基于电子舌技术的卷烟主流烟气味觉识别

为考察电子舌对不同卷烟烟气味觉的识别效果,利用电子舌系统检测了6个烤烟型和3个混合型卷烟样品主流烟气水处理液的味觉特征,并对其传感器响应信号进行了主成分分析( PCA)和判别因子分析( DFA).结果表明:①前2维主成分对卷烟品种的味觉识别贡献率达84.82％;②前2维DFA对卷烟品种的味觉识别贡献率达95.42％.结...     

基于自动机模型的构件集成软件测试要素的提取

分析了构件化软件接口、事件、上下文及内容依赖关系在集成测试中的作用,以及提取该四要素的难度;针对构件化软件的自身特点提出了一种有限状态自动机模型,利用该模型实施对四要素的提取,并通过实例验证了该模型的有效性。     

The effects of extremes and temporal scale on multifractal properties of river flow time series

For accurate forecasting of extreme events in rivers, streamflow time series with sub‐daily temporal resolution (1–6 hour) are preferable, but discharge time series for long rivers are usually available at daily or monthly resolution. In this study, the scaling properties of hourly and daily streamflow time series were measured. As an innovation, the effects of extreme values on the multifractal behavior of these series were evaluated. Interestingly, both hourly and daily discharge records led to nearly identical scaling trends and identical crossover times. Daily and hourly discharge time series appeared to be non‐stationary when the timescale ranged from 75 to 366 days. Otherwise, the signals may be considered stationary time series. In addition, the results indicated that the extreme values strongly contribute to the multifractality of the series. The width of singularity spectra decreased considerably when the extreme events were removed from both hourly and daily discharge records.     

基于图划分的正则表达式分组算法

针对多条正则表达式转换为确定型有限自动机带来的状态空间膨胀问题,借鉴图划分的思想,提出一种改进的分组算法。与原分组算法相比,该算法在分组数相同时状态数平均减少30%,在某些情况下能获得更少的分组数。实验结果证明,该算法能有效降低匹配算法的复杂度。     

抑郁症患者脑电导联选择算法及分类研究

基于EGI公司64导脑电采集系统,采集了16位青少年抑郁症患者和16位正常人静息态下闭眼4分钟的脑电数据。运用频谱不对称分析法（Spectral Asymmetry Index,SASI）和去趋势波动分析（Detrended Fluctuation Analysis,DFA）算法提取脑电时域和频域特征。针对提取的特征的导联,一方面,选择最佳电极Pz作为分类的导联,另一方面,通过遗传算法对所有导联进行筛选,将筛选后的导联特征用于分类。使用支持向量机（Support Vector Machine,SVM）在单导联和多导联的情况下,对抑郁症患者和正常人进行分类,结果发现,单导联下,使用SVM分类器对抑郁组和对照组的SASI和DFA结果进行分类,分类精度分别为45.5%和51.5%,使用遗传算法的分类精度分别为78.1%和90.6%,SASI算法的计算实时性优于DFA算法,DFA算法的准确性优于SASI算法。该研究为抑郁症的计算机辅助诊断提供了理论依据。     

滚动轴承故障诊断的多重超阶分析方法

多重分形去趋势波动分析(MF-DFA)可以获得能够表征信号内在动力学机制的多重分形谱,但是在提取滚动轴承振动信号故障特征时存在参数接近、状态混叠等问题,导致分析结果易受信号噪声等因素干扰,影响分类精度。为解决此问题,提出了多重超阶分析(MF-SOA)的方法。该方法将极值增量方法引入了多重去趋势波动分析中,对时间序列进行取极值操作;然后计算并分析获得的极值增量序列的重分形特征,通过MF-SOA方法获得的特征可以更清晰地表现出序列的内部动力学机制。最后将所提出的方法应用于滚动轴承的故障诊断中。试验数据分析结果表明,该方法对于信号的不规则程度十分敏感,并且有效改善了MF-DFA方法的缺陷,对于模式相近的故障类型有更优的区分度,提高了滚动轴承故障诊断的精度。     

一种基于合并思想的DFA最小化算法

在深入研究状态等价的充分条件的基础上．借鉴时序逻辑门电路中原始状态表化简的方法．给出基于合并思想的适用于任何DFA的最小化算法——隐含表法。     

维吾尔语名词构形词缀有限状态自动机的构造

该文主要阐述维吾尔语词干提取中使用的名词构形词缀分析DFA的构造过程。维吾尔语属于黏着语,所以维吾尔语自然语言处理系统必须实现词干提取。词干提取的主要任务从单词提取词干和连接词干词尾的构形词缀。维吾尔语单词的构形词缀按照一定的规则连接到词干词尾,这使得维吾尔语构形词缀的连接规则可用有限状态自动机形式化描述。该文首先介绍维吾尔语名词的形态结构,然后根据规则构造从右向左的有限状态自动机,最后对这个自动机进行方向翻转和转换确定自动机操作。     

Some Aspects of Synchronization of DFA

A word w is called synchronizing （recurrent, reset, directable） word of deterministic finite automata （DFA） if w brings all states of the automaton to a unique state. According to the famous conjecture of Cerny from 1964, every n-state synchronizing automaton possesses a synchronizing word of length at most （n - 1）2. The problem is still open. It will be proved that the Cerny conjecture holds good for synchronizing DFA with transition monoid having no involutions and for every n-state （n 〉 2） synchronizing DFA with transition monoid having only trivial subgroups the minimal length of synchronizing word is not greater than （n - 1）2/2. The last important class of DFA involved and studied by Schutzenberger is called aperiodic; its automata accept precisely star-free languages. Some properties of an arbitrary synchronizing DFA were established.