Energy Efficiency
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2021 ◽  
Vol 22 (6) ◽  
pp. 27-35
Author(s):  
Uliana Andrusiv ◽  
Haluna Zelinska ◽  
Halyna Kupalova ◽  
Olga Galtsova ◽  
Liliya Marynchak ◽  
...  

2021 ◽  
Vol 13 (30) ◽  
pp. 73-78
Author(s):  
Ivan Mitkov ◽  
◽  
Veselin Harizanov ◽  
Georgi Komitov ◽  
◽  
...  

The problem of feeding the population and the lack of trained staff for growing crops is increasing all over the world. This inevitably leads to a change in technology for growing crops. These new technologies rely on autonomous robotic systems for the continuous cultivation of crops without human personnel. Robots are small, smart, interconnected, lightweight machines that aim to release the person from the basic everyday pursuits. Globally, there is a trend in agriculture to automate the hard manual labor with continued increases in yields to feed the population. This article discusses the problems of determining the main energy aspects of agricultural robots. Guidelines are given for determining the energy saving of the robot, depending on the time for its long autonomous operation, the terrain to be cultivated and a number of other factors. Exemplary values of the energy required to drive the agricultural robot and the time for energy recovery through renewable energy sources have been determined.


2021 ◽  
Author(s):  
Sergey Zuev ◽  
Ruslan Maleev ◽  
Aleksandr Chernov

When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU). The energy efficiency is largely determined by the power of the generator set and the battery, which is given to the electrical network in various driving modes. Most of the existing methods for calculating power supply systems use the average values of disturbing factors (generator speed, current of electric energy consumers, voltage in the on-board network) when choosing the characteristics of the generator set and the battery. At the same time, it is obvious that when operating a motor vehicle, these parameters change depending on the driving mode. Modern methods of selecting the main parameters and characteristics of the power supply system do not provide for modeling its interaction with the power unit start-up system of a motor vehicle in operation due to the lack of a systematic approach. The choice of a generator set and a battery, as well as the concept of the synthesis of the power supply system is a problem studied in the monograph. For all those interested in electrical engineering and electronics.


2021 ◽  
Author(s):  
Nguyen Thi Thu Thao ◽  
Tran Thi Hieu ◽  
Nguyen Thi Phuong Thao ◽  
Le Quoc Vi ◽  
Hans Schnitzer ◽  
...  

Abstract BackgroundEconomic benefit has been analyzed for the yield of farming products when designing a farming system, while waste treatment also generates profitable energy products for this system. The economic factor is decisive in decision-making for applying waste treatment solutions for a small-scale farming system. A household farming system in ​​the Mekong Delta generates many kinds of organic wastes, but most of the agricultural waste resources are disposed of into the environment. MethodsThis study approaches an analysis of economic-environmental-energy (EEE) efficiency for waste treatment of an integrated livestock-orchard (LO) system on a household scale in the Mekong Delta. This novel analysis method is based on the energy content of biomass and its cost. The EEE efficiency is optimized to gain objective functions regarding energy yield efficiency, system profit, and CO2 sequestration for the treatment model. The algorithms are built for optimizing these objective functions. ResultsThe optimization results show the treatment model of pyrolysis and pelleting gain all the objective functions with high efficiency. The model is efficiently applied for the LO system that generates more than 100 kilograms of orchard residues and 3,000 kilograms of pig manure. The system with a charcoal oven and pellet machine is capable to gain energy efficiency due to its potential biofuel products, such as biochars and pellet products. A treatment model of composting, pyrolysis, and pelleting gives the best performance of overall EEE efficiency. ConclusionsThis work has proven economic benefits from integrating biogas tank, charcoal oven, and pellet machine in an integrated LO system. The system contributes not only for reducing CO2 emissions but also for supplementing secondary renewable bioenergy, as well as for increasing incomes and thus supporting livelihoods for the local farming households.


2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Guilherme Korol ◽  
Michael Guilherme Jordan ◽  
Mateus Beck Rutzig ◽  
Antonio Carlos Schneider Beck

FPGAs, because of their energy efficiency, reconfigurability, and easily tunable HLS designs, have been used to accelerate an increasing number of machine learning, especially CNN-based, applications. As a representative example, IoT Edge applications, which require low latency processing of resource-hungry CNNs, offload the inferences from resource-limited IoT end nodes to Edge servers featuring FPGAs. However, the ever-increasing number of end nodes pressures these FPGA-based servers with new performance and adaptability challenges. While some works have exploited CNN optimizations to alleviate inferences’ computation and memory burdens, others have exploited HLS to tune accelerators for statically defined optimization goals. However, these works have not tackled both CNN and HLS optimizations altogether; neither have they provided any adaptability at runtime, where the workload’s characteristics are unpredictable. In this context, we propose a hybrid two-step approach that, first, creates new optimization opportunities at design-time through the automatic training of CNN model variants (obtained via pruning) and the automatic generation of versions of convolutional accelerators (obtained during HLS synthesis); and, second, synergistically exploits these created CNN and HLS optimization opportunities to deliver a fully dynamic Multi-FPGA system that adapts its resources in a fully automatic or user-configurable manner. We implement this two-step approach as the AdaServ Framework and show, through a smart video surveillance Edge application as a case study, that it adapts to the always-changing Edge conditions: AdaServ processes at least 3.37× more inferences (using the automatic approach) and is at least 6.68× more energy-efficient (user-configurable approach) than original convolutional accelerators and CNN Models (VGG-16 and AlexNet). We also show that AdaServ achieves better results than solutions dynamically changing only the CNN model or HLS version, highlighting the importance of exploring both; and that it is always better than the best statically chosen CNN model and HLS version, showing the need for dynamic adaptability.


2021 ◽  
Author(s):  
Ibrahim Salah ◽  
M. Mourad Mabrook ◽  
Kamel Hussein Rahouma ◽  
Aziza I. Hussein

Abstract Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell MMIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.


Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5888
Author(s):  
Saulius Baskutis ◽  
Jolanta Baskutiene ◽  
Valentinas Navickas ◽  
Yuriy Bilan ◽  
Wojciech Cieśliński

Environmental pollution, energy supply and security of supply have become major issues across the world due to climate change, limited energy sources, energy price volatility and energy supply constraints. Energy availability, energy efficiency and the replacement of fossil fuels by renewable energy sources are key factors in the global development of sustainable energy. In many countries with limited fossil fuel resources, the sustainable development of renewable energy sources is an important tool in reducing dependence on imported fuels. Some alternative energy sources, such as wind, solar, tidal and hydropower, seem almost inexhaustible. With the exception of tidal energy, all of these sources have been used extensively and for a long time. This article examines the improvement of energy security and the government’s actions to promote the use of renewable energy sources, focusing on increasing energy efficiency and reducing energy intensity and dependence on energy imports in Lithuania. In addition, the article provides the state of renewable energy sources in Lithuania, aspects of sustainability and future development directions and perspectives.


Author(s):  
Paras Kumar Mishra

Lack of glucose uptake compromises metabolic flexibility and reduces energy efficiency in the diabetes mellitus (DM) heart. Although increased utilization of fatty acid to compensate glucose substrate has been studied, less is known about ketone body metabolism in the DM heart. Ketogenic diet reduces obesity, a risk factor for T2DM. How ketogenic diet affects ketone metabolism in the DM heart remains unclear. At the metabolic level, the DM heart differs from the non-DM heart due to altered metabolic substrate and the T1DM heart differs from the T2DM heart due to insulin levels. How these changes affect ketone body metabolism in the DM heart are poorly understood. Ketogenesis produces ketone bodies by utilizing acetyl CoA whereas ketolysis consumes ketone bodies to produce acetyl CoA, showing their opposite roles in the ketone body metabolism. Cardiac-specific transgenic upregulation of ketogenesis enzyme or knockout of ketolysis enzyme causes metabolic abnormalities leading to cardiac dysfunction. Empirical evidence demonstrates upregulated transcription of ketogenesis enzymes, no change in the levels of ketone body transporters, very high levels of ketone bodies, and reduced expression and activity of ketolysis enzymes in the T1DM heart. Based on these observations, I hypothesize that increased transcription and activity of cardiac ketogenesis enzyme suppresses ketolysis enzymes in the DM heart, which decreases cardiac energy efficiency. The T1DM heart exhibits highly upregulated ketogenesis compared to T2DM due to lack of insulin that inhibits ketogenesis enzyme.


2021 ◽  
Vol 19 (4) ◽  
pp. e0209-e0209
Author(s):  
Mohammad Younesi-Alamouti ◽  

Aim of study: To investigate the impact factors affecting the greenhouse environment on energy consumption and productivity. Area of study: Alborz province of Iran during the period 2018–2020. Material and methods: In this study, 18 active units of greenhouse owners in Alborz province of Iran that had necessary standards were identified. Then, upper and lower amplitudes of the variables affecting productivity and energy consumption in greenhouses were calculated using a type-2 fuzzy neural network, Matlab 2017 software. Area, temperature, energy exchange, environmental evapotranspiration and relative humidity were studied as indicators. Main results: With each unit of temperature, energy consumption and productivity increased by 0.737% and 0.741%, respectively; with each unit of energy exchange, they increased by 0.813% and 0.696%, respectively; with each unit of evaporation and transpiration of the environment, they increased by 0.593% and 0.869%, respectively; and with each unit of humidity, they increased by 0.398% and 0.509%, respectively. Research highlights: The factors affecting the greenhouse environment such as area, temperature, evapotranspiration and relative humidity had a significant effect on productivity in studying greenhouses and therefore increasing their productivity. According to the results, the model’s ability in energy consumption was better than that for energy efficiency prediction. Also, greenhouse ranking was done by FAHP method.


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