As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becomin...As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.展开更多
At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in p...At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.展开更多
为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利...为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利用高斯降权函数对其置信度进行衰减,通过判别参数对衰减后的置信度作进一步判断;在训练过程中加入采用优化NMS算法的在线难例挖掘(Online hard example mining,OHEM)技术,对马铃薯芽眼进行识别试验。试验结果表明:改进的模型识别精度为96.32%,召回率为90.85%,F1为93.51%,平均单幅图像的识别时间为0.183 s。与原始的Faster R-CNN模型相比,改进的模型在不增加运行时间的前提下,精度、召回率、F1分别提升了4.65、6.76、5.79个百分点。改进Faster R-CNN模型能够实现马铃薯芽眼的有效识别,满足实时处理的要求,可为种薯自动切块中的芽眼识别提供参考。展开更多
文摘As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.
文摘At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.
文摘为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利用高斯降权函数对其置信度进行衰减,通过判别参数对衰减后的置信度作进一步判断;在训练过程中加入采用优化NMS算法的在线难例挖掘(Online hard example mining,OHEM)技术,对马铃薯芽眼进行识别试验。试验结果表明:改进的模型识别精度为96.32%,召回率为90.85%,F1为93.51%,平均单幅图像的识别时间为0.183 s。与原始的Faster R-CNN模型相比,改进的模型在不增加运行时间的前提下,精度、召回率、F1分别提升了4.65、6.76、5.79个百分点。改进Faster R-CNN模型能够实现马铃薯芽眼的有效识别,满足实时处理的要求,可为种薯自动切块中的芽眼识别提供参考。
基金Supported by the China Postdoctoral Science Foundation(Grant No.2012M512133)the National Natural Science Foundation of China(Grant NO.41176074)the Fundamental Research Funds for the Central University(Grant NO.T013513015)
