TPC-BS: 센서 네트워크에서 이진검색 방법을 이용한 빠른 전송전력 결정 방법

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

The UK has been one of the few countries that has successfully decoupled final energy consumption from economic growth over the past 15 years. This study investigates the drivers of final energy consumption in the UK productive sectors between 1997 and 2013 using a decomposition analysis that incorporates two novel features. Firstly, it investigates to what extent changes in thermodynamic efficiency have contributed to overall changes in sectoral energy intensities. Secondly, it analyses how much of the structural change in the UK economy is driven by the offshoring of energy-intensive production overseas. The results show that energy intensity reductions are the strongest factor reducing energy consumption. However, only a third of the energy savings from energy intensity reductions can be attributed to reductions in thermodynamic efficiency with reductions in the exergy intensity of production making up the reminder. In addition the majority of energy savings from structural change are a result of offshoring, which constitutes the second biggest factor reducing energy consumption. In recent years the contributions of all decomposition factors have been declining with very little change in energy consumption after 2009. This suggests that a return to the strong reductions in energy consumption observed between 2001 and 2009 in the UK productive sectors should not be taken for granted. Given that further reductions in UK final energy consumption are needed to achieve global targets for climate change mitigation, additional policy interventions are needed. Such policies should adopt a holistic approach, taking into account all sectors in the UK economy as well as the relationship between the structural change in the UK and in the global supply chains delivering the goods and service for consumption and investment in the UK.

ObjectivesThe unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system.Materials and methodsEnergy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption.ResultsOur findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption.ConclusionImplementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs.Clinical relevance statementMRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact.Key PointsOptimization of protocol settings reduced energy consumption by 31% and imaging time by 18%.DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol.During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.

China’s economy adopts an extensive growth method. This economic growth method relies on resource input, which consumes huge energy and causes excessive pressure on resources and the environment. The economic growth rate of Fujian Province ranks among the highest in the country in recent years, and its energy consumption is still on the high side. Industrial enterprises, especially the manufacturing industry, account for the majority of energy consumption. In order to ensure the sustainable development of enterprises and society, the government needs to make plans for energy conservation and consumption reduction, policy support and enterprise energy conservation and consumption reduction measures to promote energy conservation and consumption reduction in the whole society, and strive to achieve coordinated development of economic growth and environmental protection.

Treatment of natural stones with Phase Change Materials: Experiments and computational approaches

In this paper, the problem of adversarial learning is studied for a wireless powered communication network (WPCN) in which a hybrid access point (HAP) seeks to learn the transmission power consumption profile of an associated wireless transmitter. The objective of the HAP is to use the learned estimate in order to determine the transmission power of the energy signal to be supplied to its associated device. However, such a learning scheme is subject to attacks by an adversary who tries to alter the HAP's learned estimate of the transmission power distribution in order to minimize the HAP's supplied energy. To build a robust estimate against such attacks, an unsupervised Bayesian learning method is proposed allowing the HAP to perform its estimation based only on the advertised transmisson power computed in each time slot. The proposed robust learning method relies on the assumption that the device's true transmission power is greater than or equal to advertised value. Then, based on the robust estimate, the problem of power selection of the energy signal by the HAP is formulated. The HAP optimal power selection problem is shown to be a discrete convex optimization problem, and a closed-form solution of the HAP's optimal transmission power is obtained. The results show that the proposed robust Bayesian learning scheme yields significant performance gains, by reducing the percentage of dropped transmitter's packets of about 85% compared to a conventional Bayesian learning approach. The results also show that these performance gains are achieved without jeopardizing the energy consumption of the HAP.

This paper presents an energy-efficient cooperative MAC (EECO-MAC) protocol using power control in mobile ad hoc networks. Cooperative communications improve network performance by taking full advantage of the broadcast nature of wireless channels. The power control technique improves the network lifetime by adjusting the transmission power dynamically. We propose the best partnership selection algorithm, which takes energy consumption into consideration for selection of the optimal cooperative helper to join in the transmission. Through exchanging control packets, the optimal transmission power is allocated for senders to transmit data packets to receivers. In order to enhance energy saving, space---time backoff and time---space backoff algorithms are proposed. Simulation results show that EECO-MAC consumes less energy and prolongs the network lifetime compared to IEEE 802.11 DCF and CoopMAC at the cost of delay. Performance improvement offered by our proposed protocol is apparent in congested networks where nodes have low and limited energy.

The influence of biopreparations on the reduction of energy consumption and CO2 emissions in shallow and deep soil tillage

We investigated the reduction of electrical energy consumption in thepulsed corona discharge process for the removal of nitrogenoxides. Hydrocarbon chemical additives used in the laboratory-scaleexperiment are responsible for the enhancement of the NO conversionthrough the chain reactions of free radicals, such as, R, RCO, RO,and others. Electrical energy consumption per converted NO moleculehas a minimum value of 17 eV when pentanol is injected. When ethyleneand propylene are injected, 30 and 22 eV of electrical energy consumptionare required for the conversion of a NO molecule, respectively. The ratioof the pulse-forming capacitance (Ce) to the reactor capacitance (CR)plays an important role in the energy transfer efficiency to thereactor. The maximum energy transfer efficiency of approximately 72%could be obtained by the pulse-forming capacitance, which is 3.4 timeslarger than the reactor capacitance; the maximum NO conversionefficiency was also observed with the same condition.

This paper presents an evaluation and reduction of energy consumption during railway train movement on a straight track section with reduced freight wagon mass. A theoretical model was developed to simulate energy consumption based on input parameters, including train speed, track gradient, section length, travel time, and train mass. The results indicate that energy consumption increases by 18.9% as speed rises to 90 km/h and as gradients increase to 2.0‰, while energy consumption decreases by 14.5% on a descending gradient of 1.5‰, which corresponds to the expected dynamics of railway trains. These results are supported by experiments showing that the MAPE error does not exceed 1.9%, which can confirm the accuracy of the developed model. A comprehensive analysis of the potential reduction in energy consumption with reduced freight wagon mass was also conducted. Using a freight wagon design with a reduced mass of 2.3% allows for a reduction in energy consumption by 8–89 kW·h, depending on the length of the section and the speed of movement.

RETRACTED: Calculating potential of solar energy and CO2 emissions reduction for cityscale buildings based on 3D remote sensing technologies

To determine whether energy consumption changes after selective dorsal rhizotomy (SDR) among children with cerebral palsy (CP). We retrospectively evaluated net nondimensional energy consumption during walking among 101 children with bilateral spastic CP who underwent SDR (59 males, 42 females; median age [5th centile, 95th centile] 5y 8mo [4y 2mo, 9y 4mo]) compared to a control group of children with CP who did not undergo SDR. The control group was matched by baseline age, spasticity, and energy consumption (56 males, 45 females; median age [5th centile, 95th centile] 5y 8mo [4y 1mo, 9y 6mo]). Outcomes were compared at baseline and follow-up (SDR: mean [SD] 1y 7mo [6mo], control: 1y 8mo [8mo]). The SDR group had significantly greater decreases in spasticity compared to matched controls (-42% SDR vs -20% control, p<0.001). While both groups had a modest reduction in energy consumption between visits (-12% SDR, -7% control), there was no difference in change in energy consumption (p=0.11) or walking speed (p=0.56) between groups. The SDR group did not exhibit greater reductions in energy consumption compared to controls. The SDR group had significantly greater spasticity reduction, suggesting that spasticity had minimal impact on energy consumption during walking in CP. These results support prior findings that spasticity and energy consumption decrease with age in CP. Identifying matched control groups is critical for outcomes research involving children with CP to account for developmental changes.

Energy optimization in freight train operations: Algorithmic development and testing

In today’s Wireless Communication World MMIMO (Massive Multiple Input and Multiple Output) is the sounding word and capable system for high data transmission rates in Wireless Systems and Networks (WSN). Higher Data Transmission Rates are achieved by posing many more antennas at base station, as result the power consumption should be more, and the bandwidth requirement also enhanced. To address this issue, in this article an innovative method of selecting the Transmit Antenna by making active and passive combination a novel method is proposed. This novel method not only optimizes the antenna selection but also provides the mechanism of reducing power utilization and energy consumption. Initially we consider the GSA (Gravitational Search Algorithm) with velocity parameter and proposed an alternative velocity, treated as Enhanced Velocity Gravitational Search Algorithm (EV-GAS). Moreover EV-GASA on conventional models of Greedy Algorithm, Antenna Bee Colony, Particle Swarm Optimization, Fierce fly for antenna optimization, Energy and Power consumption. Obtained outcomes are proved the EV-GAS is the prominent algorithm for high data transmission rates.

Energy saving and the development of renewable energies already form part of the daily discourse in our society. Climate change and the environmental problems arising from it affect all of us on the planet. In view of the recent energy supply crisis in Europe and in line with the EU's Sustainable Development Goals, it is necessary to accelerate the ecological transition. This means, among many other things, encouraging energy efficiency and energy savings. This research is based on the EURONET 50/50 max project, which applied a methodology with no upfront capital outlay, but based on changing user habits to save energy in educational buildings. It was carried out in twelve educational centres of the Spínola Foundation throughout Spain. Significant reductions in energy and water consumption were achieved. The maximum reduction obtained in water consumption was 29%, 24% in electricity consumption and 33% in heating consumption. The average reduction in consumption obtained was 13% compared to 11% achieved in the 50/50 max project. It was concluded that, on the journey to optimising our energy consumption, working on patterns of usage is fundamental, raising awareness and involving users in order to give rise to permanent processes over time, gradually reversing climate change.