符号分析在混沌序列模式识别中的应用研究

针对待测时间序列是来自周期信号源、混沌信号源还是随机信号源的问题,提出一种用符号谱定性判定的方法。先对时间序列量化,然后进行编码,继而绘其符号谱。通过对不同类型时间序列的符号谱的对比分析表明,符号谱比K-S熵和功率谱等其他方法更能简单有效地判定时间序列的确定性来源和模式类型。     

基于圆形麦克风阵列的声源定位改进算法

针对波达方向估计中传统互功率谱法声源方位估计准确性差、方位模糊的问题,提出了一种基于圆形麦克风阵列的声源定位改进算法,并进行了实验验证。在该改进算法中,先设计了十二元圆形麦克风阵列,由麦克风对接收语音信号的时延与相位得到相位旋转因子,再将其引入到语音信号的互功率谱中,新定义了圆形集成互功率谱,由该功率谱进行声源方位估计。仿真与实测实验结果表明,本文的圆形集成互功率谱法对声源方位进行估计,估计的准确度高于传统互功率谱法。     

基于Golomb序列调制的目标距离速度测量方法

针对连续波激光雷达在同步测量目标距离和速度的应用中存在辐射信号上限峰值功率低、测量极限距离近的问题,提出一种基于Golomb脉冲序列调制的测量信号波形,研究该方法在道路环境中同步测量目标距离和速度的可行性。首先,以一种准连续波,即伪随机（PN）码调制为例分析连续波调制方法存在的发射信号峰值功率低的问题,在此基础上讨论Golomb序列的特征,提出使用Golomb序列调制发射信号提高发射脉冲峰值功率的方法;然后,讨论Golomb序列调制的多普勒信号频谱分析方法和基于数据累加的延迟时间定位方法,实现同步测量目标的速度和距离;最后,在道路目标产生的多普勒频率范围内,通过仿真实验验证该方法的正确性。实验结果表明,在以脉冲序列构成的对多普勒信号平均采样率远低于奈奎斯特频率的条件下,仍然可以通过快速傅里叶变换（FFT）方法获得多普勒信号的频率,从而在发射平均功率恒定的条件下极大提高单脉冲的峰值功率;另外,利用Golomb序列的不等间隔特性,通过数据累加方法可以完成对激光脉冲飞行时间的定位,在保证了速度测量的同时实现对目标距离的测量。     

Predictive modular neural networks for unsupervised segmentation of switching time series: the data allocation problem

In this paper, we explore some aspects of the problem of online unsupervised learning of a switching time series, i.e., a time series which is generated by a combination of several alternately activated sources. This learning problem can be solved by a two-stage approach: 1) separating and assigning each incoming datum to a specific dataset (one dataset corresponding to each source) and 2) developing one model per dataset (i.e., one model per source). We introduce a general data allocation (DA) methodology, which combines the two steps into an iterative scheme: existing models compete for the incoming data; data assigned to each model are used to refine the model. We distinguish between two modes of DA: in parallel DA, every incoming datablock is allocated to the model with lowest prediction error; in serial DA, the incoming datablock is allocated to the first model with prediction error below a prespecified threshold. We present sufficient conditions for asymptotically correct allocation of the data. We also present numerical experiments to support our theoretical analysis.     

基于Attention-LSTM的CNC刀具破损在线检测系统

为了在数控（CNC）机床批量加工过程中对刀具破损进行检测以减少残次产品,提出一种利用机床主轴功率信息,基于Attention-LSTM的CNC生产线刀具破损在线监测方法。该方法以数控系统内置传感器作为数据源获取机床主轴功率时间序列,在数据采集环节中,需要分辨加工过程中的不同工序以及该工序所使用的刀具编号。因此,在数据采集环节中,同时对数控代码和主轴功率进行采集,使用数控代码解析方式对采集数据进行处理,完成加工过程的工序识别,再使用Attention-LSTM算法对主轴功率数据进行预测,然后利用DTW算法计算时间序列相似度。加工过程功率时间序列和标准时间序列之间的相似程度应当处于合理的阈值范围内,否则认为此次加工过程中发生刀具破损。以FANUC数控系统为平台进行实验,验证了刀具破损识别的准确率。     

Multi-resolution screening of paper formation variations on production line

This paper is concerned with a technique for detecting and monitoring abnormal paper formation variations in machine direction online in various frequency regions. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the time series block. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the kernel canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The experimental investigations performed have shown good repetitiveness and stability of the paper formation abnormalities detection results. The tools developed are used for online paper formation monitoring in a paper mill.     

基于不变网络模型和故障注入的分布式信息系统故障溯源方法

针对传统分布式信息系统故障溯源算法对于先验知识依赖严重的问题,提出一种基于不变网络与故障注入相结合的故障溯源方法.首先,利用系统日志中收集到的系统组件运行数据,构建系统的不变网络模型,在此基础上进行节点或组件故障注入及扩散建模,建立故障网络集;然后,根据原始时间序列取值情况,制定数据质量评价规则以甄别数据是否发生突变;最后,利用实际故障网络与故障网络集中故障网络局部拟合的方式进行故障溯源,并利用数据质量评价规则对该结果进行修正,实现对系统故障源的精确定位.在仿真数据集、某开源系统数据集和某电网调度系统实采数据上的实验结果表明,所提出方法具有更高的准确率.     

一种分组密码算法的功耗分析新方法

功耗分析能够有效地析出内嵌密码芯片的分组密码的密钥,Walsh谱变换是一种研究分组密码布尔函数的重要工具。该文在相关功耗分析的基础上,提出了一种基于Walsh谱的功耗分析新方法,证明了分组密码布尔函数的非线性度越高,密码防范功耗分析的能力就越弱的结论。实验表明,相对差分功耗分析、相关功耗分析而言,基于Walsh谱的功耗分析能有效地提高析出密钥的效率和精度。     

随机海浪的数值仿真与频谱分析

针对船舶航行抑制海浪干扰的需要,研究了海浪波形的建立方法。选取ITTC双参数谱作为海浪谱,通过能量等分法对其进行频率分割,计算分割频段重心频率作为谐波频率,根据长峰波海浪模型,将各次谐波进行时域合成。给出了基于能量等分法的海浪仿真算法,得出了时间和空间海浪仿真波形。采用Welch法对仿真海浪进行频谱分析,与理想海浪谱对比,仿真精度达到了1.6%,验证了该方法的有效性,为船舶航行运动参数的有效滤波提供了可靠的扰动信号。     

PGD系列直流屏电源系统在厦门机场10KV变电站的应用

介绍PGD系列直流屏电源系统在厦门机场10KV变电站的使用状况及发展阶段,通过PGD系列直流屏在厦门机场的成功运用及推广,在对其系统组成、性能、功能等方面进行详细分析的基础上,得出了PGD系列直流屏电源系统具有技术先进、性能可靠、界面友好、便于维护等优点,特别适合大型电厂和高等级变电站等重要场所的使用。     

气象回波特征随机过程的模拟

文章在Matlab环境中模拟了同时具有气象回波一阶统计特征和二阶统计特征的随机过程.首先,利用反变换法、同分布随机过程合成方法、变量变换法、Matlab函数直接生成法模拟了具有气象回波特征随机过程的一阶特征.在模拟气象回波特征的二阶特性时,选择AR模型模拟高斯功率谱,在阶数的选择上采用了一种优化简便的方法,实现了更好的模拟效果.模拟立方功率谱时,提出了在Matlab环境中采用频率采样法达到了更好的模拟效果.     

Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling

Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures; thus, sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources, and singing voices, show that this algorithm outperforms the general nonnegative matrix factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side effect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which is a difficult task for general multipitch estimation (MPE) algorithms.     

Modular neural networks for MAP classification of time series andthe partition algorithm

We apply the partition algorithm to the problem of time-series classification. We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the computation of posterior probabilities. Prediction error is used to adaptively update the posterior probability of each source. The algorithm is implemented by a hierarchical, modular, recurrent network. The bottom (partition) level of the network consists of neural modules, each one trained to predict the output of one candidate source. The top (decision) level consists of a decision module, which computes posterior probabilities and classifies the time series to the source of maximum posterior probability. The classifier network is formed from the composition of the partition and decision levels. This method applies to deterministic as well as probabilistic time series. Source switching can also be accommodated. We give some examples of application to problems of signal detection, phoneme, and enzyme classification. In conclusion, the algorithm presented here gives a systematic method for the design of modular classification networks. The method can be extended by various choices of the partition and decision components.     

基于小波变换的电力热线预警信息盲检测方法研究

电力电网系统在电力传输控制中节点分布具有动态性,容易产生热线预警,为了提高电力热线预警检测能力,提出一种基于小波变换的电力热线预警信息盲检测方法。构建电力热线预警的信号模型,采用随机线性时间序列分析方法构建电力热线预警的统计信号分析模型,对电力热线预警信号采用相关性检测方法进行时频分解,采用小波分析方法将电力热线预警信号从时域向频域转换,根据接收到的两路电力热线预警信息的关联性进行特征匹配和自动分离,实现对电力热线预警信息的盲分离和噪声干扰抑制,采用小波变换和谱特征检测方法,实现对电力热线预警的盲检测。仿真结果表明,采用该方法进行电力热线预警检测的盲分离性能较好,检测准确概率较高,提高了电力热线预警能力。     

Adaptive Time Segmentation for Improved Speech Enhancement

Single-channel enhancement algorithms are widely used to overcome the degradation of noisy speech signals. Speech enhancement gain functions are typically computed from two quantities, namely, an estimate of the noise power spectrum and of the noisy speech power spectrum. The variance of these power spectral estimates degrades the quality of the enhanced signal and smoothing techniques are, therefore, often used to decrease the variance. In this paper, we present a method to determine the noisy speech power spectrum based on an adaptive time segmentation. More specifically, the proposed algorithm determines for each noisy frame which of the surrounding frames should contribute to the corresponding noisy power spectral estimate. Further, we demonstrate the potential of our adaptive segmentation in both maximum likelihood and decision direction-based speech enhancement methods by making a better estimate of the a priori signal-to-noise ratio (SNR)$xi$. Objective and subjective experiments show that an adaptive time segmentation leads to significant performance improvements in comparison to the conventionally used fixed segmentations, particularly in transitional regions, where we observe local SNR improvements in the order of 5 dB.     

基于互补型集成经验模态分解模糊熵和回声状态网络的短期电力负荷预测

为了提高短期电力负荷预测的精度,提出一种噪声互补型集成经验模态分解(CEEMD)模糊熵和泄漏积分型ESN(LiESN)的组合预测方法。为降低对负荷序列进行局部分析的计算规模以及提高负荷预测的准确性,首先采用CEEMD模糊熵将负荷时间序列分解为具有明显复杂度差异的负荷子序列;然后,通过对各子序列进行特性分析,分别构建相应的子LiESN预测模型;最后将各子序列的预测结果叠加得到最终预测值。将CEEMD模糊熵结合LiESN的组合预测方法应用于美国新英格兰地区短期电力负荷实例中,仿真结果表明,所提出的组合预测方法具有很高的预测精度。     

基于层次分析法的停电试验与不停电检测技术等效性分析模型

为了评价停电试验与不停电检测技术的等效性,提出了一种基于层次分析法的停电试验与不停电检测技术等效性分析模型。首先根据两种技术的状态量获取过程确定了准确度、有效性与故障检出能力组成的准则层,以及数据数值准确度等七个指标构成的指标层。采用层次分析法计算各指标权重系数,建立了双层次的等效性评价指标体系。根据各指标层中各指标性质,确定各个指标层指标的量化方法及归一化计算方法。针对不停电检测和停电试验的数据数量不相等、时序不匹配的特点,提出了数据三次样条插值拟合后再取值的数据预处理方法,最终建立等效性分析模型。实例分析表明该模型可以减少人为干预,综合状态量数据获取的主要阶段来评价不停电检测对停电试验的等效性。     

Modeling of correlated resources availability in distributed computing systems

Volunteer computing systems are large-scale distributed systems with large number of heterogeneous and unreliable Internet-connected hosts. Volunteer computing resources are suitable mainly to run High-Throughput Computing (HTC) applications due to their unavailability rate and frequent churn. Although they provide Peta-scale computing power for many scientific projects across the globe, efficient usage of this platform for different types of applications still has not been investigated in depth. So, characterizing, analyzing and modeling such resources availability in volunteer computing is becoming essential and important for efficient application scheduling. In this paper, we focus on statistical modeling of volunteer resources, which exhibit non-random pattern in their availability time. The proposed models take into account the autocorrelation structure in individual and subset of hosts whose availability has temporal correlation. We applied our methodology on real traces from the SETI@home project with more than 230,000 hosts. We showed that Markovian arrival process and ARIMA time series can model the availability and unavailability intervals of volunteer resources with a reasonable to excellent level of accuracy.     

基于拉曼光谱的电力变压器绝缘故障自动定位系统设计

电力变压器绝缘故障定位是提高电力变压器稳定性的关键,提出一种基于拉曼光谱的电力变压器绝缘故障自动定位方法。采用卷积稀疏自编码器进行电力变压器绝缘故障特征检测,提取电力变压器绝缘故障的拉曼光谱特征量,根据电力变压器的能源、负荷差异性进行受扰响应特性分析,构建电力变压器绝缘故障辨识模型,采用深度学习方法进行电力变压器绝缘故障检测中的收敛性判断,根据拉曼光谱的频谱特征分布进行电力变压器绝缘故障的自动定位。结合API接口和SS/E23节点网络拓扑模型实现电力变压器绝缘故障自动定位系统设计。仿真测试结果表明,采用该方法进行电力变压器绝缘故障定位的准确性较高,故障的可视化分辨能力较强。