A small samples training framework for deep Learning-based automatic information extraction: Case study of construction accident news reports analysis

Knowledge management is crucial for construction safety management. Widely collected and well-organized safety-related documents are recognized to be significant in raising the workers' security awareness and then to prevent hazards and accidents. To improve document processing efficiency, automatic information extraction plays an important role. However, currently, automatic information extraction modeling requires large scale training datasets. It is a big challenge for the engineering industry, especially for the fields which heavily rely on the experts’ knowledge. Limited data sources, and high time and labor costs make it not practical to establish a large-scale dataset. This work proposed a natural language data augmentation-based small samples training framework for automatic information extraction modeling. With the designed cross combination-based text data augmentation algorithm, the deep neural network can be employed to build up automatic information extraction models without large-scale raw data and manual annotations. Characters semantic coding is employed to avoid word segmentation and make sure that the framework can be utilized in different writing language systems. The BiLSTM-CRF model is adopted as the detection core to conduct character classification. Through a case study of two independent accident news report datasets analysis, the proposed framework has been validated. A reliable and robust automatic information extraction model can be established, even though with small samples training.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

基于改进迭代最近点算法的接骨板贴合性快捷计算方法

为了快速计算出接骨板在断骨表面的最佳贴合位置,以减少手术中接骨板反复调整的次数,提出了一种基于改进迭代最近点（ICP）算法的接骨板贴合性快捷计算方法。首先,由医生指导在断骨表面选取贴合区域,并利用接骨板表面点的法向量之间的夹角提取接骨板的内曲面点云;然后,在对两组点云模型进行平滑处理并采用格点采样的方式来简化点云模型后,利用点云之间的特征关系进行初始配准;最后,对接骨板内曲面点云模型进行边界及内部特征关键点提取,并采用K-维树（KD-Tree）搜索邻近点,对接骨板的特征关键点和断骨表面选取区域执行ICP精确配准。以胫骨为例进行实验,实验结果表明,所提算法相较于近年所提配准算法在保持较高配准度的同时提高了配准效率,能够实现胫骨不同受损类型与接骨板之间的快速配准,并且对其他受损骨骼具有通用性。     

基于溶液图像时序特征的元素组分含量动态监测系统

针对稀土萃取过程中组分含量难以实时监测以及现有组分含量检测方法耗时、耗内存的现状,设计了一种基于溶液图像时序特征的元素组分含量动态监测系统。首先使用图像采集装置获取萃取槽体溶液的时序图像,考虑萃取液颜色特性和单一颜色空间的不全面性,采用主成分分析（PCA）方法在HSI和YUV融合的颜色空间提取图像的时序特征,并结合生产指标构造基于鲸鱼优化算法（WOA）的最小二乘支持向量机（LSSVM）分类器来对工况状态进行判断。然后当工况处于非最佳状态时,在HSV颜色空间对图像提取颜色直方图和颜色矩特征,并开发以溶液图像间的混合特征差值的线性加权值为相似度度量的图像检索系统,从而获取组分含量值。最后进行镨/钕萃取槽体混合溶液测试,结果表明该系统能够实现元素组分含量的动态监测。     

非平稳时序循环平稳趋势提取与机械故障诊断

在简单介绍非平稳时序模型周期趋势项提取方法的基础上，提出了机械故障诊断和分析方法，该方法利用旋转机械出现故障以后所呈现的循环平稳特性，使用循环平稳分析方法剔除非平稳时间序列模型中的趋势项，然后建立平稳时序模型，用该模型进行机械故障诊断分析。并运用仿真和实例对方法的正确性进行了验证。     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

一种带有“遮罩”的ASIFT特征提取算法

指出提高ASIFT算法效率的一种新途径:利用“遮罩”屏蔽源自视角模拟的无效像素区域,从而使较为耗时的区域运算仅作用于有效像素区域,以达到提高运算效率的目的.首先,分析无效像素区域产生根源,并定量分析此类区域在各区域运算环节占有的比例,指出增加“遮罩”的必要性,特别指出被处理图像的宽高比越大,则经“遮罩”处理后所获得的加速比越高;随后,给出带有“遮罩”的ASIFT算法结构;然后,给出“遮罩”的一种低存储空间、高访问效率的数据结构,并给出离线获取“遮罩”的一种解析方法;最后,通过实验证实使用“遮罩”能有效提高ASIFT算法中区域运算部分的效率,从而在一定程度上也提高了算法整体的运算效率,但效率提升幅度受到理想区域加速比限制.     

引入迁移学习的癫痫EEG信号自适应识别

在脑电图( EEG)信号识别中,EEG信号的采样环境、病人状态的多样性导致分类器训练所用的源域与分类器测试所用的目标域不匹配,分类器在目标域上表现不佳。为此,引入邻域适应策略,提出一种基于子空间相似度的改进主成分分析特征提取方法( SSM-PCA),在选择主成分时,考虑源域和目标域数据的几何和统计特性,并结合迁移学习分类器大间隔投射迁移支持向量机( LMPROJ),给出以SSM-PCA为基础的LMPROJ分类识别方法。实验结果表明,与结合PCA特征抽取技术和K近邻分类器实现的识别方法相比,该方法在识别正确率方面得到较大提升。     

基于日志分析的增量数据捕获方法研究

通过扫描数据库日志文件可以捕获变化数据,但已有研究仅适应某种特定类型的数据库管理系统并且缺乏对冗余信息的消除。针对上述不足,提出一个基于日志分析的增量数据检测及其净效应处理通用模型,描述增量检测过程中的通用处理步骤,即日志抽取、日志分析和净效应处理3个模块。通过实验对净效应处理速度、冗余数据压缩率、网络传输速度等因素进行分析,结果表明,净效应处理可以有效地减少数据的网络传输时间和更新时间,提高运行效率。     

无人直升机遥感在华北平原地裂缝监测中的应用

本文介绍了无人直升机遥感技术在华北地裂缝(隆尧段)调查中的应用,给出了低空遥感地裂缝信息采集技术框架,并通过从不同角度、不同高度空中摄影方案,展示了部分遥感监测影像。根据华北平原(隆尧段)地裂缝的发育特征、建立遥感解译标志,并对隆尧地裂缝遥感信息进行探索性提取。通过野外验证,监测效果良好。本文研究成果为在平原地区快速、大面积寻找和监测地裂缝提供了可行的低空遥感技术和方法参考。