Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

Diode-Pumped Solid-State Lasers for Inertial Fusion Energy

We have begun building the Mercury laser system as the first in a series of new generation diode-pumped solid-state lasers for inertial fusion research. Mercury will integrate three key technologies: diodes, crystals, and gas cooling, within a unique laser architecture that is scalable to kilojoule and megajoule energy levels for fusion energy applications. The primary near-term performance goals include 10% electrical efficiencies at 10 Hz and 100J with a 2–10 ns pulse length at 1.047 m wavelength. When completed, Mercury will allow rep-rated target experiments with multiple chambers for high energy density physics research.     

基于CBS模式的档案管理数字化系统的研究

随着我国经济和社会的飞速发展.人民对信息的要求日益提高.档案管理在这种新的形势下.面临巨大的挑战.传统的档案管理已经不能适应新形势的发展,档案管理数字化系统进行研究势在必行.本文主要对档案管理的涵义、数字化系统方案设计.文档图像处理和系统软件部分等进行了相应的阐述.本文的研究对以后指导档案管理工作具有一定的理论意义和现实意义.     

A high payload frequency-based reversible image hiding method

To provide both a high payload capacity and a good stego-image quality, a high payload frequency-based reversible image hiding (HPFRIH) method was presented in this paper. These performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through Haar digital wavelet transform (HDWT) method followed by an adaptive arithmetic coding method to encode the HDWT coefficients in a high-frequency band. The HPFRIH method was used to conceal both the compressed data and the secret data in the high-frequency band. This simple method can completely reconstruct the cover image only from the stego-image. Moreover, experimental results showed that the HPFRIH method offers both impressive hiding capacity and satisfied stego-image quality.     

智能车竞赛中道路的视频识别方案

视频信号的的采集和处理是一个复杂的过程,这需要程序设计者了解视频信号的构成,充分利用复合视频信号中的各种同步信息作为视频信号采集的控制信号,从而完成信号的采集。     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

混沌权值变异的Huffman树图像加密算法

提出一种采用混沌权值变异的Huff man树的图像加密算法.此算法在熵编码过程中,以基本的Huffman树为标准,利用双耦合混沌序列1对DC系数进行树的结构未变异、路径值变异的加密;再利用双耦合混沌序列2对AC系数进行码字序号的加密.论文对算法进行了仿真,并对安全性、计算复杂度、压缩比性能进行了分析.实验结果表明,该算法基本上不影响压缩效率,且计算复杂度低、安全性高和实时性好,可用于网络上的图像服务.     

基于改进傅里叶描绘子的CBIR系统

在现有的基于傅里叶描绘子的CBIR系统中,为了提高检索速度,一般需要舍去物体轮廓经傅里叶变换后的大部分高频分量.当物体轮廓在细节部分具有较高能量时,此方法不具备有效性.为尽可能保证检索准确率并兼顾检索速度,在原有傅里叶描绘子上进行扩展,避免直接舍去高频分量,引入Fisher判别分析法将描绘子映射到子空间进行降维,并保证...