Research on Vector Road Data Matching Method Based on Deep Learning

Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.

A Fingerprint Minutiae Matching Method Based on Line Segment Vector

Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.

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.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

基于FFT-Matching Pursuit的心电身份识别算法研究

研究了心电信号在身份识别中的应用,提出了基于快速傅里叶变换的匹配追踪心电特征提取算法。在预处理阶段,对心电信号进行消噪、标准化和质量检测等预处理,有效去除了心电信号中的噪声和心率变异所带来的干扰。特征提取阶段,对心电模板进行基于快速傅里叶变换的匹配追踪稀疏分解,原子的时频参数和投影值作为特征参数,然后构建支持向量机分类器,实现了身份识别。最后利用MIT-BIH的ST Change、PTB、QT等数据库测试了算法性能,获得了较高的识别率。

FAST IMAGE ENCODING ALGORITHM BASED ON MEAN-MATCH CORRELATION VECTOR QUANTIZATION

A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3～0.4 dB compared to the full-search VQ.

A new parallel algorithm for image matching based on entropy

Presents a new parallel image matching algorithm based on the concept of entropy feature vector and suitable to SIMD computer, which, in comparison with other algorithms, has the following advantages:(1)The spatial information of an image is appropriately introduced into the definition of image entropy. (2) A large number of multiplication operations are eliminated, thus the algorithm is sped up. (3) The shortcoming of having to do global calculation in the first instance is overcome, and concludes the algorithm has very good locality and is suitable for parallel processing.

A Robust Template Matching Algorithm Based on Reducing Dimensions

Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative for 2-D full search block matching algorithms. Our approach consists of three steps. In the first step the images are converted from 2-D into 1-D by summing up the intensity values of the image in two directions horizontal and vertical. In the second step, the template matching is performed among 1-D vectors using the similarity function sum of square difference. Finally, the decision will be taken based on the value of similarity function. Transformation template image and sub-images in the source image from 2-D grey level information into 1-D information vector reduce the dimensionality of the data and accelerate the computations. Experimental results show that the computational time of the proposed approach is faster and performance is better than three basic template matching methods. Moreover, our approach is robust to detect the target object with changes of illumination in the template also when the Gaussian noise added to the source image.

Smart Clothing Fabric Color Matching with Reference to Popular Colors

Color economy and market fashion trend have an increasing impact on clothing fabric color matching.Therefore,a smart clothing fabric color matching system with reference to popular colors is designed to realize the diversification of clothing color matching,which includes a palette generation module and a clothing fabrics-palette color matching network(CF-PCN).Firstly,palette generation module generates palettes referring popular colors while maintains color styles of clothing fabrics.Secondly,CF-PCN generates color matching images containing color information of palettes.The experimental results show that the color matching system has a higher average pixel ratio of palette colors and contains more palette color information.It demonstrates that the system achieves color matching innovation referring popular colors while retaining color style of clothing brands and provides designers with appropriate color matching solutions.

航空平台地磁矢量匹配导航算法研究进展

航空地磁矢量导航技术因其具有自主、无源、可靠性强的优势,在卫星导航系统受到攻击等情况下可有效发挥替代作用,在军民用领域均具有极高的战略意义和应用价值。航空平台具有飞行速度快、短时间跨越地域广的特性,对地磁矢量测量与导航方法提出高精度和高可靠性等要求。该文梳理近年来航空地磁矢量导航系统的研究与发展现状,介绍地磁矢量导航的关键技术,重点对地磁矢量匹配导航算法的研究进展进行分析。针对现有算法存在的不足,提出进一步提升算法的精度和鲁棒性、发展基于机器学习的地磁矢量匹配导航方法、推动无人机等新型航空平台地磁矢量导航技术发展等后续研究方向,意在促进航空地磁矢量导航技术的进一步发展。

考虑关联波段特性的光谱相似图像分类方法

光谱相似图像分类性能过差会增加光谱信息冗余度,降低地物勘探与军事防御等多种领域的光谱探测效率。为了多元素匀质区分光谱信息与光谱曲线,提出考虑关联波段特性的光谱相似图像分类方法。该方法首先利用光谱匹配消除光谱相似图像白色光源过曝现象。然后提取优化图像的关联波段,并将其作为聚类特征输入支持向量机中。最后根据支持向量机的输出结果,实现光谱相似图像分类。实验结果表明,所提方法分类结果清晰度较高,分类误差或像素块填色错误小,混淆矩阵中同行同列矩形块的分类精度较高。

基于深度学习语义匹配的通用智能问答系统的设计与实现

FAQ问答系统有着广泛的实际应用场景,但传统问答系统的搭建通常需要将相关领域的知识转化为一系列的规则和知识图谱,且构建过程重度依赖人力,换个场景或用户都需要大量的重复劳动。针对上述问题并结合Bert模型和向量搜索引擎Faiss,本文设计了一种基于深度学习语义匹配的FAQ问答系统的解决方案,介绍了系统的工作原理和设计流程,可以方便快速地搭建面向特定领域的问答系统,有效地减少了文本预处理的过程,实现了秒级的查询响应。系统测试发现,可以很好地对用户的查询进行语义匹配,并返回正确的答案,为构建各种问答系统提供了一种通用的解决思路。

高压直流换流阀无源宽频等效电路模型的建模

作为高压直流换流站中的关键设备之一,换流阀的宽频特性是一个颇受人们关注的问题。文中以晶闸管换流阀为对象,建立换流阀的无源宽频等效电路模型。采用矢量匹配法对晶闸管、均压电容器和阻容回路的频率阻抗特性进行有理函数逼近,得到极点—留数形式的有理多项式;利用网络综合原理将有理多项式分解和综合,得到宽频有源电路网络。依据端口无源的条件使用序列二次规划法进行端口无源优化,并使用全局优化算法进行严格无源优化,得到换流阀的无源宽频等效电路模型。

基于BERT字句向量与差异注意力的短文本语义匹配策略

短文本语义匹配是自然语言处理领域中的一个核心问题,可广泛应用于自动问答、搜索引擎等领域。过去的工作大多只考虑文本之间的相似部分,忽略了文本之间的差异部分,从而使模型无法充分利用到决定文本之间是否匹配的关键信息。针对上述问题,提出一种基于BERT字句向量与差异注意力的短文本语义匹配策略,利用BERT对句子对进行向量化表示,使用BiLSTM并引入多头差异注意力机制获取当前字向量与文本全局语义信息之间表征意图差异的注意力权重,结合一维卷积神经网络对句子对的语义特征向量进行降维,最后拼接字句向量并送入全连接层计算出2个句子之间的语义匹配度。通过在LCQMC和BQ Corpus数据集上的实验表明,该策略可以有效提取文本语义差异信息,从而使模型表现出更好的效果。

基于振动信号最优特征提取算法的风力机齿轮箱SVM故障诊断

针对风力机齿轮箱故障诊断的特征提取过程,提出基于振动信号最优特征提取算法的风力机齿轮箱SVM故障诊断方法。首先,分析3种主要特征提取算法各自适应性高的信号类型;然后,根据不同类型信号所具有的信号特性,利用信号分析对传入的振动信号进行特性提取并分类,将不同类别信号与适应性高的特征提取算法进行匹配,实现振动信号的最优特征提取;最后,将匹配算法与支持向量机模型结合实现故障诊断。对实际采集的3种齿轮故障信号进行测试与验证,结果表明该方法可有效进行最优特征提取与算法匹配,相比未经过匹配算法具有更高的故障诊断准确率。

基于匹配滤波域的PSK雷达信号有源干扰识别算法

针对复杂电磁环境下相位编码(Phase Shift Key,PSK)信号干扰识别问题,研究瞄准式干扰(Spot Jamming,SJ)、阻塞式干扰(Blocking Jamming,BJ)、线性扫频式干扰(Linear Sweep Jamming,LSJ)、灵巧噪声卷积干扰(Noise Convolution Jamming,NCJ)、灵巧噪声乘积干扰(Noise Product Jamming,NPJ)、间歇采样直接转发干扰(InterruptedSampling and Direct Repeater Jamming,ISDJ)、间歇采样重复转发干扰(Interrupted-Sampling and Periodic Repeater Jamming,ISPJ)及间歇采样循环转发式干扰(Interrupted-Sampling and Cyclic Repeater Jamming,ISCJ)的匹配滤波序列,人工提取16维特征组成特征向量放入支持向量机(Support Vector Machine,SVM)进行分类识别。仿真实验表明:干噪比(Jamming Noise Ratio,JNR)为-20~30 dB至16~30 dB时,识别率随JNR的增加而提高;JNR为-2~30 dB时,识别率为94.89%;模拟JNR为-2~30 dB的真实环境时,保留8维特征参数,算法识别率可达93.62%,仅降低1.27%。

基于自适应局部邻域条件下的点云匹配

为了应对传统迭代最近点(ICP)算法在处理复杂点云空间特征时,面临噪声干扰和数据缺失等问题导致收敛速度缓慢、配准精度不高以及鲁棒性较差等问题,本文提出了一种基于自适应局部邻域条件下的点云匹配算法。首先,采用体素网格滤波对数据进行预处理,根据不同半径邻域内邻近点的分布情况,定义邻域表面的弯曲程度,在此基础上,充分考虑到法向量分布和邻域曲率特征,从而得到更精确的特征点提取;其次,通过运用最小二乘曲面拟合方法,进一步提取出邻域曲率变化最为显著的特征点,采用快速点特征直方图(FPFH)对特征点进行描述,并通过设定距离阈值的采样一致性算法来匹配相似的特征点对,计算出关键的坐标转换参数,完成初始配准。最后,利用线性最小二乘优化点到面的ICP算法,以实现更精确的配准结果。通过一系列实验对比发现相较于现有的几种配准算法(ICP,SAC-IA+ICP,K4PCS+ICP),在存在噪声干扰和数据缺失的情况下,所提方法的配准准确度平均提高45%,配准速度平均提高38%,充分验证了该方法在应对大数据量、低重叠率点云配准方面具备出色的稳健性能。

电梯电流监测及其启动倒溜车故障诊断方法

针对电梯生产企业、使用单位和监管部门重点关注的电梯运行安全问题,结合电梯运行时经常空载的特点,考虑电梯异常状态实时监测的需求,设计了一种针对电梯主电路电流和曳引电机电流监测系统。首先基于能量阈值法判定电梯启停状态,然后提出一种基于空载启动电流信号的电梯启动倒溜车故障诊断算法,其中针对启动电流时频图,分别基于模板匹配法和支持向量机对电梯启动倒溜车故障进行设别,并通过融合决策的方式提高故障诊断的准确度;最后构建实验装置采集电梯电机电流数据,对提出的电梯启动倒溜车故障诊断算法进行验证。研究结果表明:电梯启动倒溜车故障诊断算法对四种电梯状态的平均识别率达到88.2%,对保障电梯安全运行具有一定的理论和实际意义。

基于多尺度特征融合的自主巡检视频识别摄像头图像配准方法

针对自主巡检视频识别摄像头图像受结构相似性影响导致配准精度低的问题,本文提出基于多尺度特征融合的自主巡检视频识别摄像头图像配准方法。在图像增强的基础上,采用Harris角点检测方法获取自主巡检视频识别摄像头图像结构特征,并基于特征向量构建结果,对图像特征进行多尺度融合。设计图像配准变换估计模型实现自主巡检视频识别摄像头图像配准。结果表明:在特征匹配时,本文所提方法可以有效缩短匹配时间使得融合结果更为精确,具有良好配准效果。

利用无监督预训练的轨迹深度关联

针对时空相似度算法关联轨迹的局限性,采用深度学习方法进行轨迹关联,并提出了一种基于无监督预训练的匹配神经网络训练方式。利用Geohash向量嵌入对轨迹信号做特征工程处理,构建自注意力机制神经网络结构,使用无标注轨迹数据基于遮蔽预测任务进行模型预训练;然后构建孪生匹配网络结构,加载预训练模型参数;最后使用标注轨迹对数据基于均方差损失函数微调预训练模型参数得到轨迹对匹配模型。采用Geolife GPS轨迹数据集作为评估数据集进行模型训练与测试,实验结果显示,利用无监督预训练的轨迹关联方法较现有最优算法匹配准确率提高了5个百分点,达到了96.3%,充分证明了该方法的有效性。目前轨迹关联领域基于深度学习预训练模型的研究较少,该方法具有重要的参考意义。