Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world. The estimates, analysis and predictions are based on the data gathered from Johns Hopkins Center during the time span of April 21 to June 27, 2020. We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents. The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified. The parameters of Gaussian distribution, i.e., maximum time and width, are determined through a statistical χ2-fit for the purpose of doubling times after April 21, 2020. For COVID-19 detection, we proposed a novel method based on the Histogram of Oriented Gradients (HOG) and CNN in multi-class classification scenario i.e., Normal, COVID-19, viral pneumonia etc. Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19.     

周期性时间序列数据聚类算法的改进研究

聚类分析是时间数据序列分析的一种常用手段,现有的聚类算法通常从相似性度量方面进行改进.实际的时间序列数据往往具有一定的周期性和连续性,现有的算法往往忽略时间序列数据周期性和连续性特点对聚类算法的影响.对此问题进行了研究,尝试采用延拓的方法来解决该问题,从而改善聚类的效果.初步的实验结果表明了该方法的可行性和有效性.     

传感器故障检测、分离与恢复的神经网络方法

传感器是测控系统不可缺少的部件,传感器数据的高可靠性是系统正常工作的重要保证,本文基于递归神经网络具有优良的动态系统建模能力和时间数据序列预报区能力,融合时空信息,构造出具有传感器故障检测,分离和故障恢复能力的智能传感器系统,理论 仿真结果表明,所研究系统的优良性能。     

鸟类卫星跟踪数据可视分析方法研究—以朱鹮为例

目的 鸟类跟踪技术的成熟发展使得鸟类专家可以轻松获得大量鸟类运动数据。然而，数据规模的增加使得传统方法难以有效完成数据检索和分析。研究如何辅助专家有效地分析这些数据，挖掘其中的有用信息，具有很强的实用价值。本文基于国家Ⅰ级重点保护物种朱鹮的卫星跟踪数据，从鸟类专家对数据分析的需求出发，提出了一种运动轨迹的可视分析方法。方法 基于二维地图进行多视图协同展示的交互布局方式，以及聚类分析方法等对朱鹮运动轨迹进行可视分析，挖掘朱鹮的生活状态和习性。在以上工作的基础上，设计实现了一个朱鹮运动轨迹可视分析系统。结果 本文提出的可视分析方法，允许用户从时空维度和时期（繁殖期、游荡期、越冬期）、状态（夜宿、觅食）等具有生态学意义的维度观察朱鹮运动轨迹，对运动数据进行统计分析，了解朱鹮运动行为。与现有朱鹮数据分析方法相比，本文提出的可视分析方法能够同时从多个不同维度对运动数据进行分析，针对朱鹮的生活状态和生活习性进行更深入的分析挖掘。结论 案例分析表明，基于本文提出的方法，鸟类专家可以从多个角度对朱鹮运动轨迹数据进行综合分析，达到对鸟类习性和状态进行研究挖掘的目的，并能够为其他鸟类跟踪数据分析工作提供思路和方法。     

Nervous habitat patches: The effect of hydropeaking on habitat dynamics

Alteration in the river flow regime due to intermittent hydropower production (i.e., hydropeaking) leads to biodiversity loss and ecosystem degradation worldwide. Due to the increasing shear of volatile green energy (i.e., wind and solar), hydropeaking frequency is deemed to increase in the coming decades. However, our mechanistic understanding of how the frequency of repeated hydropeaking (i.e., series of multiple events) affects ecological processes is still limited. Here, we reflect on the impacts of altered flow frequency and relative duration on the persistency of aquatic habitats. We focus on the habitats at patch-scale, being this the scale representing what organisms perceive when interacting with their environment. With a showcase we explore a temporally explicit approach to quantify altered habitat dynamics at patch-scale due to hydropeaking. We then review how changes in habitat dynamics and persistency may affect ecological processes. Our findings suggest that (i) a time-series approach allows to account for the inherent multi-event nature of hydropeaking; (ii) hydropeaking can increase the dynamics of single habitat patches by at least one order of magnitude if compared to unregulated rivers; (iii) altered habitat dynamics at the patch scale can affect the survival of more sessile species and life cycle stages (e.g., invertebrates) or the energy budget of mobile species and life cycle stages (e.g., adult fish). However, the ecological significance and potential environmental thresholds of patch-scale dynamics and persistency are still poorly investigated and need further attention. Moreover, methods for the aggregation of habitat dynamics and persistency from the patch to the reach-scale are not available yet.     

基于ATD-GC-MS技术检测铁观音做青过程环境挥发性成分的动态变化

采用全自动热脱-气相色谱-质谱法捕集和检测铁观音做青关键工艺节点的环境挥发性成分，应用短时间序列表达挖掘器（short time-series expression miner,STEM）和偏最小二乘法判别分析（partial least squaresdiscriminantanalysis,PLS-DA）对铁观音做青过程中挥发性成分进行分析。结果显示，铁观音做青环境中共鉴定出122种挥发性成分，包括醇类、酯类、烯烃类、烷烃类、醛类、芳香烃类、酮类和其他化合物8大类，其中酯类是做青环境中的主要挥发物；STEM结果表明，检测出的挥发性成分可划分成19种变化趋势，且在2种趋势模型中有显著性富集，其中仲丁醇、乙酸乙酯、异戊醛、异戊醇、正己醇、2-庚醇、己酸甲酯、甲基庚烯酮、异丁酸辛酯等挥发性成分带有特殊香气并随着做青次数的增加而显著增加；PLS-DA结果表明，摇青和晾青两部分可有效区分开，并鉴定出7种共同特征挥发性成分（变异权重参数值>1）:(Z)-乙酸-4-己烯-1-酯、反-3-己烯基丁酯、叶醇、3-己烯醛、异戊腈、(3E)-4,8-二甲基壬-1,3,7-三烯、罗勒烯异构体混合物。...     

Elucidation of mosaic patterns in gravel riverbeds using classifying flow velocity regimes obtained from a planar two-dimensional analysis

Gravel riverbeds in the middle reaches of Japanese rivers are essential habitats for various plants and animals. Disturbance from flooding is necessary for the formation of gravel riverbeds, but human control of rivers, such as dams and channelization, has altered flow and sediment regimes, thereby reducing disturbance. The flooding generates a mosaic pattern characterized by varying frequencies and intensities of disturbance in gravel riverbeds. Understanding the disturbance regimes that form mosaic patterns is important for the conservation of biodiversity in rivers. In this study, we proposed a method to extract mosaic patterns from flow velocity regimes obtained by planar two-dimensional analysis by classifying them with time-series clustering. Based on the distribution of Anaphalis margaritacea var. yedoensis on gravel riverbanks, we compared several past flooding events to identify mosaic patterns that are important for A. margaritacea var. yedoensis. The study site is the Echi River, which flows through Shiga Prefecture in Japan and into Lake Biwa. Using a unmanned aerial vehicle (UAV), orthomosaic images with an average ground resolution of 3.3 mm/pixel were created, and colony polygons of A. margaritacea var. yedoensis were created using image detection and visual correction. Hydraulic analysis was performed using iRIC ver2.3 (Nays2DH ver1.0). Time-series clustering was used to classify the flow velocity regimes for each computed mesh into 30 clusters. The relationship between the clusters of each flooding event and the distribution of A. margaritacea var. yedoensis was evaluated. Mosaic patterns were created by classifying the flow velocity regimes of each computational mesh calculated by planar 2D analysis into clusters using time-series clustering. After analyzing the relationship between each cluster and the area of distribution of A. margaritacea var. yedoensis, the first flooding event was determined to be the mosaic pattern that best explained the distribution of A. margaritacea var. yedoensis. Cluster 1, the “low peak, short duration type,” was considered the growth center of A. margaritacea var. yedoensis. The method used in this study is an innovative approach for obtaining mosaic patterns that quantifies these five elements of the disturbance regime.     

金刚石磨粒变切深划擦无氧铜的声发射及其时间序列建模

试验研究了单颗金刚石磨粒以不同切深划擦无氧铜的声发射信号特征,对不同切深下的声发射信号进行平稳化,确定合适的时间序列模型阶次和模型识别,建立了金刚石划擦无氧铜的声发射时间序列自回归(Auto Regressive,AR)模型。研究表明:随着切深的增加,声发射特征参数和最大振幅随之增大,AR模型的各特征向量与切深之间具有较好的线性关系,合理的AR模型可较好地表征单颗金刚石磨粒划擦无氧铜的声发射信号特征,并可以实时分析金刚石磨粒的划擦深度。