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1.
One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity. 相似文献
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以甲烷-二氧化碳重整制合成气为实例,设计探究性实验,将合成气的制备和现代分析技术应用于化工专业实验的教学实践中以提高学生的创新和实践能力。实验包括催化剂的制备,催化剂的性能评价和催化剂的表征等3大部分。采用工业最常用的浸渍法制备含有不同助剂的Ni/X/γ-Al2O3(X为Co,Fe,MgO,CeO2)催化剂,以甲烷-二氧化碳重整反应评价其催化性能,并采用XRD、H2-TPR、BET和TG对催化剂的微观结构进行表征。结合催化剂的性能评价结果和表征结果,探讨不同助剂对镍基催化剂性能的改善效果及机制。通过开设该实验,可以让学生了解化工学科的前沿知识以及现代分析技术的基本原理和用途,掌握专业的实验操作、数据处理和谱图绘制方法,提高学生的专业素养和综合能力。 相似文献
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This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network(CNN).To promote the detection performance and efficiency,we proposed a scheme based on wavelet packet(WP)decomposition and CNN.The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection.The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results.This function-mapping-relation style detection scheme can detection efficiency significantly.In this work,instrument effects are con-sidered,and the noise are computed from a power spectral density(PSD)equivalent to the Advanced LIGO design sensitivity.The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection.On efficiency,the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering. 相似文献
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PolyethyleneiminePEImodified silver nanoclustersAg NCswere synthesized through chemical reduction method by using PEI as stabilizerAgNO3 as silve source and ascorbic acid as reducing agentand it was applied to the detection of cobalt ions. It was found that the fluorescence of the silver nanoclusters was quenched by Co2+ for the aggregation of PEI-Ag NCs caused by the coordination between cobalt ion and amino group on polyethyleneimine. Under the optimized experimental conditionsthe fluorescence quenching degree of PEI-Ag NCs was piecewise linear with the concentration of cobalt ion in the range of 8.30×10-7-4.17×10-4 mol/L and the detection limit was 0.41 μµmol/L. © 2022, Youke Publishing Co.,Ltd. All rights reserved. 相似文献
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This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coefficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank. 相似文献
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《Stochastic Processes and their Applications》2020,130(5):2954-2973
We consider a stochastic search model with resetting for an unknown stationary target with known distribution . The searcher begins at the origin and performs Brownian motion with diffusion constant . The searcher is also armed with an exponential clock with spatially dependent rate , so that if it has failed to locate the target by the time the clock rings, then its position is reset to the origin and it continues its search anew from there. Denote the position of the searcher at time by . Let denote expectations for the process . The search ends at time . The expected time of the search is then . Ideally, one would like to minimize this over all resetting rates . We obtain quantitative growth rates for as a function of in terms of the asymptotic behavior of the rate function , and also a rather precise dichotomy on the asymptotic behavior of the resetting function to determine whether is finite or infinite. We show generically that if is of the order , with , then is of the order ; in particular, the smaller the asymptotic size of , the smaller the asymptotic growth rate of . The asymptotic growth rate of continues to decrease when with ; now the growth rate of is more or less of the order . Note that this exponent increases to when increases to and decreases to 2 when decreases to 1. However, if , then , for . Our results suggest that for many distributions supported on all of , a near optimal (or optimal) choice of resetting function in order to minimize will be one which decays quadratically as for some . We also give explicit, albeit rather complicated, variational formulas for . For distributions with compact support, one should set off of the support. We also discuss this case. 相似文献
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