We gratefully thank Wuhan Urban Construction Group 2023 Annual Scientific Research Project (NO.7) for funding this work.

1. Concrete sample image acquisition
<ol>
	<li>Transport concrete to the workplace, where it will be poured by the pump truck.</li>
	<li>To capture images, switch on the shooting equipment by moving the power key switch to the right and turning it to the ON position. Adjust the camera&#39;s mode knob to the green automatic mode, ensuring the camera lens is parallel to the concrete surface, and press the Shutter Key. Capture 20 image samples of non-vibrated concrete, saving them in .jpg format with an acquisition resolution of 1024 x 1024 pixels, as illustrated in Figure 1.</li>
	<li>Insert the vibrating part of the concrete vibration equipment (plug-in concrete vibrating rod) into the concrete. Connect the power supply, and then switch on the concrete vibration equipment by setting it to the ON position.</li>
	<li>When there is some collapse on the surface of the concrete (caused by the vibration discharging air inside the concrete, leading to the sinking of surface aggregates to fill the gaps, resulting in concrete collapse) and some cement slurry appears, keep the camera lens parallel to the concrete surface and press the Shutter Key. Collect 20 image samples of vibrating concrete, as shown in Figure 2.</li>
	<li>Continue operating the vibration equipment. When there is no obvious collapse on the concrete surface, no cement slurry appears, and no bubbles are generated, stop the vibration process, and capture 20 image samples of vibrated concrete, as shown in Figure 3.</li>
</ol>
2. Sample image gray binarization
<ol>
	<li>Utilize the imread() function of MATLAB software to read the .jpg file as the unit format data of 1024 pixels x 1024 pixels x 3 color channels, representing the red, green, and blue channel values of the image.</li>
	<li>Next, apply the MATLAB function rgb2gray to convert the image to grayscale, denoted by the format gray value = rgb2gray(jpg file name). Calculate the gray value of each pixel according to Equation (1), and save the gray value as the unit8 format data of 1024 x 1024. 
	GR(i,j) = 0.2989R(i,j) + 0.587G(i,j) + 0.114B(i,j)&#160; &#160; (1) 
	where, GR(i,j) is the gray value of the pixel point, R(i,j)&#160;is the red channel value of the pixel point, G(i,j) is the green channel value of the pixel point, and B(i,j)&#160; is the blue channel value of the pixel point.</li>
	<li>Alter the binary value of the gray values greater than the pixel threshold &#952; to 1 and set the binary value of the gray values less than the &#952; pixel to 0.</li>
	<li>After obtaining the binarized gray results, save the outcome as 1024 x 1024 logical format data. In this case, &#952; represents the binarization threshold, and its values are 50, 100, 150, and 200 for different vibration state concrete sample images, as depicted in Figure 5, Figure 6, and Figure 7.</li>
</ol>
3. Calculation of directional gradient eigenvalue
<ol>
	<li>Calculate the horizontal and vertical binary gradient of each pixel in the image using the following equation28 
	Tp = R(x, y + 1) - R(x, y - 1), Th = R(x+1, y) - R(x-1, y) 
	where Th is the horizontal binarized gradient, Tp is the vertical binarized gradient, R is the binarized logical format data, x is the row number of the binarized matrix, and y is the column number of the binarized matrix.</li>
	<li>Calculate the binary gradient direction and size of each point using the following equation29 
	<img alt="Equation 1" src="/files/ftp_upload/65731/65371eq01.jpg" style="margin: auto;" /> 
	where T is the binary gradient size, &#945;T is the binary gradient direction, Th is the horizontal binary gradient, and Tp is the vertical binary gradient.</li>
	<li>Determine the size of the image segmentation block, denoted as n, where the value of n is [ 1,9]. Set the segmentation line for each n pixel in the y direction along the x direction, effectively dividing the image into n x n square blocks based on the segmentation line&#39;s position. Any parts of the image that cannot form complete square blocks are then removed.</li>
	<li>Partition binary gradient direction&#160;&#945;T (value of the binary gradient direction &#945;T is [ 0&#176;, 360&#176;]) into m parts, resulting in m directional gradient statistical angle intervals. Proceed to calculate the gradient statistical value for each block&#39;s gradient statistical angle interval.
	<ol>
		<li>Based on the binary gradient direction of each pixel in the block, classify the pixels into the appropriate gradient statistical angle interval of each direction.</li>
		<li>Sum the binary gradient of the pixels in the gradient statistical angle interval of each direction in the counterclockwise direction to obtain the gradient statistical value of that interval. The obtained results for gradient statistical angle interval directional gradient statistics are depicted in Figure 8, Figure 9, and Figure 10 for block sizes n equal to 8, 128, and 512, respectively .</li>
	</ol>
	</li>
</ol>
4. Constructing directional gradient feature vector
<ol>
	<li>Divide samples into required calculation areas, where each calculation area consists of four adjacent blocks based on the block results obtained in step 3.3. For instance, considering 16 x 16 resolution pixels and a block size of 4 x 4, the image is divided into (16 / 4-1) x ( 16 / 4-1 ) = 9 calculation areas.</li>
	<li>Calculate the statistical value of the directional gradient within angle interval of the gradient statistics of each block in the calculation area. Subsequently, obtain the feature vector with the directional gradient statistics as the component.</li>
	<li>Combine the directional gradient feature vectors from each calculation area to obtain the image&#39;s directional gradient feature vector.</li>
</ol>
5. SVM training
<ol>
	<li>Randomly select 42 samples from the three vibration states to create the training group, leaving the remaining 18 samples as the test group.</li>
	<li>Utilize the fitcecoc function of MATLAB for SVM training; the format is 
	SVM = fitcecoc (trainingFeatures, Trainingeigenvalue) 
	where SVM is the support vector machine to be trained, trainingFeatures is the training group image directional gradient feature vector, and Trainingeigenvalue is the characteristic value of the vibration state of the training group. The characteristic values of the vibration state of the non-vibrated concrete, vibrating concrete, and vibrated concrete samples are 1, 2, and 3, respectively.</li>
	<li>Save the trained SVM using the Save function in .mat format.</li>
</ol>
6. Verification of SVM recognition accuracy
<ol>
	<li>Using MATLAB&#39;s prediction function, input the test group sample image&#39;s directional gradient feature vector into the trained SVM to obtain the calculated value of the vibration state feature for each test sample. The format is as follows: 
	testgroupcalculateseigenvalues = predict (SVM, testFeature) 
	where testgroupcalculateseigenvalues is the calculated value of the vibration state feature, and SVM is the support vector machine trained in step 5. testFeature is the directional gradient feature vector of the test group sample image.</li>
	<li>Determine the test set sample recognition results by feeding the test set into the trained SVM. Count the number of samples for which the test set recognition results match the actual state, and then calculate the recognition accuracy by dividing the number of correct samples by the total number of test set samples.
	<ol>
		<li>If the test recognition accuracy is higher than 94%, consider the SVM recognition as effective. If it is lower than 94%, return to step 1.1 and adjust the binarization threshold, block size n, and the number of directional gradient statistical intervals m.</li>
	</ol>
	</li>
</ol>

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

<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>

The authors have nothing to disclose.

This paper utilizes the support vector machine (SVM) to learn the image features of various concrete vibration state samples. Based on the machine learning outcomes, a concrete vibration state recognition method based on image recognition is proposed. To enhance the recognition accuracy, it is crucial to control the parameters of the three key steps: image segmentation, image binarization, and directional gradient eigenvalue extraction. According to the test results, a smaller binarization threshold is employed to prepro...

Concrete is a fundamental building material extensively used in the construction industry. During pumping, the concrete frequently develops voids that require compaction through vibration. Inadequate vibration may result in a honeycombed concrete surface, while excessive vibration can lead to concrete segregation1,2. The quality of vibration operation significantly impacts the strength3,4,5,6 and durability of the formed concrete structures7,8. Cai et al.9,10 conducted a study that combined experimental research with numerical analysis to investigate the influence mechanism of vibration on aggregate settlement and concrete durability. The findings revealed that vibration time and aggregate particles exert a substantial impact on aggregate settlement, while aggregate density and the plastic viscosity of the cement-based material have minimal effects. Vibration causes aggregate deposition at the bottom of the concrete specimens. Moreover, as the vibration time increases, the chloride ion concentration decreases at the bottom of the concrete specimens while significantly increasing at the top9,10.
Currently, the assessment of concrete vibration state relies predominantly on manual judgment. As the construction industry continues to progress through intelligent reforms, robot operations have emerged as the future direction11,12. Consequently, a crucial challenge in intelligent vibration operations is how to enable robots to identify the vibration state of concrete.
The histogram of the oriented gradient is a technique that utilizes the intensity gradient of pixels or the distribution of edge directions as a descriptor to characterize the representation and shape of objects in images13,14. This approach operates on the local grid cells of the image, providing robust stability in characterizing image changes under various geometric and optical conditions.
Zhou et al.15 proposed a method for directly extracting directional gradient features from Bayer mode images. This approach omits numerous steps in calculating the directional gradient by matching the color filter column with the gradient operator, thereby significantly reducing the computational requirements for directional gradient image recognition. He et al.16 utilized the directional gradient histogram as the underlying feature and employed the mean clustering algorithm to classify rail fasteners and determine whether the fasteners are defective. The recognition results indicated that the histogram of the oriented gradient feature exhibited high sensitivity to fastener defects, meeting the needs of railway maintenance and repair. In another study, Xu et al.17 preprocessed face image features using Gabor wavelet filtering and reduced the dimension of feature vectors through binary coding and the HOG algorithm. The average recognition accuracy of the method is 92.5%.
The support vector machine (SVM)18 is used to map the vector into a high-dimensional space and establishes a separating hyperplane with a suitable direction to maximize the distance between two parallel hyperplanes. This allows for the classification of support vectors19. Scholars have improved and optimized this classification technology, leading to its application in various fields such as image recognition20,21, text classification22, reliability prediction23, and fault diagnosis24.
Li et al.25 developed a two-stage SVM model for seismic failure pattern recognition, focusing on three seismic failure modes. The analysis results indicate that the proposed two-stage SVM method can achieve more than 90% accuracy for the three failure modes. Yang et al.26 integrated an optimization algorithm with the SVM to simulate the relationship between the five ultrasonic parameters and the stress of the loaded concrete. The performance of an unoptimized SVM is unsatisfactory, particularly in the low-stress stage. However, traversing the model optimized by the algorithm yields improved results, albeit with lengthy computation times. In comparison, the particle swarm optimization optimized SVM significantly reduces the calculation time while delivering optimal simulation results. Yan et al.27 employed SVM technology and introduced a precision-insensitive loss function to predict the elastic modulus of high-strength concrete, comparing its prediction accuracy against the traditional regression model and neural network model. The research findings demonstrate that the SVM technology produces a smaller prediction error for elastic modulus compared to other methods.
This paper collects image samples of concrete under various vibration states and describes the concrete&#39;s different states using the directional gradient histogram technique. The directional gradient is employed as a feature vector for training the SVM, and the study focuses on the viability of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. Additionally, the paper analyzes the influence mechanism between three key parameters-binarization threshold, directional gradient statistical block size, and directional gradient statistical interval number-in the feature extraction process of the directional gradient histogram and the recognition accuracy of the SVM.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 &#39; concrete strength test and evaluation standard &#39; ( 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&#39;s programming interface provides development tools for improving code quality maintainability and maximizing performance. 
			It provides tools for building applications using custom graphical interfaces. 
			It provides tools for combining MATLAB-based algorithms with external applications and languages</td>
		</tr>
		<tr>
			<td>Processor&#160;</td>
			<td>Intel</td>
			<td>12th Gen Intel(R) Core (TM) i7-12700H @ 2.30GHz</td>
			<td>64-bit Win11 processor&#160;</td>
		</tr>
	</tbody>
</table>

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

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

This protocol aims to analyze how the three-vector calculation parameters of the directional gradient feature affect the accuracy of the SVM in identifying the concrete vibration state. The primary calculation parameters of the directional gradient feature vector include the directional gradient statistical block size, the number of directional gradient statistical angle intervals, and the binary gray threshold. This section uses three main calculation parameters as variables to design the test. The test parameter levels...

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

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

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

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873 Views.  Wuhan University. The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

Video: Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth.&#160;However, unlike a stereo-based method, DFP uses a digital video projector to replace one of the cameras1. The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera&#8217;s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation.
Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop.&#160;DFP systems can also achieve the same measurement resolution as the camera.&#160;For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in1-5). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30&#160;Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame6,7.&#160;Binary defocusing DFP methods can achieve even greater speeds8.
Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis9, facial animation10, cardiac mechanics studies11, and fluid surface measurements, but many other potential applications exist.&#160;This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.

This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces.&#160;It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses ...

Selective Area Modification of Silicon Surface Wettability by Pulsed UV Laser Irradiation in Liquid Environment

The wettability of silicon (Si) is one of the important parameters in the technology of surface functionalization of this material and fabrication of biosensing devices. We report on a protocol of using KrF and ArF lasers irradiating Si (001) samples immersed in a liquid environment with low number of pulses and operating at moderately low pulse fluences to induce Si wettability modification. Wafers immersed for up to 4 hr in a 0.01% H2O2/H2O solution did not show measurable change in their initial contact angle (CA) ~75&#176;. However, the 500-pulse KrF and ArF lasers irradiation of such wafers in a microchamber filled with 0.01% H2O2/H2O solution at 250 and 65 mJ/cm2, respectively, has decreased the CA to near 15&#176;, indicating the formation of a superhydrophilic surface. The formation of OH-terminated Si (001), with no measurable change of the wafer&#8217;s surface morphology, has been confirmed by X-ray photoelectron spectroscopy and atomic force microscopy measurements. The selective area irradiated samples were then immersed in a biotin-conjugated fluorescein-stained nanospheres solution for 2 hr, resulting in a successful immobilization of the nanospheres in the non-irradiated area. This illustrates the potential of&#160;the method for selective area biofunctionalization and fabrication of advanced Si-based biosensing architectures. We also describe a similar protocol of irradiation of wafers immersed in methanol (CH3OH) using ArF laser operating at pulse fluence of 65 mJ/cm2 and in situ formation of a strongly hydrophobic surface of Si (001) with the CA of 103&#176;. The XPS results indicate ArF laser induced formation of Si&#8211;(OCH3)x compounds responsible for the observed hydrophobicity. However, no such compounds were found by XPS on the Si surface irradiated by KrF laser in methanol, demonstrating the inability of the KrF laser to photodissociate methanol and create -OCH3 radicals.

We report on a process of in situ alteration of HF treated Si (001) surface into a hydrophilic or hydrophobic state by irradiating samples in microfluidic chambers filled with&#160;H2O2/H2O solution (0.01%-0.5%) or methanol solutions using pulsed UV laser of a relative low pulse fluence.

The wettability of silicon (Si) is one of the important parameters in the technology of surface functionalization of this material and fabrication of ...

Fourier-Based Diffraction Analysis of Live Caenorhabditis elegans

This manuscript describes how to classify nematodes using temporal far-field diffraction signatures. A single C. elegans is suspended in a water column inside an optical cuvette. A 632 nm continuous wave HeNe laser is directed through the cuvette using front surface mirrors. A significant distance of at least 20-30 cm traveled after the light passes through the cuvette ensures a useful far-field (Fraunhofer) diffraction pattern. The diffraction pattern changes in real time as the nematode swims within the laser beam. The photodiode is placed off-center in the diffraction pattern. The voltage signal from the photodiode is observed in real time and recorded using a digital oscilloscope. This process is repeated for 139 wild type and 108 &#34;roller&#34; C. elegans. Wild type worms exhibit a rapid oscillation pattern in solution. The &#34;roller&#34; worms have a mutation in a key component of the cuticle that interferes with smooth locomotion. Time intervals that are not free of saturation and inactivity are discarded. It is practical to divide each average by its maximum to compare relative intensities. The signal for each worm is Fourier transformed so that the frequency pattern for each worm emerges. The signal for each type of worm is averaged. The averaged Fourier spectra for the wild type and the &#34;roller&#34; C. elegans are distinctly different and reveal that the dynamic worm shapes of the two different worm strains can be distinguished using Fourier analysis. The Fourier spectra of each worm strain match an approximate model using two different binary worm shapes that correspond to locomotory moments. The envelope of the averaged frequency distribution for actual and modeled worms confirms the model matches the data. This method can serve as a baseline for Fourier analysis for many microscopic species, as every microorganism will have its unique Fourier spectrum.

Fourier-Based Diffraction Analysis of Live Caenorhabditis elegans

This manuscript describes how to distinguish different nematodes using far-field diffraction signatures. We compare the locomotion of 139 wild type and 108 &#34;Roller&#34; C. elegans by averaging frequencies associated with the temporal Fraunhofer diffraction signature at a single location using a continuous wave laser.

This manuscript describes how to classify nematodes using temporal far-field diffraction signatures. A single C. elegans is suspended in a water column ...

Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

Solid-state electrolytes are a promising replacement for current organic liquid electrolytes, enabling higher energy densities and improved safety of lithium-ion (Li-ion) batteries. However, a number of setbacks prevent their integration into commercial devices. The main limiting factor is due to nanoscale phenomena occurring at the electrode/electrolyte interfaces, ultimately leading to degradation of battery operation. These key problems are highly challenging to observe and characterize as these batteries contain multiple buried interfaces. One approach for direct observation of interfacial phenomena in thin film batteries is through the fabrication of electrochemically active nanobatteries by a focused ion beam (FIB). As such, a reliable technique to fabricate nanobatteries was developed and demonstrated in recent work. Herein, a detailed protocol with a step-by-step process is presented to enable the reproduction of this nanobattery fabrication process. In particular, this technique was applied to a thin film battery consisting of LiCoO2/LiPON/a-Si, and has further been previously demonstrated by in situ cycling within a transmission electron microscope.

Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

A protocol for the fabrication of electrochemically active LiPON-based solid-state lithium-ion nanobatteries using a focused ion beam is presented.

Solid-state electrolytes are a promising replacement for current organic liquid electrolytes, enabling higher energy densities and improved safety of ...

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Flexible photodetectors have been intensely studied for the use of curved image sensors, which are a crucial component in bio-inspired imaging systems, but several challenging points remain, such as a low absorption efficiency due to a thin active layer and low flexibility. We present an advanced method to fabricate a flexible phototransistor array with an improved electrical performance. The outstanding electrical performance is driven by a low dark current owing to deep impurity doping. Stretchable and flexible metal interconnectors simultaneously offer electrical and mechanical stabilities in a highly deformed state. The protocol explicitly describes the fabrication process of the phototransistor using a thin silicon membrane. By measuring I-V characteristics of the completed device in deformed states, we demonstrate that this approach improves the mechanical and electrical stabilities of the phototransistor array. We expect that this approach to a flexible phototransistor can be widely used for the applications of not only next-generation imaging systems/optoelectronics but also wearable devices such as tactile/pressure/temperature sensors and health monitors.

We present a detailed method to fabricate a deformable lateral NIPIN phototransistor array for curved image sensors. The phototransistor array with an open mesh form, which is composed of thin silicon islands and stretchable metal interconnectors, provides flexibility and stretchability. The parameter analyzer characterizes the electrical property of the fabricated phototransistor.

Flexible photodetectors have been intensely studied for the use of curved image sensors, which are a crucial component in bio-inspired imaging systems, ...

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Soft pneumatic network actuators have become one of the most promising actuation devices in soft robotics which benefits from their large bending deformation and low input. However, their monotonous bending motion form in two-dimensional (2-D) space keeps them away from wide applications. This paper presents a detailed fabrication method of soft pneumatic network actuators with oblique chambers, to explore their motions in three-dimensional (3-D) space. The design of oblique chambers enables actuators with tunable coupled bending and twisting capabilities, which gives them the possibility to move dexterously in flexible manipulators, to become biologically inspired robots and medical devices. The fabrication process is based on the molding method, including the silicone elastomer preparation, chamber and base parts fabrication, actuator assembly, tubing connections, checks for leaks, and actuator repair. The fabrication method guarantees the rapid manufacturing of a series of actuators with only a few modifications in the molds. The test results show the high quality of the actuators and their prominent bending and twisting capabilities. Experiments of the gripper demonstrate the advantages of the development in adapting to objects with different diameters and providing sufficient friction.

Here we present a fabrication method of soft pneumatic network actuators with oblique chambers. The actuators are capable of generating coupled bending and twisting motions, which broadens their application in soft robotics.

Soft pneumatic network actuators have become one of the most promising actuation devices in soft robotics which benefits from their large bending ...

A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure

In this study, we fabricated a flexible 3D mesh structure with periodic voids by using a 3D lithography method and applying it to a vibration energy harvester to lower resonance frequency and increase output power. The fabrication process is mainly divided into two parts: three-dimensional photolithography for processing a 3D mesh structure, and a bonding process of piezoelectric films and the mesh structure. With the fabricated flexible mesh structure, we achieved the reduction of resonance frequency and improvement of output power, simultaneously. From the results of the vibration tests, the meshed-core-type vibration energy harvester (VEH) exhibited 42.6% higher output voltage than the solid-core-type VEH. In addition, the meshed-core-type VEH yielded 18.7 Hz of resonance frequency, 15.8% lower than the solid-core-type VEH, and 24.6 &#956;W of output power, 68.5% higher than the solid-core-type VEH. The advantage of the proposed method is that a complex and flexible structure with voids in three dimensions can be relatively easily fabricated in a short time by the inclined exposure method. As it is possible to lower the resonance frequency of the VEH by the mesh structure, use in low-frequency applications, such as wearable devices and house appliances, can be expected in the future.

In this study, we fabricated a flexible 3D mesh structure and applied it to the elastic layer of a bimorph cantilever-type vibration energy harvester for the purpose of lowering resonance frequency and increasing output power.

In this study, we fabricated a flexible 3D mesh structure with periodic voids by using a 3D lithography method and applying it to a vibration energy ...

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction (HFrEF), broadly overlooking heart failure with preserved ejection fraction (HFpEF), which has more recently become a dominant form of heart failure worldwide. Motivated by the paucity of HFpEF in silico representations, two distinct computational models are presented in this paper to simulate the hemodynamics of HFpEF resulting from left ventricular pressure overload. First, an object-oriented lumped-parameter model was developed using a numerical solver. This model is based on a zero-dimensional (0D) Windkessel-like network, which depends on the geometrical and mechanical properties of the constitutive elements and offers the advantage of low computational costs. Second, a finite element analysis (FEA) software package was utilized for the implementation of a multidimensional simulation. The FEA model combines three-dimensional (3D) multiphysics models of the electro-mechanical cardiac response, structural deformations, and fluid cavity-based hemodynamics and utilizes a simplified lumped-parameter model to define the flow exchange profiles among different fluid cavities. Through each approach, both the acute and chronic hemodynamic changes in the left ventricle and proximal vasculature resulting from pressure overload were successfully simulated. Specifically, pressure overload was modeled by reducing the orifice area of the aortic valve, while chronic remodeling was simulated by reducing the compliance of the left ventricular wall. Consistent with the scientific and clinical literature of HFpEF, results from both models show (i) an acute elevation of transaortic pressure gradient between the left ventricle and the aorta and a reduction in the stroke volume and (ii) a chronic decrease in the end-diastolic left ventricular volume, indicative of diastolic dysfunction. Finally, the FEA model demonstrates that stress in the HFpEF myocardium is remarkably higher than in the healthy heart tissue throughout the cardiac cycle.

This work introduces two computational models of heart failure with preserved ejection fraction based on a lumped-parameter approach and finite element analysis. These models are used to evaluate the changes in the hemodynamics of the left ventricle and related vasculature induced by pressure overload and diminished ventricular compliance.

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction ...