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.     

Study of delay-independent decentralized guaranteed cost control for large scale systems

This study introduces delay independent decentralized guaranteed cost control design method based on two controller structures for nonlinear uncertain interconnected large scale systems with time delays. First, a set of equivalent Takagi-Sugeno (T-S) fuzzy models are extended to represent the systems. Then a decentralized state-feedback guaranteed cost performance controller is proposed for the fuzzy systems. Based on delay independent Lyapunov functional approach, some sufficient conditions for the existence of the controller can be cast into the feasible problem of LMIs irrespective of the sizes of the time delays so that the system can be asymptotically stabilized for all considered uncertainties whose sizes are not larger than their bounds. Finally, the minimizing approach is proposed to search the suboptimal upper bound value of guaranteed cost function. Moreover, the corresponding conditions are extended into the generalized dynamic output-feedback close-loop system. Finally, the better control performances of the proposed methods are shown by the simulation examples.     

基于FPGA与ARM单片机的CAN总线分析仪设计

针对CAN总线网络通信质量分析、测试和验证的需要,论证了一种基于ARM单片机与FPGA完成的CAN总线分析仪设计;该分析仪采用集成的CAN控制器与专用总线电平采样双通道信息采集硬件结构;采用单片机及基于FPGA的专用电路完成对CAN网络的实时通信数据的收集与监控;采用PC机完成数据分析与参考信息显示;文章详细讨论了分析仪硬件的具体设计;分析了CAN总线通信波特率的自动检测、总线故障的检测与定位方法;最后结合软件设计给出了所设计的CAN总线分析仪的实测试验结果,可实现总线报文的正常监测、总线状态分析与错误检测功能。     

基于FFT图像差值在轮胎印痕识别中的研究

轮胎印痕识别对交通事故的现场勘测及事后侦查有着十分重要的作用.介绍了轮胎印痕识别在交通肇事逃逸案侦破中的具体应用,研究了基于FFT的图像差值原理,提出了基于FFT图像差值算法的轮胎印痕识别方法,并分析了具体的试验数据,结果显示,该方法有利于提高轮胎印痕识别的成功率,为快速查找肇事逃逸车辆提供了理论基础和科学依据.     

一种改进小波收缩阈值的信号消噪算法

小波变换是一种时频分析的方法,在信号处理领域广泛应用.文章在经典的小波软、硬阈值消噪方法的基础上,提出了一种基于小波分析的改进阈值信号消噪方法.Matlab仿真结果表明,此法同时克服了软、硬阈值方法的缺点,其消噪效果较好.     

计算机在医学领域的应用

根据医学领城应用计算机技术的现状,对医学实验、辅助诊断、图像处理、医院综合管理等方面的应用做简要概述,并举例说明计算机在医疗领域的具体应用.     

一种基于RSSI的车载WiFi相遇时间预测方法

随着WiFi技术的普及,在移动车辆上使用路边AP进行数据通信变得越来越容易.使用车载WiFi通信有一个关键问题是如何选择最优的AP.针对该问题,提出了一种通过监测AP接收信号强度RSSI的变化趋势来预测车辆与AP相遇时间的算法.车载WiFi通过预测不同AP的相遇时间选择预测时间较长的AP.实验分析表明,在确定无线电信号衰减因子的情况下, 通过多次采样可以减小误差,相遇时间预测结果的准确性较高.算法提高了平均连接时间,减少了切换次数,增强了通信稳定性.     

A novel color image watermarking scheme in nonsampled contourlet-domain

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust color image watermarking scheme against geometric distortions. Based on the support vector regression (SVR) and nonsubsampled contourlet transform (NSCT), we propose a new color image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the geometrically invariant space is constructed by using color image normalization, and a significant region is obtained from the normalized color image by utilizing the invariant centroid theory. Then, the NSCT is performed on the green channel of the significant region. Finally, the digital watermark is embedded into host color image by modifying the low frequency NSCT coefficients, in which the HVS masking is used to control the watermark embedding strength. In watermark detection, according to the high correlation among different channels of the color image, the digital watermark can be recovered by using SVR technique. Experimental results show that the proposed color image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.