k-Bitonic sort

Ak-bitonic sort which generalizes the bitonic sort is proposed. The theorem of the bitonic sort, which merges two monotonic sequences into one order sequence, is extended into the theorem ofk-bitonic sort. Thek-bitonic sort merges (K (=2k or 2k?1) monotonic sequences into one order sequence in $\left\lceil {log_2 K} \right\rceil \left\lceil {log_2 N} \right\rceil - \tfrac{{\left\lceil {log_2 K} \right\rceil (\left\lceil {log_2 K} \right\rceil - 1)}}{2}$ steps, where $k = \left\lceil {\tfrac{K}{2}} \right\rceil $ is an integer andk≥1. Thek-bitonic sort is the Batcher's bitonic sort whenk=1.

基于SORT映射的IRCMFDE在旋转机械故障诊断中的应用

针对旋转机械振动信号的强非线性和非平稳性,导致故障特征提取困难的问题,提出了一种基于SORT映射的改进精细复合多尺度波动散布熵(IRCMFDE)和蝙蝠算法优化的相关向量机(BA-RVM)的旋转机械故障诊断方法。首先,利用SORT映射函数替换了精细复合多尺度波动散布熵(RCMFDE)方法的正态累积分布函数,同时对RCMFDE方法的粗粒化方式进行了改进,提出了基于SORT映射的IRCMFDE方法;随后,利用IRCMFDE方法提取了旋转机械振动信号的故障特征,构造了故障特征集;最后,采用BA-RVM分类器对旋转机械的故障类型进行了智能化的识别和分类;将基于IRCMFDE和BA-RVM的故障诊断方法应用于滚动轴承、离心泵和齿轮箱的实验数据分析,并将其与现有故障诊断方法进行了对比分析。研究结果表明:基于IRCMFDE和BA-RVM的故障诊断方法能够有效地识别旋转机械的故障状态,识别准确率分别达到了100%、98%和99%,相比基于RCMFDE、精细复合多尺度熵、精细复合多尺度模糊熵、精细复合多尺度排列熵和精细复合多尺度散布熵的故障特征提取方法,该故障诊断方法的效率和平均识别准确率均优于对比方法,其更适合应用于旋转机械的在线实时故障监测。

Bubble-sort网络的一类条件连通度

[目的]为评价网络容错性,以具有高对称性和递归结构的Bubble-sort网络为研究对象,确定其h-extra r-分支边连通度.[方法] Bubble-sort网络Bn可以分解成n个子图Bn(i),其中Bn(i)是由点集{x_(1)x_(2)…x_(n):x_(n)=i}(1≤i≤n)导出的子图,并且Bn(i)同构于B_(n-1),利用它的结构特点,用数学归纳法推理证明了主要结果.[结果]确定了bubble-sort网络的h-extra r-分支边连通度cλ2/3(B_(n))=4n-10(n≥4).[结论]研究了bubble-sort网络的一类条件连通度,可用于衡量网络的可靠性.今后将继续深入研究bubble-sort网络的其他条件连通度.

基于SORT算法的图像轨迹跟踪混合控制方法

当目标物体被其他物体部分或完全遮挡时,目标的有效特征点数量会逐渐减少,跟踪器无法继续准确地锁定目标,导致目标轨迹中断。为此,文中研究基于SORT算法的图像轨迹跟踪混合控制方法。选取FCOS算法,利用特征金字塔结构,依据检测头层输出的目标分类得分、位置回归结果以及中心度检测图像目标。将目标检测结果作为卡尔曼滤波器的输入,利用离散控制过程系统描述视频图像中的目标运动状态,预测目标轨迹。利用SORT算法控制图像目标检测结果与目标轨迹预测结果进行级联匹配与IoU匹配,输出匹配成功的目标,即图像目标轨迹跟踪结果。实验结果表明,该方法可有效地跟踪视频图像目标轨迹,未出现ID变更情况,轨迹中断占比低于0.2%。

Comparative analysis shows high level of lineage sorting in genomic regions with low recombination in the extended Picea likiangensis species complex

Genome-scale data,while promising for illuminating phylogenetic relationships,frequently pose a conundrum by yielding conflicting topologies and highly variable gene tree distributions(Pease et al.,2016).This complexity likely arises from the reticulate evolution observed in many taxa,where genetic information exchange occurs through diverse biological processes.

Digital Twin Technology of Human-Machine Integration in Cross-Belt Sorting System

The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.

A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.

Phylotranscriptomic discordance is best explained by incomplete lineage sorting within Allium subgenus Cyathophora and thus hemiplasy accounts for interspecific trait transition

The transition of traits between genetically related lineages is a fascinating topic that provides clues to understanding the drivers of speciation and diversification.Much can be learned about this process from phylogeny-based trait evolution.However,such inference is often plagued by genome-wide gene-tree discordance(GTD),mostly due to incomplete lineage sorting(ILS)and/or introgressive hybridization,especially when the genes underlying the traits appear discordant.Here,by collecting transcriptomes,whole chloroplast genomes(cpDNA),and population genetic datasets,we used the coalescent model to turn GTD into a source of information for ILS and employed hemiplasy to explain specific cases of apparent“phylogenetic discordance”between different morphological traits and probable species phylogeny in the Allium subg.Cyathophora.Both concatenation and coalescence methods consistently showed the same phylogenetic topology for species tree inference based on single-copy genes(SCGs),as supported by the KS distribution.However,GTD was high across the genomes of subg.Cyathophora:~27%e38.9%of the SCG trees were in conflict with the species tree.Plasmid and nuclear incongruence was also present.Our coalescent simulations indicated that such GTD was mainly a product of ILS.Our hemiplasy risk factor calculations supported that random fixation of ancient polymorphisms in different populations during successive speciation events along the subg.Cyathophora phylogeny may have caused the character transition,as well as the anomalous cpDNA tree.Our study exemplifies how phylogenetic noise can be transformed into evolutionary information for understanding character state transitions along species phylogenies.

改进Bot-SORT的边坡落石监测方法

针对边坡落石监测中存在的目标尺寸小、石块与背景特征差距小、落石目标运动速度快等问题,提出一种基于检测的改进Bot-SORT多目标跟踪算法。在检测部分对YOLOv7模型进行改进,引入注意力机制,提升模型对石块特征的提取能力,并使用归一化高斯Wasserstein距离作为真值框与预测框的距离度量方式,降低模型对小目标的漏检率;在跟踪部分引入GIoU距离匹配方式,有效跟踪快速运动的落石。通过实景拍摄及Unity仿真方式建立训练及测试数据集,消融实验和对比实验结果表明,本文改进算法能够有效提高落石的检测率和跟踪精度。

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

Accelerating Large-Scale Sorting through Parallel Algorithms

This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.

利用无人机航拍视频结合YOLOv3模型和SORT算法统计云杉数量

准确、快速地统计苗木数量对苗圃的运营和管理具有重要意义,是提高苗圃运营和管理水平的有效方式。为快速准确统计完整地块内苗木数量,该研究选取云杉为研究对象,以无人机航拍完整地块云杉视频为数据源,提出一种基于YOLOv3(You Only Look Once v3,YOLOv3)和SORT(Simple Online and Realtime Tracking,SORT)的云杉数量统计方法。主要内容包括数据采集、YOLOv3检测模型构建、SORT跟踪算法和越线计数算法设计。以平均计数准确率(Mean Counting Accuracy,MCA)、平均绝对误差(Mean Absolute Error,MAE)、均方根误差(Root Mean Square Error,RMSE)和帧率(Frame Rate,FR)为评价指标,该方法对测试集中对应6个不同试验地块的视频内云杉进行数量统计的平均计数准确率MCA为92.30%,平均绝对误差MAE为72,均方根误差RMSE为98.85,帧率FR 11.5帧/s。试验结果表明该方法能够快速准确统计完整地块的云杉数量。相比SSD+SORT算法,该方法在4项评价指标中优势显著,平均计数准确率MCA高12.36个百分点,帧率FR高7.8帧/s,平均绝对误差MAE和均方根误差RMSE分别降低125.83和173.78。对比Faster R-CNN+SORT算法,该方法在保证准确率的基础上更加快速,平均计数准确率MCA仅降低1.33个百分点,但帧率FR提高了10.1帧/s。该研究从无人机航拍视频的角度为解决完整地块的苗木数量统计问题做出了有效探索。

基于YOLOv3和Deep SORT的草原牛跟踪系统

设计了一种基于深度学习算法的草原牛跟踪系统。融合YOLOv3目标检测算法与Deep SORT目标跟踪算法实现对草原牛的检测跟踪,结合比例—积分—微分(PID)算法控制云台(PTZ)摄像头稳定跟随草原牛转动。在内蒙古苏尼特左旗牧场进行现场实验测试,实验结果表明:系统运行稳定,对草原牛检测准确率较高,跟踪效果较好,可以实现未检测到草原牛时自动巡航、对多只草原牛自动跟踪、以及指定跟踪单只草原牛的功能。

基于YOLO v5s和改进SORT算法的黑水虻幼虫计数方法

目前农业环境下的无序目标的精确计数有很高的应用需求,这种计数对其生物量、生物密度管理起到了重要的指导作用。如黑水虻幼虫目标追踪过程中,追踪对象具有高速和非线性的特征,常规算法存在追踪目标速度不足和丢失目标后的再识别困难等问题。针对以上问题,本文提出了一种改进SORT算法,通过改进卡尔曼滤波模型的方式提升目标追踪算法的快速性和准确性,提升了计数的精度。另外,针对黑水虻幼虫目标识别过程中幼虫性状的多样性和混料导致的复杂背景问题,本文通过实验对比多种深度学习网络性能选定YOLO v5s算法提取图像多维度特征,提升了目标识别精度。实验结果表明:在划线计数方面,本文提出的改进SORT算法与原模型相比,平均精度从91.36%提升到95.55%,提升4.19个百分点,通过仿真和实际应用,证明了本文模型的有效性;在目标识别方面,使用YOLO v5s模型在训练集上帧率为156 f/s,mAP@0.5为99.10%,精度为90.11%,召回率为99.22%,综合性能优于其他网络。

基于绘制历史的sort-first集群绘制负载平衡方法

负载平衡对sort-first集群并行绘制系统的性能有很大影响,但静态负载平衡方法适应性较差。而动态方法中,基于几何数据遍历的方法不具备实用性,基于粗粒度几何数据组织的方法在实际中得到应用,但依赖于对几何数据的预处理。为此,提出了一种实用的、基于绘制历史的负载平衡方法:它不依赖于几何数据,用以前帧的绘制时间和负载分布信息作为未来帧绘制负载分配计算的输入。在集群绘制平台上的测试和比较表明,该方法有很好的效果。

基于改进Faster R-CNN和Deep Sort的棉铃跟踪计数

棉铃作为棉花重要的产量与品质器官,单株铃数、铃长、铃宽等相关表型性状一直是棉花育种的重要研究内容。为解决由于叶片遮挡导致传统静态图像检测方法无法获取全部棉铃数量的问题,提出了一种以改进Faster R-CNN、Deep Sort和撞线匹配机制为主要算法框架的棉铃跟踪计数方法,以实现在动态视频输入情况下对盆栽棉花棉铃的数量统计。采用基于特征金字塔的Faster R-CNN目标检测网络,融合导向锚框、Soft NMS等网络优化方法,实现对视频中棉铃目标更精确的定位;使用Deep Sort跟踪器通过卡尔曼滤波和深度特征匹配实现前后帧同一目标的相互关联,并为目标进行ID匹配;针对跟踪过程ID跳变问题设计了掩模撞线机制以实现动态旋转视频棉铃数量统计。试验结果表明:改进Faster R-CNN目标检测结果最优,平均测量精度mAP75和F1值分别为0.97和0.96,较改进前分别提高0.02和0.01;改进Faster R-CNN和Deep Sort跟踪结果最优,多目标跟踪精度为0.91,较Tracktor和Sort算法分别提高0.02和0.15;单株铃数计数结果决定系数、均方误差、平均绝对误差和平均绝对百分比误差分别为0.96、1.19、0.81和5.92%,与人工值具有较高一致性,开发的棉铃跟踪软件可以实现对棉铃的有效跟踪和计数。

基于Quick Sorting的快速分页排序算法

提出了分页排序的概念和基于Quick Sorting的快速分页排序算法(Quick Page Sorting) 以及基于Hint缓存机制的算法实现技术。实验表明,在数万至数百万数据总量情况下,Quick Page Soring的速度比Quick Sorting快10倍左右,大大提高了应用系统的响应速度。

基于VFNet-Improved和Deep Sort的棉花黄萎病病情分级

棉花是全球最重要的经济作物之一,而黄萎病是世界主要棉花生产区的第一大病害,黄萎病病原菌通过感染棉花的根部使叶片萎蔫、褪色以致脱落,导致棉花质量和产量严重下降。国家标准将患黄萎病叶片划分为5个等级,传统检测方法主要依赖人工,存在主观、低效、重复性差等问题,因此提出一种以VFNet-Improved、Deep Sort和撞线匹配机制为主要算法框架的棉花黄萎病病情分级方法,实现在旋转视频输入情况下对患病叶片的数量统计和病情等级的划分。研究首先基于VFNet目标检测网络,融合多尺度训练、动态卷积等优化方法,实现对旋转视频中患病叶片的精准定位;然后采用Deep Sort跟踪器实现前后帧同一叶片的相互关联,并针对跟踪过程ID跳变问题设计了掩膜撞线匹配机制;最后使用OpenCV对经过掩膜线的叶片进行特征提取与患病分级的划分。试验结果表明,VFNet-Improved可以有效改善棉花患病叶片识别精度,mAP75达到0.906,较改进前VFNet模型提升了0.012,帧率FPS为12.9帧/s;Deep Sort跟踪器跟踪效果MOTA为0.835,对患病叶片数量统计结果R2、RMSE、MAE与MAPE分别为0.890、5.138、4.300和14.967%,与人工统计值具有较高一致性。本研究为棉花黄萎病病情精准、高效鉴定提供一种新的科学工具,对棉花抗病品种筛选和遗传机制解析具有重要意义。