熵产控制体方法对压气机近零间隙流动损失的演变规律

以澄清近零间隙流动结构及流动损失随间隙的变化规律为目标,提出熵产控制体分析方法,可精确量化流场任意位置的局部不可逆损失.数值模拟发现,间隙在0.4%叶高范围内(称近零间隙)效率水平最高且出现一对应峰值效率最高的最佳非零间隙.熵产控制体分析表明:通道内总熵产随间隙变化规律与效率一致;间隙变化对动叶端区20%~80%弦长范围内的熵产改变最为明显,主导了总熵产的变化;间隙变化时熵产改变的流动区域不同,这是由于动叶端区主导损失的流动结构发生改变造成的.进一步结合流场结构,探讨近零间隙流动损失演变的可能原因:间隙为零时壁面剪切力是造成损失的主导因素;最佳间隙时流动均匀,损失最小.      

纪念信息论创始人山农

信息论创始人山农 ( C.E.Shannon)于 2 0 0 1年 2月 2 4日逝世。为纪念这位世纪科学老人的辞世 ,缅怀他为人类的科学事业所作的伟大贡献 ,特撰写此文     

纪念信息论创立五十周年

１９４８年，山农（Ｃ．Ｅ．Ｓｈａｎｎｏｎ）首创信息论，至今信息论在促进现代科学技术发展上已作出重大贡献；介绍了信息概念的发展，评述了山农的信息定义、信道容量定理、山农采样定理和保密通信理论。最后，介绍横断科学的兴起、概念及其意义。     

加热圆管流动热阻力及熵产的数值分析

本文利用数值计算方法,对圆管加热流动的热阻力和熵产进行了综合分析研究。以空气加热圆管为例,通过联立求解流体力学基本方程和熵产方程,获得了热阻力、熵产及其随加热量、管长而变化的关系。数值分析结果表明,加热流动中的热阻力与熵产二者之间存在着对应相关的关系。     

基于熵权的灰色关联概率数据关联算法

在多目标信息融合中,通过数据关联判断来自不同传感器的数据是否代表同一个目标,传统数据关联一般采用联合概率数据关联算法,单纯依靠状态量测且计算复杂。而基于灰色关联的概率数据关联算法,能够利用目标的多个特征信息进行数据关联,且计算简单准确。特征信息一般由专家主观判断给出。提出了基于熵权的灰色关联概率数据关联算法,由量测数据自适应的给出各特征的权重系数,不依赖于专家。仿真表明,该关联算法具有很好的工程实用性。     

基于样本熵的语音信号检测算法

船舶自身发出的巨大噪声,严重影响了通讯设备通讯端船舶工作者的正常语音交流和通讯,须要进行语音信号增强,将语音信号从背景噪声中检测分离出来。为此,文章在分析语音信号与船舶背景噪声信号时域序列复杂性基础上,提出基于信号序列样本熵的语音信号检测算法。该算法将含噪信号进行分帧后,首先计算每帧信号的样本熵,并根据自适应预设阈值对信号进行初始判断；然后,利用信号通常持续一定时间的连续性,对信号初始判断进行平滑,得到信号的最终判别类别,实现对语音信号的检测。经过大量实测实验数据的验证,环境噪声信号的辨别准确度为 90.33%,语音信号为 95.05%,该算法取得了较好的判别精度。     

Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast. Thus safety of aircraft taking off and landing and air flight can be guaranteed. Thresholding is a kind of simple and effective method of cloud classification. It can realize automated ground-based cloud detection and cloudage observation. The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect. Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation. In view of the above-mentioned problems, multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization （UPSO） are proposed. Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy. In addition, exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster. Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy. In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection, the UPSO algorithm is adopted. It can find the optimal thresholds quickly and accurately. As a result, the real-time processing of segmentation of groundbased cloud image can be realized. The experimental results show that, in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy, the proposed methods can extract the boundary shape, textures and details feature of cloud more clearly. Therefore, the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.     

Scatter factor confidence interval estimate of least square maximum entropy quantile function for small samples

Classic maximum entropy quantile function method (CMEQFM) based on the probabil-ity weighted moments (PWMs) can accurately estimate the quantile function of random variable on small samples, but inaccurately on the very small samples. To overcome this weakness, least square maximum entropy quantile function method (LSMEQFM) and that with constraint condition (LSMEQFMCC) are proposed. To improve the confidence level of quantile function estimation, scatter factor method is combined with maximum entropy method to estimate the confidence inter-val of quantile function. From the comparisons of these methods about two common probability distributions and one engineering application, it is showed that CMEQFM can estimate the quan-tile function accurately on the small samples but inaccurately on the very small samples (10 sam-ples); LSMEQFM and LSMEQFMCC can be successfully applied to the very small samples;with consideration of the constraint condition on quantile function, LSMEQFMCC is more stable and computationally accurate than LSMEQFM; scatter factor confidence interval estimation method based on LSMEQFM or LSMEQFMCC has good estimation accuracy on the confidence interval of quantile function, and that based on LSMEQFMCC is the most stable and accurate method on the very small samples (10 samples).     

The entropy in groups – a clue to galaxy formation

The entropy in the hot X-ray gas in groups of galaxies is a fossil of the process of galaxy formation The amount of entropy in these low mass systems considerably exceeds that predicted from structure formation models. To explain these results requires “extra” energy which is a relic of the process of star formation and active galaxy heating. We present new XMM results on the entropy and entropy profiles. These results are inconsistent with pre-heating scenarios which have been developed to explain the entropy floor in groups but are broadly consistent with models of structure formation which include the effects of heating and/or the cooling of the gas. The total entropy in these systems provides a strong constraint on all models of galaxy and group formation, and on the poorly defined feedback process which controls the transformation of gas into stars and thus the formation of structure in the universe.     

基于距离代价函数和信息熵的发动机性能参数估计研究

研究发动机部件性能参数变化规律,对于减少维修次数和推动视情维修具有重要意义。针对测量参数个数少于待估性能参数的情况,给出了一种通过构建代价函数和优化算法的参数估计方法。原代价函数只考虑当前点参数,缺少与前面点参数的联系,因此结合自组织神经网络,构造了包含以前与当前点参数的距离代价函数。并提出了一种快速的参数估计方法。由于准确的部件性能参数很难获取,并且参数趋势估计不同于单纯的点估计问题,以对应的测量参数为基础,利用信息熵方法评定部件性能参数估计效果。进一步得到距离代价函数对应的参数信息熵为0.6805,优于原代价函数的估计结果。最后通过实例验证了参数估计方法的有效性。 