面向可重构扫描网络的锁定隔离安全结构

为使可重构扫描网络免受未经授权的访问、恶意仪器对传输数据的窜改和嗅探3种安全攻击的影响,提出了锁定隔离安全结构.该结构首先把彼此不具有安全威胁的仪器分成一组,通过控制隔离信号实现组与组之间的单独访问,以防止恶意仪器对传输数据的窜改和嗅探.然后通过使用锁段插入位保护关键的仪器,只有扫描网络中处于特定位置的多个键值被设置成特定值(0,1序列)时锁段插入位才能打开,能加大未经授权访问的难度.此外,为解决仪器分组多导致硬件开销大和布线困难的问题,提出了仪器分组算法,根据仪器间的安全关系构建无向图,然后对无向图求极大独立集,能有效地减少仪器分组数.在ITC 02基准电路上的实验结果表明,与国际上同类方法相比,所提的安全结构打开锁段插入位所需要的时间增大了7倍,在面积、功耗和布线上的平均减少百分比分别为3.81%,9.02%和4.55%.     

基于动态优先级策略的多AGV无冲突路径规划

自动导引运输车(AGV)路径规划影响柔性制造车间的高效、平稳生产.分析了多AGV在车间执行运输任务时常见的冲突类型,提出了一种基于动态优先级策略的多AGV无冲突路径规划方法.该方法通过时间窗检测冲突类型,并将等待时间、行驶距离、任务紧急程度作为量化动态优先级的考虑因素,进而利用动态优先级作为等待、二次路径规划两种冲突消减方法的选择依据.案例研究表明,在已知各AGV可行路径时,运用所提方法不仅能够实现多AGV的无冲突路径规划,而且能够提升多AGV完成运输任务的整体效率.     

基于动态优先级策略的多AGV无冲突路径规划

自动导引运输车(AGV)路径规划影响柔性制造车间的高效、平稳生产.分析了多AGV在车间执行运输任务时常见的冲突类型,提出了一种基于动态优先级策略的多AGV无冲突路径规划方法.该方法通过时间窗检测冲突类型,并将等待时间、行驶距离、任务紧急程度作为量化动态优先级的考虑因素,进而利用动态优先级作为等待、二次路径规划两种冲突消减方法的选择依据.案例研究表明,在已知各AGV可行路径时,运用所提方法不仅能够实现多AGV的无冲突路径规划,而且能够提升多AGV完成运输任务的整体效率.     

Global Avalanche Characteristics of Boolean Functions by Concatenation

In order to measure the correlation propeties of two Boolean functions,the global avalanche characteristics of Boolean functions constructed by concatenation are discussed,i.e.,f_1‖f_2and f_1‖f_2‖f_3‖f_4.Firstly,for the function f = f_1‖f_2,the cross-correlation function of f_1,f_2 in the special condition are studied.In this case,f,f_1,f_2 must be in desired form.By computing their sum-of-squares indicators,the crosscorrelation function between f_1,f_2 is obtained.Secondly,for the function g = f_1‖f_2‖f_3‖f_4,by analyzing the relation among their auto-correlation functions,their sum-of-squares indicators are investigated.Based on them,the sum-of-squares indicators of functions obtained by Canteaut et al.are investigated.The results show that the correlation property of g is good when the correlation properties of Boolean functions f_1,f_2,f_3,f_4 are good.     

基于多层注意力机制—柔性AC算法的机器人路径规划

针对行动者—评论家（AC）算法存在的经验学习样本维度高、策略梯度模型鲁棒性低等问题,依据多代理系统的信息协作优势,构建注意力机制网络并作为代理体,引入多层并行注意力机制网络模型对AC算法进行改进,提出一种基于多层并行注意力机制的柔性AC算法。将其用于解决动态未知环境下的机器人路径规划问题,可增强行动者的策略梯度鲁棒性并降低评论家的回归误差,实现机器人路径规划最优方案的快速收敛。实验结果表明,该算法有效克服机器人路径规划的局部最优,具有计算速度快、稳定收敛的优点。     

枝向量矩阵反馈环算法的MATLAB实现*

找到系统中的全部反馈环是系统动力学模型分析的基础。针对这一问题,比较了几种常见的反馈环计算方法,得出在时间复杂度上矩阵算法优于行列式算法。在计算机上用MATLAB实现了基于流率基本入树和强简化流率基本入树枝向量矩阵计算所有反馈环的矩阵算法,分析了算法的复杂性,并给出了相应的算例。     

TEM IMAGING TECHNIQUE

This imaging technique is used for approximate interpretation of TEM data including ungrounded loopsource and grounded line source soundings.Loop source data can be collected as central-loop soundings or asout-of-loop soundings with a short or long offset configuration.The imaging method is based on the same princi-ple as that employed by Fullager(1989),with apparent resistivities computed directly from time-derivative data,db/d,and then associated with depths according to image theory.     

基于平方位置误差下限的优化功率分配方案

在无线传感网络中,节点定位是基于位置的应用基本要求。然而,现多数文献仅关注定位精度,而忽略了能量消耗对定位精度的影响。为此,针对基于接收信号强度RSSI(Received Signal Strength Indicator)定位方案,提出基于平方位置误差下限SPEB(Squared Position Error Bound)的优化功率分配SPEB-OPA(SPEB-Based Optimal Power Allocation)方案,目的在于最小化能量消耗。在SPEB-OPA算法中,将SPEB作为评定定位精度的参数,并推导出SPEB表达式,然后建立优化功率分配的目标函数,并考虑到锚节点位置存在误差。仿真结果表明,提出的SPEB-OPA方案极大地减少了功率消耗。当误差门限T=8时,SPEB-OPA方案的功率消耗比统一功率分配UPA(Uniform Power allocation)方案减少至50%。     

Combination of bottleneck feature extraction and dereverberation for distant-talking speech recognition

The performance of speech recognition in distant-talking environments is severely degraded by the reverberation that can occur in enclosed spaces (e.g., meeting rooms). To mitigate this degradation, dereverberation techniques such as network structure-based denoising autoencoders and multi-step linear prediction are used to improve the recognition accuracy of reverberant speech. Regardless of the reverberant conditions, a novel discriminative bottleneck feature extraction approach has been demonstrated to be effective for speech recognition under a range of conditions. As bottleneck feature extraction is not primarily designed for dereverberation, we are interested in whether it can compensate for other carefully designed dereverberation approaches. In this paper, we propose three schemes covering both front-end processing (cascaded combination and parallel combination) and back-end processing (system combination). Each of these schemes integrates bottleneck feature extraction with dereverberation. The effectiveness of these schemes is evaluated via a series of experiments using the REVERB challenge dataset.