Statistical investigation of a dehumidification system performance using Gaussian process regression

Swift performance assessment of dehumidification systems, in design stage and while operation of the system is of substantial importance for commercialization and wide implementation of this technology. This paper presents a novel statistical model, employing Gaussian Process Regression (GPR) to investigate performance of a solar/waste energy driven dehumidification/regeneration cycle with a solid adsorbent bed. The statistical model takes thousands of operating conditions derived from a numerical model to predict the performance of the system. This predictive tool directly correlates the main operating parameters with the performance parameters of the system. The operating parameters considered in this study are: temperature, relative humidity and flow rate of process air, temperature of regeneration air, length of the desiccant bed, solar radiation intensity and operating time, and the selected performance parameters are: moisture extraction efficiency for the dehumidification cycle and moisture removal efficiency for the regeneration cycle. The model is evaluated by three metrics, namely: root mean square error (RSME), mean absolute percentage error (MAPE), and coefficient of determination (R2). The maximum RSME and MAPE for moisture extraction are only 0.045, 0.21%, and for moisture removal efficiencies are 0.082 and 0.39%, respectively, while the R2 value is derived as 0.97. The developed model is used to investigate the impact of four selected operating parameters on system performance. Additionally, the system performance is predicted for randomly generated operating conditions as well as warm and humid climates. The developed GPR model provides a swift and highly accurate predictive tool for design of the dehumidification systems and for commercialization of the investigated dehumidification systems

Heart sounds analysis using wavelets responses and support vector machines

Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation for further definition of the present pathology if any. A novel algorithm for heart sounds segmentation is also presented. The decision making is accomplished by means of support vector machines (SVM) classifier which is fed by characteristic features extracted from PCGs basing on wavelet filter banks coefficients so that PCG signals are classified into five classes: normal heart sound (NHS), aortic stenosis (AS), aortic insufficiency (Al) mitral stenosis (MS), and mitral insufficiency (MI). The SVM was trained on a low-dimensional feature space, and tested on relatively a big dataset in order to show its generalization capability

Sparse coding joint decision rule for ear print recognition

Human ear recognition has been promoted as a profitable biometric over the past few years. With respect to other modalities, such as the face and iris, that have undergone a significant investigation in the literature, ear pattern is relatively still uncommon. We put forth a sparse coding-induced decision-making for ear recognition. It jointly involves the reconstruction residuals and the respective reconstruction coefficients pertaining to the input features (co-occurrence of adjacent local binary patterns) for a further fusion. We particularly show that combining both components (i.e., the residuals as well as the coefficients) yields better outcomes than the case when either of them is deemed singly. The proposed method has been evaluated on two benchmark datasets, namely IITD1 (125 subject) and IITD2 (221 subjects). The recognition rates of the suggested scheme amount for 99.5% and 98.95% for both datasets, respectively, which suggest that our method decently stands out against reference state-of-the-art methodologies. Furthermore, experiments conclude that the presented scheme manifests a promising robustness under large-scale occlusion scenarios

Decomposing global solar radiation into its diffuse and direct normal radiation

This work presents a model based on Radial Basis Function (RBF) to estimate the diffused solar radiation (DSR) and direct normal radiation (DNR) fractions of solar radiation from global solar radiation in a semiarid area in Algeria based on a database measured between 2013 and 2015. The data has been collected at Applied Research Unit for Renewable Energies, (URAER) at Ghardaia city situated in the south of Algeria. The experimental results show that RBF model estimates DNR and DSR with high performance. The difference between the measured and the predicted values show a normalised Root Mean Square Error (nRMSE) of 0.033 and 0.065 for DNR and DSR, respectively. The obtained values of Determination Coefficient (R²) and Correlation Coefficient (R) are: 97.3%, 98.60%, respectively for DNR and 88.89%, 91.12% For DSR