Earticle

Hypervelocity impact by space debris can cause direct damage to spacecraft internal subsystem, and even lead to catastrophic failure of manned spacecraft. To research the crater characteristics of different shape of projectiles hypervelocity impact on semi infinite thick aluminum alloy plates, Lagrange and SPH (smoothed particle hydrodynamics) coupling method in AUTODYN is used. By analyzing the influence on crater characteristics by different impact velocity of spherical projectile, different ratio between length and diameter and different impact velocity of cylindrical projectile, we obtain the law of crater characteristics of the semi infinite thick plates. The research results have reference value and engineering application of guiding significance to the study of spacecraft space debris shield structure design.

With the fast development of industry and computer science technology, the image color recognition has been a hot topic. The prior research focus more on sensor and hardware based approaches which are not intelligent or convenient. In this paper, we present a novel image segmentation combined color recognition algorithm through boundary detection and deep neural network. The deep learning algorithm can largely increase the accuracy of classification whereas cut down the processing time consumed, we adopt the deep neural network and support vector machine to extract image features both in RGB and YUV color spaces. Boundary detection in sudden change, by contrast, is more global in nature, such as texture, so need to integrate the whole information of the image. Under the guidance, we modify the current segmentation methods with boundary detection technique to serve as the pre-processing step before classifying colors. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise. Further analysis is also conducted in the final section.

This paper describes a new color image edge detection method. Edge detection application based on fuzzy set theory is introduced in this paper. First of all, we design fuzzy inference engine to detect image edges. In inference engine step, two Gaussian membership functions in two directions (horizontal and vertical) are used for input signals. Finally, two triangle and one Gaussian membership functions were used for defuzzification step. By using fuzzy process and defuzzification, we obtain a relationship between input and output values. Simulation results show the proposed method outperformed conventional edge detection methods.

This study tried to determine how teachers viewed continuity in the English curriculum between elementary and middle school. To achieve this goal, the authors implemented a survey toward 103 teachers and analyzed their quantitative and qualitative responses. While most participants comprehended the English curriculum at their own school level, only a few teachers understood English curricula at other school levels. The study revealed that middle school teachers had only a vague understanding of classroom activity as they had not usually utilized such activities in their daily teaching. Furthermore, the teachers thought negatively about continuity in activity, difficulty, and amount of learning. The results led this study to suggest 35 new questions designed to examine seven factors involved in English teachers’ cognition of continuity of the English curriculum.

In this paper, we propose an improved method for the removal of additive Gussian white noise from PET-CT images. Different from the traditional sparse representation based denoising methods, our method is composed of two distinctively steps such as the preliminary denoise and the detail compensation. By constructing a sparse representation model, denoising is formulated as an optimization problem that can be solved on an over-complete dictionary. The proposed method effectively trains this dictionary by using K-SVD algorithm with atom replace model. Then the preliminary denoised image is reconstructed through improved OMP algorithm with the fidelity factor of SSIM (Structural Similarity). The detail compensation image is obtained by using the difference between the noisy image and the preliminary de-noised image, and the improved OMP algorithm is employed again to get the denoised detail compensation image. Finally, the final denoised image is reconstructed by adding the denoised detail compensation image to the preliminary denoised image. Experiment results have shown that the proposed method is better than some other denoising methods in terms of PSNR and SSIM.

In this study, we used the STEAM education program on climate change as a high-school science club activity and analyzed its effectiveness. Ten rounds of lessons and a 1-day field trip for collecting fossil leaves were carried out. The overall satisfaction of students with the STEAM program was highly positive (the mean score of 4.76 on a 5-point Likert scale). Additionally, the score was high when combined with the optimized level of difficulty, as it gave a sense of challenge and originality to the students’ tasks (the score of 4.53). The students liked the process and direct experience of the STEAM educational factors and convergence thinking across various academic fields with the common subject, namely, that of climate change, and fossil analysis. It is worth noting that, prior to participation in the program, the students did not previously encounter academic learning of this type based on hands-on experience. Furthermore, the students experienced enjoyed creativity, character education, and planning ways to reduce climate change during the program.

This paper puts towards the improvement of the HMM algorithm, then proposed the HMM state identification model of the electronic equipment. It is verified by examples of the experiment, which results show that the HMM state recognition effect is better, recognition rate can reach 94%.Finally, and the experimental results were analyzed, and found the number of hidden states and have bigger influence on the model of training samples.

With the development of network and video compression technologies, more and more video applications emerged and enriched people’s daily life. Since high data rate of videos could exhaust bandwidth and starve data streams, DiffServ (Differentiated services) is proposed to balance synchronous transmission of two stream types. However, most existed researches aimed at receiving quality promotion of video streams, leading to poor performance of data streams. In this paper we try to find how to reserve proper amount of resources for video streams by evaluating transmission of various video sequences. Results show that (1) video transmission performance is content aware and is determined by data rate, data rate variation and continuous burst of the video. Reserved bandwidth of a video should be proportional to its data rate. If data rate variation is significant, more bandwidth should be reserved. If continuous burst occurs, additional bandwidth is required. (2) If accurate data rate and its variation can be obtained and data rate variation and/or continuous burst are remarkable, adaptive bandwidth reservation is recommended. Otherwise, fixed reservation is better. (3) Large buffer size in routers will improve the video receiving quality.