Owing to the unreliability of wireless link and the resource constraints of embedded devices in terms of energy, processing power, and memory size in low power and lossy networks （LLNs）, network congestion may occur...Owing to the unreliability of wireless link and the resource constraints of embedded devices in terms of energy, processing power, and memory size in low power and lossy networks （LLNs）, network congestion may occur in an emergency and lead to significant packet loss and end-to-end delay. To mitigate the effect of network congestion, this paper proposes a centralized congestion control routing protocol based on multi-metrics （CCRPM）. It combines the residual energy of a node, buffer occupancy rate, wireless link quality, and the current number of sub-nodes for the candidate parent to reduce the probability of network congestion in the process of network construction. In addition, it adopts a centralized way to determine whether the sub-nodes of the congested node need to be switched based on the traffic analysis when network congestion occurs. Theoretical analysis and extensive simulation results show that compared with the existing routing protocol, the performance of CCRPM is improved significantly in reducing the probability of network congestion, prolonging average network lifetime, increasing network throughput, and decreasing end-to-end delay.展开更多
Background information is provided about the Web 2.0 related term altmetrics. This term is placed in the context of the broader field of informetrics. The term influmetrics is proposed as a better term for altmetrics....Background information is provided about the Web 2.0 related term altmetrics. This term is placed in the context of the broader field of informetrics. The term influmetrics is proposed as a better term for altmetrics. The importance of considering research products and not just scientific publications is highlighted. Issues related to peer review and making funding decisions within a multi-metric approach are discussed and brought in relation with the new metrics field.展开更多
窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进...窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。展开更多
双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型...双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型预测控制(generalized extended state observer based muti-model predictive control,GESOMMPC)方法。首先,建立了基于间隙度量(gap-metric)的多模型控制对象用于逼近非线性系统;其次,设计了扩张状态观测器估计系统耦合的集总扰动,并作为前馈信号输入到预测控制器中;最后,设计基于扰动前馈的多模型预测控制器实现对双母管系统的控制。实验结果表明,相对于PID方法,所提方法在满足电热负荷的同时,可以在允许范围内保持母管压力稳定,且动态偏差更小,过渡过程时间更短。展开更多
Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract usef...Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.展开更多
行人重识别任务旨在跨相机下检索出特定的行人图像.虽然行人重识别任务得到了快速发展,在检索精度上得到很大的提升,但是依然面临着行人重识别模型在新的数据集上泛化能力有限,以及在无监督领域自适应任务中无法避免的伪标签噪声的问题...行人重识别任务旨在跨相机下检索出特定的行人图像.虽然行人重识别任务得到了快速发展,在检索精度上得到很大的提升,但是依然面临着行人重识别模型在新的数据集上泛化能力有限,以及在无监督领域自适应任务中无法避免的伪标签噪声的问题.针对目前无监督领域自适应任务中由于聚类算法的局限性而导致伪标签出现噪声的问题,提出一种基于多度量融合的无监督领域自适应行人重识别算法.具体而言,多度量融合算法是在目标域上使用DBSCAN(density-based spatial clustering of applications with noise)聚类算法对特征空间的行人特征进行聚类时,通过多个特征相似度度量函数线性加权的方式,计算行人之间的特征相似度,从而在目标域上生成更为准确的伪标签,之后利用该伪标签微调模型.通过在Market1501→DukeMTMC-reID和DukeMTMC-reID→Market1501上大量的实验,证明多度量融合算法有效提升了行人重识别模型在无监督领域自适应任务上的检索精度.展开更多
基金supported by the National Natural Science Foundation of China (61379159)the Foundation and Frontier Research Project of Chongqing (cstc2015jcyjBX0085)
文摘Owing to the unreliability of wireless link and the resource constraints of embedded devices in terms of energy, processing power, and memory size in low power and lossy networks （LLNs）, network congestion may occur in an emergency and lead to significant packet loss and end-to-end delay. To mitigate the effect of network congestion, this paper proposes a centralized congestion control routing protocol based on multi-metrics （CCRPM）. It combines the residual energy of a node, buffer occupancy rate, wireless link quality, and the current number of sub-nodes for the candidate parent to reduce the probability of network congestion in the process of network construction. In addition, it adopts a centralized way to determine whether the sub-nodes of the congested node need to be switched based on the traffic analysis when network congestion occurs. Theoretical analysis and extensive simulation results show that compared with the existing routing protocol, the performance of CCRPM is improved significantly in reducing the probability of network congestion, prolonging average network lifetime, increasing network throughput, and decreasing end-to-end delay.
基金supported by the National Natural Science Foundation of China (7101017006, 71173187)
文摘Background information is provided about the Web 2.0 related term altmetrics. This term is placed in the context of the broader field of informetrics. The term influmetrics is proposed as a better term for altmetrics. The importance of considering research products and not just scientific publications is highlighted. Issues related to peer review and making funding decisions within a multi-metric approach are discussed and brought in relation with the new metrics field.
文摘窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。
文摘双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型预测控制(generalized extended state observer based muti-model predictive control,GESOMMPC)方法。首先,建立了基于间隙度量(gap-metric)的多模型控制对象用于逼近非线性系统;其次,设计了扩张状态观测器估计系统耦合的集总扰动,并作为前馈信号输入到预测控制器中;最后,设计基于扰动前馈的多模型预测控制器实现对双母管系统的控制。实验结果表明,相对于PID方法,所提方法在满足电热负荷的同时,可以在允许范围内保持母管压力稳定,且动态偏差更小,过渡过程时间更短。
文摘Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.
文摘行人重识别任务旨在跨相机下检索出特定的行人图像.虽然行人重识别任务得到了快速发展,在检索精度上得到很大的提升,但是依然面临着行人重识别模型在新的数据集上泛化能力有限,以及在无监督领域自适应任务中无法避免的伪标签噪声的问题.针对目前无监督领域自适应任务中由于聚类算法的局限性而导致伪标签出现噪声的问题,提出一种基于多度量融合的无监督领域自适应行人重识别算法.具体而言,多度量融合算法是在目标域上使用DBSCAN(density-based spatial clustering of applications with noise)聚类算法对特征空间的行人特征进行聚类时,通过多个特征相似度度量函数线性加权的方式,计算行人之间的特征相似度,从而在目标域上生成更为准确的伪标签,之后利用该伪标签微调模型.通过在Market1501→DukeMTMC-reID和DukeMTMC-reID→Market1501上大量的实验,证明多度量融合算法有效提升了行人重识别模型在无监督领域自适应任务上的检索精度.
