Krylov subspace method based on data preprocessing technology

The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.     

Exploration of TCM Masters Knowledge Mining

Traditional Chinese medicine （TCM） has a rich knowledge about human health and disease by its special way evolved along a very long history. As modern medicine is achieving much progress, arguments and disputes toward TCM never end. To avoid losing precious knowledge of living TCM masters, endeavors have been engaged to systematic collection of those knowledge of TCM masters, such as their growth experiences, effective practical cases toward diseases and typical therapeutic principles and methods. Knowledge mining methods have been expected to explore some useful or hidden patterns to unveil some mysteries of the TCM system. In the paper, some computerized methods are applied toward those collected materials about some living TCM masters in China mainland to show a different way of exposing essential ideas of those TCM masters by correspondence visualization which aims to help people understand TCM holistic views toward disease and body, and facilitate tacit knowledge transfer and sense-making of the essence of TCM. The work is one kind of qualitative meta-synthesis of TCM masters＇ knowledge.     

The urokinase receptor and integrins in cancer progression

Enhanced levels of expression of urokinase receptor (uPAR) and certain integrins have been linked to cancer cell progression. This has classically been attributed to matrix degradation via the activation of the urokinase (uPA)/plasmin system and modulation of cell motility and survival through integrin engagement. More recently, uPAR has been shown to play multiple roles independent of protease activity. Specifically, uPAR has been shown to be intimately involved in the regulation of cell adhesion, migration and proliferation in part through interactions with other membrane partners, including integrins. The goal of this review is to summarize recent insights in the function of uPAR/integrin interactions, to provide a framework for understanding the importance of these interactions in the context of cancer, and to highlight its potential as a target for therapeutic intervention.     

基于蚁群系统的仿真机器人足球攻防转换策略

为了提高机器人足球的整体攻防能力,在动态攻防转换策略中建立了基于蚁群系统的混合通信机制,既增强了足球机器人的协作能力,又避免了足球机器人陷入冲突或任务死锁状态,增加了攻防转换的灵活性。在该通信机制的基础上采用攻防区域划分的方法,以球的位置为驱动信息,通过信息素来确定变换队形。在FIRA仿真平台中实验表明,基于蚁群混合通信机制的动态攻防转换策略使得仿真足球机器人在比赛过程中表现出更优异的性能,改善了球队的整体攻防能力,可有效应用于机器人足球比赛中。     

一种新型多DSP并行计算结构及其应用

传统的雷达信号处理系统的设计方法是针对特定应用的,因此系统的通用性差,而具有超级计算机体系结构的通用高速实时雷达信号处理系统有望解决这一问题。该系统的关键部件为担负具体计算任务的处理结点。首先提出了一种新型的、由5片ADSP-2106x构成的多DSP并行计算结构。它具有运算能力强、I/O带宽大、通信手段多样、能灵活地改变拓扑结构、可扩展、通用性强等特点。并且以此并行计算结构为核心设计实现了通用高速实时雷达信号处理系统的处理结点。     

基于时间-距离-多普勒像的编队目标架次检测

针对实际编队目标在距离上的分布特性和目标多普勒频率的变化规律,利用联合时频变换代替传统傅立叶变换,建立目标时间-距离-多普勒立体像,提出一种基于立体像的目标架次检测方法。该方法不仅能给出清晰的瞬时距离-多普勒像,而且能反映目标距离-多普勒像的时间演变特性,较好地解决了目标在距离上的分布和多普勒像的模糊。实测回波数据验证了该方法的有效性,成功地实现了4架编队飞机目标的架次检测。     

基于电子商务环境的自动化立体仓库系统研究

随着信息技术的高速发展,电子商务思想日益为企业所接受,越来越多的企业开始尝试其使用,同时也对物流自动化系统的开发提出了更高的要求。以某物资供应中心自动配送系统为例,探讨了基于电子商务环境条件下自动化立体仓库系统的管理控制与作业模式。     

一种高效挖掘高维数据的频繁闭合模式算法

为了克服传统高维数据挖掘频繁闭合模式算法迭代产生子表,引起算法执行时间长和存储开销大等问题,提出了一种高效挖掘高维数据的频繁闭合模式的算法EMHCP. EMHCP算法采用一种新型结构位图表来压缩存储数据,在仅扫描数据库一次后,建立位图转换表.根据位图转换表来构建混合树结构,采用深度优先的方式和有效的剪枝策略高效挖掘出所有的闭合模式.从而有效地缩小了搜索空间,加快了处理速度.通过在生物数据库应用的实验结果表明, EMHCP算法比已有的CARPENTER和TD-close等算法更为有效.     

A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning

Software Defect Prediction(SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction(CPDP). In this work,we propose a transfer-leaning algorithm(TSboo...     

基于FPGA的交流伺服系统电流环带宽扩展

在电流环的数学模型基础上,分析了电流环带宽与电流环路延时的关系,比较了几种典型电流环时序下产生的延时,对电流采样和PWM占空比更新时序进行了改进,并在FPGA中得到了具体的验证和实现.实验结果表明:这种改进的电流环时序克服了原有电流环时序的问题,能在不改变功率器件开关频率和不损失输出电压能力的基础上减小电流环路延时,从而提高电流环带宽,改善电流环和速度环的控制性能.