Robust Non-Parametric Mortality and Fertility Modelling and Forecasting : Gaussian Process Regression Approaches

Funding: This research received no external funding. Acknowledgments: The authors thank the editor and the reviewers for their very helpful and constructive comments.Peer reviewedPublisher PD

A Novel Mathematical Model of the Solar Assisted Dehumidification and Regeneration Systems

This paper introduces a state-of-the-art modelling technique to investigate the performance of solar assisted dehumidification and regeneration cycles. The dehumidification/regeneration system investigated in this study employs a solid adsorbent bed and enables use of both solar energy and returning warm air to deliver efficient dehumidification and regeneration of the treated air. Study of literature revealed a huge gap between model results and industrial performance of such systems. Hence, the modelling work presented in this paper employs Gaussian Process Regression (GPR) technique to close the gap between model outputs and real-life operation parameters of the system. An extensive amount of laboratory tests were also carried out on the dehumidification/regeneration system and model predictions were validated through comparison with experimental results. The model predictions were found to be in good agreement with experimental results, with maximum error not exceeding 10%. The GPR technique enables simultaneous analysis of a vast quantity of key system parameters derived from mathematical models and laboratory tests. The system parameters investigated in this study include: temperature, relative humidity and flow rate of process air, and temperature of regeneration air, solar radiation intensity, operating time, moisture extraction efficiency of the dehumidification cycle and moisture removal efficiency of the regeneration cycle. Investigation of both modelling and experimental results revealed that efficiencies of the both dehumidification and regeneration cycles decrease as relative humidity of the process air increases. The increase in regeneration temperature leads to an increase in regeneration efficiency whereas; it does not have a significant impact on the dehumidification efficiency. A similar trend was also observed when solar intensity were increased. The proposed technique reduced the complexity of model by eliminating the need for heat and mass transfer calculations; reduced the performance gap between model results and real-life performance data, and increased the reliability of model outputs by showing a good agreement with experimental results. The GPR based mathematical model delivers an effective design and performance evaluation tool for the solar assisted dehumidification and regeneration systems and provides an unprecedented opportunity for commercializing such systems

ANALYSIS OF THREE DIFFERENT MACHINE LEARNING ALGORITHMS FOR SWE ESTIMATION OVER WESTERN COLORADO USING SPACE-BASED PASSIVE MICROWAVE RADIOMETRY

This study compares the performance of three different machine learning algorithms used for snow water equivalent (SWE) estimation. Inputs to these algorithms include passive microwave (PMW) brightness temperature (Tb) observations at 10.65 GHz, 18.7 GHz, and 36.5 GHz at both vertical and horizontal polarization as collected by the Advanced Microwave Scanning Radiometer (AMSR-2). The three algorithms include: 1) support vector machine (SVM) regression, 2) long short-term memory (LSTM) networks, and 3) Gaussian process (GP) regression. In-situ SWE measurements from the SNOTEL network collected across western Colorado is used as the training “targets” during the training procedure. The performance of the algorithms is evaluated using a number of different metrics including, but not limited to correlation coefficient, mean square error (MSE), and bias. The evaluation is conducted over a range of different elevations and different land cover classifications in order to assess algorithm performance across a broad range of snowpack conditions. Preliminary results suggest the LSTM algorithm is computationally more efficient during the training process as compared to the other algorithms, yet yields a similar level of performance. Some limitations, however, have been found in the study, including poor performance during deep snow conditions, which is likely related to signal “saturation” within the PMW Tb’s used during the supervised training process. Additionally, algorithm performance is strongly dependent on the amount of training data such that too little training data results in poor performance by the algorithm at successfully reproducing inter-annual variability. The strengths and limitations of these different machine learning algorithms for snow mass estimation will be discussed

Improving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: utilizing Gaussian process regression with a new method to estimate dynamic input uncertainty

To recover the actual responsivity for the Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g., unstable, noisy, and with gaps) time series of its in situ calibration factors (V0) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian process (GP) regression provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied to a synthetic signal and showed significantly smaller RMSEs than a Gaussian process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR V0 time series at three ground sites. The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results at the three test sites (i.e., HI02 at Mauna Loa, Hawaii; IL02 at Bondville, Illinois; and OK02 at Billings, Oklahoma) demonstrated that the agreement among aerosol optical depths (AODs) at the 368&thinsp;nm channel calculated using V0 determined by the GP mean function and the equivalent AERONET AODs were consistently better than those calculated using V0 from standard techniques (e.g., moving average). For example, the average AOD biases of the GP method (0.0036 and 0.0032) are much lower than those of the moving average method (0.0119 and 0.0119) at IL02 and OK02, respectively. The GP method's absolute differences between UV-MFRSR and AERONET AOD values are approximately 4.5&thinsp;%, 21.6&thinsp;%, and 16.0&thinsp;% lower than those of the moving average method at HI02, IL02, and OK02, respectively. The improved accuracy of in situ UVMRP V0 values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.</p

Développement d’une nouvelle approche d’essais pour l’évaluation de systèmes avancés d'assistance et d'aide à la conduite (ADAS) dans les véhicules intelligents ou hautement automatisés sous de multiples conditions

Les systèmes avancés d’aide à la conduite (ADAS) doivent passer des tests intensifs avant d’être mis en production, mais les tests avec des véhicules réels prennent beaucoup de temps, sont coûteux, difficiles à reproduire et présentent des risques. Des essais avec des véhicules réels seront toujours nécessaires à l’avenir, mais ne seront pas suffisants pour répondre à la totalité des exigences de fiabilité et de sécurité. Pour démontrer les performances attendues d’un système ADAS dans un véhicule intelligent ou autonome, les méthodes d’évaluation doivent inclure des simulations, des essais sur piste et des essais sur route [25]. Les véhicules intelligents et autonomes continueront à rendre la conduite plus facile et plus sécuritaire. Néanmoins, la question ultime reste à savoir quelle est l'approche d'évaluation optimale à proposer, qui aura la capacité de valider le comportement et les performances attendues des systèmes embarqués dans des véhicules intelligents et autonomes pendant tous les stades: développement, démonstration et déploiement. À notre connaissance, la plupart des études / propositions qui tentent de combiner plusieurs approches (2 ou 3) sont appliquées pendant le stade de développement. Cette thèse présente une nouvelle approche d'évaluation des systèmes avancés d’aide à la conduite dans un véhicule intelligent ou hautement automatisé conduit par une entité externe pendant la phase de déploiement. Cette approche permet à la fois d'identifier un ensemble de pires scénarios pour une application ADAS donnée et de combiner les trois approches d’évaluation mentionnées précédemment. Pour ce faire, cette étude propose plusieurs solutions qui sont regroupées en trois parties. La première « Synthèse de scénarios, stratégie d’échantillonnage et simulations » comprend une description du système ADAS à évaluer et de ses différents critères d'évaluation, propose une synthèse des scénarios de tests les plus pertinents avec les paramètres de fonctionnement de chaque scénario. Ensuite, nous traitons la base de données FOT en implémentant une stratégie d'échantillonnage appropriée et à la fin de cette partie, des tests virtuels sont mis en oeuvre dans un environnement de simulation véhiculaire. La deuxième partie « Évaluation et classification des risques » se concentre sur la collecte des résultats de simulation, puis sur l’évaluation et la classification du risque de chaque test, ce qui nous permet ensuite de récupérer les niveaux de risques et d’avoir une estimation approximative de l’ensemble de scénarios défavorables. La troisième partie « Validation » traite les résultats des essais sur piste de l’ADAS étudié et des différentes techniques d'apprentissage automatique et ensembliste utilisées pour créer son modèle prédictif. Ensuite, le traitement de la base de données FOT et l’implémentation d’une stratégie d'échantillonnage plus avancée et à la fin la collecte des résultats de prédiction, puis sur l’évaluation de risque de chaque test et sur sa classification à l’aide d’une technique de classification non supervisée, ce qui nous permet de construire et sélectionner finalement un ensemble des pires scénarios

Gaussian process regression method for forecasting of mortality rates

Gaussian process regression (GPR) has long been shown to be a powerful and effective Bayesian nonparametric approach, and has been applied to a wide range of fields. In this paper we present a new application of Gaussian process regression methods for the modelling and forecasting of human mortality rates. The age-specific mortality rates are treated as time series and are modelled by four conventional Gaussian process regression models. Furthermore, to improve the forecasting accuracy we propose to use a weighted mean function and the spectral mixture covariance function in the GPR model. The numerical experiments show that the combination of the weighted mean function and the spectral mixture covariance function provides the best performance in forecasting long term mortality rates. The performance of the proposed method is also compared with three existing models in the mortality modelling literature, and the results demonstrate that the GPR model with the weighted mean function and the spectral mixture covariance function provides a more robust forecast performance