The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

The use of computational intelligence (CI) in solar photovoltaic (SPV) systems has been on the rise due to the increasing computational power, advancements in power electronics and the availability of data generation tools. CI techniques have the potential to reduce energy losses, lower energy costs, and facilitate and accelerate the global adoption of solar energy. In this context, this review paper aims to investigate the role of CI techniques in the advancements of SPV systems. The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking systems. A performance comparison between CI techniques and conventional methods is also carried out to prove the importance of CI in SPV systems. The findings confirmed the superiority of CI techniques over conventional methods for every application studied and it can be concluded that the continuous improvements and involvement of these techniques can revolutionize the SPV industry and significantly increase the adoption of solar energy

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys. Rev.

Feature Papers in Electronic Materials Section

This book entitled "Feature Papers in Electronic Materials Section" is a collection of selected papers recently published on the journal Materials, focusing on the latest advances in electronic materials and devices in different fields (e.g., power- and high-frequency electronics, optoelectronic devices, detectors, etc.). In the first part of the book, many articles are dedicated to wide band gap semiconductors (e.g., SiC, GaN, Ga2O3, diamond), focusing on the current relevant materials and devices technology issues. The second part of the book is a miscellaneous of other electronics materials for various applications, including two-dimensional materials for optoelectronic and high-frequency devices. Finally, some recent advances in materials and flexible sensors for bioelectronics and medical applications are presented at the end of the book

A comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques

The dissertation includes a comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques. Chapter 1 introduces the reader to the entire work of the Ph. D. thesis and to its main topic of research, which is focused on structural and microstructural characterization of colloidal quantum-dots. The following Chapter is dedicated to the description of conventional and unconventional characterization methods at the nanoscale, discussing their limits and potentiality in characterizing real nano-systems. Chapter 3 serves as a mathematical description of the DSE, and its implementation in the DebUsSy suite for the characterization of real ensembles of nanosized samples. Therein, the data collection and reduction procedures are also reported, together with a brief section in which the DSE to PDF approaches are compared. The need of introducing strains and defects in the complex atomistic model of CdSe nanocrystals makes it necessary to describe these defects, with a brief state of the art of their characterization methods (Chapter 4). Chapter 5 is completely dedicated to describing the computational model used for the characterization of cQDs and its use as a part of the overall data analysis strategy. The final Chapters focus on the application of the model to real systems in which its potentiality and sensitivity are tested on different materials, disclosing new size-dependent fault driven relaxation and faceting features in CdSe cQDs. An additional section presents an alternative method for the characterization of metallic NPs with larger sizes, but (much) lower stacking fault probabilities

Silicon Nanodevices

This book is a collection of scientific articles which brings research in Si nanodevices, device processing, and materials. The content is oriented to optoelectronics with a core in electronics and photonics. The issue of current technology developments in the nanodevices towards 3D integration and an emerging of the electronics and photonics as an ultimate goal in nanotechnology in the future is presented. The book contains a few review articles to update the knowledge in Si-based devices and followed by processing of advanced nano-scale transistors. Furthermore, material growth and manufacturing of several types of devices are presented. The subjects are carefully chosen to critically cover the scientific issues for scientists and doctoral students

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

A comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques

The dissertation includes a comprehensive multi-scale modeling of defective CdSe colloidal nanocrystals through advanced X-ray scattering techniques. Chapter 1 introduces the reader to the entire work of the Ph. D. thesis and to its main topic of research, which is focused on structural and microstructural characterization of colloidal quantum-dots. The following Chapter is dedicated to the description of conventional and unconventional characterization methods at the nanoscale, discussing their limits and potentiality in characterizing real nano-systems. Chapter 3 serves as a mathematical description of the DSE, and its implementation in the DebUsSy suite for the characterization of real ensembles of nanosized samples. Therein, the data collection and reduction procedures are also reported, together with a brief section in which the DSE to PDF approaches are compared. The need of introducing strains and defects in the complex atomistic model of CdSe nanocrystals makes it necessary to describe these defects, with a brief state of the art of their characterization methods (Chapter 4). Chapter 5 is completely dedicated to describing the computational model used for the characterization of cQDs and its use as a part of the overall data analysis strategy. The final Chapters focus on the application of the model to real systems in which its potentiality and sensitivity are tested on different materials, disclosing new size-dependent fault driven relaxation and faceting features in CdSe cQDs. An additional section presents an alternative method for the characterization of metallic NPs with larger sizes, but (much) lower stacking fault probabilities