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.

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。

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。

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。

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。

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。

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.

基于静态峰值分布的超声波回波信号检测方法研究

超声波气体流量计在气体流量计量方面具有独特的优势而被广泛应用,然而超声波信号在管道内传播,受流速、噪声等因素的影响,超声波回波信号幅值会发生衰减,造成回波信号定位错误导致严重的计量误差。尤其在高流速情况下,使用常用的方法很难确定特征点,导致测量到错误的结果。为此,研究超声回波信号的峰值特性,提出一种基于归一化静态峰值分布的超声波回波信号的超声气体流量计数字信号处理方法。在静态条件下对超声回波信号进行归一化,提取超声波回波信号峰值分布得到静态峰值参考波。在动态条件下,判断回波信号峰值的与静态峰值参考波的对应关系,确定特征点后计算超声波传播时间。该数字信号处理方法在以MSP430微控制器为核心的数字系统中实现,并进行气体流量标定实验,可验证该方法和所开发的数字系统的有效性。

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TCN和Transformer的鸡胚心跳混淆信号分类方法

鸡蛋胚胎培养法是制备禽流感疫苗常用的方法,快速准确地对鸡蛋胚胎进行成活性分类并将死胚从活胚中尽早剔除可以有效避免因胚胎死亡导致的细菌或霉菌污染,对孵化效率的提高有着重要意义。目前,主要以鸡胚心跳信号作为分辨死胚和活胚的依据。然而,鸡蛋活胚在注入禽流感病毒96 h后,其心跳信号特征介于普通活胚和死胚之间,易与死胚混淆,本文将该类数据称为鸡胚心跳混淆信号,单独作为一类加入数据集,将原本死胚、活胚二分类改为死胚、普通活胚和96 h活胚三分类,根据信号特征设计了绝对值均值标准化预处理方法,增强原始数据特征以提升数据可分类性,并针对全局特征和细节特征提出了一种基于时间卷积网络(Temporal convolutional network,TCN)和Transformer的残差结构浅层双分支网络结构(Residual fully temporal convolutional with transformer network,RFTNet)。实验结果表明,本文提出的三分类绝对值均值标准化预处理方法和RFTNet双分支网络在鸡胚混淆数据集分类任务中展现出良好性能,检测准确率高达99.75%。此外,在精确率、召回率和F1值3个评价指标上分别达到99.75%、99.74%和99.75%,进一步验证了本文方法的有效性。

基于深度学习的OFDM-IM信号检测方法

正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术在无线通信领域中拥有着重要地位,但OFDM系统中存在子载波间干扰和较高的峰均比的缺点,使得OFDM系统在信号检测方面的表现不太理想。针对OFDM系统中信号检测性能较差的问题,提出一种基于自归一化网络的索引调制(Index Modulation for Self Normalizing Network,IM-SNN)算法,并采用4QAM、8QAM、16QAM的调制方式验证系统的信号检测性能。结果表明,所提出的算法提高了接收端解调信号的性能,有效增强了信号检测的能力,并表现出优于传统技术中最大似然检测(Maximum Likelihood Detection,MLD)算法及现有技术中基于深度神经网络的索引调制(Index Modulation in Deep Neural Network,IM-DNN)算法的系统误码率及网络损失。在3种调制方式下,性能改善0.6~8 dB。

基于BP神经网络的光伏发电设备故障检测方法研究

针对光伏发电设备工作过程中存在的故障检测低下问题,设计了集采样、稳压、采样保持和故障采集于一体的集成电路,并提出了故障检测方法。通过分布式测控网络,使系统能够实时、准确地获得光伏发电设备的运行状态。故障检测装置自带计算处理模块智能对比检测数据,为实现故障检测算法提供数据支撑。为了更好地识别光伏发电设备的故障,提出了基于反向传播(BP)神经网络引入可信度检测度量参数的故障监测算法。该算法不仅能够消除误差,并且有很强的抗干扰性。试验结果证明,在不同的干扰信噪比下,检测故障的时间仅在5 ms左右,能够自主地对光伏发电设备运行过程中产生的故障进行识别,极大地提高了光伏发电设备故障检测的工作效率。

微动信号模拟及其在微动勘探中的应用

对于简正振型叠加法合成的微动信号,使用空间自相关法计算的频散曲线与理论频散曲线对比,发现两者具有较好的一致性。模拟微动信号时涉及许多参数,这些参数对微动勘探开展具有重要意义。对这些参数进行了数值试验,试验结果表明:震源距离和台阵尺寸对结果有明显影响,在复杂地层情况下应考虑高阶面波,在安静的观测场地应用小尺寸台阵往往能获得更高频率的频散信息从而提高浅层分辨率。因此,在实际开展微动勘探时,应考虑震源分布、台阵尺寸和高阶面波等因素对勘探结果的影响。