竖直上升管的煤粉密相气力输送阻力特性的实验研究

煤粉密相气力输送是气流床煤粉气化工艺中的关键单元技术。通过实验室装置（管径20, 50 mm）和半工业化装置（管径42 mm）的煤粉密相气力输送竖直上升管压降测试,建立了可用于煤粉密相高压气力输送竖直上升管道的压降预测模型,总体偏差在±20%以内,可满足工业装置设计和优化操作的需求。固相静压降占总压降比例达35%~70%,体现了高浓度输送特性;且在管径一定的条件下,与固相弗洛德数近似成线性关系。在固相弗洛德数和管径一定的条件下,可通过竖直上升管压降估算出相应的固相质量流率,从而为工业装置上煤粉质量流率在线测定提供一定的参考。     

成都市蚊密度中的混沌现象

目的 2006—2021年成都市城区蚊密度呈现宽幅震荡趋势，可将其一维时间序列拓展到多维相空间去进行混沌分析，以确定数据序列的非线性特征。方法 使用L.Cao算法确定嵌入维数和关联维数，后对数据序列进行相空间重构，由雅可比(Jacobian)方法计算最大李雅普诺夫(Lyapunov)指数。结果 当自相关函数衰减为0时，成都市蚊密度数据序列嵌入时延为τ=2,estimateEmbeddingDim函数确定的嵌入维数为m=7,系统的关联维数介于2～3。lyapunov.max函数计算所得的最大李雅普诺夫指数为λ₁=0.024,证明该动力系统至少有1个李雅普诺夫指数大于0,提示蚊密度有混沌特征。结论 成都市蚊密度分析可使用非线性方法，预测结果能较好地表征城区蚊密度的变化趋势和规律，对20个月内蚊密度中混沌现象短期预测精度较高。     

音乐信号的特征分析及脑神经系统特征探讨

音乐具有无穷的魅力，它与人的脑神经系统有密切的联系。本文用非线性理论对音乐信号进行了深入分析，得出了经典乐曲的关联维数均值一般在5至6．5之间，而Lyapunov指数很小，基本接近于零，可以认为经典钢琴曲是一个接近于准周期信号。其它乐曲的维数较大，Lyapunov指数相对也大些，可以认为是一个弱混沌信号。论文还对脑神经系统进行了探讨，并根据音乐的特征，对脑神经系统的特征进行了推测。     

水平管粉煤密相气力输送压差信号的小波分析

水平管中密相气力输送的压差信号间接反映了水平管中的流动状态，本文用小波分析方法对粉煤密相气力输送的压差信号进行了分析，发现粉煤在水平管中密相输送的压差信号存在分形，分形维数为1.2～1.5。     

1/fα噪声对计算混沌动力系统最大Lyapunov指数的影响

最大Lyapunov指数是诊断和描述动力系统混沌的重要参数。本文在仿真计算的基础上,发现1/fα噪声对计算混沌动力系统最大Lyapunov指数具有显著影响。在同一噪声水平下,白噪声的影响最为显著,随功率谱指数α的增大,其影响逐渐减弱;随着噪声水平的增加,1/fα噪声对计算混沌动力系统最大Lyapunov指数的影响越显著。当α≥2.0时,在噪声水平为0.2的情况下,计算得到的最大Lyapunov指数的误差小于10%。     

密相气力输送参数的人工神经网络预测

在水平管道中，用压缩空气和氢气对煤粉和小米进行密相气力输送实验，利用改进的BP神经网络对不同气量下的固体质量流率进行预测。结果表明，BP网络能对不同实验条件下的固体质量流率进行较好的预测。并画出不同气量下，固体质量流量的等值图。根据此图，可对密相气力输送参数进行初步优化，控制固体的输送量，减少密相气输送中的盲目操作。     

煤粉剪切特性及其影响因素分析

为了研究煤粉的剪切特性及其影响因素,借助ShearTrac Ⅱ剪切系统,获得了煤粉的屈服轨迹,并将煤粉与典型粉体玻璃微珠的剪切特性进行了比较,分析表明煤粉更难剪切,因此重点研究了剪切速率、剪切位移和预压应力对煤粉剪切特性的影响。Jenike剪切标准D6128 00的剪切速率范围(1~3 mm/min)对煤粉介质依然适用,在此范围内可适当提高剪切速率以节省测试时间;为确保煤粉介质达到剪切峰值并进入稳态区,剪切位移设计应不小于8 mm。此外,煤粉内聚力和流动指数随着预压应力的增加而增大,应根据实际应用选择与其相对应的预压应力范围。     

分形、混沌在医药方面的应用

在传统的Euclid几何学中，拓扑维数是一个很重要的概念，其涉及的Euclid维都是整数，我们经常用到的是1、2维、3维等，点是0维，直线是1，平面是2维，空间几何体是3维，在Einstein相对论中，则讨论了时空4维世界；热力学所研究的n维相空间，其维数n可以是大于4的整数；至于科学技术研究中涉及的n维因素空间，影响事件进行过程的因素每增加一个，即相当于维数增1,这里的n也是整数。但人类的生活空间仅仅依靠Euclid维是不够的。     

脑电重构吸引子复杂度的测量

选用３０例脑电图，用混沌学方法分析脑波的复杂性。他们的重构吸引子存在明显差异，这种差异可用分形几何的方法测量构成吸引子的状态点迹，即以同等的最小和最大单位面积块全部覆盖吸引子点迹时的块数为复杂度的得分数。得分的高低与被测对象的实际情况相对照，二者总符合率达９４．３％。     

小颗粒滴流床反应器中液相轴向返混的研究

对三种形状六种尺寸的小颗粒填料,在滴流床中气液两相并流向下流动时液相的轴向返混程度作了研究。实验表明:随着液速的增加,填料颗粒的毕克力Pe_L值增大,返混程度降低;填料颗粒的球形度越小,Pe_L值就越小,返混程度就越大;填料颗粒的尺寸对返混也有影响,尤其是对非球形颗粒,尺寸越大,则Pe_L值越大。当气液两相流速不断增大时,床层中的流动状态从滴流区转变为脉动区,则液体性质对Pe_L值的影响也不同。给出了两个流区的Pe_L关联式。     

Automated Detection of Anesthetic Depth Levels Using Chaotic Features with Artificial Neural Networks

Monitoring the depth of anesthesia (DOA) during surgery is very important in order to avoid patients' interoperative awareness. Since the traditional methods of assessing DOA which involve monitoring the heart rate, pupil size, sweating etc, may vary from patient to patient depending on the type of surgery and the type of drug administered, modern methods based on electroencephalogram (EEG) are preferred. EEG being a nonlinear signal, it is appropriate to use nonlinear chaotic parameters to identify the anesthetic depth levels. This paper discusses an automated detection method of anesthetic depth levels based on EEG recordings using non-linear chaotic features and neural network classifiers. Three nonlinear parameters, namely, correlation dimension (CD), Lyapunov exponent (LE) and Hurst exponent (HE) are used as features and two neural network models, namely, multi-layer perceptron network (feed forward model) and Elman network (feedback model) are used for classification. The neural network models are trained and tested with single and multiple features derived from chaotic parameters and the performances are evaluated in terms of sensitivity, specificity and overall accuracy. It is found from the experimental results that the Lyapunov exponent feature with Elman network yields an overall accuracy of 99% in detecting the anesthetic depth levels.     

局部狭窄动脉中血液流动的稳定性与混沌

目的：研究动脉局部狭窄时血液流动的稳定性与混沌特性。方法采用有限差分方法,将偏微分方程组约化为常微分方程组。结果求得了动力系统的平衡解与稳定性,求得了动力系统最大的Ｌｙａｐｕｎｏｖ指数。结论动力系统的混沌运动与血液流动的湍流存在着关联。     

Secured Medical Images - a Chaotic Pixel Scrambling Approach

In this paper, a cryptosystem is proposed to encrypt 16-bit monochrome DICOM image using enhanced chaotic economic map. A new enhanced chaotic economic map (ECEM) is designed from the chaotic economic map which has better bifurcation nature and positive Lyapunov exponent values. In order to improve the sternness of the encryption algorithm, the enhanced chaotic map is employed to generate the pixel permutation, masking, and swapping sequences. The substitution operation is introduced in-between the standard permutation and diffusion operations. The robustness of the proposed image encryption algorithm is measured by various analyses such as histogram, key sensitivity, key space, number of pixel change rate (NPCR), unified average change intensity (UACI), information entropy and correlation coefficient. The results of the security analyses are compared with existing algorithms to validate that the proposed algorithm is better in terms of larger key space to resist brute force attacks and other common attacks on encryption.     

Determining Fractal Dimension of Umbilical Artery Doppler Signals Using Hurst Exponent

Doppler signals from the umbilical artery of 20 women with normal pregnancy between 18 and 20 weeks of gestation were recorded. The AR spectral analysis method has been used to obtain the Doppler sonograms of umbilical artery belonging to normal pregnant subjects and fractal dimension curves were calculated using Hurst exponent. RI; PI and S/D indexes have been calculated from the maximum frequency envelope of Doppler sonograms and from the fractal dimension curve. Area under the curve from ROC curve for RI, PI and S/D indexes derived from maximum frequency waveform were calculated as 0.931, 0.959, 0.938, respectively and area under the curve for RI, PI and S/D indexes derived from fractal dimension curve were calculated as 0.933, 0.961, and 0.941, respectively. These results show that, the Doppler indexes derived from fractal dimension curve are as sensitive as Doppler indexes derived from maximum velocity curve. Power Spectral Density graphics were derived from Doppler signals and Hurst exponent values calculated to evaluate the blood flow changing during pregnancy. ROC curve for PSD_HURST index was calculated as 0.97. According to this result, PSD_HURST index is more sensitive to detect the blood flow changing than traditional Doppler indexes.     

EEG Signal Analysis: A Survey

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.     

Correlation Dimension Analysis of Doppler Signals in Children with Aortic Valve Disorders

In this study, the correlation dimension analysis has been applied to the aortic valve Doppler signals to investigate the complexity of the Doppler signals which belong to aortic stenosis (AS) and aortic insufficiency (AI) diseases and healthy case. The Doppler signals of 20 healthy subjects, ten AS and ten AI patients were acquired via the Doppler echocardiography system that is a noninvasive and reliable technique for assessment of AS and AI diseases. The correlation dimension estimations have been performed for different time delay values to investigate the influence of time delay on the correlation dimension calculation. The correlation dimension of healthy group has been found lower those found in AI and AS disorder groups and the correlation dimension of AS group has also been found higher than those found in AI group, significantly. The results of this study have indicated that the aortic valve Doppler signals exhibit high level chaotic behaviour in AI and AS diseases than healthy case. Additionally, the correlation dimension analysis is sensitive to the time delay and has successfully characterized the blood flow dynamics for proper time delay value. As a result, the correlation dimension can be used as an efficient method to determine the healthy or pathological cases of aortic valve.     

糖尿病自主神经病变的非线性心率变异性分析

目的通过对糖尿病患者进行心率变异性(HRV)分析,研究糖尿病自主神经病变的早期诊断方法。方法随机选择34名糖尿病(包括22例伴有明显的自主神经病变并发症)患者,采用基于LabVIEW的虚拟仪器开发平台,获得5 min相邻R波间期的时间信号,采用非线性动力学研究方法,包括Allan因子、李雅普诺夫指数、近似熵、分形维数、复杂度、小波变换标准差和非线性能量算子进行心率变异性分析,并与对照组进行对比。结果非线性动力学研究方法对糖尿病患者HRV分析的结果表现出很强的特异性,尤其是李雅普诺夫指数、近似熵和非线性能量算子。结论HRV分析的非线性动力学研究方法在评价自主神经状态与诊断自主神经病变方面具有重要价值,能够为临床诊断和治疗自主神经病变提供参考依据,是提高糖尿病自主神经病变早期诊断率的有效途径。     

糖尿病自主神经病变的非线性心率变异性分析

目的 通过对糖尿病患者进行心率变异性(HRV)分析，研究糖尿病自主神经病变的早期诊断方法。方法 随机选择34名糖尿病(包括22例伴有明显的自主神经病变并发症)患者，采用基于LabVIEW的虚拟仪器开发平台，获得5min相邻R波间期的时间信号，采用非线性动力学研究方法，包括Allan因子、李雅普诺夫指数、近似熵、分形维数、复杂度、小波变换标准差和非线性能量算子进行心率变异性分析，并与对照组进行对比。结果 非线性动力学研究方法对糖尿病患者HRV分析的结果表现出很强的特异性，尤其是李雅普诺夫指数、近似熵和非线性能量算子。结论 HRV分析的非线性动力学研究方法在评价自主神经状态与诊断自主神经病变方面具有重要价值，能够为临床诊断和治疗自主神经病变提供参考依据，是提高糖尿病自主神经病变早期诊断率的有效途径。     

气温对亚热带季风气候内陆城市登革热传播的影响

目的 以江西省樟树市为例探究气温对登革热传播的影响,为亚热带季风气候内陆城市登革热监测预警提供理论依据。方法 收集樟树市2019年本地感染登革热病例及同时期气温数据,利用核密度分析、标准差椭圆、交叉相关分析等方法分析了登革热时空变化及其与气温因子的关系。结果 樟树市登革热发病具有明显的时空分异特征。从时间上看,2019年8—9月登革热发病数先升高后降低,在8月29日达到峰值,日新增病例为64例。随后新增病例开始减少,9月11日以后无新增病例。从空间上看,人口密集、卫生条件相对较差的老城区（淦阳街道、鹿江街道及福成街道）登革热病例密度最高,而随着与市中心城区距离的增大登革热病例密度逐渐递减,登革热有从中心城区向周边区域辐射的趋势,主要向东北和西南方向扩散。相关分析显示,登革热病例数与最高气温、积温呈正相关关系,并存在明显的滞后效应,以滞后5~10 d,滑动平均7~10 d的相关性最高。登革热病例扩散率与最高气温、最低气温以及积温均呈正相关关系,但未见明显的滞后效应。结论 气温对登革热的传播与扩散有重要影响,及时掌握最高气温、最低气温、积温的变化趋势,尽早采取措施是登革热防控的关键。     

基于混沌量子粒子群算法的置换流水车间调度

流水车间调度问题广泛存在于企业生产过程中,优化的调度方案可以提高企业生产效率,降低生产成本。提出了基于混沌量子粒子群优化算法并应用于求解置换流水车间调度问题,该算法在量子粒子群算法(QPSO)的基础上,引入了混沌机制,在保持QPSO算法收敛速度快的同时,利用混沌机制的遍历性,克服了QPSO易陷入局部极小值的缺点。同时提出了一种新的混沌变量到工件排序的编码方案,能够完整保留混沌的遍历性。仿真结果验证了所提出的新的调度算法能更好地探索更优解,同时不失去量子粒子群算法的收敛速度。