基于LMD的PWM整流电路故障特征提取新方法

脉冲宽度调制(PWM)整流电路结构日益复杂,对其可靠运行提出了更高的要求;对局域均值分解(LMD)用于PWM整流电路的故障特征提取进行研究,提出一种基于LMD和加权频带能量法的特征提取新方法;该方法通过逐步抽取调频调幅成分将故障信号在频域上展开,然后基于信号能量的频带分布特点,充分考虑各频带成分与故障的相关性,构造故障特征向量,实现特征提取;最后以PWM整流电路为例进行仿真,相电压380V,仿真时间0.5s,0.1s时注入故障;结果表明,该方法能有效地提取故障信号的特征,并降低特征向量的维数。     

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.     

社交网站监管法律问题研究

本文着重分析了社交网站常见的法律问题,并通过比较研究各国的做法,试图总结出我国加强社交网站管理的监管措施。     

创新实践教学模式,培养复合型人才

针对目前高校现行实践教学体制存在的弊端,在分析产生原因的基础上,提出一种科学的实践教学模式,该模式与信息与计算科学专业培养的目标以及社会发展的内在需求相结合,在信息处理和软件开发能力培养方面取得一定效果,增强了学生就业竞争力。     

A level set method based on the Bayesian risk for medical image segmentation

This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.     

网络信息安全问题的法律探析

网络技术的发展以及应用深刻地影响着人类社会,在给人类带来巨大便利的同时也带来了诸如网络信息安全问题等隐患,这不仅在技术层面给我们提出新的问题,在法律层面也提出了更高的要求。本文介绍了对网络信息安全问题进行法理探讨的背景,并且在分析了国内外研究现状的基础上提出在法律层面解决网络信息安全问题的初步构想。     

浅谈计算机信息技术在图书馆中的应用

在社会日益发展的今天,传统的图书馆服务模式已经无法适用新形势的发展要求,数字化、网络化等计算机信息技术的出现并应用,促使图书馆从传统功能模式向开放式、网络式的信息服务模式转变,由单一功能向多功能转变,从根本上推动了图书馆事业的发展。     

搜索引擎与个人信息安全

搜索引擎的发展给我们的工作和生活带来了便利,同时给我们的个人信息安全带来了威胁。该文从搜索引擎的原理出发,简要分析了个人信息泄露的途径,并提出了避免信息泄露的思路和方法。     

An Achilles heel: denial of service attacks on Australian critical information infrastructures

Critical, or national, information infrastructure protection, referred to as either CIIP or NIIP, has been highlighted as a critical factor in overall national security by the United States, the United Kingdom, India and the European Community. As nations move inexorably towards so-called ‘digital economies’, critical infrastructure depends on information systems to process, transfer, store and exchange information through the Internet. Electronic attacks such as denial of service attacks on critical information infrastructures challenge the law and raise concerns. A myriad of issues potentially plague the protection of critical information infrastructures owing to the lack of legal regulation aimed at ensuring the protection of critical information infrastructures. This paper will highlight the legal concerns that relate to the denial of service attacks on critical information infrastructures and provide an introductory overview of the law as it relates to CIIP in Australia.