我们衷心感谢武汉城建集团2023年度科研专项（NO.7）对这项工作的资助。

1.具体样品图像采集
<ol><li>将混凝土运输到工作场所，由泵车浇筑。</li>
	<li>要拍摄图像，请通过向右移动电源键开关并将其转到 ON 位置来打开拍摄设备。将相机的模式旋钮调整到绿色自动模式，确保相机镜头与混凝土表面平行，然后按 下快门键。捕获 20 个非振动混凝土的图像样本，将它们保存为采集分辨率为 1024 x 1024 像素的.jpg格式，如 图 1 所示。</li>
	<li>将混凝土振动设备的振动部分（插入式混凝土振动杆）插入混凝土中。连接电源，然后通过将其设置为 ON 位置来打开混凝土振动设备。</li>
	<li>当混凝土表面出现一些坍塌（由混凝土内部释放空气的振动引起，导致表面骨料下沉以填充缝隙，导致混凝土坍塌）并出现一些水泥浆时，保持相机镜头平行于混凝土表面并按 下快门键。收集 20 个振动混凝土的图像样本，如 图 2 所示。</li>
	<li>继续操作振动设备。当混凝土表面无明显塌陷时，不出现水泥浆，不产生气泡，停止振动过程，采集20张振动混凝土图像样本，如 图3所示。</li>
</ol>2. 样本图像灰度二值化
<ol><li>利用 MATLAB 软件的 imread（） 函数将 .jpg 文件读取为 1024 像素 x 1024 像素 x 3 个颜色通道的单位格式数据，表示图像的红色、绿色和蓝色通道值。</li>
	<li>接下来，应用 MATLAB 函数 rgb2gray 将图像转换为灰度，用格式 gray value = rgb2gray（jpg file name） 表示。根据公式（1）计算每个像素的灰度值，并将灰度值保存为1024 x 1024的unit8格式数据。 GR（i，j） = 0.2989R（i，j） + 0.587G（i，j） + 0.114B（i，j） （1） 其中，GR（i，j） 是像素点的灰度值，R（i，j） 是像素点的红色通道值，G（i，j） 是像素点的绿色通道值，B（i，j） 是像素点的蓝色通道值。</li>
	<li>将大于像素阈值 θ 的灰度值的二进制值更改为 1，并将小于 θ 像素的灰度值的二进制值设置为 0。</li>
	<li>获得二值灰度结果后，将结果另存为 1024 x 1024 逻辑格式数据。在本例中，θ 表示二值化阈值，对于不同振动状态的混凝土样品图像，其值分别为 50、100、150 和 200，如 图 5、 图 6 和 图 7 所示。</li>
</ol>3. 定向梯度特征值的计算
<ol><li>使用以下公式28 计算图像中每个像素的水平和垂直二进制梯度 Tp = R（x， y + 1） - R（x， y - 1）， Th = R（x+1， y） - R（x-1， y） 其中 Th 是水平二值化梯度，Tp 是垂直二值化梯度，R 是二值化逻辑格式数据，x 是二值化矩阵的行号，y 是二值化矩阵的列号。</li>
	<li>使用以下公式29 计算每个点的二进制梯度方向和大小 <img alt="Equation 1" src="/files/ftp_upload/65731/65371eq01.jpg" style="margin: auto;"> 其中 T 是二进制梯度大小，αT 是二进制梯度方向，Th 是水平二进制梯度，Tp 是垂直二进制梯度。</li>
	<li>确定图像分割块的大小，表示为 n，其中 n 的值为 [ 1,9]。沿 x 方向在 y 方向上设置每个 n 像素的分割线，根据分割线的位置有效地将图像分成 n x n 个正方形块。然后，图像中无法形成完整方形块的任何部分都将被删除。</li>
	<li>将T α二进制梯度方向（T α二进制梯度方向的值为 [ 0°， 360°]）划分为 m 个部分，得到 m 个方向梯度统计角度区间。继续计算每个块的梯度统计角度间隔的梯度统计值。<ol><li>根据块中每个像素的二进制梯度方向，将像素分类为每个方向的适当梯度统计角度区间。</li>
		<li>将每个方向的梯度统计角度间隔内像素在逆时针方向上的二进制梯度求和，得到该间隔的梯度统计值。 图 8、 图 9 和 图 10 分别描绘了块大小 n 等于 8、128 和 512 的梯度统计角度间隔方向梯度统计的结果。</li>
	</ol></li>
</ol>4. 构建定向梯度特征向量
<ol><li>将样本划分为所需的计算区域，其中每个计算区域由四个相邻的模块组成，基于步骤 3.3 中获得的模块结果。例如，考虑 16 x 16 分辨率像素和 4 x 4 的块大小，图像分为 （16 / 4-1） x （ 16 / 4-1 ） = 9 个计算区域。</li>
	<li>计算计算区域内各块的梯度统计量的角度间隔内的方向梯度统计值。随后，得到以方向梯度统计量为分量的特征向量。</li>
	<li>组合来自每个计算区域的方向梯度特征向量，以获得图像的方向梯度特征向量。</li>
</ol>5. 支持向量机培训
<ol><li>从三种振动状态中随机选择 42 个样本创建训练组，剩余 18 个样本作为测试组。</li>
	<li>利用 MATLAB 的 fitcecoc 功能进行 SVM 训练;格式是 SVM = fitcecoc （trainingFeatures， Trainingeigenvalue） 其中SVM为待训练的支持向量机，trainingFeatures为训练组图像定向梯度特征向量，Trainingeigenvalue为训练组振动状态的特征值。非振动混凝土、振动混凝土和振动混凝土样品的振动状态特征值分别为 1、2 和 3。</li>
	<li>使用 Save 函数以 .mat 格式保存经过训练的 SVM 。</li>
</ol>6. SVM识别精度的验证
<ol><li>使用 MATLAB 的预测函数，将测试组样本图像的方向梯度特征向量输入到经过训练的 SVM 中，以获得每个测试样本的振动状态特征的计算值。格式如下: testgroupcalculateseigenvalues = predict （SVM， testFeature） 其中 testgroupcalculatesegenvalues 是振动状态特征的计算值，SVM 是在步骤 5 中训练的支持向量机。testFeature 是测试组样本图像的方向梯度特征向量。</li>
	<li>通过将测试集输入到经过训练的 SVM 中来确定测试集样本识别结果。计算测试集识别结果与实际状态匹配的样本数，然后通过将正确样本数除以测试集样本总数来计算识别准确率。<ol><li>如果测试识别准确率高于 94%，则认为 SVM 识别有效。如果低于 94%，则返回步骤 1.1 并调整二值化阈值、块大小 n 和方向梯度统计区间数 m。</li>
	</ol></li>
</ol>

用于识别混凝土振动状态的定向梯度直方图和 SVM

<ol>
	<li>Jiang, L., Tian, Z., Wang, K., Sun, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+segregation+of+concrete+under+vibration+based+on+electrical+method.">Estimating the segregation of concrete under vibration based on electrical method.</a> Concrete. 1, 41-44 (2023).</li><li>Ren, B., Ye, Z., Wang, D., Wu, B., Tan, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+segregation+degree+of+hardening+concrete+using+improved+Mask+R-CNN.">Evaluation of segregation degree of hardening concrete using improved Mask R-CNN.</a> Journal of Hydroelectric Engineering. 41 (5), 93-102 (2022).</li><li>Hu, J., Qin, M., Wang, H., Liu, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Study+on+the+influence+of+vibration+frequency+on+the+performance+of+concrete.">Study on the influence of vibration frequency on the performance of concrete.</a> Highway. 65 (12), 111-114 (2020).</li><li>Bian, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+on+characterization+of+evaluation+indexes+for+vibration+compaction+of+fresh+concrete.">Experimental study on characterization of evaluation indexes for vibration compaction of fresh concrete.</a> Journal of Hydroelectric Engineering. 39 (2), 67-80 (2020).</li><li>Liu, Z., Zhou, M., Bai, J., Mou, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+vibration+time+on+the+strength+and+homogeneity+of+rubber+concrete.">Influence of vibration time on the strength and homogeneity of rubber concrete.</a> Industrial Construction. 42, 509-512 (2012).</li><li>Cheng, Y., Gou, Z., Wang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Testing+Investigation+on+Effects+of+Mixing,+Vibrating+and+Curing+on+Strength+of+High-Performance+Concrete.">Testing Investigation on Effects of Mixing, Vibrating and Curing on Strength of High-Performance Concrete.</a> Journal of Northeastern University (Natural Science). 31 (12), 1790-1793 (2010).</li><li>Zhao, Y., Chen, S., Liu, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+mechanism+of+high-frequency+vibration+on+concrete+antifreeze+and+application+in+construction+of+tunnels+in+cold+regions.">Influence mechanism of high-frequency vibration on concrete antifreeze and application in construction of tunnels in cold regions.</a> Industrial Construction. 44 (5), 101-105 (2014).</li><li>Quan, L., Tian, B., Li, S., He, Z., He, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolution+characteristics+of+flexural+fatigue+performance+of+dense+concrete+consolidated+with+high+frequency+vibration+applied+in+airport+pavement.">Evolution characteristics of flexural fatigue performance of dense concrete consolidated with high frequency vibration applied in airport pavement.</a> Journal of Traffic and Transportation Engineering. 20 (2), 34-45 (2020).</li><li>Cai, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+coarse+aggregate+settlement+induced+by+vibration+on+long-term+chloride+transport+in+concrete:+a+numerical+study.">Influence of coarse aggregate settlement induced by vibration on long-term chloride transport in concrete: a numerical study.</a> Materials and Structures. 55 (9), 1-18 (2022).</li><li>Cai, Y., Liu, Q. F., Yu, L., Meng, Z., Avija, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+experimental+and+numerical+investigation+of+coarse+aggregate+settlement+in+fresh+concrete+under+vibration.">An experimental and numerical investigation of coarse aggregate settlement in fresh concrete under vibration.</a> Cement and Concrete Composites. 122 (7), 104153 (2021).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+application+of+concrete+vibrating+robot+system+for+high+arch+dam.">Development and application of concrete vibrating robot system for high arch dam.</a> Journal of Hydraulic Engineering. 53 (6), 631-654 (2022).</li><li>Chen, C., Li, X., Qiu, Z., Yao, W., Zhu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+Progress+of+Construction+Robots.">Research Progress of Construction Robots.</a> Journal of Architecture and Civil Engineering. 39 (4), 58-70 (2022).</li><li>Shen, H., Zhang, W., Liu, J., Qiu, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+Prospect+of+Construction+Robots+for+High+Rise+Buildings.">Development and Prospect of Construction Robots for High Rise Buildings.</a> Construction Technology. 46 (8), 105-108 (2017).</li><li>Dalal, N., Triggs, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histograms+of+Oriented+Gradients+for+Human+Detection.">Histograms of Oriented Gradients for Human Detection.</a> IEEE Computer Society Conference on Computer Vision &amp; Pattern Recognition. , (2005).</li><li>Zhou, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gradient-based+Feature+Extraction+From+Raw+Bayer+Pattern+Images.">Gradient-based Feature Extraction From Raw Bayer Pattern Images.</a> IEEE Transactions on Image Processing. (99), 1 (2021).</li><li>He, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Railway+Fastener+Defects+Detection+under+Various+Illumination+Conditions+using+Fuzzy+C-Means+Part+Model.">Railway Fastener Defects Detection under Various Illumination Conditions using Fuzzy C-Means Part Model.</a> Transportation Research Record. 2675 (4), 271-280 (2021).</li><li>Xu, X., Quan, C., Ren, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+expression+recognition+based+on+Gabor+Wavelet+transform+and+Histogram+of+Oriented+Gradients.">Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients.</a> IEEE International Conference on Mechatronics &amp; Automation. , (2015).</li><li>Cortes, C., Vapnik, V. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Support+Vector+Networks.">Support Vector Networks.</a> Machine Learning. 20 (3), 273-297 (1995).</li><li>Burges, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Tutorial+on+Support+Vector+Machines+for+Pattern+Recognition.">A Tutorial on Support Vector Machines for Pattern Recognition.</a> Data Mining and Knowledge Discovery. 2 (2), 121-167 (1998).</li><li>Yang, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+Pleurotus+Ostreatus+From+Different+Producing+Areas+Based+on+Mid-Infrared+Spectroscopy+and+Machine+Learning.">Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning.</a> Spectroscopy and Spectral Analysis. 43 (2), 577-582 (2023).</li><li>Chaabane, S. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Face+recognition+based+on+statistical+features+and+SVM+classifier).">Face recognition based on statistical features and SVM classifier).</a> Multimedia Tools and Applications. 81 (6), 8767-8784 (2022).</li><li>Saleh, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experiments+with+SVM+to+classify+opinions+in+different+domains.">Experiments with SVM to classify opinions in different domains.</a> Expert Systems with Applications. 38 (12), 14799-14804 (2011).</li><li>Zhang, Y., Liu, Y., Wang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliability+Prediction+of+Coal+Mine+Water+Disasters+Emergency+Rescue+System+Based+on+Improved+SVM.">Reliability Prediction of Coal Mine Water Disasters Emergency Rescue System Based on Improved SVM.</a> Journal of Zhengzhou University (Engineering Science). 36 (3), 115-119 (2015).</li><li>Cao, Y., Song, D., Hu, X., Sun, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fault+Diagnosis+of+Railway+Point+Machine+Based+on+Improved+Time-Domain+Multiscale+Dispersion+Entropy+and+Support+Vector+Machine.">Fault Diagnosis of Railway Point Machine Based on Improved Time-Domain Multiscale Dispersion Entropy and Support Vector Machine.</a> Acta Electronica Sinica. 51 (1), 117-127 (2023).</li><li>Li, Q., Yuze, C., Yu, B., Ning, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-stage+support+vector+machine+method+for+failure+mode+classification+of+reinforced+concrete+columns.">Two-stage support vector machine method for failure mode classification of reinforced concrete columns.</a> Engineering Mechanics. 39 (2), 148-158 (2022).</li><li>Yang, Y., Zhang, W., Yu, H., Chai, W., Liu, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+on+the+relationships+between+ultrasonic+parameters+and+the+stress+state+in+loaded+concrete+based+on+improved+support+vector+machines).">Analysis on the relationships between ultrasonic parameters and the stress state in loaded concrete based on improved support vector machines).</a> Journal of Vibration and Shock. 42 (2), 175-224 (2023).</li><li>Yan, K., Shi, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+elastic+modulus+of+normal+and+high+strength+concrete+by+support+vector+machine.">Prediction of elastic modulus of normal and high strength concrete by support vector machine.</a> Construction &amp; Building Materials. 24 (8), 1479-1485 (2010).</li><li>Hussein, I. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully-automatic+identification+of+gynaecological+abnormality+using+a+new+adaptive+frequency+filter+and+histogram+of+oriented+gradients+(HOG).">Fully-automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients (HOG).</a> Expert Systems. 39 (3), 12789 (2022).</li><li>Chandrakala, M., Devi, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-stage+classifier+for+face+recognition+using+HOG+features.">Two-stage classifier for face recognition using HOG features.</a> Materials Today: Proceedings. 47, 5771-5775 (2021).</li><li>Long, C., Yichi, Z., Zhangkai, L., Dandan, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-Light+Image+Enhancement+Based+on+RAW+Domain+Image.">Low-Light Image Enhancement Based on RAW Domain Image.</a> Journal of Computer-Aided Design &amp; Computer Graphics. 35 (2), 303-311 (2023).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+application+of+concrete+vibrating+robot+system+for+high+arch+dam.">Development and application of concrete vibrating robot system for high arch dam.</a> Journal of Hydraulic Engineering. 53 (6), 631-654 (2022).</li></ol>

作者没有什么可透露的。

本文利用支持向量机（SVM）学习各种混凝土振动状态样本的图像特征。基于机器学习结果，提出了一种基于图像识别的具体振动状态识别方法。为了提高识别精度，控制图像分割、图像二值化和定向梯度特征值提取这三个关键步骤的参数至关重要。根据测试结果，采用较小的二值化阈值对具体样品图像进行预处理，并利用128 pixels x 128 pixel的图像分割块大小。统计角度区间的方向梯度数设置为 12。在 1024 分辨率的图像样本集中，机器学习结果的识别精度最高。 
由于不同施工现场的环境差异很大，该方法具有精度调整机制。当识别精度达不到要求时，可以扩大或降低上述三个关键参数作为可行的解决方案，有效缓解环境变化导致的识别精度误差。需要注意的是，这种方法在很大程度上依赖于工作面的照明条件。在低照度条件下捕获图像可能会导致识别精度下降。为了减少对照明条件的依赖，已经探索了30个低照明图像增强算法，但它们显着增加了识别时间和硬件要求。目前，尚无有效的技术能同时保证低照明识别精度和识别效率。该方法利用定向梯度技术直方图来表征混凝土振动状态的特征，包括混凝土振动过程中的表面塌陷、水泥浆漂浮和气泡。与现有技术相比，这种方法大大减少了所需的样本数量，并降低了计算机性能要求。使用配备 2.30 GHz CPU 的笔记本电脑，识别过程只需 15 秒即可完成 SVM 的训练空间差异。当适当设置提取参数时，识别精度也有所提高31.
将来，该协议可用于指挥建筑机器人的振动操作，允许在混凝土达到所需的振动水平时及时停止操作，从而避免因振动不足而导致的质量问题。此外，该方法还可以根据施工现场的纹理特性修改机器人操作，例如结构平整度检测、工作面泥浆去除和其他相关过程。

混凝土是建筑行业广泛使用的基本建筑材料。在泵送过程中，混凝土经常产生空隙，需要通过振动压实。振动不足可能导致混凝土表面呈蜂窝状，而过度振动会导致混凝土离体 1,2。振动操作的质量显着影响成型混凝土结构的强度 3,4,5,6 和耐久性 7,8。Cai等[9,10]将实验研究与数值分析相结合，研究了振动对骨料沉降和混凝土耐久性的影响机制。结果表明，振动时间和骨料颗粒对骨料沉降有很大影响，而骨料密度和水泥基材料的塑性粘度影响最小。振动导致骨料沉积在混凝土试样的底部。此外，随着振动时间的增加，混凝土试样底部的氯离子浓度降低，而顶部的氯离子浓度显着增加9,10。
目前，混凝土振动状态的评估主要依赖于人工判断。随着建筑业通过智能化改革不断进步，机器人操作已成为未来的发展方向11,12。因此，智能振动操作的一个关键挑战是如何使机器人能够识别混凝土的振动状态。
定向梯度直方图是一种利用像素的强度梯度或边缘方向的分布作为描述符来表征图像中物体的表示和形状的技术13,14。这种方法在图像的局部网格单元上运行，在表征各种几何和光学条件下的图像变化时提供了强大的稳定性。
周等[15 提出了一种直接从拜耳模式图像中提取方向梯度特征的方法。通过将滤色器列与渐变算子进行匹配，这种方法省略了计算定向梯度的许多步骤，从而大大降低了定向渐变图像识别的计算要求。He et al.16 利用方向梯度直方图作为底层特征，采用平均聚类算法对钢轨紧固件进行分类，确定紧固件是否存在缺陷。识别结果表明，定向梯度特征直方图对紧固件缺陷具有较高的敏感性，满足铁路养护和维修的需要。在另一项研究中，Xu 等人 17 使用 Gabor 小波滤波对人脸图像特征进行了预处理，并通过二进制编码和 HOG 算法降低了特征向量的维度。该方法的平均识别准确率为92.5%。
支持向量机（SVM）18用于将向量映射到高维空间中，并建立具有合适方向的分离超平面，以最大化两个平行超平面之间的距离。这允许对支持向量进行分类 19.学者们对这种分类技术进行了改进和优化，使其在图像识别20,21，文本分类22，可靠性预测23和故障诊断24等各个领域得到应用。
Li等[25 ]开发了用于地震破坏模式识别的两阶段SVM模型，重点关注三种地震破坏模式。分析结果表明，所提出的两阶段支持向量机方法在3种失效模式下均能达到90%以上的准确率。Yang等[26 ]将优化算法与支持向量机相结合，模拟了5个超声参数与加载混凝土应力之间的关系。未优化的 SVM 的性能不尽如人意，尤其是在低应力阶段。然而，遍历由算法优化的模型会产生更好的结果，尽管计算时间很长。相比之下，粒子群优化优化的 SVM 显著缩短了计算时间，同时提供了最佳的仿真结果。Yan等[27 ]采用支持向量机技术，引入精度不敏感损失函数对高强混凝土的弹性模量进行预测，并将其预测精度与传统回归模型和神经网络模型进行比较。研究结果表明，与其他方法相比，SVM技术对弹性模量的预测误差更小。
本文收集了混凝土在各种振动状态下的图像样本，并利用定向梯度直方图技术描述了混凝土的不同状态。将定向梯度作为特征向量进行定向训练，重点关注使用定向梯度直方图-支持向量图技术识别混凝土振动状态的可行性。此外，还分析了方向梯度直方图特征提取过程中的二值化阈值、方向梯度统计块大小和方向梯度统计区间数这三个关键参数之间的影响机制，以及支持向量机的识别精度。

<table><tbody><tr><td>camera</td><td>SONY</td><td>A6000</td><td>The sensor size is 23.5x15.6mm, the maximum acquisition resolution is 1440 * 1080, and the effective pixel is 24.3 million.</td></tr><tr><td>concrete</td><td>Wuhan Construction Changxin Technology Development Co., Ltd.</td><td>C30 pumping concrete</td><td>According to the standard of &#039; concrete strength test and evaluation standard &#039; ( GB / T 50107-2010 ), the standard value of cubic compressive strength is 30 MPa pumping concrete.</td></tr><tr><td>Matlab</td><td>MathWorks</td><td>Matlab R2017a</td><td>MATLAB&#039;s programming interface provides development tools for improving code quality maintainability and maximizing performance.&lt;br/&gt; It provides tools for building applications using custom graphical interfaces.&lt;br/&gt; It provides tools for combining MATLAB-based algorithms with external applications and languages</td></tr><tr><td>Processor&amp;nbsp;</td><td>Intel</td><td>12th Gen Intel(R) Core (TM) i7-12700H @ 2.30GHz</td><td>64-bit Win11 processor&amp;nbsp;</td></tr></tbody></table>

image recognition and parameter analysis of concrete vibration state based on support vector machine

本文采用定向梯度直方图技术提取了不同振动状态下捕获的混凝土图像样本的特征。利用支持向量机（SVM）学习图像特征与振动状态之间的关系。机器学习结果随后用于评估混凝土振动状态的可行性。同时，分析了方向梯度直方图计算参数对识别精度的影响机理。结果验证了采用定向梯度直方图-SVM技术识别混凝土振动状态的可行性。识别准确率最初随着方向梯度的块大小或统计间隔数量的增加而增加，然后逐渐降低。识别准确率也随着二值化阈值的增加而线性降低。通过使用分辨率为1024 x 1024像素的样本图像并优化特征提取参数，可以达到100%的识别准确率。

该协议旨在分析方向梯度特征的三向量计算参数如何影响支持向量机识别混凝土振动状态的精度。方向梯度特征向量的主要计算参数包括方向梯度统计块大小、方向梯度统计角度区间数和二进制格雷阈值。本节使用三个主要计算参数作为变量来设计测试。测试参数级别详见 表 1。对分辨率为 1024 x 1024 像素的混凝土图像样本共进行了 100 次测试。与 表1 中描述的参数相对应的测试结果如 表2所示。
不同二元灰度阈值-SVM识别结果分析 表2 显示了SVM在不同二值化阈值下的平均识别精度，二值化阈值与识别准确率的关系如 图4所示。当块大小和统计区间数固定时，SVM的识别准确率通常呈下降趋势，并随着二值化阈值的增加而下降。值得注意的是，当二值化阈值落在 100 到 150 的范围内时，识别准确率会显着降低。需要进一步调查以了解这种现象背后的原因及其对SVM除法计算的影响。
在本节中，按照步骤2.1中描述的方法和步骤3.1中描述的实验设计，对非振动混凝土、振动混凝土和振动混凝土的图像样本进行二值化。使用的二值灰度阈值为 50、100、150、200 和 250，因此每个状态都有二值灰度图像，如 图 5、 图 6 和 图 7 所示。
如 图5所示，随着二值化阈值的降低，非振动混凝土图像样品的二值图像中的白色区域显着减少。在二值化阈值 250 时，二值图像显示为纯黑色。 在图6中，具有二值化阈值的振动混凝土图像样本的二元灰度图像的变化趋势与非振动混凝土样本相似，但振动混凝土图像样本的白色区域减少更为明显。此外， 图7 显示了黑色部分和白色区域的组合，反映了混凝土在不同振动状态下的表面纹理特征。振动混凝土的二进制灰度图像也随着二值化阈值的降低而减小。例如，当二值化阈值设置为 50 和 100 时，振动混凝土的二进制灰色图像趋于白色。在阈值为 150 时，它看起来与其他两种状态相似，但当阈值超过 150 时，二进制图像趋于黑色。值得注意的是，当二值化阈值介于 100 和 150 之间时，二值图像特征会发生显著变化。
本文的特征向量提取依赖于图像样本的定向梯度。将二值化阈值从 50 增加到 100 可减少白色和黑色像素之间的接触面积。这种减少会影响像素方向梯度的统计信息，因为它取决于每个像素之间的像素值变化。接触面积越大，SVM 特征向量中的分量就越少于 0，因此混凝土振动状态特征的表示更加全面。二值化阈值对识别精度的变化主要是由于方向梯度特征向量中 0 个分量数量的变化。此外，当二值化阈值从 150 提高到 250 时，二值图像样本的白色区域显着减少。因此，相应的识别精度也大大降低，进一步支持了这一规则。
不同方向梯度统计块大小-SVM识别结果 本节计算了梯度统计在不同方向上的统计块大小识别精度，如 表2 所示。随后，计算梯度统计在各个方向上的统计块大小识别精度的平均值;结果如 图 8 所示。
图 8 演示了 1024 分辨率混凝土图像样本的 SVM 识别与定向梯度的统计块大小之间的关系。这种关系可以用公式（2）来表示。
y=0.09+0.144x-0.01x2 （2）
通过块扫描方法20计算图像样本特征向量。同时，当块较小时，特征向量表征二进制图像的局部特异性。这导致不同振动状态的具体样本图像具有相似的局部特异性，导致特征向量中有大量 0 个分量。因此，如此多的 0 分量会对 SVM 分割造成大量干扰，导致识别精度降低，特别是对于块大小为 8 像素的 1024 像素图像。
随着块大小的增加，特征向量所反映的局部特异性逐渐减弱，特征向量表征图像样本的区域特异性，如 图10所示。因此，特征向量中的 0 个分量数量减少，从而减少了 SVM 分裂过程中的干扰。因此，SVM的识别精度提高了。
但是，当块大小进一步增加，超过 32 像素时，特征向量中 0 个分量的数量继续减少。但它也会导致 SVM 训练集的特征向量维度减少。在这一点上，对SVM识别精度的影响主要源于特征维度的缺失。尽管如此，特征向量仍然设法在具体图像中捕获一定程度的特异性。如 图11所示，当块尺寸扩大到一定程度时，具有不同振动状态的混凝土图像样本中每个块的定向梯度特征仍表现出显著差异。这一观察结果解释了为什么当块大小变得过大时识别准确率会降低，尽管下降幅度相对较小。
定向梯度统计角度区间数-SVM识别结果 在本节中，计算 了表2 中所示的方向梯度统计区间数量的识别精度。随后，计算方向梯度统计区间数的平均识别精度。结果如 图 12 所示。
从 图 12 中可以明显看出，随着方向梯度统计区间数量的增加，支持向量机对混凝土振动状态的识别精度先上升，然后下降。这种关系可以用公式（3）表示
y=-0.45+0.2x-0.007x2 （3）
梯度方向统计区间数与识别精度之间的影响机制是由于图像特征提取参数的变化。这导致图像样本的特征向量的特定表征能力发生变化。在本节中，截获了中度振动混凝土的部分图像样本。当网格大小为4时，方向梯度特性的计算结果为6、9、12和15，如 图13所示。
如图13A，B所示，当方向梯度统计区间数设置为6时，每个区间的大小为60°。考虑到计算块大小为 4x4，每个块中有 16 个像素。对于较大的间隔大小，多个像素的方向梯度落在单个间隔内。当区间大小较大时，这会导致图像样本的特征向量中 0 个分量的数量增加。因此，它会影响支持向量机的训练结果和识别精度。但是，当方向梯度统计区间数为 9 时，角度划分变得更加精细，从而减少区间内没有像素的情况。因此，图像样本的特征向量中 0 个分量的数量也减少了，从而提高了特征向量的图像特定表示能力。然而，与图13C和图13D相比，当方向梯度统计区间数从12增加到15时，方向梯度特征计算结果区间中0的像素数增加。因此，特征向量表征样本图像特异性的能力降低。这种表征能力的降低归因于方向梯度统计区间大小的进一步减小。具体来说，只有一个像素的间隔现在分为两个区间:一个是单个像素，另一个是空区间。因此，空间隔数量的增加会导致特征向量中出现更多的 0 分量，最终导致识别精度降低。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65731/65371fig01.jpg"> 图 1:非振动混凝土的图像。 在没有振动操作的情况下拍摄的泵送混凝土图像。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig01large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65731/65371fig02.jpg"> 图2:振动混凝土的图像。 泵送混凝土振动操作中的图像样本。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig02large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65731/65371fig03.jpg"> 图3:振动混凝土样品的图像。 泵送混凝土振动操作完成时的图像样本。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig03large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65731/65371fig04.jpg"> 图4:二值化阈值-识别准确率关系。 二值化阈值对支持向量机识别精度的影响。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig04large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65731/65371fig05.jpg"> 图 5:非振动混凝土的二进制灰度图像。 设置不同二值化阈值时非振动混凝土图像的二值化处理结果。（A） 二进制门槛为50。（B） 二进制门槛为100。（C） 二进制门槛为150。（D） 二进制门槛为200。（E） 二进制门槛为250。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig05large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65731/65371fig06.jpg"> 图 6:振动混凝土的二进制灰度图像。 设置不同二值化阈值时振动混凝土图像的二值化处理结果。（A） 二进制门槛为50。（B） 二进制门槛为100。（C） 150 处的二值化阈值。（D） 二进制门槛为200。（E） 二进制门槛为250。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig06large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65731/65371fig07.jpg"> 图 7:振动混凝土的二进制灰度图像。 设置不同二值化阈值时振动混凝土图像的二值化处理结果。（A） 二进制门槛为50。（B） 二进制门槛为100。（C） 二进制门槛为150。（D） 二进制门槛为200。（E） 二进制门槛为250。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig07large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65731/65371fig08.jpg"> 图 8:定向梯度统计块大小识别精度图。 方向梯度统计块大小对支持向量机识别精度的影响。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig08large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65731/65371fig09.jpg"> 图 9:块大小为 8 像素的定向梯度特征提取结果示意图。 当块大小为 8 像素时，梯度特征会产生三种振动状态方向。（A）非振动混凝土，（B）振动混凝土，（C）振动混凝土。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig09large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/65731/65371fig10.jpg"> 图 10:块大小为 128 像素的定向梯度特征提取结果示意图。 当块大小为 128 像素时，梯度特征会产生三种振动状态方向。（A）非振动混凝土，（B）振动混凝土，（C）振动混凝土。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig10large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/65731/65371fig11.jpg"> 图 11:块大小为 512 像素的不同振动状态下混凝土样品图像的定向梯度提取结果。 当块大小为 512 像素时，梯度特征会产生三种振动状态方向。（A）非振动混凝土，（B）振动混凝土，（C）振动混凝土。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig11large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/65731/65371fig12.jpg"> 图12:方向梯度统计区间数字识别精度关系。 方向梯度统计区间number 对支持向机识别精度的影响 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig12large.jpg" target="_blank">请点击此处查看此图的较大版本.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/65731/65371fig13.jpg"> 图13:不同定向梯度统计区间数量定向梯度特性的计算结果。 在设置不同的定向梯度统计区间时，得到样本的定向梯度特征结果。（A） 6 个方向梯度统计区间，（B） 9 个方向梯度统计区间，（C） 12 个方向梯度统计区间，（D） 15 个方向梯度统计区间。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/65371fig13large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
表 1:SVM 识别测试因子水平。 分析了方向梯度特征矢量的计算参数对支持向机识别混凝土振动状态精度的影响。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/Table%201_RE.xlsx" target="_blank">请按此下载此表格。</a>
表2:方向梯度直方图参数分析测试结果。 基于 表1中的测试方案，得到了识别准确率结果。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65731/Table%202_Re.xlsx" target="_blank">请按此下载此表格。</a>

Watch this Scientific Journal Video about Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines at JoVE.com

作者聚焦:使用定向梯度直方图技术和支持向量机进行高效图像识别 

本文所述的协议利用定向梯度直方图技术提取了各种振动状态下混凝土图像样品的特征。它采用支持向量机进行机器学习，从而产生了一种具有最小训练样本要求和低计算机性能要求的图像识别方法。

基于支撑矢量机的混凝土振动状态图像识别与参数分析

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different ...

本文描述的协议利用定向梯度直方图技术提取了各种振动单元下混凝土图像样本的特征。它采用支持向量机进行机器学习，从而产生具有最小训练样本要求和低计算机性能要求的成像识别方法。这种方法大大减少了所需的样本数量，并降低了计算机性能要求。使用相当于 2.3 GHz 中央处理器的笔记本电脑，识别过程只需 50 秒即可完成支持的向量机的列车空间微分。使用 128 个项目和 128 个项目以下大小的图像分割。统计角度反转的方向向量数设置为 12。在224分辨率的图像处理过程中，机器学习结果的识别效果最佳。

image-recognition-parameter-analysis-concrete-vibration-state-based

Nanyang Technological University

Research

Education

JoVE Core

JoVE Journal

Civil Engineering

Engineering

Unit 1

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Mixing, Placing and Curing Concrete 

SCIENTISTS IN ACTION

881 次观看.  Wuhan University. 本文描述的协议利用定向梯度直方图技术提取了各种振动单元下混凝土图像样本的特征。它采用支持向量机进行机器学习，从而产生具有最小训练样本要求和低计算机性能要求的成像识别方法。这种方法大大减少了所需的样本数量，并降低了计算机性能要求。使用相当于 2.3 GHz 中央处理器的笔记本电脑，识别过程只需 50 秒即可完成支持的向量机的列车空间微分。使用 12...

视频: 作者聚焦:使用定向梯度直方图技术和支持向量机进行高效图像识别 

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the behavioral correlate for the sense of ownership. Previously, the estimation of proprioceptive drift was limited to one spatial dimension, such as height, width, or depth. As the hand can move freely in 3D, measuring proprioceptive drift in only one dimension is not sufficient for the estimation of the drift in real life situations. In this article, we provide a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion by combining an objective behavioral measurement (hand position tracking) and subjective, phenomenological assessment (subjective assessment of hand position and questionnaire) with a sophisticated machine learning approach. This technique permits not only an investigation of the underlying mechanisms of the sense of ownership and agency but also assists in the rehabilitation of a missing or paralyzed limb and in the design rules of real-time control systems with a self-body-like usability, in which the operator controls the system as if it were part of his/her own body.

This article describes a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion and combining a psychophysical procedure with an analysis using machine learning.

对2D平面本体感受漂移的支持向量机可视化方法

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the ...

科研

行为学

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With designs governed by the dynamic performance under human-induced loads, a strong demand exists for the verification and refinement of currently available load models. The present contribution uses a 3D inertial motion tracking technique for the characterization of the in-field pedestrian behavior. The technique is first tested in laboratory experiments with simultaneous registration of the corresponding ground reaction forces. The experiments include walking persons as well as rhythmical human activities such as jumping and bobbing. It is shown that the registered motion allows for the identification of the time variant pacing rate of the activity. Together with the weight of the person and the application of generalized force models available in literature, the identified time-variant pacing rate allows to characterize the human-induced loads. In addition, time synchronization among the wireless motion trackers allows identifying the synchronization rate among the participants. Subsequently, the technique is used on a real footbridge where both the motion of the persons and the induced structural vibrations are registered. It is shown how the characterized in-field pedestrian behavior can be applied to simulate the induced structural response. It is demonstrated that the in situ identified pacing rate and synchronization rate constitute an essential input for the simulation and verification of the human-induced loads. The main potential applications of the proposed methodology are the estimation of human-structure interaction phenomena and the development of suitable models for the correlation among pedestrians in real traffic conditions.

A protocol is presented for the characterization of the in-field pedestrian behavior and the simulation of the resulting structural response. Field-tests demonstrate that the in situ identified pacing rate and synchronization rate among the participants constitute an essential input for the simulation and verification of the human-induced loads.

基于该特征场行人行为人类引起的振动模拟

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With ...

工程学

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for determining optimal sensor and forcing locations typically require data, either simulated or measured, from the damaged structure. Embedded sensitivity functions provide an approach for determining the best available sensor location to detect damage with only data from the healthy structure. In this video and manuscript, the data acquisition procedure and best practices for determining the embedded sensitivity functions of a structure is presented. The frequency response functions used in the calculation of the embedded sensitivity functions are acquired using modal impact testing. Data is acquired and representative results are shown for a residential scale wind turbine blade. Strategies for evaluating the quality of the data being acquired are provided during the demonstration of the data acquisition process.

The data acquisition procedure for determining embedded sensitivity functions is described. Data is acquired and representative results are shown for a residential scale wind turbine blade.

数据采集​​协议确定嵌入式灵敏度功能

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for ...

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...

环境科学

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

The rapid development of X-ray imaging techniques with image processing and analysis skills has enabled the acquisition of CT images of granular soils with high-spatial resolutions. Based on such CT images, grain-scale mechanical behavior such as particle kinematics (i.e., particle translations and particle rotations), strain localization and inter-particle contact evolution of granular soils can be quantitatively investigated. However, this is inaccessible using conventional experimental methods. This study demonstrates the exploration of the grain-scale mechanical behavior of a granular soil sample under triaxial compression using synchrotron X-ray micro-tomography (&#956;CT). With this method, a specially fabricated miniature loading apparatus is used to apply confining and axial stresses to the sample during the triaxial test. The apparatus is fitted into a synchrotron X-ray tomography setup so that high-spatial resolution CT images of the sample can be collected at different loading stages of the test without any disturbance to the sample. With the capability of extracting information at the macro scale (e.g., sample boundary stresses and strains from the triaxial apparatus setup) and the grain scale (e.g., grain movements and contact interactions from the CT images), this procedure provides an effective methodology to investigate the multi-scale mechanics of granular soils.

The protocol describes procedures to acquire high-spatial resolution computed tomography (CT) images of a granular soil during triaxial compression, and to apply image processing techniques to these CT images to explore the grain-scale mechanical behavior of the soil under loading.

使用同步加速器 X 射线微断层扫描技术，在剪切下对颗粒土壤的故障和相关的颗粒级机械性能进行可视化

The rapid development of X-ray imaging techniques with image processing and analysis skills has enabled the acquisition of CT images of granular soils ...

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments. Effective design of these ASF systems requires an in-depth understanding of how the parameters of specific feedback mechanism affect user perception and reaction to stimuli. This article presents a method for applying vibrotactile stimuli to human participants and measuring their response. Rotating mass vibratory motors are placed at pre-defined locations on the participant's thigh, and controlled through custom hardware and software. The speed and accuracy of participants' volitional responses to vibrotactile stimuli are measured for researcher-specified combinations of motor placement and vibration frequency. While the protocol described here uses push-buttons to collect a simple binary response to the vibrotactile stimuli, the technique can be extended to other response mechanisms using inertial measurement units or pressure sensors to measure joint angle and weight bearing ratios, respectively. Similarly, the application of vibrotactile stimuli can be explored for body segments other than the thigh.

This article describes a technique for applying vibrotactile stimuli to the thigh of a human participant, and measuring the accuracy and reaction time of the participant&#39;s volitional response for various combinations of stimulation location and frequency.

一种意志的运动反应的及时性和准确性，以振动触觉刺激评价方法

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments.  Effective design ...

生物工程

<meta charset="utf8"></head><body>使用计算机视觉系统研究冰淇淋融化行为

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of starting time and rate of melting. However, the aspect of ice cream during melting is also important because shape retention is linked to a good structure of the product. The protocol proposed here illustrates a computer vision system (CVS) that can be used to support the already existing gravitational methodology to calculate two new melting indices related to shape retention and melting rate. Pictures of ice cream during melting are taken every 15 min for a total of 90 min. Afterward, digital images are elaborated using a purposely developed image processing method to calculate ice cream area, height, and width. The ratio between height and width at each melting time, referred to&#160;the ratio at time 0 (Rt/R0), is an index of the shape retention of ice cream, while the area at the different melting times referred to the area at time 0 (At/A0) is related to the melting rate. This computer vision system allows the obtaining of highly sensitive and reliable results, and it can be applied not only to ice cream but also to different food matrices, such as whipped milk cream or egg albumen.

<meta charset="utf8"></head><body>作者聚焦：通过计算机视觉系统进行创新的冰淇淋融化行为分析

Here, we present a protocol based on a computer vision system (CVS) to determine the melting behavior of multi-phase food systems.

用于评估冰淇淋融化行为的计算机视觉系统

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of ...

通过随机点运动图中的形状识别评估视觉

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

The standard visual acuity measurements rely on stationary stimuli, either letters (Snellen charts), vertical lines (vernier acuity) or grating charts, processed by those regions of the visual system most sensitive to the stationary stimulation, receiving visual input from the central part of the visual field. Here, an acuity measurement is proposed based on discrimination of simple shapes, that are defined by motion of the dots in the random dot kinematograms (RDK) processed by visual regions sensitive to motion stimulation and receiving input also from the peripheral visual field. In the motion-acuity test, participants are asked to distinguish between a circle and an ellipse, with matching surfaces, built from RDKs, and separated from the background RDK either by coherence, direction, or velocity of dots. The acuity measurement is based on ellipse detection, which with every correct response becomes more circular until reaching the acuity threshold. The motion-acuity test can be presented in negative contrast (black dots on white background) or in positive contrast (white dots on black background). The motion defined shapes are located centrally within 8 visual degrees and are surrounded by RDK background. To test the influence of visual peripheries on centrally measured acuity, a mechanical narrowing of the visual field to 10 degrees is proposed, using opaque goggles with centrally located holes. This easy and replicable narrowing system is suitable for MRI protocols, allowing further investigations of the functions of the peripheral visual input. Here, a simple measurement of shape and motion perception simultaneously is proposed. This straightforward test assesses vision impairments depending on the central and peripheral visual field inputs. The proposed&#160;motion-acuity test advances the capability of standard tests to reveal spare or even strengthened vision functions in patients with injured visual system, that until now remained undetected.

作者聚焦:通过基于运动的周边视觉测试评估中央视力丧失患者的视力

A novel motion-based acuity test that allows the assessment of central and peripheral visual processing in low-vision and healthy individuals, along with goggles limiting peripheral vision compatible with MRI protocols, are described here. This method offers a comprehensive vision assessment for functional impairments and dysfunctions of the visual system.

运动敏锐度测试，用于使用运动定义形状的视野敏锐度测量

The standard visual acuity measurements rely on stationary stimuli, either letters (Snellen charts), vertical lines (vernier acuity) or grating charts, ...

基于支撑矢量机的混凝土振动状态图像识别与参数分析

副本

摘要

探索更多视频

此视频中的章节

对2D平面本体感受漂移的支持向量机可视化方法

基于该特征场行人行为人类引起的振动模拟

数据采集协议确定嵌入式灵敏度功能

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

使用同步加速器 X 射线微断层扫描技术，在剪切下对颗粒土壤的故障和相关的颗粒级机械性能进行可视化

一种意志的运动反应的及时性和准确性，以振动触觉刺激评价方法

用于评估冰淇淋融化行为的计算机视觉系统

用于评估冰淇淋融化行为的计算机视觉系统

用于评估冰淇淋融化行为的计算机视觉系统

运动敏锐度测试，用于使用运动定义形状的视野敏锐度测量

基于支撑矢量机的混凝土振动状态图像识别与参数分析

副本

摘要

探索更多视频

此视频中的章节

对2D平面本体感受漂移的支持向量机可视化方法

基于该特征场行人行为人类引起的振动模拟

数据采集​​协议确定嵌入式灵敏度功能

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

使用同步加速器 X 射线微断层扫描技术，在剪切下对颗粒土壤的故障和相关的颗粒级机械性能进行可视化

一种意志的运动反应的及时性和准确性，以振动触觉刺激评价方法

用于评估冰淇淋融化行为的计算机视觉系统

用于评估冰淇淋融化行为的计算机视觉系统

用于评估冰淇淋融化行为的计算机视觉系统

运动敏锐度测试，用于使用运动定义形状的视野敏锐度测量

数据采集协议确定嵌入式灵敏度功能