光反馈激光器非线性仿真研究

一些传统计算方法对非线性系统的分析存在着一些困难。针对光反馈半导体激光器收发系统微分方程组的特点,对非线性性质做出了数学上的分析,考虑到在光反馈半导体激光器收发系统中,其微分方程含有延迟因子,无法直接利用多节点上的值的加权和来近似计算斜率。基于龙格-库塔法提出了一种有效的数值仿真算法,并在matlab平台上对给定参数条件下的发射端与接收端的归一化光场以及同步误差进行了仿真,证明了接收端光场信号可以很快与发射端光场信号一致,仿真结果表明仿真算法能有效对系统进行仿真分析。     

基于二重差分法的光流场运动检测

对视频图像序列中运动目标的检测,是计算机视觉领域长期关注的问题.传统的Horn-Schunck光流算法非常耗时,不利于在视频图像序列中做实时分析,故提出采用结合背景差分法与帧间差分法的二重差分法(DS)来做光流算法的预处理.实验结果也表明二重差分法的光流算法(DSOF)比单纯用Horn-Schunck方法计算光流效率更高,用时更短,能更好地满足对视频图像序列做实时运动检测分析的需求.     

光通信中一种有效的差分脉冲位置宽度调制

考虑脉冲位置调制(PPM)在解调时需要严格的时钟同步和符号同步,而且功率越高,时隙越窄的问题,结合差分脉冲位置调制(DPPM)和脉冲宽度调制(PWM)提出了一种有效的调制方式--差分脉冲位置宽度调制(DPPM+PWM).研究了采用DPPM+PWM的红外通信系统在加性高斯白噪声干扰下的性能,对DPPM+PWM的符号结构、发射功率、带宽需求、传输容量以及误包率等特性进行了分析,并通过仿真与开关键控(OOK),PPM,PPM等调制方式进行了比较.结果表明DP-PM+PWM频带利用率高,传输容量大,不需要符号同步,且增加了脉冲信号的长度,大大简化系统实现,是一种很有实际应用价值的调制方式.     

基于关联规则的分布式通信网告警相关性研究

描述了基于数据挖掘的通信网告警相关性分析.在分布式数据库中直接运用序列算法效率很低,因为这需要大量的额外通信.为此提出了一种有效的分布式关联规则挖掘算法--EDMA,它通过局部剪枝与全局剪枝来最小化候选项集数目和通信量.在局部站点上运用先进的压缩关联矩阵CMatrix统计局部项集支持数.此外还利用项目剪枝与交易剪枝共同来减少扫描时间.最后仿真验证了EDMA比其他经典分布式算法有更高的运算效率、更低的通信开销以及更好的可扩展性.     

P2P入网机制研究

P2P节点入网机制是构建P2P网络的基础.不同拓扑结构的P2P网络,节点加入机制也各不相同.提出了4种P2P入网模型,给出了相应的性能指标,对P2P入网机制进行分析、对比,有助于P2P网络自组织构造的进一步研究.     

基于动态贝叶斯网络的社交圈归属匹配模型

为了帮助社交网中新成员寻找与之最为合适的社交圈,尝试采用动态贝叶斯网（DBN）理论解决社交网分析应用中成员（节点）与社交圈（集合）的匹配问题。将圈内成员个人的多项兴趣爱好程度作为描述社交圈基本属性的特征向量,对每一类圈子建立了带有辅助信息形式的DBN模型,求解最大输出概率即为最佳匹配对象。结果表明,在客观测试和主观评价两方面,该模型都收到了较为满意的结果。     

电子编务在现代化科技期刊编辑出版中的应用

通过对电子编务在现代化科技期刊编辑出版中的应用探讨,使我们进一步认识电子编务不仅可以提升编辑在日常编辑工作的效率,使得编辑出版流程环节简化、规范,强化稿件管理,提高出版效率,而且对编辑部在竞争激烈的科技期刊出版环境下生存和发展能力的提高起着重要的作用。     

A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.