potential coincides with the local chemical potential in the trap center, measurements of the central density as a function of the chemical potential gives access to the information on the bulk compressibility of the Bose-Hubbard model. Supplemented with time-of-flight measurements of the coherence properties, the measurement of compressibility enables one to discern among the various possible phases realized by bosons in an optical lattice with or without external (periodic or random) potentials -- e.g. superfluid, Mott insulator, band insulator, and Bose glass. We theoretically demonstrate the trap-squeezing investigation of the above phases in the case of bosons in a one-dimensional optical lattice, and in a one-dimensional incommensurate superlattice.Comment: 27 pages, 26 figures. v2: added references and further discussion of the local-density approximation