Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Diagnostyka

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Education Quality Detection Method Based on the Probabilistic Neural Network Algorithm

Wu Changdong, Jiang Hua, Wang Ping

Diagnostyka

2020

Vol. 21, No. 4

79--86

The traditional education quality detection method is too single and unreasonable, which is not suitable to evaluate students' ability comprehensively. In this paper, the probabilistic neural network (PNN) algorithm is used to detect the education quality by considering the important impact between the various achievements of students. PNN algorithm originates from Bayesian decision rule, and it uses the non-linear Gaussian Parzen window as the probability density function. As PNN model has the virtues of strong nonlinear and anti-interfering ability, it is fit to detect the education quality by classifying the students' achievements. Besides, the influences of different evaluation models on classification accuracy and efficiency are also discussed in this paper. Furthermore, the effect of spread value on PNN model is also discussed. Finally, the actual data are used to detect the education quality. Experimental results show that the detection accuracy can reach 95%, and the detection time is only 0.0156s based on the proposed method. That is to say, the method is a very practical detection algorithm with high accuracy and efficiency. Moreover, it also provides a reference for how to further improve the teaching quality.

Catenary image enhancement method based on curvelet transform with adaptive enhancement function

Wu Changdong

Diagnostyka

2019

Vol. 20, No. 2

3--10

In the process of catenary failure diagnosis system based on image processing technique, some catenary images present low contrast, which need to be enhanced. Curvelet transform has the high directional sensitivity and anisotropy, which is suitable for image enhancement because of its optimal sparse representation of image with rich details and edges. First, the catenary image is decomposed by Curvelet transform to get its high and low frequency coefficients, then adjust the high frequency coefficients using the enhancement function. Afterwards, combine the high frequency coefficients and low frequency coefficients by the inverse Curvelet transform, and thus to get the enhanced catenary image. In this paper, Curvelet transform is compared with the traditional enhancement methods. The experimental results show that the proposed method can effectively enhance the low contrast catenary images, the catenary insulator, arm, hanger, pillar and locator part become visible, the details become more obvious. Moreover, as for the online application of catenary failure diagnosis system, efficiency is another important consideration. The experimental results also show that the cost time of catenary image enhancement is within a few tens of seconds, which meets the requirements of catenary failure diagnosis system.