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Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Frontiers of Structural and Civil Engineering 2024年第3期18卷 334-349页

作者： Longjian LI Li YANG Zhongyu HAO Xiaoli SUN gongfa chen School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China JSTI Group Guizhou Engineering Survey and Design Co. Ltd.Guangzhou 510800China Guangzhou Municipal Engineering Testing Co. Ltd.Guangzhou 510520China

Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.

关键词： ground penetrating radar gprMax forward modeling sample generation Swin-YOLOX object detection

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Optimal CNN-based semantic segmentation model of cutting slope images

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Frontiers of Structural and Civil Engineering 2022年第4期16卷 414-433页

作者： Mansheng LIN Shuai TENG gongfa chen Jianbing LV Zhongyu HAO School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China JSTI Group Guangdong Inspection and Certification Co.Ltd Nanjing 210000China

This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.

关键词： slope damage image recognition semantic segmentation feature map visualizations

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Digital image correlation-based structural state detection through deep learning

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Frontiers of Structural and Civil Engineering 2022年第1期16卷 45-56页

作者： Shuai TENG gongfa chen Shaodi WANG Jiqiao ZHANG Xiaoli SUN School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China Earthquake Engineering Research&Test Center Guangzhou UniversityGuangzhou 510405China Guangzhou Municipal Engineering Testing Co. Ltd.Guangzhou 510520China

This paper presents a new approach for automatical classification of structural state through deep learning.In this work,a Convolutional Neural Network(CNN)was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame;the input was a series of vibration signals,and the output was a structural state.The digital image correlation(DIC)technology was utilized to collect vibration information of an actual steel frame,and subsequently,the raw signals,without further pre-processing,were directly utilized as the CNN samples.The results show that CNN can achieve 99%classification accuracy for the research model.Besides,compared with the backpropagation neural network(BPNN),the CNN had an accuracy similar to that of the BPNN,but it only consumes 19%of the training time.The outputs of the convolution and pooling layers were visually displayed and discussed as well.It is demonstrated that:1)the CNN can extract the structural state information from the vibration signals and classify them;2)the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN;3)the CNN has better anti-noise ability.

关键词： structural state detection deep learning digital image correlation vibration signal steel frame

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Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments

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The Innovation 2024年第1期5卷 127-138页

作者： Zhen Zhang Chun Li Wenhui Wang Zheng Dong gongfa Liu Yuhui Dong Yi Zhang National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029China Beijing Synchrotron Radiation Facility Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China

Synchrotron tomography experiments are transitioning into multifunctional,cross-scale,and dynamic characterizations,enabled by new-generation synchrotron light sources and fast developments in beamline instrumentation.However,with the spatial and temporal resolving power entering a new era,this transition generates vast amounts of data,which imposes a significant burden on the data processing end.Today,as a highly accurate and efficient data processing method,deep learning shows great potential to address the big data challenge being encountered at future synchrotron beamlines.In this review,we discuss recent advances employing deep learning at different stages of the synchrotron tomography data processing pipeline.We also highlight how applications in other data-intensive fields,such as medical imaging and electron tomography,can be migrated to synchrotron tomography.Finally,we provide our thoughts on possible challenges and opportunities as well as the outlook,envisioning selected deep learning methods,curated big models,and customized learning strategies,all through an intelligent scheduling solution.

关键词： processing pipeline outlook

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Slope stability analysis based on big data and convolutional neural network

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 882-895页

作者： Yangpan FU Mansheng LIN You ZHANG gongfa chen Yongjian LIU School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China

The Limit Equilibrium Method(LEM)is commonly used in traditional slope stability analyses,but it is time-consuming and complicated.Due to its complexity and nonlinearity involved in the evaluation process,it cannot provide a quick stability estimation when facing a large number of slopes.In this case,the convolutional neural network(CNN)provides a better alternative.A CNN model can process data quickly and complete a large amount of data analysis in a specific situation,while it needs a large number of training samples.It is difficult to get enough slope data samples in practical engineering.This study proposes a slope database generation method based on the LEM.Samples were amplified from 40 typical slopes,and a sample database consisting of 20000 slope samples was established.The sample database for slopes covered a wide range of slope geometries and soil layers’physical and mechanical properties.The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model.The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers,and it can quickly obtain the safety factor and stability state of the slopes.Moreover,the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%,and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation.Therefore,the CNN can predict the safety factor of real slopes.In particular,the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN,which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability limit equilibrium method convolutional neural network database for slopes big data

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A Precision-Positioning Method for a High-Acceleration Low-Load Mechanism Based on Optimal Spatial and Temporal Distribution of Inertial Energy

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Engineering 2015年第3期1卷 391-398页

作者： Xin chen Youdun Bai Zhijun Yang Jian Gao gongfa chen The Key Laboratory of Mechanical Equipment Manufacturing & Control Technology of Ministry of Education Guangdong University of Technology

High-speed and precision positioning are fund a mental requirements for high-acceleration low-load mechanisms in integrated circuit(IC) packaging equipment. In this paper, we derive the transient nonlinear dynamicresponse equations of high-acceleration mechanisms, which reveal that stiff ness, frequency, damping, and driving frequency are the primary factors. Therefore, we propose a new structural optimization and velocity-planning method for the precision positioning of a high-acceleration mechanism based on optimal spatial and temporal distribution of inertial energy. For structural optimization, we first reviewed the commonly flexible multibody dynamic optimization using equivalent static loads method(ESLM), and then we selected the modifled ESLM for optimal spatial distribution of inertial energy; hence, not only the stiff ness but also the inertia and frequency of the real modal shapes are considered. For velocity planning, we developed a new velocity-planning method based on nonlinear dynamic-response optimization with varying motion conditions. Our method was verifled on a high-acceleration die bonder. The amplitude of residual vibration could be decreased by more than 20% via structural optimization and the positioning time could be reduced by more than 40% via asymmetric variable veloci ty planning. This method provides an effective theoretical support for the precision positioning of high-acceleration low-load mechanisms.

关键词：高加速度空间分布能量最优时间分布惯性定位方法负载非线性动力方程

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半监督TS网络模型在齿轮故障诊断中的应用

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机械科学与技术 2024年第7期43卷 1249-1256页

作者：陈保家阮宇豪陈法法肖文荣李公法陶波三峡大学水电机械设备设计与维护湖北省重点实验室湖北宜昌443002 重庆大学机械传动国家重点实验室重庆400030 武汉科技大学冶金装备及其控制教育部重点实验室武汉430081

为解决在工业大数据条件下,有标签样本少导致机械故障诊断准确率低的问题,提出了一种半监督神经网络模型。该方法采用协同训练的方式,从时域和频域两个维度训练教师网络(T),将无标签数据转化为高质量的伪标签数据。再利用转化后的伪标... 详细信息

为解决在工业大数据条件下,有标签样本少导致机械故障诊断准确率低的问题,提出了一种半监督神经网络模型。该方法采用协同训练的方式,从时域和频域两个维度训练教师网络(T),将无标签数据转化为高质量的伪标签数据。再利用转化后的伪标签数据训练学生网络(S),通过对数据进行评判和计分,避免网络过拟合。最后通过得分函数,对伪标签数据进行阶梯筛选成为有标签数据。齿轮故障诊断结果表明:TS网络在仅有少量有标签数据的情况下,故障分类准确率达90.31%,与其他半监督方法相比,准确率高出15%~20%。在信噪比(SNR)为5、0、-5的条件下,模型可以达到86.81%、78.00%、52.78%的诊断准确率。

关键词：齿轮故障诊断伪标签学习抗噪性协同训练

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TRV介导的溪荪VIGS转化体系的构建与鉴定

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植物研究 2024年第1期44卷 132-138页

作者：刘桂伶徐诺史恭发王玲东北林业大学园林学院哈尔滨150040

病毒诱导基因沉默(Virus-induced gene silencing,VIGS)技术为缺乏稳定遗传转化体系的植物进行基因功能分析提供了可能,为解决缺少稳定遗传转化体系的植物进行基因功能验证的需求,以单子叶植物溪荪(Iris sanguinea)为材料,克隆IsPDS基... 详细信息

病毒诱导基因沉默(Virus-induced gene silencing,VIGS)技术为缺乏稳定遗传转化体系的植物进行基因功能分析提供了可能,为解决缺少稳定遗传转化体系的植物进行基因功能验证的需求,以单子叶植物溪荪(Iris sanguinea)为材料,克隆IsPDS基因的特异性片段并构建其VIGS重组载体pTRV2-IsPDS,通过注射法侵染溪荪叶片,结果显示:pTRV1和pTRV2-IsPDS的菌液OD_(600)调至1.8~2.0后重悬,重悬液OD600调至0.8~1.0,通过注射器叶脉注射方式侵染溪荪叶片效果最佳。在室外温度为15~20℃的下午6~8时进行,用1 mL注射器针头扎伤溪荪叶片外表皮,沿着平行脉缓慢注射重悬液1mL至溪荪叶片维管束中,14 d左右会出现明显的白化表型。在出现表型变化的植株和空载组中均检测到TRV1和TRV2的病毒载体,白化植株的IsPDS表达量显著低于对照组。侵染液配制中通过提高携带病毒载体的农杆菌的浓度提高侵染效率,且接种后无需遮光。

关键词：溪荪 IsPDS基因 VIGS体系

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改进蚁群算法的火灾环境疏散路径规划研究

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计算机工程与应用 2024年第8期60卷 309-319页

作者：杜云刘小雨贾科进丁力黄公发河北科技大学电气工程学院石家庄050000

针对建筑物火灾人员疏散问题,以保证火灾人员安全撤离为目标,提出火灾环境下改进蚁群算法的路径规划模型。构建方向性信息函数,当搜索方向靠近目标节点,方向性信息函数的值增大使信息素差异化;根据火灾影响程度建立火灾等级函数,使转移... 详细信息

针对建筑物火灾人员疏散问题,以保证火灾人员安全撤离为目标,提出火灾环境下改进蚁群算法的路径规划模型。构建方向性信息函数,当搜索方向靠近目标节点,方向性信息函数的值增大使信息素差异化;根据火灾影响程度建立火灾等级函数,使转移概率随着火灾等级的增大而减小,减少蚁群寻路的盲目性;通过分析火灾影响因素建立等效长度并构建启发函数,避免陷入局部最优;结合火灾等级函数自适应调整信息素挥发系数,加快了火灾路径信息素挥发速度,提升算法全局搜索能力;同时在信息素更新策略中引入奖惩系数和模糊控制,提升疏散系统的鲁棒性以及路径平滑度,并对全局信息素进行限制,以平衡算法的局部开发与全局搜索能力。经仿真实验验证,改进的蚁群算法在有无火灾的情况下都能高效地规划出疏散路线。

关键词：蚁群算法方向性信息函数等效长度模糊控制火灾人员疏散

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合肥光源飞扫控制系统研制

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核技术 2024年第2期47卷 31-40页

作者：张天哲孔安博陈凯陈留国刘功发中国科学技术大学国家同步辐射实验室合肥230029

合肥光源实验站现在的扫描实验全部是基于步进扫描模式的。这种步进模式下由于运动机构存在一定的死区时间,实验耗时长,效率低。为了提高实验效率,设计了一种基于硬件触发的飞扫控制系统,可以实现实验过程的快速连续扫描。该飞扫控制系... 详细信息

合肥光源实验站现在的扫描实验全部是基于步进扫描模式的。这种步进模式下由于运动机构存在一定的死区时间,实验耗时长,效率低。为了提高实验效率,设计了一种基于硬件触发的飞扫控制系统,可以实现实验过程的快速连续扫描。该飞扫控制系统包括同步信号采集模块、同步运动控制模块以及软件控制模块,使用EPICS(Experimental Physics and Industrial Control System)架构实现设备控制,并基于Bluesky完成实验流程控制以及数据采集。随后在合肥光源的软X射线磁性圆二色实验站上进行了飞扫控制系统的部署和测试。测试结果表明,在满足采谱性能指标的前提下,该飞扫控制系统可将单次采谱时间从几十分钟量级降低至分钟量级,显著提高了实验效率和用户体验。

关键词：飞扫 FPGA Bluesky EPICS

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