Chinese-based research on subject-covered information retrieval supervised by textual semantic domain

By means of analysis of artificial intervention in ready-retrieved text, training set used to compare with new texts from large-scale real texts corpus is provided. It is based on the data-originated presentation of training set that a special formula to calculate semantic cohesion between new texts and training set is devised. The semantic cohesion of new text is the average value of semantic evaluation of all elements involved, and semantic evaluation of an element depends on its semantic relevance with the training set and on the semantic ratio of its domain to synonymous domain. In terms of empirical verification a conclusion is drawn that semantic cohesion is the key measurement standard of textual retrieval. Despite the advantages of textual retrieval, limitations of formula-raised condition and analyst＇s accomplishments make the analysis involved in this paper imperfect.     

试析中西方绘画形式异同之思想根源

中国人在绘画艺术上走的道路与西方绘画不同,究其思想根源在于中国文化中的艺术精神。一方面是孔孟儒家所显现的"中和之美"的典型;另一方面是由老庄道家所显现出来的彻底的纯艺术精神的性格。中国艺术家在儒道思想的影响下,致力于自身与天道的融合,西方的画家则是在希腊文化和基督教文明人文主义思想的光环中完成自己理想的嬗变。     

基于DSP的电源控制系统的软硬件开发

介绍了一种基于DSP的逆变电源控制系统,详细阐述了系统组成结构和软硬件设计.该系统采用TMS320F2812作为主控制器,最大程度的减少了硬件电路的搭建,实现了对逆变电源的数字控制.实验结果证明,该系统具有良好的稳压功能,既能满足一定控制精度要求,又能满足实时性要求,可以很好的控制115V/400Hz逆变电源的输出.     

Dual-band reflective polarization converter based on metasurface

In this paper, a dual-band and reflective polarization converter based on metasurface is proposed. Its unit cell is composed of two layers of metal plates separated by a dielectric substrate. The simulation results show that the proposed converter is able to convert x- or y-polarized incident waves into cross-polarized waves perfectly in frequency bands of 6.75—10.59 GHz and 17.78—19.61 GHz, and the polarization conversion ratio (PCR) is nearly 100%, which can also convert linearly polarized waves into circularly polarized waves at four frequencies. It can be widely used in applications of radar satellites, antenna design and telecommunication with the function of realizing polarization conversion in two bands and achieving high PCR simultaneously.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

一种新型单层递归神经网络解决非光滑伪凸优化问题

非光滑伪凸优化问题是一类比较特殊的非凸优化问题,常出现在各类科学与工程应用中,因此具有很大的研究价值.针对现有神经网络模型解决非光滑伪凸优化问题存在的不足,该文基于微分包含理论,提出一种新型单层递归神经网络模型.通过理论分析,证明了神经网络状态解在有限时间内收敛到可行域,且永驻其中,最终神经网络状态解收敛于原优化问题的最优解.最后,通过数值实验,验证了所提理论的有效性.与现有的神经网络相比,该文所提神经网络模型结构简单仅为单层,不需要提前计算罚参数,且对初始点选取没有任何特殊的要求.