Damage detection in beams from modal and wavelet analysis using a stationary roving mass and noise estimation

This paper uses the Continuous Wavelet Transform Analysis on mode shapes for damage identification. The wavelet analysis is applied to the difference in the mode shapes between a healthy and a damaged state. The paper also includes a novel methodology for estimating the level of noise of the experimental mode shapes based on a standard Signal to Noise Ratio (SNR). The estimated SNRs are used for identifying and making emphasis on the less noisy data. Moreover, a mass attached to the structure is considered to enhance the sensitivity of the structure to damage. Modal analysis is performed for different positions of the mass along the beam. The results obtained for all the positions of the mass are combined so an averaging process is implicitly applied. The paper presents the results from an experimental test of a cantilever steel beam with different severity levels of damage at the same location. The results show that the use of the attached mass reduces the effect of noise and increases the sensitivity to damage. Little damage can be identified with the proposed methodology even using a small number of sensors and only the first five bending modes.     

电磁精细探测法在隐伏型导水地质裂缝勘探中的应用

针对传统隐伏型导水地质裂缝勘探方法存在勘测精度较差的问题,提出电磁精细探测法探析隐伏型导水地质裂缝。依照屏蔽系数、实测场强和理论场强数据绘制综合曲线图,通过该图获取隐伏型导水地质裂缝所处位置几何阴影范围,采用层析成像法得到网格化的工作面,获取隐伏型导水地质图像。通过图像直接观测隐伏型导水地质工作面裂缝所处位置,在此基础上,观测四个电磁场分量,采用正交电磁场分量计算介质视电阻率,依据计算视电阻率数值和视电阻率分布状态研究裂缝发育情况和裂缝富水程度。结果表明:采用该方法能较为精准地获取隐伏型导水地质裂缝位置。通过裂缝位置进一步检测出隐伏型导水地质裂缝最大发育高度为63.5 m。当视电阻数值不断增加时,隐伏型导水地质裂缝和裂缝富水性逐渐减小,与实际情况较为相符,说明该种方法探析效果较好。     

Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes

Anomaly detection and location in crowded scenes have attracted a lot of attention in computer vision research community recently due to the increased applications of intelligent surveillance improve security in public. We propose a novel parallel spatial-temporal convolution neural networks model to detect and localize the abnormal behavior in video surveillance. Our approach contains two main steps. Firstly, considering the typical position of camera and the large number of background information, we introduce a novel spatial-temporal cuboid of interest detection method with varied-size cell structure and optical flow algorithm. Then, we use the parallel 3D convolution neural networks to describe the same behavior in different temporal-lengths. That step ensures that the most of behavior information in cuboids could be captured, also insures the reduction of information unrelated to the major behavior. The evaluation results on benchmark datasets show the superiority of our method compared to the state-of-the-art methods.     

基于残差决策的自动飞行控制系统远程实时故障诊断

针对自动飞行控制系统结构复杂、关联部件众多,发生故障时诊断时间长,从而影响飞机运行效率的问题,提出一种基于飞机通信寻址报告系统(ACARS)的远程实时故障诊断方案。首先,分析自动飞行控制系统的故障特点,设计搭建检测滤波器;然后,利用ACARS数据链实时发送的自动飞行控制系统的关键信息进行相关部件的残差计算,并根据残差决策算法进行故障诊断及定位;最后,针对不同故障部件残差间的差异大、决策门限无法统一的缺点,提出基于二次差值的残差决策改进算法,减缓了检测对象的整体变化趋势,降低了随机噪声和干扰的影响,避免了将瞬态故障诊断为系统故障的情况。实验仿真结果表明,基于二次差值的改进残差决策算法避免了多决策门限的复杂性,在采样时间为0.1 s的情况下,故障检测所需时间大约为2 s,故障检测时间大幅降低,有效故障检测率大于90%。     

基于局部放电法的矿用高压电缆信号去噪算法研究

干扰噪声直接影响局部放电法有效检测矿用高压电缆故障。基于局部放电法，综合采用理论计算、仿真实验、现场试验的方法，对比分析了短时傅里叶变换和傅里叶分析去噪法的原理和优缺点，提出了一种矿用高压电缆的局部放电去噪算法——小波阈值去噪法，同时，选择了合理的阈值函数和去噪流程。基于此，采用白噪声和连续周期信号作为高压电缆的干扰噪声，进行了模拟仿真实验。结果表明，小波阈值去噪法可有效抑制白噪声，其中，Db2小波性能和去噪效果最好；同时，现场试验结果显示，去噪后信噪比得到了显著增加，验证了小波阈值去噪法的合理性和可靠性。     

光伏用电致发光缺陷检测仪空间分辨率的量化评估

针对光伏用电致发光缺陷检测仪空间分辨率目视判定重复性、准确性差,并且难以量化等问题,提出一套基于卷积神经网络模型的空间分辨率量化评估方法。参照相关标准JJF(闽)1088-2018,采取不同的拍摄条件拍摄并切割出空间分辨率线对图像,对图像进行人工分类。设计卷积神经网络结构,采用卷积层、池化层和全连接层结构,并使用已分类完成的空间分辨率线对图像对模型进行训练。最终使用测试集对模型进行评估,结果表明,模型在测试集上的判别正确率达到99.2%。该方法满足使用要求,可取代目视判别,提高了光伏用电致发光缺陷检测仪空间分辨率判定的准确性与重复性,并为其量化提出了新的解决思路。由于太阳电池片用光致发光缺陷检测仪与光伏用电致发光缺陷检测仪的成像方式类似,该方法可兼容太阳电池片光致发光检测仪的性能评估。     

Performance of near-infrared dyes as effective contrast agents for breast cancer detection through simulation of photoacoustic imaging

ABSTRACT

Targeted photoacoustic imaging using exogenous contrast agents can potentially improve early detection of breast cancer, even at significant depths inside the breast. In this study, computer simulations were performed to compare the photoacoustic performance of 11 different near-infrared (NIR) dyes for detecting tumours deep inside the breast tissue. It was observed that the three high performing NIR dyes produced at least two-fold contrast enhancement of a spherical breast tumour embedded at 4?cm depth inside the breast than those of the corresponding endogenous contrast agents. These three selected dyes were employed to visualize small blood vessels deep inside the breast tissue. Although methylene blue provided the best contrast in visualizing tumour blood vessels at depths beyond 3?cm, considering other factors such as availability of suitable targeting agent, indocyanine green at 800?nm may be preferred over all other dyes for deep breast imaging applications.     

变分布的量子行为粒子群优化算法求解工程约束优化问题

针对工程形状设计领域中带有多个约束条件的非线性设计优化问题,提出了一种自适应的基于高斯分布的量子行为粒子群优化(AG-QPSO)算法。通过自适应地调整高斯分布,AG-QPSO算法能够在搜索的初始阶段有很强的全局搜索能力,随着搜索过程的进行,算法的局部搜索能力逐渐增强,从而满足了算法在搜索过程不同阶段的需要。为了验证算法的有效性,在压力容器和张弦设计问题这两个工程约束优化问题上进行50轮独立实验。实验结果表明,在满足所有约束条件的情况下,AG-QPSO算法在压力容器设计问题上取得了5890.9315的平均解和5885.3328的最优解,在张弦设计问题上取得了0.01096的平均解和0.01096的最优解,远优于标准粒子群优化(PSO)算法、具有量子行为的粒子群优化(QPSO)算法和高斯量子行为粒子群(G-QPSO)算法等现有的算法的结果,同时AG-QPSO算法取得的结果的方差较小,说明该算法具有很好的鲁棒性。