Name: Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training
Uploaded: 2024-01-05T13:50:00
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calculation of directional gradient eigenvalue for support vector machine training

Somut titreşim durumlarını tanımlamak için yönlü gradyan histogramı ve SVM

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.

Yazar Spotlight: Yönlü Gradyan Histogram Tekniği ve Destek Vektör Makinelerini Kullanarak Verimli Görüntü Tanıma 

Destek vektör makinesine dayalı beton titreşim durumunun görüntü tanıma ve parametre analizi

The protocol described in this paper utilized the directional gradient histogram technique to extract the characteristics of a concrete image samples under various vibration cells. It employs a support vector machine for machine learning, resulting in imaging recognition method with minimal trained sample requirements and low computer performance demands. This approach significantly reduce the number of samples required, and lowers the computer performance requirements.With a laptop equivalent at 2.3 gigahertz central processing units, the recognition process completes the train space differentiation of the supported vector machine within just 50 seconds. The image segmentation below size of 128 projects and 128 projects is utilized. The number of directional vectors for statistical angle inverse is set to 12.In the 224 resolution image process, the best recognition occurrence for machine learning result is achieved.

Watch this Scientific Journal Video about Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training at JoVE.com

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training  

Destek Vektör Makinesi Eğitimi için Yönlü Gradyan Özdeğerinin Hesaplanması

Somut görüntüleri ikili hale getirdikten sonra, bu denklemi kullanarak görüntüdeki her pikselin yatay ve dikey ikili gradyanını hesaplayın. Bu denklemi kullanarak her noktanın ikili gradyan yönünü ve boyutunu hesaplayın. Ardından, n olarak temsil edilen görüntü segmentasyon bloğunun boyutunu belirleyin.Görüntüyü n'ye n'lik kare bloklara bölmek için, yatay eksen ve dikey eksen boyunca her n piksel için bir segmentasyon çizgisi ayarlayın. Pikselleri, bloktaki her pikselin ikili degrade yönüne bağlı olarak her yöndeki uygun degradenin istatistiksel açı aralığına göre sınıflandırın. Bu aralığın gradyan istatistiksel değerini elde etmek için, her yönün gradyan istatistiksel açı aralığındaki piksellerin ikili gradyanını saat yönünün tersine toplayın.Gradyan istatistiksel açı aralığı yönlü gradyan istatistikleri için elde edilen sonuçları kaydedin. Ardından, numuneleri, her alanın dört bitişik bloktan oluştuğu kesin hesaplama alanlarına bölün. Belirtilen hesaplama alanındaki her blok için açı aralığı içindeki yönlü gradyanın istatistiksel değerini hesaplayın.Ardından, bileşen olarak yönlü gradyanın istatistikleriyle özellik vektörünü oluşturun. Görüntünün yönlü gradyan özellik vektörünü elde etmek için her hesaplama alanından türetilen yönlü gradyan unsur vektörlerini birleştirin. Blok boyutu belirli bir dereceye kadar genişletildikçe, farklı titreşim durumlarına sahip beton görüntü örneklerinin her bir bloğundaki yönlü gradyan özellikleri önemli farklılıklar gösterir.

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JoVE Journal

Engineering

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

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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Support Vector Machine (SVM) Training to Identify the Vibration State of Concrete

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