Investigating renewable energy systems using artifcial intelligence techniques

This research investigated applying Artificial Intelegence (AI) and Machine Learning (ML) to renewable energy through three studies. The first study characterized and mapped the recent research landscape in the field of AI applications for various renewable energy systems using Natural Language Prcoessing (NLP) and ML models. It considered published documetns at Scopus database in the period (2000-2021). The second study built hybrid Catboost-CNN-LSTM architecture pipeline to predict an industrial-scale biogas plant’s daily biogas production and investigate the feedstock components importance on it. The third study investigated prediciting biogas yield of various subtrates and the significance of each organic component (carbohydrates, proteins, fats/lipids, and legnin) in biogas production using hybrid VAE-XGboost model. The first study showed seven main metatopics and ascent of "deep learning (DL)" as a prominent methodology led to an increase in intricate subjects, including the optimization of power costs and the prediction of wind patterns. Also, a growing utilization of DL approaches for the analysis of renewable energy data, particularly in the context of wind and solar photovoltaic systems. The research themes and trends observed in the first study signify substantial recent investments in advanced AI learning techniques. The developed Catboost-CNN-LSTM pipeline achived a significant results and presented a superior approach when compared to previous relevant studies by eliminating the requirement for feature engineering, enabling direct prediction of biogas yield without the need for converting it into a classification task. The VAE-XGboost pipeline could ovcercome data limitation in the field and produced significant results. It has shown that the "fats" category is the most influential group on the methane production in biogas plants, however, “proteins” illustrated the lowest impact on biogas production

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Efficient Learning Machines

Computer scienc

Towards Automated Design of Complex Modular Systems Inspired by Nature

Katedra kybernetik

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma

Evolutionary Deep Convolutional Neural Networks for Medical Image Analysis

Medical image segmentation is a procedure to analyse an image’s content to find an organ, cancer, tumour, or possible abnormalities. Since hospitals and medical centres generate billions of images daily worldwide, manual analysis of the images is frustrating. Therefore there is a need to improve automatic techniques to examine the content of images. Deep Convolutional Neural Networks (DCNNs) are one of the most reliable and successful approaches to analyse images’ content. However, the main problem is a lack of rules to design a network, and trial and error is the usual approach to find a network structure along with its training parameters. Regarding the diversity of medical images, existing with various types of noises and artefacts, the limited number of available labelled medical images, and limited available computational facilities, designing a CNN for medical image analysis is even more complicated. Because of the importance of medical image segmentation, during the last decade, various CNNs are designed manually; however, most of these networks work well for the segmentation of a specific dataset or application. One of the solutions to address this problem is developing networks automatically. Neuroevolution, which is the combination of an evolutionary algorithm and Neural Networks (NNs), can automatically design a network. Evolutionary algorithms are relatively easy to understand and implement; however, they need considerable computation to evolve a network. Since Nerouevolution is computationally demanding, there is very limited previous work regarding applying Neuroevolution for medical image segmentation. Existing works just set up a part of the parameters to develop a network and have been applied to a limited number of datasets. The most significant drawbacks of existing works are lack of robustness and generalizability; also, most of them are computationally expensive. In this thesis, several Neroevolutionary-based frameworks are developed for 2D and 3D medical image segmentation. Firstly, a new block-based encoding model is developed to generate variable length 2D Deep Convolutional Neural Networks (DCNNs). The proposed encoding model could find appropriate values for several hyperparameters to create and train a DCNN. Also, a Genetic Algorithm (GA) is employed to evolve the generated networks. Besides, a comprehensive analysis is done to find an appropriate population size and generations, and consequently, an improved model is developed. In addition, to improve the results’ quality, an ensemble of found networks is utilised for final segmentation. Then to find a 3D evolutionary network, two approaches are examined. According to the proposed 2D model, a 3D model is developed to generate a population of 3D networks and evolve the 3D networks to find an appropriate 3D network for 3D medical image segmentation. Since evolving 3D networks is computationally expensive, a second approach is also introduced. In the second approach, the possibility of using a 2D evolutionary model to create a 3D network is examined and named Converted 3D network. Because of the diversity of medical images and the complexity of medical image analysis, sometimes more complicated CNN is needed. To address this issue, also another evolutionary model is developed in this thesis to generate more accurate and complex DCNNs using the combination of Dense and Residual blocks. In the proposed DenseRes model, a new encoding model is introduced, which is able to create a variable-length network with variable filter sizes within a block. In the DenseRes model, all required parameters to generate and train a network are included in the search. Most of the time, the Region Of Interest (ROI) is a small part of a medical image with almost the similar colour and texture of the surrounding organs. Therefore, more precise network architectures, like attention networks, are needed to process the images. To do so, two different approaches are introduced in this thesis to develop evolutionary attention networks. First, a 2D evolutionary attention model is proposed that is able to find an appropriate attention gate to transfer the block’s input to its output. Since some useful information will be lost during the downsampling in DCNNs, another 2D and 3D evolutionary attention framework is introduced to address this issue. In this model, besides creating a network structure along with its training parameters, an evolutionary algorithm is employed to find an appropriate model to recover and transfer feature maps from downsampling to the upsampling part of a network. The effectiveness of the proposed models is examined using various publicly available datasets. Results are compared with multiple manual and automatically designed models. The significant findings of this thesis can summarise as: (1) the proposed models obtain much better segmentation accuracy compared to state-of-the-art models, also, the proposed models are computationally cheap, even for developing 3D evolutionary networks; (2) converting a 2D evolutionary model to a 3D model is a reliable, fast, and accurate approach to create 3D networks; (3) including more constructive parameters in the search space can lead to more precise networks; (4) the initial population plays a significant role in the final results and decreasing training time; moreover, using variable filter sizes within a block can obtain better results compared to using a fixed one; (5) recovering a downsampling’s feature maps and transferring them to the corresponding upsampling part can considerably improve segmentation accuracy; (6) the proposed models are robust and general such that they can be applied for the segmentation of various medical images (CT and MRI) for different organs and tumour segmentation; (7) all the proposed encoding models are compatible with conventional crossover and mutation techniques, without any extra effort to create a new crossover technique or using a method to check the correctness of layers’ sequences

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe