A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Left Ventricular Systolic Intraventricular Flow Field Assessment in Hyperthyroidism Patients Using Vector Flow Mapping

Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological condition. This study examined use of new VFM and flow field for assessment of left ventricular （LV） systolic hemodynamics in patients with simple hyperthyroidism （HT）. Thirty-seven simple HT patients were enrolled as HT group, and 38 gender- and age-matched healthy volunteers as control group. VFM model was used to analyze LV flow field at LV apical long-axis view. The follow- ing flow parameters were measured, including peak systolic velocity （Vs）, peak systolic flow （Fs）, total systolic negative flow （SQ） in LV basal, middle and apical level, velocity gradient from the apex to the aortic valve （AV）, and velocity according to half distance （V1/2）. The velocity vector in the LV cavity, stream line and vortex distribution in the two groups were observed. The results showed that there were no significant differences in the conventional parameters such as left ventricular ejection fraction （LVEF）, left ventricular end-diastolic diameter （LVEDD） and left atrium diameter （LAD） between HT group and control group （P〉0.05）. Compared with the control group, a brighter flow and more vortexes were detected in HT group. Non-uniform distribution occurred in the LV flow field, and the stream lines were discontinuous in HT group. The values of Vs and Fs in three levels, SQ in middle and basal levels, AV and V1/2 were higher in HT group than in control group （P〈0.01）. AV was positively correlated with serum free thyroxin （FT4） （r=0.48, P〈0.01）. Stepwise multiple regression analysis showed that LVEDD, FT4, and body surface area （BSA） were the influence factors of △V. The unstable left ventricular sys- tolic hydrodynamics increased in a compensatory manner in simple PIT patients. The present study in- dicated that VFM may be used for early detection of abnormal ventricle contraction in clinical settings.

LIC color texture enhancement algorithm for ocean vector field data based on HSV color mapping and cumulative distribution function

Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image,a new color texture enhancement algorithm based on the Line Integral Convolution(LIC)for the vector field data is proposed,which combines the HSV color mapping and cumulative distribution function calculation of vector field data.This algorithm can be summarized as follows:firstly,the vector field data is convoluted twice by line integration to get the gray texture image.Secondly,the method of mapping vector data to each component of the HSV color space is established.And then,the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image.Thirdly,the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values.Finally,both the gray texture image and the color image are fused to get the color texture.The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data.Furthermore,the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.

Benson proper efficiency for vector optimization of generalized subconvexlike set-valued maps in ordered linear spaces

Several equivalent statements of generalized subconvexlike set-valued map are established in ordered linear spaces. Using vector closure, we introduce Benson proper efficient solution of vector optimization problem. Under the assumption of generalized subconvexlikeness, scalarization, multiplier and saddle point theorems are obtained in the sense of Benson proper efficiency.

ε-strongly Efficient Solutions for Vector Optimization with Set-valued Maps

In locally convex Hausdorff topological vector spaces,ε-strongly efficient solutions for vector optimization with set-valued maps are discussed.Firstly,ε-strongly efficient point of set is introduced.Secondly,under the nearly cone-subconvexlike set-valued maps,the theorem of scalarization for vector optimization is obtained.Finally,optimality conditions of ε-strongly efficient solutions for vector optimization with generalized inequality constraints and equality constraints are obtained.

Wavelet neural network based watermarking technology of 2D vector maps

A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.

基于ISOMAP-DE-SVM的Cz单晶硅等径阶段掉苞预测

针对目测法无法及时发现直拉单晶硅在等径生长阶段发生的掉苞问题,本文提出一种基于ISOMAP-DE-SVM的掉苞预测模型,可以在掉苞现象发生之前发出警告。首先剔除方差较小的参数,采用斯皮尔曼相关系数法剔除冗余参数,采用最大互信息法检验剩余参数的非线性相关性;然后将关键参数的均值和标准差作为等度量映射和多维放缩的输入,得到两份样本数据;最后将这两份样本数据分别输入到经过差分算法、遗传算法优化的支持向量机预测模型,得到4份预测结果。预测结果表明:基于ISOMAP-DE-SVM的预测模型具有收敛速度快、准确度高的特点,平均预测准确率可以达到96%;同时,所使用的方法揭示了单晶硅等径阶段的数据具有非线性特点。通过实际应用验证表明模型具有一定的工程实用价值。

Information hiding scheme for vector maps based on fingerprint certification

An information hiding scheme for vector maps is presented to identify the source after the vector map is leaked in some key application areas. In this scheme, the fingerprint image of the map owner can be converted into a character string as the watermark, and then the watermark will be embedded into the coordinate descriptions of the attribute file by the "0-bit value" programming method. This programming algorithm ensures that the accuracy is lossless and the graphics is unchanged for any vector map. Experiments show that the presented hiding scheme has stable robustness, the average similarity rate is 97.2% for fingerprints matching and the false non-match rate is 1.38% in the blocking test. In the opening test, the former reaches 84.46% and the latter reaches 5.56%.

On the basis of the morphology of the T-wave alternans: A Poincare mapping method research

Presently T-wave alternans (TWA) has become a clinical index of non-invasive diagnosis for heart sudden death prediction, and detecting T-wave alternate accurately is particularly important. This paper introduces an algorithm for detecting TWA using Poincare mapping method which is a technique for nonlinear dynamic systems to display periodic behavior. Sample series of beat to beat cycles were selected to prepare Poincare mapping method. Vector Angle Index (VAI), which is the mean of the difference between θi (the angle between the line connecting the i point to the origin and the X axis) and 45 degrees was used to present the presence or absence of TWA. The value of 0.9 rad ≤ VAI ≤ 1.03 rad is accepted as a level determinative for presence of TWA. VAI via Poincare mapping method (PM) is used for correlation analysis with T-wave alternans voltage (Vtwa) by way of the spectral method (SM). The cross-correlation coefficient between Vtwa and VAI is γ = 0.8601. The algorithm can identify the absence and presence of TWA accurately and provide idea for further study of TWA-PM.

Traditional Knowledge System Based GIS Mapping of Antimalarial Plants: Spatial Distribution Analysis

The native communities have been using their unique traditional knowledge system (TKS), culture, indigenous skills and expertise since the ancient times. India has witnessed its legacy from the time of Charaka & Susruta for TKS of medicinal plants. The objective of the study is to carry out inter-disciplinary work by integrating ethno-medicinal findings with Geographical Information System (GIS) tools to develop spatio-temporal maps covering antimalarial plants prevalent in three rural districts of Eastern Uttar Pradesh (UP), India. Two sources Flora Gorakhpurensis & Flora of Upper Gangetic Plains have been considered to evaluate all possible antimalarials prevalent in the study region and are cross validated with research papers and journals. GPS coordinates were recorded for marked locations and under GIS environment maps of antimalarials are generated to highlight geographical distribution of such plants. Further, these are analysed with respect to various natural plant habitats.?48 plants belonging to 25 families were found and its geographical distribution is illustrated through series of GIS maps. The developed map highlights the geographical location of antimalarial plants and facilitates easy access of plant’s natural habitat. It is believed that the work would help researchers to find out the novel antimalarials towards open source drug discovery projects.

基于S-ISOMAP和DAG-SVM的齿轮声学故障诊断方法

齿轮故障声信号特征数据集具有高维和非线性特性,等距映射(ISOMAP)降维算法通过构造距离矩阵来测量样本点之间的测地距离,具有处理复杂非线性数据的能力,但其本身是一种无监督算法,不能有效利用样本点间的标签信息。文中设计了一种结合有监督等距映射(S-ISOMAP)算法和有向无环图支持向量机(DAG-SVM)的故障诊断方法,主要包括特征提取、降维和模式识别三个部分。利用梅尔频率倒谱系数(MFCC)提取齿轮故障声信号的特征信息,建立高维特征数据集,在计算欧式距离时引入调节因子,构建有监督的S-ISOMAP降维算法对高维MFCC特征数据集进行降维。引入有向无环图,构建DAG-SVM分类器实现多分类。实验结果表明,该方法能有效准确的识别出旋转机齿轮的故障状态,识别准确率达到94.67%,S-ISOMAP相较ISOAMP、局部线性嵌入的降维效果更好,分类识别准确率更高。

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era.However,medical data is susceptible to leaks and hacks during transmission.This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage,as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms.First,a pre-trained GoogLeNet network is used in this paper,based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset,so that the pre-trained network can further learn the deep convolutional features in the medical dataset,and then the trained network is used to extract the stable feature vectors of medical images.Then,a two-dimensional Henon chaos encryption technique,which is more sensitive to initial values,is used to encrypt multiple different types of watermarked private information.Finally,the feature vector of the image is logically operated with the encrypted multiple watermark information,and the obtained key is stored in a third party,thus achieving zero watermark embedding and blind extraction.The experimental results confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

基于MapGIS的林业地理信息栅格图像数据矢量化方法

在林业工作中,进行制图、评价和决策分析时,矢量数据是必不可少的数据源,但有时获得矢量数据必须从纸质介质上获得。通过扫描纸质图件,获得软件可以识别、处理的栅格图像数据,使用MapGIS矢量化功能结合使用ArcGIS软件进行数据处理,可以方便、快捷和准确地获得矢量化数据。文章对这一过程进行了分析和论述。

基于Isomap-MPA-LSSVM软测量模型的PVC聚合过程转化率预测

针对聚氯乙烯(PVC)聚合过程转化率难以实时获取,提出了一种基于Isomap-MPA-LSSVM软测量模型预测氯乙烯单体(VCM)转化率的方法。首先,为消除数据间共线性,采用等度量映射(Isomap)算法对原始数据特征进行提取,得到模型的输入变量。其次,用海洋捕食者算法(MPA)优化最小二乘支持向量机(LSSVM)的参数,并建立模型。最后,进行仿真实验。结果表明,软测量模型相较于MPA-LSSVM与LSSVM模型,预测的平均绝对误差分别降低了43.03%和59.92%,均方根误差分别降低了30.27%和52.24%,显著提升了VCM转化率的预测精度。所提模型能精确预测VCM转化率,对提高PVC的产品质量具有重要参考意义。

一种曲线数据压缩的自编码器神经网络方法

矢量空间数据的压缩是减少存储空间及节省网络传输带宽最直接有效的途径。本文利用自编码器神经网络构建了一种面向矢量曲线数据的压缩和解压模型。该模型针对不同规模和复杂度的曲线数据,首先进行分段和重采样处理进而规范化输入向量,然后通过优化自编码器结构和调整损失函数实现了高精度的压缩解码模块,最后通过弧段数据的坐标闭合差分配确保了数据的稳定性。对山西、湖南、江西3省的1∶100万县级弧段进行了压缩和还原试验,分析发现:数据还原精度随压缩率变小而变小,当压缩率小于35%后,数据还原精度呈波动趋势,25%为满足精度要求的适宜压缩率。比较傅里叶级数及贝塞尔曲线拟合方法发现本文模型压缩精度和处理速度在一定的压缩率范围内存在优势,同时该模型说明了深度学习在提取空间要素几何特征方面有一定的潜力。

面向地质图的知识图谱构建及智能问答应用

海量地质图件蕴含着丰富的地学基础知识及专家经验知识。地质图主要表达了通过区域地质调查、矿产地质调查所获取的地球表面的地质知识(如地层单元、岩体、断裂等)。如何快速地从矢量地质图件中抽取地质知识并形成知识服务是目前地学知识图谱及知识服务研究的前沿。由于传统的地质图知识抽取主要依赖人工方式进行综合分析,本文聚焦于矢量地质图件知识表达与抽取研究,提出了一种地质图知识表达框架,提取地质图中所包含的地质实体及关系,将地质图信息以知识图谱的形式表达,并开展了基于地质矢量知识图谱的智能问答应用。最后以江西省于都县银坑幅矢量数据集为例开展实验验证分析,结果表明,本文方法能够较为全面地获取地质图中各个地质对象的信息,提高了地质图语义表达的效果,同时也可以提高地质学习人员对地质图的理解和认识,让计算机能够大规模获取地质图的知识内容。

MapGene2Chrom基于Perl和SVG语言绘制基因物理图谱

遗传图谱表现形式简洁明了,为分析遗传规律、克隆基因提供了便利。Gbrowse、MapViewer等工具虽然能够协助研究人员绘制相似形式的物理图谱,但有很大的局限性:(1)数据需提前布置好;(2)输出结果无法灵活修改。鉴于此,文章基于Perl和SVG语言,开发了一款生物辅助作图软件MapGene2Chrom的本地版与网页版,该软件能够依据输入数据快速绘制相应的物理图谱。该软件输入数据格式简单,输出结果易于修改,图片格式为SVG矢量图,具有很好的移植性,以期为研究人员绘制物理图谱提供便利。

基于MapGIS的地质图制作方法探究

MapGIS是集图形、图像、地质、地理、遥感、人工智能、计算机科学为一体的智能软件系统。运用MapGIS软件,能够方便地绘制各种地质图、地形图,提高制图效率,缩短制图时间。以徐州市基岩地质图为例,对在MapGIS中制作地质图的过程和方法进行了探讨和分析,提出了一些方法及原则。

顾及多级空间索引结构的矢量地图局部加密方法

随着地理信息产业的蓬勃发展,地图与各类生活服务深度融合,地图从专业化应用逐步走向大众化应用。大数据时代的到来,为地图数据的分发、共享、获取与动态更新等提供了极大便利,但伴随而来的是地图数据传输过程中面临的信息泄露、窃取等安全隐患问题也日益严峻。现有的矢量地图加密方法大多是在经典密码学算法基础上扩展而来,虽然已有加密方法考虑到矢量地图的特征,但是现有方法大多仍以整个地图或图层为加密单元,在用户需要访问某个局部的感兴趣区域时需要对整个地图文件或图层进行解密,对于矢量地图的按需局部加密仍未提出有效方案,还难以满足大数据时代地图按需用图、众包地图数据采集与云端更新等应用场景对矢量地图局部加密的应用需求。为此,本文从矢量地图的数据结构特点出发,提出了一种顾及多级空间索引的矢量地图局部加密方法。通过点、线、面不同矢量地图数据的加密试验分析,结果表明:①本文方法可以有效实现矢量地图的局部加解密;②相比于现有加密方法,本文方法采用局部加密策略,且支持多级分块并行加密,具有更高的加密效率;③本文针对不同加密单元采用不同加密参数,显著提高了加密算法的抗攻击能力。