Magnetic Tile Research Articles

Mutual inductance calculation of circular coils arbitrary positioned with magnetic tiles for wireless power transfer system

The mutual inductance between the primary and secondary coils is an important parameter in the wireless power transfer system, and the mutual inductance calculation of coils in many complicated circumstances has been studied. However, the mutual inductance calculation of two circular coils in arbitrary relative position with magnetic tiles have not been solved. In this study, an analytical formula of the mutual inductance with respect to the parameters of the system such as arbitrary relative position, geometry, and the properties of the magnetic tiles is proposed. The proposed formula is derived from the proposed parameter vector method, and the proposed parameter vector method can not only be used to explore the tendencies of the mutual inductance in arbitrary relative position, but also provide a fairly concise and general method for mutual inductance calculation. The finite-element analysis and experimental results validated the reliability and accuracy of the proposed formula, and an accuracy of a few parts in a thousand can be readily attainable.

Gender differences in mothers’ spatial language use and children’s mental rotation abilities in Preschool and Kindergarten

Mental rotation has emerged as an important predictor of success in Science, Technology, Engineering, and Math (STEM). By the age of 4.5years, boys outperform girls in these abilities. Because parents use less spatial language with girls at this age (Pruden and Levine, 2017), the amount of spatial language that children are exposed to at home is believed to be one potential contributor to this disparity in preschool age children. To date, it is unclear if this gender difference continues when children enter school. To address this question in this study, children not yet in kindergarten (PreK) and kindergarten and first grade children (K+) were recorded playing with their mothers using magnetic tile toys followed by children's completion of a mental rotation task. As expected, mothers used more spatial language with PreK boys than girls; however, this relationship reversed in K+, with mothers using significantly more spatial language with girls than boys. Surprisingly, despite this, boys outperformed girls on the mental rotation task. These findings suggest that although girls' exposure to spatial language is higher after entry into school, this language difference is not reflected in increases in mental rotation abilities. Thus, earlier exposure to spatial language may be vital for the development of mental rotation abilities.

GSPSO-LRF-ELM: Grid Search and Particle Swarm Optimization-Based Local Receptive Field-Enabled Extreme Learning Machine for Surface Defects Detection and Classification on the Magnetic Tiles

Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. However, existing work focuses mainly on the detection rather than the classification. In this article, we propose GSPSO-LRF-ELM that is the grid search (GS) and the particle swarm optimization- (PSO-) based local receptive field-enabled extreme learning machine (ELM-LRF) for the detection and classification of the surface defects on the magnetic tiles. In the ELM-LRF classifier, the balance parameter C and the number of feature maps K via the GS algorithm and the initial weight Ainit via the PSO algorithm are optimized to improve the performance of the classifier. The images used in the experiments are from the dataset collected by Institute of Automation, Chinese Academy of Sciences. The experiment results show that the proposed algorithm can achieve 96.36% accuracy of the classification, which has significantly outperformed several state-of-the-art approaches.

Ferrite Magnetic Tile Defects Detection Based on Nonsubsampled Contourlet Transform and Texture Feature Measurement

The ferrite magnetic tiles are widely used in industry field. At present, the defects detection in ferrite magnetic tile surfaces is done by manual work. In order to improve the defects detection efficiency and prevent missed and false detection, an automatic detection system applied to the magnetic tiles non-destructive detection was proposed based on computer vision. A suit of the automatic defects detection equipment used for magnetic tile surfaces was designed so that we can adjust the position and angle of the lightings and cameras programmatically to meet the requirement of different location for different kinds of magnetic tiles. To solve the problem of automatically detect defects from magnetic tile images which are with dark colors and low contrasts, a new hybrid algorithm which combines nonsubsampled Contourlet transform and Laws texture feature measurement was proposed to eliminate the influence of the grinding textures and extract defects. In this methodology the original image was first decomposed by nonsubsampled Contourlet transform, the characteristics of the decomposition coefficients are analyzed by Laws texture feature measurement. Then according to the texture energies of the restructured image, a segmentation threshold was determined to reset the decomposition coefficients. Finally the image was reconstructed with the reconstruction coefficients, the grinding textures were eliminated and defects were obtained by Canny operator. The experimental results show that based on the proposed method, the grinding textures can be eliminated effectively, the defects can be extracted accurately, and the accuracy rate of extraction defects can achieve 93.57%. The automatic detection system can provide an effective solution to magnetic tile defects detection industry.

FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile

Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this article, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module, and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.

Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses Gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify Gabor energy spectra to get the center number of fuzzy C-means (FCM) clustering. Moreover, the user-defined Gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack, damage, pit and other defects on the magnetic tile surface.

Surface defect saliency of magnetic tile

Computer vision builds a connection between image processing and industrials, bringing modern perception to the automated manufacture of magnetic tiles. In this article, we propose a real-time model called MCuePush U-Net, specifically designed for saliency detection of surface defect. Our model consists of three main components: MCue, U-Net and Push network. MCue generates three-channel resized inputs, including one MCue saliency image and two raw images; U-Net learns the most informative regions, and essentially it is a deep hierarchical structured convolutional network; Push network defines the specific location of predicted surface defects with bounding boxes, constructed by two fully connected layers and one output layer. We show that the model exceeds the state of the art in saliency detection of magnetic tiles, in which it both effectively and explicitly maps multiple surface defects from low-contrast images. The proposed model significantly reduces time cost of machinery from 0.5 s per image to 0.07 s and enhances detection accuracy for image-based defect examinations.

Crack detection in magnetic tile images using nonsubsampled shearlet transform and envelope gray level gradient

A novel algorithm based on nonsubsampled shearlet transform (NSST) and envelope gray level gradient (EGLG) is proposed for detecting low-contrast cracks in variably illuminated magnetic tile images. The algorithm first adopted NSST to decompose the original images into multiple subbands at different directions and scales. Then a novel column filtering based on EGLG was employed to remove the uneven background in the approximation subband, and a shearlet coefficient variance discriminator (SCVD) was used to eliminate interferences of noise and textures in the detail subbands. Finally the cracks were extracted from the reconstructed images. To verify the validity of the proposed algorithm, extensive experiments were conducted in a novel machine vision based system and its comparison with traditional algorithms was given. Experimental results show that this method achieves an accuracy rate of 95.5% in detecting cracks longer than 0.9mm with an average runtime of 0.576s, and outperforms traditional methods in terms of accuracy and robustness.

Internal defect inspection in magnetic tile by using acoustic resonance technology

This paper focuses on the validity of a nondestructive methodology for magnetic tile internal defect inspection based on acoustic resonance. The principle of this methodology is to analyze the acoustic signal collected from the collision of magnetic tile with a metal block. To accomplish the detection process, the separating part of the detection system is designed and discussed in detail in this paper. A simplified mathematical model is constructed to analyze the characteristics of the impact of magnetic tile with a metal block. The results demonstrate that calculating the power spectrum density (PSD) can diagnose the internal defect of magnetic tile. Two different data-driven multivariate algorithms are adopted to obtain the feature set, namely principal component analysis (PCA) and hierarchical nonlinear principal component analysis (h-NLPCA). Three different classifiers are then performed to deal with magnetic tile classification problem based on features extracted by PCA or h-NLPCA. The classifiers adopted in this paper are fuzzy neural networks (FNN), variable predictive model based class discrimination (VPMCD) method and support vector machine (SVM). Experimental results show that all six methods are successful in identifying the magnetic tile internal defect. In this paper, the effect of environmental noise is also considered, and the classification results show that all the methods have high immunity to background noise, especially PCA-SVM and h-NLPCA-SVM. Considering the accuracy rate, computation cost problem and the ease of implementation, PCA-SVM turns out to be the best method for this purpose.

Writing on the walls: Supporting 21st century thinking in the material classroom

A school's walls are taken-for-granted spaces, not seen as central to teaching. This article asks: How do teachers facilitate writing on secondary classroom and hallway walls that helps to build rich text environments through texts that are local, material, and multimodal? Further, how are those texts reflective of literacy practices appropriate for the 21st Century, resistant to standardized curriculum, and built on students' literate lives? Findings suggest that teachers used butcher paper, markers, post-its, magnetic tile words, and walls to engage students in 21st Century literacy practices. And, even within a test-focused curriculum, students produced an array of multimodal compositions.

Defect detection in magnetic tile images based on stationary wavelet transform

A novel approach using stationary wavelet transform (SWT) is proposed for automatically detecting low-contrast defects under various light conditions in magnetic tile images. In this method, the uneven background was removed by Sobel operation. Then the index low-pass filtering and the nonlinear enhancement were respectively used to eliminate the interference and enhance the target in subbands produced by SWT. To verify the validity of the proposed algorithm, extensive experiments were conducted in a novel machine vision based system. As the result shows, the proposed method achieves an accuracy rate of 92.86% in detecting various defects in magnetic tile surfaces with the average operation time of 0.5190s, and is superior to traditional methods in terms of the high reliability and accuracy.

A novel surface defect inspection algorithm for magnetic tile

In this paper, we propose a defect extraction method for magnetic tile images based on the shearlet transform. The shearlet transform is a method of multi-scale geometric analysis. Compared with similar methods, the shearlet transform offers higher directional sensitivity and this is useful to accurately extract geometric characteristics from data. In general, a magnetic tile image captured by CCD camera mainly consists of target area, background. Our strategy for extracting the surface defects of magnetic tile comprises two steps: image preprocessing and defect extraction. Both steps are critical. After preprocessing the image, we extract the target area. Due to the low contrast in the magnetic tile image, we apply the discrete shearlet transform to enhance the contrast between the defect area and the normal area. Next, we apply a threshold method to generate a binary image. To validate our algorithm, we compare our experimental results with Otsu method, the curvelet transform and the nonsubsampled contourlet transform. Results show that our algorithm outperforms the other methods considered and can very effectively extract defects.

Inspection of magnetic tile internal cracks based on impact acoustics

An automatic system is developed for internal cracks detection in magnetic tiles based on the impact acoustics, using wavelet packet transform (WPT), principal component analysis (PCA) and hidden Markov model (HMM). In this system, the detecting device is considered as core part to collect and analyse the impact sounds. The original impact sounds are first decomposed up to six levels based on WPT to extract the features. PCA is then performed for dimension reduction and clustering analysis. By adopting the features extracted based on WPT and optimised by PCA as inputs, an HHM classifier is developed for automatic inspection. The results of classification show that the accuracy rate is 100%, demonstrating that the system has significant potential in detecting magnetic tile internal cracks.

Automatic classification of magnetic tiles internal defects based on acoustic resonance analysis

A novel signal processing method using wavelet packet transform (WPT), linear discriminant analysis (LDA) and support vector machine (SVM) is presented for detecting internal defects in magnetic tile. In this methodology, the acoustic signal obtained by a mechanical system based on acoustic resonance is analyzed. WPT is applied to extract the normalized features of the signal. The internal defects are identified by SVM based on the extracted features optimized by LDA and a constraint algorithm. The experimental results demonstrate that the presented approach can be employed for a promising application of automatic detection of internal defects in magnetic tile.

磁瓦表面缺陷的机器视觉检测方法

In order to improve the accuracy and reduce the prediction time of detection of magnetic tile surface defect,a method of the feature selection and the bias classification is proposed.While offline training,the subgraphs which are generated from the transformation by Gabor filters are fused.Then the texture features of the pictures are extracted.The Relief algorithm is improved to extract the feature subset which have a strong correlation with category and remove redundant features.In order to decrease the miss rate of defective magnetic tile,the bias classification is performed before used LSSVM to predict the categories.It is proved that the proposed method can achieve about 99.09%as the accuracy rate of the defect magnet and the overall accuracy rate is about 96.89%.Compared with the original method,the online prediction only costs 67.4ms which decreased by nearly 1/4.

Detection of surface crack defects on ferrite magnetic tile

A new approach is proposed for automatically detecting crack defects with dark colors and low contrasts in magnetic tile images using the fast discrete curvelet transform (FDCT) and texture analysis. In this methodology the original images were first decomposed and reconstructed based on the FDCT. Then the thresholds of decomposition coefficients were calculated by texture feature measurements. With these thresholds the surface textures in the images can be eliminated. Finally by extracting contours from the reconstructed images, the expected images without textures but with crack defects contours were obtained. Experimental results show that the proposed method could eliminate the contours of the textures, and extract from the image cracks longer than 0.8mm.

Extraction Method for the Surface Image Defects of the Magnetic Tile Based on Adaptive Gaussian Filtering

This paper proposed a defects extraction method based on one-dimensional adaptive Gaussian filtering. To overcome the difficulties as various defects, low contrast, complex background and overall uneven brightness, this method designed a new one-dimensional adaptive Gaussian filter based on the defect size. According to the gray change of the magnetic tiles surface image in the different regions, we progressed the Gaussian filtering based on the selective one-dimensional scan line by region, and made the width of the filter can automatically adjust with the defect size, simulated the threshold value curve, and extracted the magnetic tiles surface defects. The experiment shows that this method can accurately and quickly extract the various types of defects of the magnetic tile surface.

Magnetic Shielding of Levitation Melting Devices

Magnetic shielding of levitation melting induction heating devices does not only reduce the stray field of the device; it has also an influence on the position of the workpiece. The aim is to investigate the change in magnetic field, and the change in steady-state position of the workpiece caused by adding copper or soft magnetic shields to the device. The calculation of the force on the workpiece and the magnetic field around the induction heater is done by either a 2-D or 3-D finite element model, or by a circuit model combined with analytical expressions for the magnetic field and the force. By adding an axisymmetric copper shield surrounding the inductor (shielding by induced currents), the levitated workpiece moves downward, for the same excitation current. By adding a nonaxisymmetric wall of soft magnetic ferrite tiles at one side of the induction heater (shielding by flux-shunting), the workpiece moves upward, and it shifts away from the axial symmetry axis.

Self-Assembly of 3D Magnetic Tiles

Self-assembly is a term most commonly associated with biological processes, especially cellular manufacture of DNA. By utilizing a four element code, cells are able to actively reproduce the genetic framework of life. By applying a similar principle to the macro-scale, tangible 3D objects can be fashioned by a coded series of simple building blocks. The goal of this paper is to examine the fabrication needs of macro-scale 3D self-assembly. A simple way of showing 3D self-assembly is by manufacturing a chain of tiles implanted with permanent magnets. The tiles are coded by the relative orientations of two pairs of embedded NdFeB magnets. At each tile interface, the chain is bent by the simultaneous attractive and repulsive forces of the magnet pairs. Using this information, chains are coded to automatically fold into any shape without self-intersection. The principles outlined above will be shown through physical demonstrations wherein a coded chain self-assembles into a 3D object. To accomplish this, the chain is injected into a buoyant environment through a chute of comparable diameter. Beyond the scope of this project, future challenges to 3D self-assembly include applications to MEMS-scale projects and applications to practical macro-scale products.