75 research outputs found

    Machine learning for accelerating the discovery of high-performance low-cost solar cells

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    Solar energy has the potential to enhance the operation of electronic devices profoundly and is the solution to the most important challenge facing humanity today. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. However, since photovoltaic (PV) technology is a mature and reliable method for converting the Sun’s vast energy into electricity, innovation in developing new materials and solar cell architectures is becoming more important to increase the penetration of PV technologies in wearable and IoT applications. Moreover, artificial intelligence (AI) is touted to be a game changer in energy harvesting. The thesis aims to optimize solar cell performance using various computational methods, from solar irradiance and solar architecture to cost analysis of the PV system. The thesis explores the PV cell architectures that can be used for optimized cost/efficiency trade-offs. In addition, machine learning (ML) algorithms are incorporated to develop reconfigurable PV cells based on switchable complementary metal-oxide-semiconductor (CMOS) addressable switches, such that the output power can be optimized for different light patterns and shading. The first part of the thesis presents a critical literature review of a range of ML techniques applied for estimating solar irradiance, followed by a review on accurately predicting the levelized cost of electricity (LCOE) and return on investment (ROI) of a PV system and lastly, presents a systematic review (SR) on the discovery of solar cells. Furthermore, the literature review consists of a thorough systematic review that reveals that ML techniques can speed up the discovery of new solar cell materials and architectures. The review covers a broad range of ML techniques that focus on producing low-cost solar cells. Additionally, a new classification method is introduced based on data synthesis, ML algorithms, optimization, and fabrication process. The review finds that Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) is the most promising method for designing low-cost organic solar cell architecture. Therefore, the first part of the thesis critically evaluates the existing ML techniques and guides researchers in discovering solar cells using ML techniques. The literature review also discusses the recent research work done for predicting solar irradiance and evaluating the LCOE and ROI of the PV system using various time-series forecasting techniques under ML algorithms. Secondly, the thesis proposes an ML algorithm for accurately predicting solar irradiance using the wireless sensor network (WSN) relying on batteries that need constant replacement and are hazardous waste. Therefore, WSNs with solar energy harvesters that scavenge energy from the Sun are proposed as an alternative solution. Consequently, the ML algorithms that enable WSN nodes to accurately predict the amount of solar irradiance are presented so that the node can intelligently manage its energy. The nodes use the panel’s energy to power its internal electronic components, such as the processor and transmitter, and charge its battery. Accordingly, this helps the node access an exact amount of solar irradiance predictions to plan its energy utilization more efficiently, thereby adjusting the operation schedule depending on the expected solar energy availability. The ML models were based on historical weather datasets from California, USA, and Delhi, India, from 2010 to 2020. In addition, the process of data pre-processing, followed by feature engineering, identification of outliers, and grid search to determine the most optimized ML model, is evaluated. Compared with the linear regression (LR) model, the support vector regression (SVR) model showed accurate solar irradiance forecasting. Moreover, from the predicted output calculated results, it was also found that the models with time duration of 1 year and 1 month have much better forecasting results than 10 years and 1 week, with both root square mean error (RMSE) and mean absolute error (MAE) less than 7% for California, USA. Consecutively, the third part of the thesis evaluates the parameter LCOE using demographic variables. Moreover, LCOE facilitates economic decisions and quantitative comparisons between energy generation technologies. Previous methods for calculating the LCOE were based on fixed singular input values that do not capture the uncertainty associated with determining the financial feasibility of a PV project. Instead, a dynamic model that considers important demographic, energy, and policy data that include interest rates, inflation rates, and energy yield is proposed. All these parameters will undoubtedly vary during a PV system’s lifetime and help determine a more accurate LCOE value. Furthermore, comparisons between different ML algorithms revealed that the ARIMA model gave an accuracy of 93.8% for predicting the consumer price of electricity. Moreover, the proposed model with two case studies from the United States and the Philippines is evaluated in detail. Results from these case studies revealed that LCOE values for the State of California could be almost 30% different (5.03 ¢/kWh for singular values in comparison to 7.09¢/kWh using our ML model), which can distort the risk or economic feasibility of a PV power plant. Additionally, the ML model predicts the ROI of a grid-connected PV plant in the Philippines to be 5.37 years instead of 4.23 years which gives a clear indication to the client for making an accurate estimation for the cost analysis of a PV plant

    Optimizing Plant Water Use Efficiency for a Sustainable Environment

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    The rising shortage of water resources in crop-producing regions worldwide and the need for irrigation optimisation call for sustainable water savings. The allocation of irrigation water will be an ever-increasing source of pressure because of vast agricultural demands under changing climatic conditions. Consequently, irrigation has to be closely linked with water-use efficiency with the aim of boosting productivity and improving food quality, singularly in those regions where problems of water shortages or collection and delivery are widespread. The present Special Issue (SI) showcases 19 original contributions, addressing water-use efficiency in the context of sustainable irrigation management to meet water scarcity conditions. These papers cover a wide range of subjects including (i) interaction mineral nutrition and irrigation in horticultural crops, (ii) sustainable irrigation in woody fruit crops, (iii) medicinal plants, (iv) industrial crops, and (v) other topics devoted to remote sensing techniques and crop water requirements, genotypes for drought tolerance, and agricultural management. The studies were carried out in both field and laboratory surveys, with modelling studies also being conducted, and a wide range of geographic regions are also covered. The collection of these manuscripts presented in this SI updates on and provides a relevant contribution for efficient saving water resources

    IoT and Sensor Networks in Industry and Society

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    The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

    A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

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    Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

    An Energy-Autonomous Smart Shirt employing wearable sensors for Users’ Safety and Protection in Hazardous Workplaces

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    none4siWearable devices represent a versatile technology in the IoT paradigm, enabling noninvasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where aWiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.Special Issue “Innovative Materials, Smart Sensors and IoT-based Electronic Solutions for Wearable Applications”, https://www.mdpi.com/journal/applsci/special_issues/Materials_Sensors_Electronic_Solutions_Wearable_ApplicationsopenRoberto De Fazio, Abdel-Razzak Al-Hinnawi, Massimo De Vittorio, Paolo ViscontiDE FAZIO, Roberto; Al-Hinnawi, Abdel-Razzak; DE VITTORIO, Massimo; Visconti, Paol

    Energy Harvesting Techniques for Internet of Things (IoT)

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    The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments

    The Machine that Lives Forever

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    Design an intelligent micromachine that can self-power and sustain from environmental energy scavenging to achieve an autonomous device that can communicate at will with peers indefinitely. Explore sleep/wake hibernation strategies coupled with food scavenging off-grid traits to identify the tightest work to sleep efficiency schedule, incorporating adaptive reconfiguration to manage significant environmental impacts. Capture, store and manage background radiations and stray RF signals to feed on in a continued effort to make intelligent survival decisions and oversee management protocols. Ensure that every micro Watt of usable energy gets extracted from every part of the harvest and then forward-scheduled it for productive use. Finally, employ natures tricks and experience to introduce essential personality traits, pursuing maximising survival numbers and increasing dispersal target area sizes of large self-sufficient wireless sensor deployments. This research intends to provide a closely coupled software-hardware foundation that aids implementers in intelligently harnessing and using tiny amounts of ambient energy in a highly autonomous way. This platform then continues on to explore ways of maximising the efficient usage of the harvested energy using various hibernation/wake strategies and then making objective comparisons with proposed intelligent energy management protocols. Finally, the protocol extends to enable the device to manage its personal survival possibilities so the devices can use an evolutional personality-based approach to deal with the unknown environmental situations they will encounter. This work examines a machine that can self-power and sustain from environmental energy scavenging with the aim to live forever. Living forever implies a brain (microcontroller) that can manage energy and budget for continuous faculty. With these objectives, sleep/wake/hibernation and scavenging strategies are examined to efficiently schedule resources within a transient environment. Example harvesting includes induced and background radiation. Intelligent, biologically-inspired strategies are adopted in forward-scheduling strategies given temporal energy relative to the machine’s function (the Walton)

    Editorial for Special Issue on “Electronic Systems and Energy Harvesting Methods for Automation, Mechatronics and Automotive”

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    none3noElectronic apparatus have become essential components of civil and industrial systems, including the automotive, home and building automation, Industrial IoT (Internet of Things) and control applications, and playing an essential role in improving security, efficiency, manageability, and rapid feedback [1–3]. Indeed, the increasing demands of electronic systems have led to innovations and findings in electronic networks for automotive and automation plants, replacing efficiently and securely mechanical and hydraulic sections [4]. Also, the researchers have focused their attention on meeting the increasing power demand of vehicles equipment, developing 42-V automotive systems. Moreover, smart buildings and homes represent a very actual research topic in the scientific community, aimed to improve energy conservation and the liveability of everyday life environments, thanks to IoT solutions [5]. In fact, smart homes and buildings comprise innovative solutions enabling communication between users and the infrastructure, as well as performing advanced monitoring tasks, like surveillance, light and water management, HVAC (heating, ventilation and air conditioning) system management, smart energy monitoring and elderly care. IoT technology employs sensors to detect the environmental temperature for the HVAC system, water and energy consumption, and health monitoring and decision-making systems to assist elderly people and detect fires [6,7]. The scientific community is concentrating their efforts to design innovative infrastructures, management models as well as operating scenarios to make production activities simpler and more efficient [8]. In this field, IoT is one of the key elements triggering this revolution, enabling communications between machines (M2M), thus creating a manufacturing environment human-free. The combination of M2M, IoT and CPS (cyber physical systems) makes the manufacturing systems more robust, reliable and efficient. Besides, cloud computing constitutes a powerful tool, promising to solve several difficult issues with previous productive architectures. For instance, in [9], a novel architecture integrating cloud computing, IoT, and smart devices, was presented. The model uses modern manufacturing technologies, allowing highly configurable, flexible manufacturing processes involving human and robotic participants. This Special Issue aimed to cover a wide range of disciplines and application fields, collecting innovative studies on advanced sensing and energy harvesting technologies and applications in automotive, automation and mechatronics fields. The introduced innovations could mitigate the impact of human activities on the environment and revolutionize the production process by employing eco-sustainable production models, preventing climate change and natural resources waste. A total of 5 papers have been published in this special issue; the paper covers a wide range of topics but is deemed relevant to the topics covered by the special issues. The authors are from geographically distributed countries such as Italy, Mexico, Spain, and China. This reflects the great impact of the proposed topic and the effective organization of the guest editorial team of this special issue.openPaolo Visconti, Nicola Ivan Giannoccaro, Roberto de FazioVisconti, Paolo; Giannoccaro, NICOLA IVAN; DE FAZIO, Robert

    How to Use Litigation Technology to Prepare & Present Your Case at Trial October 27, 2021

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    Meeting proceedings of a seminar by the same name, held October 27, 2021

    Renewable Energy

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    Renewable Energy is energy generated from natural resources - such as sunlight, wind, rain, tides and geothermal heat - which are naturally replenished. In 2008, about 18% of global final energy consumption came from renewables, with 13% coming from traditional biomass, such as wood burning. Hydroelectricity was the next largest renewable source, providing 3% (15% of global electricity generation), followed by solar hot water/heating, which contributed with 1.3%. Modern technologies, such as geothermal energy, wind power, solar power, and ocean energy together provided some 0.8% of final energy consumption. The book provides a forum for dissemination and exchange of up - to - date scientific information on theoretical, generic and applied areas of knowledge. The topics deal with new devices and circuits for energy systems, photovoltaic and solar thermal, wind energy systems, tidal and wave energy, fuel cell systems, bio energy and geo-energy, sustainable energy resources and systems, energy storage systems, energy market management and economics, off-grid isolated energy systems, energy in transportation systems, energy resources for portable electronics, intelligent energy power transmission, distribution and inter - connectors, energy efficient utilization, environmental issues, energy harvesting, nanotechnology in energy, policy issues on renewable energy, building design, power electronics in energy conversion, new materials for energy resources, and RF and magnetic field energy devices
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