Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

降雨监测与预报技术在防洪减灾中的应用进展

洪水灾害突发性强,成灾速度快,对人民生命和财产安全造成较大的威胁。降雨作为洪水灾害致灾因子,数据的精确度对防洪减灾具有重要意义。以降雨监测与预报技术为切入点,对雨量站点观测、天气雷达降雨估计及预报、降雨数值预报、卫星遥感反演的现状进行了总结,通过分析时空降尺度方法及多源数据融合技术在降雨监测与预报中的应用,揭示了其在提升降雨数据“量”与“型”准确度方面的效果。研究表明:降雨监测与预报技术在当前取得了显著进展,但在山丘区和城市环境空间的复杂地形方面仍面临分辨率受到限制及精确性、时效性不足的问题。多源数据融合能提高降雨数据精度、时空覆盖能力和预测准确性,优化算法模型、融合“空-天-地”多源数据形成高分辨率预报是未来的研究方向。

面向防洪“四预”的闽江水口洪水预报调度系统建设与应用

为提升闽江水口流域防洪调度水平,建设闽江水口洪水预报调度系统。系统利用开放式的设计理念,采用B/S系统结构开发,融合多源降雨数据,集洪水预报与防洪调度为一体,提升洪水预报精细化水平和水工程联合调度能力。采用多源降雨信息融合技术,集成洪水预报模型库,构建流域洪水预报方案,开发流域智能化运行平台,实现流域多源降雨信息共享应用、洪水滚动预报、智能调度,延长洪水预报预见期,提高预报精度。系统已应用于闽江水口电厂,可提高水电站洪水防御能力和水能利用效益。

Carbon Emission Evaluation in Jinan Western New District based on Multi-source Data Fusion

Carbon emissions caused by human activities are closely related to the process of urbanization,and urban land utilization,function vitality and traffic systems are three important factors that may influence the emission levels.For clarifying the space structure of a low-carbon eco-city,and combining the concept of"Combining Assessment with Construction"to track and contrast the construction of the low-carbon eco-city,this research selects quantifiable low-carbon eco-city spatial characteristics as indicators,and evaluates and analyzes the potential carbon emissions.Taking the Jinan Western New District as an example,diversity of construction land,travel carbon emission potential,and density and accessibility of adjacent road networks in the overall urban planning were measured.After the completion of the new urban area,the evaluation mainly reflected certain factors,such as the mixed degree of urban functions,the density of urban functions,the walking distance to bus stops and the density and number of bus stops.Dividing the levels and adding equal weights after index normalization,the carbon emission potential is evaluated at the two levels of the overall and fragmented areas.The results show that:(1)The low-carbon emission potential areas in the planning scheme basically reached the planned goals.(2)There is inconsistency between districts and indicators in the planning scheme.The diversity of construction land and the accessibility of the adjacent road network are relatively small;however,there is a large difference between the travel carbon emission potential and the road network accessibility.(3)Carbon emission potential after completion did not reach the planned expectation,and the low-carbon emission potential plots were concentrated in the Changqing Old City Area and Central Area of Dangjia Town Area.(4)The carbon emission indicators varied greatly in different areas,and there were serious imbalances in the density of public transportation lines and the mixed degree of urban functions.

A heuristic cabin-type component alignment method based on multi-source data fusion

In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assisted assembly(MAA) and force-driven assembly. In MAA,relative pose between components is directly measured to guide assembly, while in force-driven assembly, only contact state can be recognized according to measured six-dimensional force and torque(6 D F/T) and the process is completed based on preset assembly strategy. Aiming to improve the efficiency of force-driven cabin-type component alignment, this paper proposed a heuristic alignment method based on multi-source data fusion. In this method, measured 6 D F/T, pose data and geometric information of components are fused to calculate the relative pose between components and guide the movement of pose adjustment platform. Among these data types, pose data and measured 6 D F/T are combined as data set. To collect the data sets needed for data fusion, dynamic gravity compensation method and hybrid motion control method are designed. Then the relative pose calculation method is elaborated, which transforms collected data sets into discrete geometric elements and calculates the relative poses based on the geometric information of components.Finally, experiments are conducted in simulation environment and the results show that the proposed alignment method is feasible and effective.

Application Research on Two-Layer Threat Prediction Model Based on Event Graph

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

Design and Realization of Block Level Augmented Reality Three-Dimensional Map

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

卫星降雨数据在高山峡谷地区的代表性与可靠性

以长江上游金沙江流域典型高山峡谷地区为研究对象,用该区域地面观测降雨量数据对TRMM PR 3B42 V6产品进行了3 h、日、月3个时间尺度的有效性评估,旨在为开展区域卫星与地面降水数据融合的流域水文模拟及预报奠定数据基础。分别采用了线性回归方法分析降雨量相关性、经验正交函数-奇异值分解方法(EOF-SVD)分析降雨量主要模态空间分布特征、相对偏差Bias、错报率RFA和探测率PD指标对该卫星产品进行了精度评定。研究结果表明:研究区该卫星产品与地面观测数据在3个时间尺度存在显著的线性时间和空间相关性,但相关程度随时间尺度的减小而减弱;二者在空间分布上总体具有一致性特征,但在高海拔、大坡度区域表现出较为显著的差异;相对偏差指标显示2008—2010年降雨量均值相对偏差在±10%的概率密度百分数为36.08%;随高程的增加,卫星数据RFA呈逐渐增加趋势变化,PD呈逐渐减小趋势变化;总体上小雨对误差的贡献最大,大雨峰值误差贡献次之,时段降雨量偏差随时间尺度的增加逐渐减小,而随高程的增加卫星数据的探测精度下降。因此,对于类似的高山峡谷流域,要应用该卫星产品进行日、3 h尺度水文模拟及预报,有必要对流域卫星数据和地面观测数据进行融合,充分发挥两种数据的优势。

多源降雨数据融合及其水文应用研究进展

降水作为水文循环中最活跃的因子和导致洪水灾害最常见的因素,其数据精度对于洪水预报和防洪减灾具有重要意义。雨量站、雷达和卫星遥感产品是获取降水量资料的3个重要来源。单一来源的降水数据不确定性较大,因此多源降雨数据融合技术得到了广泛的应用。本文总结了降雨资料的来源,介绍了多卫星降水集成以及客观分析法、地质统计学法、最优插值法、尺度递归估计法和贝叶斯模型平均法5种常用的多源降雨数据融合方法。本文还介绍了一些主流的国际和国内的多源降雨融合产品和融合降水数据在水文研究中的应用,最后对未来多源降雨数据融合及其在水文研究中的应用进行了展望。

基于融合资料的天津短时强降水环境物理量可信度及特征分析

针对2009—2017年6—9月天津地区140次短时强降水天气过程,将NCEP FNL(1°×1°)全球分析资料与地面气象观测数据融合,计算天津地区短时强降水的环境物理量参数,通过偏差和偏差区间占有率等分析融合环境物理量的可信度,并在大量样本统计基础上给出不同月份的短时强降水环境参量特征和指标。结果表明:(1)基于NCEP FNL分析资料与地面气象观测数据融合的环境物理量在短时强降水潜势判断中具有较高的可信度,融合CAPE、LI、LCL平均绝对误差分别为260.7 J/kg、0.9℃、14 hPa,与融合前的NCEP FNL物理量相比绝对误差分别降低了58.1%、48.0%、49.0%。(2)不同月份短时强降水发生所必需的水汽、热力和能量等环境条件差异显著,TPW、K、LI、CAPE、LCL和Z0均呈现明显的月变化特征。(3)若以75%短时强降水发生的环境条件作为预报指标,7—8月TPW、K、CAPE、Z0、LCL物理量阈值极为相近,短时强降水多发生在TPW>45 kg/m2、K>32℃、CAPE>835 J/kg、LCL>882 hPa、Z0>4300 m条件下,6月物理量指标要求明显降低,如TPW>34 kg/m2、K>30℃、CAPE>353 J/kg、LCL>880 hPa、Z0>3900 m,9月预报指标要求则最低。

青海高原气象地质灾害特征及致灾雨量研究

基于2008~2019年青海地质灾害的灾情记录和CLDAS融合数据,分析青海高原滑坡、泥石流和崩塌等气象地质灾害的时空分布,研究诱发气象地质灾害的降水量和土壤湿度变化特征,确定了灾害预警条件。结果表明:近12a青海高原气象地质灾害共发生了23次,灾害易发区在西宁市、海南州、黄南州和玉树州,7月是气象地质灾害发生次数最多的月份。有效降水量和土壤湿度是气象地质灾害的重要影响因子,灾害预警条件为:当天及前一天有效降水量之和达到10mm或持续有效降水量达到18mm,并同时满足0~10cm和10~40cm的土壤体积含水量差值≤0.03mm^(3)/mm^(3)或其中一个深度的土壤体积含水量≥0.27mm ^(3)/mm^(3)。

利用数据融合算法提高雷达面雨量监测精度

雷达面雨量自动监测系统是国内水文行业近年发展起来的一种新型雨量监测系统,通过对雷达面雨量自动监测系统监测数据与遥测雨量站监测数据进行对比评估分析,发觉二者之间整体相关性较好,但在具体数据精度上存在一定差异。结合已建遥测雨量站与人工观测的雨量数据,利用数据融合算法完善雷达面雨量系统的估值算法,经验证使用距离权重法可在一定程度上提高雷达监测雨量数据的精度。

基于灰色关联法的月降雨量预测

【目的】提高降雨量预测精度,为农业、水利等相关部门提供决策依据。【方法】鉴于月降雨量时间序列具有显著的多尺度特征,开展了数据驱动下基于模型融合的月降雨量预测研究,应用灰色EGM(1,1)模型和自适应模糊神经网络系统(ANFIS)分别预测了年尺度与月尺度下的月降雨量,采用灰色关联法将2个预测结果进行数据融合。利用澳大利亚维多利亚8个站点降雨数据验证所提出方法,并将预测结果进行了与单一灰色EGM(1,1)、ANFIS、人工神经网络(ANN)、自回归积分滑动平均模型(ARIMA)与聚类回归法(CLR)模型预测结果对比。【结果】模型融合预测结果精度高于单一EGM(1,1)、ANFIS、ANN及ARIMA模型预测结果,并在8个站点中的5个取得了最佳预测效果,其中中部地区(Ballarat和Cape Otway站点)及东部地区(Dookie,Wangaratta和Orbost站)预测均方根误差为28.2～37.2 mm,西部地区(Dimboola,Edenhope和Dunkeld站点)预测均方根误差为20.8～23.4 mm。【结论】所提出的模型融合预测法可行,为月降雨量预测提供了新思路。

基于改进的湿润地区站点与卫星降雨数据融合的洪水预报精度分析

目前卫星降雨数据是水文研究中重要的数据来源,但其精度尚未达到洪水预报的要求。将TRMM3B42RT、TRMM 3B42、GPM IMERG Early及GPM IMERG Late分别与站点实测降雨数据融合,并利用HEC-HMS模型进行洪水模拟,从而改进卫星降雨洪水预报精度。结果表明,卫星降雨数据在经过与实测降雨数据的融合处理后,误差明显降低,相关系数由0.7提高到0.9以上,进而提高了模拟洪水的精度,在洪水预报方面有一定的可行性。

基于多传感器数据融合分析的路堑滑坡模型试验研究

以成都天府国际机场高速公路TJ04标段K17+700~K17+900段滑坡为典型代表,通过地质分析及室内模型试验相结合的方法,运用数据融合及统计学概率运算方法,对降雨作用下路堑边坡的变形破坏过程进行研究。结果表明:(1)通过地质分析认为K17+700~K17+900段滑坡形成主要原因是地表水入渗及滑坡前缘路基开挖;(2)通过模型试验研究已开挖路基在降雨条件下的变形特征,得出其变形机理为:降雨入渗→边坡局部溜坍→地表水入渗加剧→土体含水率上升→孔隙水压力增大→边坡失稳→整体牵引式滑移;(3)通过传感器数据融合及统计学概率分析得出,随着降雨历时增长,含水率的变化曲线与累计降雨量之间呈正相关,相关系数为0.600 15具有显著相关性,孔隙水压力的变化曲线与含水率之间正相关,相关系数为0.921 74具有高度相关性,说明雨水入渗是土体含水率与孔隙水压力增长的直接原因。

Generation of daily snow depth from multi-source satellite images and in situ observations

Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.

大渡河流域逐日降雨数据融合的误差分析

为有效结合不同来源降雨资料的优势,发展多源降雨融合技术,采用数据融合方法,生成融合逐日降雨数据(以下称为Grid数据),再与气象观测站点资料、ERA5和NCEP2格点资料进行误差对比,探讨Grid数据在大渡河流域的适用性。结果表明:(1)Grid数据基本能完整展示大渡河流域的日均降雨分布,总体的、各区域内的降雨分布与观测数据展示出的分布规律基本吻合;(2)从插值站点来看,Grid数据的降雨量量级与观测数据差距小,总体的、各区域内的最大降雨量和最小降雨量与观测数据接近,表现优于ERA5和NCEP2数据;(3)从标准差、均方根误差来看,Grid数据的误差要明显小于EAR5和NCEP2数据;(4)从2008-2018年全流域、1~4分区的面雨量时间序列来看,Grid数据与观测数据较ERA5和NCEP2数据更接近。综上,Grid融合数据能较好地反映大渡河流域实际降雨情况。