The application of digital image recognition to the analysis of two-dimensional fingerprints

High-performance liquid chromatography (HPLC) fingerprint has been commonly used in the quality control and assessment of herbal medicines, and two-dimensional (2D) fingerprint obtained by means of HPLC-diode array detector (HPLC/DAD) can provide higher reliability. In this paper, an approach to the analysis of the 2D HPLC/DAD fingerprints, which was based on digital image recognition techniques, was developed for the first time. First, wavelet transform was employed to eliminate noise signal in the 2D fingerprint, and then the 2D fingerprint was converted into grayscale image. Second, several features of the image were calculated, and hierarchical clustering. This approach was applied to the qualitative analysis of the different samples of coptis chinensis, and the clustering result of samples was all highly consistent with the real situation. Based on the densities in grayscale image, three components in standard samples were quantitative analyzed, and the obtained correlation coefficients between concentration and grayscale density were more than 0.999. Our study indicated that the analysis of the 2D HPLC/DAD fingerprint was successful based on the idea and techniques of digital image recognition techniques, and this proposed approach provided a new pathway for the analysis of two-dimensional spectrums.     

基于连续模型的人员疏散仿真分析

行人流连续模型直观地反映人群疏散过程中的疏散特征,本文基于行人流连续模型。研究行人在典型疏散场景下的疏散特征．在COMSOL中建立行人流连续模型及其方程,通过编写MATLAB代码,实现了连续模型及其循环求解框架．利用快速扫描法求解Eikonal方程得到背景场值,在每一步迭代循环中将背景场值作为模型的初始变量导入,调用COMSOL计算模块求解模型的瞬态控制方程．通过两个标准算例,重现了典型的行人流自组织现象,验证了连续模型的合理性．结果表明,本文的疏散仿真分析模型和计算程序是可靠的,疏散仿真分析可以为实际工程中的人员疏散方案的制定以及平面设计与安全布置等方面提供技术支撑．     

底板岩层水力学特性原位测试研究

本文介绍了井下煤层底板岩层水力学特性（含单位涌水量、水压、渗透系数、裂隙导高带等）的钻孔原位测试方法,以太原市东山煤矿为例,详细给出了煤层底板石炭纪太原组和本溪组岩层的含水性态（水压、单位涌水量、渗透系数）及其奥灰水裂隙导高带等测试方法和结果,对承压水上采煤问题具有重要的参考价值。     

Learning Visible Thermal Person Re-Identification via Spatial Dependence and Dual-Constraint Loss

Visible thermal person re-identification (VT Re-ID) is the task of matching pedestrian images collected by thermal and visible light cameras. The two main challenges presented by VT Re-ID are the intra-class variation between pedestrian images and the cross-modality difference between visible and thermal images. Existing works have principally focused on local representation through cross-modality feature distribution, but ignore the internal connection of the local features of pedestrian body parts. Therefore, this paper proposes a dual-path attention network model to establish the spatial dependency relationship between the local features of the pedestrian feature map and to effectively enhance the feature extraction. Meanwhile, we propose cross-modality dual-constraint loss, which adds the center and boundary constraints for each class distribution in the embedding space to promote compactness within the class and enhance the separability between classes. Our experimental results show that our proposed approach has advantages over the state-of-the-art methods on the two public datasets SYSU-MM01 and RegDB. The result for the SYSU-MM01 is Rank-1/mAP 57.74%/54.35%, and the result for the RegDB is Rank-1/mAP 76.07%/69.43%.     

基于图像特征检测和匹配的无人船航行姿态角提取方法

计算机视觉的飞速发展, 使得采用视觉技术辅助无人船航行成为可能. 在无人船巡航过程中, 获取船体航向是航行控制的必备基础. 特征匹配是无人船相关视觉技术中的重要部分, 是目标识别和定位等功能的关键步骤. 获取无人船运动姿态的基本步骤是对图像前后帧进行有效的特征提取和匹配. 针对水域环境中的图像静态特征提取速度慢、精度低的问题, 本文提出一种图像匹配方法以求取无人船的航行姿态角. 首先对图像预处理, 并对有效区域进行特征提取. 其次, 设计一种基于描述子相似度的初始特征匹配策略. 再其次, 筛选特征匹配对, 优化模型参数. 最后, 通过前后帧旋转矩阵计算航行姿态角. 实验表明, 该方法能有效提取无人船的航行姿态角.     

基于彩色结构光的自由曲面三维重建方法

彩色结构光三维重建过程中,系统的非线性耦合以及待测曲面的拓扑结构等均会对结构光解码产生影响,从而导致调制条纹漏检和颜色码误识别.为解决这一问题,提出一种基于彩色编码结构光的三维重建方法.利用YUV颜色通道对调制条纹进行滤波差分投影处理,通过调制条纹的波形分布提取中心特征线;利用颜色聚类方法精确获取调制条纹的颜色码信息值...     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

基于小波特征的M型星自动识别方法

M型星对研究恒星的演化具有重要价值.在我国正在实施的大型巡天项目(LAMOST项目)中,急需M型星的自动识别系统.本文给出了一种自动识别M型星的新方法,该方法由以下主要步骤组成:①选取一定波长范围的光谱进行5层小波变换,从第5层小波系数中提取出小波特征;②利用小波特征检测M型星特征频率和吸收带位置;③根据特征频率和吸收带位置的检测结果进行M型星识别.大量真实光谱数据实验表明,本文方法十分有效,识别率高达97.56%.     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.