Cloud Height Estimation with a Single Digital Camera and Artificial Neural Networks

Clouds influence the local weather, the global climate and are an important parameter in the weather prediction models. Clouds are also an essential component of airplane safety when visual flight rules (VFR) are enforced, such as in most small aerodromes where it is not economically viable to install instruments for assisted flying. Therefore it is important to develop low cost and robust systems that can be easily deployed in the field, enabling large scale acquisition of cloud parameters. Recently, the authors developed a low-cost system for the measurement of cloud base height using stereo-vision and digital photography. However, due to the stereo nature of the system, some challenges were presented. In particular, the relative camera orientation requires calibration and the two cameras need to be synchronized so that the photos from both cameras are acquired simultaneously. In this work we present a new system that estimates the cloud height between 1000 and 5000 meters. This prototype is composed by one digital camera controlled by a Raspberry Pi and is installed at Centro de Geofísica de Évora (CGE) in Évora, Portugal. The camera is periodically triggered to acquire images of the overhead sky and the photos are downloaded to the Raspberry Pi which forwards them to a central computer that processes the images and estimates the cloud height in real time. To estimate the cloud height using just one image requires a computer model that is able to learn from previous experiences and execute pattern recognition. The model proposed in this work is an Artificial Neural Network (ANN) that was previously trained with cloud features at different heights. The chosen Artificial Neural Network is a three-layer network, with six parameters in the input layer, 12 neurons in the hidden intermediate layer, and an output layer with only one output. The six input parameters are the average intensity values and the intensity standard deviation of each RGB channel. The output parameter in the output layer is the cloud height estimated by the ANN. The training procedure was performed, using the back-propagation method, in a set of 260 different clouds with heights in the range [1000, 5000] m. The training of the ANN has resulted in a correlation ratio of 0.74. This trained ANN can therefore be used to estimate the cloud height. The previously described system can also measure the wind speed and direction at cloud height by measuring the displacement, in pixels, of a cloud feature between consecutively acquired photos. Also, the geographical north direction can be estimated using this setup through sequential night images with high exposure times. A further advantage of this single camera system is that no camera calibration or synchronization is needed. This significantly reduces the cost and complexity of field deployment of cloud height measurement systems based on digital photography

Improving the convergence of gene expression programming in impedance spectroscopy

Peer Reviewe

SMS-Coastal, a new Python tool to manage MOHID-based coastal operational models

This paper presents the Simulation Management System for Operational Coastal Hydrodynamic Models, or SMS-Coastal, and its novel methodology designed to automate forecast simulations of coastal models. Its working principle features a generic framework that can be easily configured for other applications, and it was implemented with the Python programming language. The system consists of three main components: the Forcing Processor, Simulation Manager, and Data Converter, which perform operations such as the management of forecast runs and the download and conversion of external forcing data. The SMS-Coastal was tested on two model realisations using the MOHID System: SOMA, a model of the Algarve coast in Portugal, and BASIC, a model of the Cartagena Bay in Colombia. The tool proved to be generic enough to handle the different aspects of the models, being able to manage both forecast cycles.UID/00350/2020 CIMA; COMPETE2020, NORTE 2020; COMPETE2020, NORTE 2020, and FCT, AEROS Constellation project [grant number AAC 04/SI/2019]; ASTRIIS project [grant number 14/SI/2019-46092-ASTRIIS].info:eu-repo/semantics/publishedVersio

Gene expression programming and genetic algorithms in impedance circuit identification

Impedance circuit identification through spectroscopy is often used to characterize sensors. When the circuit topology is known, it has been shown that the component values can be obtained by genetic algorithms. Also, gene expression programming can be used to search for an adequate circuit topology. In this paper, an improved version of the impedance circuit identification based on gene expression programming and hybrid genetic algorithm is presented to both identify the circuit and estimate its parameters. Simulation results are used to validate the proposed algorithm in different situations. Further validation is presented from measurements on a circuit that models a humidity sensor and also from measurements on a viscosity sensor

Simulador de Carga Mecânica, em Tempo Real, para Accionamento Eléctrico

O objectivo deste trabalho é implementar um simulador, em tempo real, de carga mecânica por forma a aferir os diferentes comportamentos de uma máquina eléctrica em função do tipo de carga a accionar. O protótipo desenvolvido permite ensaiar o comportamento de qualquer tipo de máquina rotativa, inserida num accionamento eléctrico, para vários tipos de carga mecânica. De forma a estudar o comportamento de uma máquina eléctrica para um conjunto alargado de cargas mecânicas foram implementadas várias cargas típicas, cujo binário de carga pode depender do tempo ou da velocidade. São apresentadas características de implementação do sistema desenvolvido, que faz uso de um travão magnético e de um autómato programável, e resultados experimentais obtidos para vários tipos de carga

Guidance and Leakage properties of chiral optical fibers

The field theory of guided waves in optical fibers with step-index profiles and in which both core and cladding are chiral isotropic media is developed. We show that both surface and semileaky modes can propagate in optically active fibers. To shed light on the guidance and leakage properties of chiral isotropic fibers we present a physical interpretation and several numerical results. The new leakage effect associated with semileaky modes is an important property that cannot be neglected in the analysis of chiral optical fibers but that, nevertheless, has been systematically disregarded in the literature

MATHEMATICAL MODELLING AND SIMULATION TECHNIQUES REVIEW FOR HYBRID PHTOVOLTAIC THERMAL SYSTEM

In this work, solar hybrid photovoltaic thermal (PVT) panel’s different mathematical modelling and simulation techniques are described. Mathematical and thermal model development and simulation technique is considered as the initial conditions to simulate the system’s behavior. This article discusses about improved modelling and implementation technique of hybrid PVT technology to enhance the panels effective efficiency by increasing energy output. Characterization and parameters identification including sensitivity analysis by using adapted numerical methods is studied and analyzed. Different types of simulation models are reviewed to establish by analyzing mathematical, thermal and physical model with advance computational tool

Analysis of a Non-Iterative Algorithm for the Amplitude and Phase Difference Estimation of Two Acquired Sinewaves

In this paper, a non-iterative algorithm for amplitude and phase difference estimation of two acquired sinewaves is presented and analyzed. The method is based on the least-squares fitting of ellipses where the common signal frequency is eliminated from the algorithm

A New Approach of Total Least Square Algorithm for Parameter Extraction of a Photovoltaic Panel: A Comparison Study

The degradation of the photovoltaic panels due to their long outdoor exposure leads to a variation in their internal parameters. The function describing the I-V characteristic of the photovoltaic cell is known to be non-linear and implicit, with five unknown parameters. To identify these photovoltaic parameters, the cost function used for optimization is the Total Least Square function (TLS), defined as a sum of quadratic terms representing the quantification of the errors between a mathematical model and a measured set of experimental data, which is usually accompanied by measurement uncertainties (measuring current and voltage). However, the studies that have been done so far to extract the photovoltaic parameters work with the Ordinary Least Square (OLS) function defined as the sum of quadratic terms representing the quantification of the errors between a mathematical model and a measured set of experimental data of one variable (current only), because it is facile to apply, especially in the case of nonlinear models. Nether less, taking into account both differences in measurement uncertainties accompanying both variables will help to achieve more efficient optimization and more precise results. This work presents a new iterative, simple to implement algorithm that can calculate the value of the Total Least Square function at each step of the optimization process. The results are then compared with the ones obtained by applying the OLS function

Pathways and hot spots of floating and submerged microplastics in Atlantic Iberian marine waters: a modelling approach

Plastic pollution has been observed in many marine environments surrounding the Iberian Peninsula, from the surface water to deeper waters, yet studies on their pathways and accumulation areas are still limited. In this study, a global ocean reanalysis model was combined with a particle-tracking Lagrangian model to provide insights into the pathways and accumulation patterns of microplastics originating in southern Portuguese coastal waters (SW Iberian). The study investigates microplastics floating on the surface as well as submerged at different water depths. Model results suggest that the North Atlantic Gyre is the main pathway for microplastics in surface and subsurface waters, transporting the microplastics southwards and eastwards towards the Mediterranean Sea and the Canary Islands. Currents flowing out of the Mediterranean Sea act as the main pathway for microplastics in deep waters, transporting the microplastics along western Iberia. An average residence time of twenty days in the coastal waters suggests that microplastics do not accumulate close to their sources due to their relatively fast transport to adjacent ocean areas. Notably, a significant proportion of microplastics leave the model domain at all depths, implying that SW Iberia may act as a source of microplastics for the adjacent areas, including the Mediterranean Sea, Morocco, the Canary Islands, Western Iberia, and the Bay of Biscay.info:eu-repo/semantics/publishedVersio