Robust adaptive output stabilization using dynamic normalizing signal

For a class of nonlinear systems with dynamic uncertainties, robust adaptive stabilization problem is considered in this paper. Firstly, by introducing an observer, an augmented system is obtained. Based on the system, we construct an exp-ISpS Lyapunov function for the unmodeled dynamics, prove that the unmodeled dynamics is exp-ISpS, and then obtain a dynamic normalizing signal to counteract the dynamic disturbances. By the backstepping technique, an adaptive controller is given, it is proved that all the signals in the adaptive control system are globally uniformly ultimately bounded, and the output can be regulated to the origin with any prescribed accuracy. A simulation example further demonstrates the efficiency of the control scheme.

Normalized intervertebral disc MRI signal as a biomarker of pain

The drop in the MRI signal intensity, analysed without any normalisation, was found related to the intervertebral disc degeneration, but its association with low back pain remains controversial. The authors developed the analysis of MR signal intensity distribution (AMRSID) method that analyzes the 3D distribution of the normalized T2-weighted MR signal intensity within the intervertebral disc using descriptive statistics of histograms and weighted centers. In this study, we hypothesized that the distribution of the normalized MRI signal intensity within T2- weighted images of the intervertebral disc is a bio-marker of low back pain (LBP) independently of age and disc degenerescence. The aims were to: 1) characterize intervertebral disc degeneration in vertebral fracture from MR T1-weighted and T2-weighted images;2) evaluate the sensitivity of the normalized MRI signal distribution to the presence of LBP, discs height loss and aging. We prospectively studied 22 patients who underwent an MRI acquisition within 48h after an accidental lumbar vertebral fracture. The presence of prefracture low back pain, spinal stenosis, annular disruption, intervertebral disc height loss was noted from each patient’s medical record. The presence of Modic changes, High-Intensity Zones (HIZs) and vertebral endplate perforations was recorded from MRI. The descriptive statistics of the normalized T2-weighted signal were compared using one-way ANOVAs and a principal component analysis was proposed. MRI, associated to normalisation of the signal intensity and principal component analysis, offers a remarkable potential for in-vivo imaging and analysis of vertebral fractures and adjacent tissues for the patient’s follow-up. The mean normalized MRI signal intensity of the adjacent intervertebral disc to the vertebral fracture was found to be a bio-marker of pain, independently of age and disc degeneration. However, the parameters describing the distribution of the normalized signal intensity were found to be not sensitive to the presence of low back pain, discs height loss and aging. Further studies need to be performed to detect small abnormalities that may explain the presence of LBP.

Wigner-Matrix-Based Normality Test and Application to Weak Signal Detection in SISO/SIMO Systems

Based on the asymptotic spectral distribution of Wigner matrices, a new normality test method is proposed via reforming the white noise sequence. In this work, the asymptotic cumulative distribution function （CDF） of eigenvalues of the Wigner matrix is deduced. A numerical Kullback-Leibler divergence of the empiric-d spectral CDF based on test samples from the deduced asymptotic CDF is established, which is treated as the test statistic. For validating the superiority of our proposed normality test, we apply the method to weak SIPSK signal detection in the single-input single-output （SISO） system and the single-input multiple-output （SIMO） system. By comparing with other common normality tests and the existing signal detection methods, simulation results show that the proposed method is superior and robust.

New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.

Design Method Based on Normalized Functions for a Toroidal Inductor Applied to Power-Quality Evaluation

The design method for a low-cost toroidal inductor is proposed as an alternative to power-quality evaluation. The method is based on well-known tools by the engineers in which is presented the relationships that exist between equivalent circuit and transfer function of a toroidal inductor. The proposed design method has been explained with normalized functions based on physical parameters of a toroidal inductor. This work presents the main arguments of the suggested methodology and as demonstration of the design method as function of normalized parameters, is developed a current-signal sensor which has been validated in the laboratory by the EN-50160-2-2 standard to evaluate the power quality in home use loads.

Research of the Adaptive Beamformer Based on the Frequency Energy Normalization

An algorithm of broadband minimum variance distortionless response(MVDR) based on the frequency energy normalization is proposed.First,every narrowband frequency component of the broadband signal is normalized by the total narrowband energy of all array elements,and the narrowband power is calculated by MVDR.Finally,final spatial energy spectrum can be obtained by averaging or summing all results of every narrowband frequency bin.Any prior-information about the noise or the signal is unnecessary for the proposed method in this paper.The processing gain of the proposed method compared to the conventional broadband MVDR can be obtained as long as the amplitude fluctuation of the array noise frequency spectrum is severer than that of the target signal.The validity of the method is validated by the optimal signal detection theory.Simulation and real data are used to validate the performance of the method.Analysis results show that about 4 dB processing gain compared to the general broadband MVDR can be reached by the proposed method.

The statistical fluctuation of a single-mode laser system driven by coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts

Using the linear approximation method, this paper studies the statistical property of a single-mode laser driven by both coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts, and calculates the steady-state mean normalized intensity fluctuation and intensity correlation time. It analyses the influences of the modulation signal, the net gain coefficient, the noise and its correlation form on the statistical fluctuation of the laser system respectively. It is found that the coloured pump noise modulated by the signal has a great suppressing action on the statistical fluctuation of the laser system; the pump noise self-correlation time and the specific frequency of modulation signal have the result that the statistical fluctuation tends to zero. Furthermore, the ＇colour＇ correlation of pump noise has much influences on the statistical fluctuation of the laser system. Increasing the intensity of pump noise will augment the statistical fluctuation of the laser system, but the intensity of quantum noise and the coefficient of cross-correlation between its real and imaginary parts have less influence on the statistical fluctuation of the laser system. Therefore, from the conclusions of this paper the statistical property can be known and a theoretical basis for steady operation and output of the laser system can be provided.

基于阶次分析的声发射信号归一化方法研究

转速直接影响声发射信号,不同转速下采集得到的声发射信号不能直接进行对比。因此,该文提出一种基于阶次分析的声发射信号归一化方法。该方法通过等角度重采样,将不同转速下的声发射信号映射到阶次域,从而消除转速对声发射信号的影响。采用机械密封试验台采集不同转速下的声发射信号进行验证,试验结果表明该方法可以有效地去除转速对声发射信号的影响。

网络药理学结合分子对接技术探讨黄芪-莪术促进肺癌肿瘤血管正常化的潜在机制

目的通过网络药理学联合分子对接技术探讨黄芪-莪术促进肺癌肿瘤血管正常化的潜在作用机制,为进一步研究寻找方向。方法利用公共数据库(TCMSP)检索并获取黄芪-莪术活性成分及相关靶点,通过基因卡、OMIM、TTD、药物GKB数据库和药物库数据,发现肺癌血管正常化的相关靶点,Met细胞景观插件ClueGO平台用于基因本体(GO)富集分析和京都基因组百科全书(KEGG)通路富集分析,最后利用AutoDock及Vina软件进行分子对接打分,Pymol软件进行对接结果的可视化展示。结果共筛选出486个黄芪-莪术生物活性成分和184个黄芪-莪术与肺癌肿瘤血管正常化相关的靶点;根据生物信息学分析,槲皮素、异鼠李素、山奈酚、7-O-甲基异脲醇、芒柄花黄素、毛芯异黄酮可能是黄芪-莪术调控肺癌肿瘤血管正常化的潜在活性成分,而RB1、MAPK1、EGFR、ESR1、AKT1、HIF1A、TP53、STAT1和CCND1是潜在的起效靶点;根据富集和分子对接结果显示异鼠李素与STAT1之间具有较高的结合亲和力,JAK2/STAT1通路可能是黄芪-莪术调控肺癌血管正常化的主要作用通路。结论黄芪-莪术促进肺癌肿瘤血管正常化作用的潜在机制可能是IFN-γ-JAK2/STAT1通路。

基于大数据分析技术的无线通信网络信号异常诊断方法

由于传统方法在无线通信网络信号异常诊断中应用效果不佳,不仅漏诊率比较高,而且诊断准确度比较低,无法达到预期的诊断效果,为此提出基于大数据分析技术的无线通信网络信号异常诊断方法。利用信号采集器捕获网络信号,并对网络信号数据归一化和标准化处理,利用大数据分析技术提取到网络信号特征,根据信号特征识别诊断网络异常状态,以此完成基于大数据分析技术的无线通信网络信号异常诊断。经实验证明,设计方法漏诊率低于1%,诊断准确度在99%以上,为无线通信网络信号异常诊断提供了参考依据。

基于归一化模板匹配算法的动作电位检测IC

针对多通道神经元动作电位(AP)信号采集硬件系统中,前端采集IC因电极阻抗、应用环境、系统功耗以及系统面积等因素而导致原始信号信噪比较低的问题,设计一种基于归一化模板匹配算法的AP信号检测IC。该检测IC基于模板匹配算法,并引入信号归一化以去除冗余信息。为确保芯片的实时性并降低功耗,芯片整体采用三级流水线结构,各个模块间插入门控时钟,同时可通过SPI总线配置更新其工作状态。在SMIC 180 nm数字工艺下,裸片面积为0.98 mm^(2),配置模板信息时功耗为701μW,完全工作时功耗为8.07 mW,且采用QFN48封装形式。测试结果表明,所设计的AP信号检测IC具有较好的抗噪性能,可用于信噪比较低的环境来降低信号传输带宽,即使在-10 dB信噪比环境下,评价模型综合性能的F1-Score仍可达到94.65%。

基于GRNN-MC的变压器振动信号预测

变压器振动信号是评估其工作状态的重要参数之一,与绕组松动或变形等隐患密切相关,为揭示变压器振动信号的变化趋势,本文提出了一种基于广义回归神经网络和马尔科夫链修正的变压器振动信号预测方法。即分别以变压器运行电压、负载电流和振动信号归一化特征频率为输入和输出建立变压器振动信号广义回归神经网络预测模型,然后引入马尔科夫链并结合负载电流的变化对振动信号计算结果进行修正以获得最终的预测结果。对某500 kV变压器振动在线监测信号的分析结果表明:经马尔科夫链修正后的变压器广义回归神经网络振动信号预测模型预测精度高,可为变压器绕组运行状态的振动监测技术提供重要参考。

基于鲸鱼优化和批量规范化卷积神经网络的振动信号去噪

由于初始权值的随机选取,传统卷积神经网络模型易陷入局部最优解,难以从噪声振动信号中提取纯净信号。针对这一问题,提出鲸鱼优化算法和批量规范化卷积神经网络相结合的振动信号去噪方法。该方法通过批量规范化层对隐层中的参数分布进行归一化,采用鲸鱼优化算法对网络权值参数进行寻优,解决网络模型存在局部最优的问题。将含噪振动信号的幅度谱和噪声信号的时域波形分别作为网络的训练特征和目标,充分利用振动信号在时频域上的分布特性,通过残差学习实现去噪的目的。实验表明,与小波阈值去噪方法、EMD方法和卷积神经网络相比,所提方法有效提升了信噪比,降低了均方误差和平均绝对误差,有效保留了振动信号原始特征,并增强了其去噪能力。

An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals

Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.

铁路专用线特殊条件下站间联系电路设计方案探讨

当铁路专用线长度较短时,由于接轨站与专用线车站之间无设置区间闭塞的硬件条件,两站联锁系统就只能通过站间联系电路进行关联,而站联电路不属于定型电路,其继电器组合也不属于以往的定型组合,因此电路设计需要根据联锁的内部逻辑、站前工程技术条件进行综合考虑,而不能简单选择类似工程项目的竣工图纸直接套用。为此,基于某竣工的铁路专用线项目,结合联锁逻辑,将站前工程技术条件转化为信号工程的设计要素,逐步呈现设计要素的分析过程、解决过程,解释在站间联系电路中,哪些信息需要驱动采集,如何进行驱动采集;然后汇总为计算机联锁施工图中的站间联系电路图、组合及内部配线图;最后根据该专用线的实际运营状况,加入轨道电路电码化的相关内容,将站联电路中需要驱动采集的特殊轨道继电器信息进行说明,供同行设计者在站间联系电路设计时参考。

RANTES和STAT3在难治性支原体肺炎患儿外周血中的表达及意义

目的探究受激活调节正常T细胞表达和分泌因子(RANTES)、信号转导子和转录激活子3(STAT3)在儿童支原体肺炎外周血中的表达,并评估其对难治性支原体肺炎(RMPP)的预测价值。方法选取2023年8—10月在河南省儿童医院郑州儿童医院住院的儿童支原体肺炎患儿103例,患儿均接受全程治疗,根据患儿治疗1周后是否仍出现持续发热、病情加重情况将患儿分为MPP组和RMPP组,对两组患儿外周血中RANTES、STAT3表达情况进行检测,并分析两组患儿相关实验室指标情况。收集两组患儿的一般资料,对可能诱发RMPP的因素进行单因素和多因素分析。结果RMPP组RANTES水平及STAT3 mRNA相对表达水平均高于MPP组(P<0.05)。RMPP组中性粒细胞计数、白细胞计数、C反应蛋白、血小板计数、乳酸脱氢酶以及红细胞沉降率等实验室指标均高于MPP组(P<0.05)。经单因素及多因素分析,RANTES和STAT3水平、乳酸脱氢酶水平和抗生素使用均与RMPP的发生有关(P<0.05)。结论RANTES和STAT3表达水平升高可以提示儿童支原体肺炎预后不良。

分数阶傅里叶变换数值计算中的量纲归一化

针对分数阶傅里叶变换(FRFT)快速算法中所要求的量纲归一化与实际工程计算脱节的问题,对量纲归一化进行了研究,提出了离散尺度变换和数据补零/截取2种实用的量纲归一化方法,研究了2种方法对chirp信号参数估计的影响,导出了采用离散尺度化方法时,归一化前后的chirp信号参数的变换关系.通过仿真实例说明FRFT快速算法能够应用于实际工程计算.

基于速度截断分布和流量的车队离散模型

为解决Pacey模型中车速分布假设与实际不符的问题,假设车速服从在最小速度和最大速度之间的截断正态分布,利用分段函数构造了车队到达下游交叉口的流量分布函数,建立了基于交通流量的车队离散模型,研究了下游交叉口流量与上游交叉口断面离去流量之间的关系.以相邻交叉口实测数据为例分析车队流量分布模式,研究结果表明,与Robertson流量模型相比,本文提出的速度截断正态分布更符合实际情况;本文模型能更精确地描述车队流量通过下游交叉口的变化趋势,确保流量守恒.

基于统计回归模型的红外人脸温度归一化

为了减小环境温度对红外人脸图像的影响,本文提出了一种基于统计回归模型的红外人脸温度归一化方法.为了得到环境温度与红外人脸温谱图在对应像素点灰度之间的关系,将环境温度改变值和对应人脸上的温度变化值作为研究对象,利用统计回归方法对这两个对象进行二次多项式拟合即可得到环境温度变化和对应的人脸上温度变化的函数关系.通过得到的函数关系,建立归一化模型对红外图像进行温度归一化处理,减小环境温度对红外人脸识别的影响.实验结果表明:相对于归一化前的图像,温度归一化后的红外人脸图像与参考图像之间的信噪比有了明显改善,本文提出的归一化方法提高了红外人脸识别识别率.

基于FFT的快速跳频同步信号存在性检测

提出了在宽带快速跳频通信系统中的一种基于FFT的同步信号存在性检测算法,给出了干扰环境中在频域上计算信号归一化信噪比的限幅法、相邻跳做差法、二次限幅法、限幅做差再限幅法四种方法,分别进行了理论分析、计算机仿真和性能的比较。在白噪声环境和多音干扰环境下,四种方法计算出来的归一化信噪比与理论值差别不大,误差在±3 dB内;在部分频带噪声干扰环境下,二次限幅法与限幅做差再限幅法性能基本一致,明显优于限幅法和相邻两跳做差法,改善5 dB以上,其中二次限幅法实现简单并更为实用.