基于启发式规则优化的网页元素提取方法

网页信息提取方法中的启发式规则,是识别网页标签信息、利用网页节点分析结果、针对网页不同内容、完成信息提取的重要手段。本研究在对现有启发式规则进行研究分析的基础上,提出了几种优化的启发式规则,实现对网页标题、发布时间、来源以及正文内容等元素信息的精准提取。本研究进一步提出了运用编辑距离算法实现正文内容提取准确率的判定,并提出阙值优化方法,克服了正文提取中噪声节点多、内容识别不完全的缺陷,大大提高了提取的准确度。     

基于遥感变化检测的石漠化信息自动提取

滇黔桂地区被划定为限制开发区后,石漠化的研究变得越来越热,而信息提取是这些研究的重要内容。当前石漠化信息提取主要依据静态指标进行,鉴于静态方法的不足,在动态机理剖析的基础上,综合考虑石漠化的静态景观及其动态属性,以贵州和云南交界区域为实验区,1992年和2001年两个时相的TM/ETM+影像为数据源,研究遥感变化检测方法在石漠化信息自动提取中的应用。在几何校正和辐射校正的基础上对两个时相的影像实施差值变化检测,将实验区分为变化区和无变化区两类,根据石漠化机理进一步将变化区分为石漠化区和逆石漠化区,完成石漠化信息识别,经验证Kappa系数为0.854。结果表明:遥感变化检测方法对石漠化有较好的识别效率和精度,能够为石漠化的综合防治提供科学依据。     

波谱匹配支持的遥感专题地物自适应提取

为贴合地物表现的多样性和复杂性,提出一种波谱匹配支持下的遥感影像专题地物自适应提取方法,经过专题地物端元选取、波谱匹配、影像自动分割、"整体-局部"的空间转换,以及局部针对性、精细化地迭代逼近等一系列相互衔接的算法,全面、准确地提取遥感影像上的专题地物。通过在ETM+影像上水体和裸地的提取实验,并与最大似然法和SVM分类结果比较,证明了该方法对多样性专题地物提取的有效性和普适性。     

基于APR—SVM的音频分类方法

音频分类在多媒体应用中十分广泛,主要有时域分析和频域分析方法。文中提出了一种基于自适应间距比（APR）算法和支持向量机（svrd）算法的音频分类方法,先用APR算法区分语音与非语音;对于非语音,再通过SVM进行音频分类。APR算法是比较PR参数和阈值来区分语音和非语音,它和信噪比密切相关;而将非语音分成四组：音乐,汽车,会议,雨声,提取特征因子。实验结果表明：文中设计的分类器的精度达到93．75％以上,能很好地把各类型音频分开。     

稀疏差分网络和多监督哈希用于高效图像检索

针对基于深度哈希的图像检索中卷积神经网络（CNN）特征提取效率较低和特征相关性利用不充分的问题,提出一种融合稀疏差分网络和多监督哈希的新方法SDNMSH（sparse difference networks and multi-supervised hashing）,并将其用于高效图像检索。SDNMSH以成对的图像作为训练输入,通过精心设计的稀疏差分卷积神经网络和一个监督哈希函数来指导哈希码学习。稀疏差分卷积神经网络由稀疏差分卷积层和普通卷积层组成。稀疏差分卷积层能够快速提取丰富的特征信息,从而实现整个网络的高效特征提取。同时,为了更加充分地利用语义信息和特征的成对相关性,以促进网络提取的特征信息能够更加有效地转换为具有区分性的哈希码、进而实现SDNMSH的高效图像检索,采用一种多监督哈希（MSH）函数,并为此设计了一个目标函数。在MNIST、CIFAR-10和NUS-WIDE三个广泛使用的数据集上进行了大量的对比实验,实验结果表明,与其他先进的深度哈希方法相比,SDNMSH取得了较好的检索性能。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

基于SPOT-5多光谱影像的矿区塌塘水体提取方法研究

针对基于遥感影像的水体提取方法存在水体提取不完整和误提的现象,提出了一种基于SPOT-5多光谱影像的矿区塌塘水体提取方法。在利用波段合成增加一个可用波段的基础上对已有的水体提取方法进行适当的改进,并基于决策树分类器和改进后的方法进行矿区水体的四级提取,保证了水体提取的完整性,同时减少了误提率;最后利用实测数据对水体提取的精度进行了评定。试验结果表明,基于决策树分类器的水体提取方法具有较高的精度,能满足矿区实际应用的需要。     

基于小波变换的泄露故障特征提取研究

为研究液压系统管路泄漏对压力脉动信号的影响,提出利用改进的小波消噪算法和小波包对压力脉动信号进行消噪和特征提取;针对传统小波变换阈值函数在去噪处理中存在的恒定偏差、不连续等缺点,提出一种改进阈值函数和新阈值相结合的新方法,将该方法与平移不变量方法相结合,避免了传统小波变换去噪时在不连续点存在的Pseudo-Gibbs现象;通过利用改进小波消噪方法和小波包对FESTO试验系统采集的正常和故障压力脉动信号进行分析比较,研究结果表明,不同工况下压力脉动信号3个主要能量频带的分布特性,可以作为泄露检测和识别的故障特征。     

基于TLM2．0的硬件虚拟平台设计

利用OVP（Open Virtual Platform）提供的模型创建基于TLM2．0的硬件虚拟平台,可以为嵌入式软件提供一个快速有效的开发环境,实现软件和硬件的协同设计。提早开发出的软件也可以对系统虚拟平台进行功能验证,分析系统性能,确定系统的最优架构。