Automatic Recognition Method of Letter Images in English Self-Learning Based on Partial Differential Equation Method

According to the current situation of knowledge popularization, students simply rely on the knowledge learned in the classroom that is far from adapting to the development of modern society; so, every student needs to have the consciousness and ability of independent learning. The research of the English self-help learning system based on partial differential equation method comes into being with information network technology as the foundation for survival and development. The existing partial differential equation recognition models based on average curvature motion are all edge-based and need to use the external force defined by the image gradient to attract the zero level set (evolution curve) to move to the target edge and finally stay on the target edge. Therefore, it is difficult to obtain ideal results when extracting fuzzy or discrete boundaries (perceptual boundaries), and it is very sensitive to the selection of initial contour and noise. To solve this problem, this paper proposes a new recognition model of partial differential equations based on mean curvature motion. This overcomes some defects of existing edge models because it is region-based and does not require image gradient as a condition to stop evolution. The proposed model can avoid manual initial curve selection and allow stopping conditions to be set in the algorithm. In addition, in the numerical solution of partial differential equations, the existing model uses upwind difference scheme, and the semi-implicit additive operator separation method is adopted in this paper. Some other layers are added, and some hyperparameters are adjusted when the convolutional neural networks of inception PDEs are constructed by stacking the structure of inception PDEs. In the contrast experiment with the prototype, the software and hardware environment are the same, and the input is exactly the same. For the handwritten English alphabet data set, the variant structure can obtain more than 90% of the training accuracy and verification accuracy, which is better than the experimental accuracy of the prototype. In addition, because the inception PDE structure contains fewer parameters than the prototype, it is more computationally efficient and takes less training time per batch than the prototype.

Kinerja OpenMP pada Pengolahan Citra dengan Model Curvature Motion

<p>Evolusi dari sebuah bentuk geometri meliputi perubahan curvature yang terdapat dalam<br />bentuk tersebut. Perubahan curvature ini tidak lepas dari perpindahan titik-titik pembentuknya<br />dan diformulakan sebagai Mean Curvature Motion (MCM). MCM telah dipelajari secara<br />mendalam untuk menyelesaikan salah satunya kurva Jordan pada pemodelan fisis. Pada jurnal<br />ini, solusi MCM diaproksimasi menggunakan skema finite difference dan disimulasikan ke<br />dalam paralel OpenMP. Untuk menghitung performansi paralel, dilakukan simulasi sebanyak<br />10 niter berbeda pada thread sejumlah 2, 4, dan 8. Dari simulasi yang telah dilakukan,<br />didapatkan hasil bahwa performa paralel lebih membutuhkan waktu komputasi yang lebih<br />rendah daripada serial. Selain itu, didapat pula rata-rata efisiensi kode paralel menggunakan<br />2 Thread lebih tinggi daripada menggunakan 4 Thread dan 8 Thread. Sebagai contoh pada<br />ukuran niter 50000, kecepatan masing-masing 2, 4, dan 8 Thread adalah 180.422, 156.002,<br />dan 333.243 s, serta efisiensi masing-masing 2, 4, dan 8 Thread adalah 113%, 66%, dan<br />34,8%.</p>

Design and analysis of high contact ratio spiral bevel gears by modified curvature motion method

This paper presents a new method to design a seventh-order transmission error for high contact ratio spiral bevel gears by the modified curvature motion method to reach the purpose of reducing or eliminating gear vibration and noise. In this paper, firstly, based on the predesigned seventh-order transmission error, the polynomial coefficients of transmission error curve can be obtained. Secondly, a method named modified curvature motion method is used to generate the spiral bevel gear with the predesigned transmission error. Lastly, based on TCA and LTCA, we verify the feasibility of the modified curvature motion method to generate spiral bevel gear with seventh-order transmission error, and the meshing impact of gear set with the seventh-order and second-order function of transmission error is analyzed and compared. The results of a numerical example show that the seventh-order transmission error acquired by the modified curvature motion method can effectively reduce the meshing impact of spiral bevel gears. The tooth modification method and meshing impact analysis method can serve as a basis for developing a general technique of flank modification for spiral bevel gears.

Image Restoration Using Adaptive Region-Wise p-Norm Filter with Local Constraints

In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387–405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance [Formula: see text]) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank–Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures.