Joint optimization of channel allocation and power control for cognitive radio networks with multiple constraints

Wireless Networks - With the increasing demands for wireless communication, efficiently using the spectrum resource has always been an important research topic. In this paper, the problem of N...     

Fault Prediction Based on the Kernel Function for Ribbon Wireless Sensor Networks

There exist several applications of wireless sensor networks in which the reliable operation can be crucial. Fault prediction is a critical problem in reliability theory for ribbon wireless sensor networks (RWSNs). Accurate fault prediction can effectively improve the availability of the WSNs system. In this paper, we evaluated the network performance for RWSNs, studied the basic theory of kernel functions, proposed a new failure prediction method based on kernel function, and selected the radial basis function as kernel function failure prediction models from two aspects of node hardware failures and network failures for fault prediction. Theoretical evidence and experimental results have shown that the proposed algorithmic prediction method has higher accuracy of 12 and 15% than that of GRNN and PNN respectively. Finally, we provided extensive numerical results to demonstrate the usage and efficiency of the proposed algorithms and complement our theoretical analysis.     

白酒检测技术在生产中的应用研究进展

白酒检测是对白酒进行深层次研究的必备手段,对控制白酒质量、指导生产、维持白酒市场稳定等有着积极作用。该文总结了色谱技术、光谱技术、核磁共振技术以及仿生传感器技术在白酒检测中应用现状及研究成果,分别阐述了不同检测技术在品牌、产地、酒龄鉴别、品质监控,真伪鉴别以及风味物质测定,等级划分等方面的应用情况,并对不同检测技术的建模方法在实际应用中存在的优势与不足进行对比分析。针对目前白酒检测过程中的难点与不足,对白酒检测技术在白酒生产、质量控制、市场维护中的应用发展进行展望,可为白酒的数字化发展以及从业人员的研究工作提供参考。     

基于酒精度重建模型的摘酒技术研究

为提高摘酒酒精度,稳定基酒质量,达到分段摘酒的目的,该研究以酒精水溶液密度、温度、酒精体积分数关系为映射,利用最 小二乘法建立了基酒密度、酒精度和温度的最优模型,以音叉密度计监测基酒密度,通过模型转换为酒精度后,对比实测酒精度与 预测酒精度之间的差异,并在模型建立的基础上,设计了一种分段摘酒装置。 结果表明,所建模型预测平均差、平均相对误差分别在 1.36～2.84、2.2%～6.5%,无显著波动性差异,稳定性良好。 该模型配合分段摘酒装置,为白酒工业提供了一种摘酒技术手段,有利于 提升摘酒品质与产量。     

基于FT-NIR光谱技术结合KPCA-MD-SVM对白酒基酒的快速判别

为保证在摘酒过程中基酒分段的准确,研究通过利用傅里叶变换近红外设备对整个摘酒过程中的基酒样品进行光谱采集,使用支持向量机（SVM）对最优预处理的基酒光谱建立基酒分段模型,其模型训练集的正确率为93.02%,测试集判别率为90.08%。为减少建模时间和提高模型的可靠性,使用核主成分分析（KPCA）对基酒光谱数据降维,并对此建立基酒分段模型。其训练集正确率为94.81%,测试集判别率为90.75%,相比无KPCA分析时的分段模型训练集高1.79%,测试集高0.67%。为进一步提高模型的判别能力,使用马氏距离（MD）剔除了降维后的异常数据样品,创建的基酒分段模型训练集对基酒段数的正确率为98.72%,测试集正确率为98.75%。剔除异常样品后的分段模型的训练集正确率提高了3.91%,测试集判别率提高了8%。以上研究表明了KPCA+MD+ SVM基酒分段模型能对基酒进行快速判别,为近红外光谱在自动化摘酒方面提供了一种理论可能。     

Optimization of heat-based cache replacement in edge computing system

The Journal of Supercomputing - With high-speed development of smart devices, abundant data are generated at the edge of the network. Edge computing has three characteristics: low response delay,...     

A model for oxidation-induced stress analysis of Ni-based anode-supported planar solid oxide fuel cell

A critical hurdle in realizing commercially viable anode-supported solid oxide fuel cell (SOFC) is the re-oxidation of cermet anode during the cell abnormal operation. In this paper, the analysis of energy dispersive X-ray spectroscopy on a partially oxidized half-cell demonstrates a particular inhomogeneous oxidation mechanism that a portion of anode near the air/anode interface is oxidized with a graded NiO content, while the remaining region hold a reduced status. Based on this observation, an analytical oxidation-induced stress model is developed to provide in-depth information about the mechanical behavior of the half-cell suffered from various oxidation. The dependences of the mechanical performance of half-cell on the NiO distribution and thickness of oxidation-graded zone are revealed. The results show that an increase in oxidation-graded zone elevates the stress level of electrolyte, but decreases the curvature under the same global degree of oxidation (DoO). In addition, the influence of thickness of oxidation-graded zone on the electrolyte failure probability is also investigated. According to these results, we conclude that the thickness of oxidation-graded zone should be as thin as possible in order to delay the electrolyte cracking.