Modal identification to non-stationary random excitation based on wavelet transform and co-integration theory

A kind of method of modal identification subject to ambient excitation is presented. A new synthesis stationary signal based on structural response wavelet transform and wavelet coefficient processes co-integration is obtained. The new signal instead of structural response is used in identifying the modal parameters of a non- stationary system, combined with the method of modal identification under stationary random excitation-the NExT method and the adjusted continuous least square method. The numerical results show that the method can eliminate the non-stationarity of structural response subject to non-stationary random excitation to a great extent, and is highly precise and robust.

Second-Order Solutions for Random Interfacial Waves Under Steady Uniform Currents

In the present research, the study of Song (2004) for random interfacial waves in two-layer fluid is extended to the case of fluids moving at different steady uniform speeds. The equations describing the random displacements of the density interface and the associated velocity potentials in two-layer fluid are solved to the second order, and the wave-wave interactions of the wave components and the interactions between the waves and currents are described. As expected, the extended solutions include those obtained by Song (2004) as one special case where the steady uniform currents of the two fluids are taken as zero, and the solutions reduce to those derived by Sharma and Dean (1979) for random surface waves if the density of the upper fluid and the current of the lower fluid are both taken as zero.

Modeling of random wave transformation with strong wave-induced coastal currents

The propagation and transformation of multi-directional and uni-directional random waves over a coast with complicated bathymetric and geometric features are studied experimentally and numerically. Laboratory investigation indicates that wave energy convergence and divergence cause strong coastal currents to develop and inversely modify the wave fields. A coastal spectral wave model, based on the wave action balance equation with diffraction effect （WABED）, is used to simulate the transformation of random waves over the complicated bathymetry. The diffraction effect in the wave model is derived from a parabolic approximation of wave theory, and the mean energy dissipation rate per unit horizontal area due to wave breaking is parameterized by the bore-based formulation with a breaker index of 0.73. The numerically simulated wave field without considering coastal currents is different from that of experiments, whereas model results considering currents clearly reproduce the intensification of wave height in front of concave shorelines.

Study on Current-Random Wave Forces Acting on a Framework

The forces of random wave plus current acting on a simplified offshore platform (jacket) model have been studied numerically and experimentally. The numerical results are in good agreement with experiments. The mean force can be approximated as a function of equivalent velocity parameter and the root-mean-square force as a function of equivalent significant wave height parameter.

Second-order solutions for random interfacial waves in N-layer density-stratified fluid with steady uniform currents

In the present paper, the random interfacial waves in N-layer density-stratified fluids moving at different steady uniform speeds are researched by using an expansion technique, and the second-order asymptotic solutions of the random displacements of the density interfaces and the associated velocity potentials in N-layer fluid are presented based on the small amplitude wave theory. The obtained results indicate that the wave-wave second-order nonlinear interactions of the wave components and the second-order nonlinear interactions between the waves and currents are described. As expected, the solutions include those derived by Chen （2006） as a special case where the steady uniform currents of the N-layer fluids are taken as zero, and the solutions also reduce to those obtained by Song （2005） for second-order solutions for random interfacial waves with steady uniform currents if N = 2.

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

A Statistical Method for Characterizing CMOS Process Fluctuations in Subthreshold Current Mirrors

A novel method to characterize CMOS process fluctuations in subthreshold current mirrors （SCM） is reported. The proposed model is succinct in methodology and calculation complexity compared with previous statistical models. However,it provides favorable estimations of CMOS process fluctuations on the SCM circuit, which makes it promising for engineering applications. The model statistically abstracts physical parameters, which depend on the IC process, into random variables with certain mean values and standard deviations, while aggregating all the random impacts into a discrete martingale. The correctness of the proposed method is experimentally verified on an SCM circuit implemented in an SMIC 0.18μm CMOS 1P6M mixed signal process with a conversion factor of 100 in an input range from 100pA to lμA. The pro- posed theory successfully predicts - 10% of die-to-die fluctuation measured in the experiment, and also suggests the -lmV of threshold voltage standard deviation over a single die,which meets the process parameters suggested by the design kit from the foundry. The deviations between calculated probabilities and measured data are less than 8%. Meanwhile, pertinent suggestions concerning high fluctuation tolerance subthreshold analog circuit design are also made and discussed.

A Robust Low Power Chaos-Based Truly Random Number Generator

This paper presents a low power,truly random number generator （TRNG） based on a simple chaotic map of the Bernoulli shift,which is extended to remain robustness in implementation. The map is realized by switched-current techniques that can fully integrate it in a cryptosystem on a chip. A pipelined architecture post-processed by a simple XOR circuit is used to improve the entropy. The TRNG is fabricated in an HJTC 0.18μm CMOS mixed signal process,and the statistical properties are investigated by measurement results. The power consumption is only 1.42mW and the truly random output bit rate is 10Mbit/s.

Transcranial direct current stimulation for auditory verbal hallucinations:a systematic review of clinical trials

Transcranial direct current stimulation(tDCS)has been reportedly beneficial for different neurodegenerative disorders.tDCS has been reported as a potential adjunctive or alternative treatment for auditory verbal hallucination(AVH).This study aims to review the effects of tDCS on AVH in patients with schizophrenia through combining the evidence from randomized clinical trials(RCTs).The databases of PsycINFO(2000–2019),PubMed(2000–2019),EMBASE(2000–2019),CINAHL(2000–2019),Web of Science(2000–2019),and Scopus(2000–2019)were systematically searched.The clinical trials with RCT design were selected for final analysis.A total of nine RCTs were eligible and included in the review.Nine RCTs were included in the final analysis.Among them,six RCTs reported a significant reduction of AVH after repeated sessions of tDCS,whereas three RCTs did not show any advantage of active tDCS over sham tDCS.The current studies showed an overall decrease of approximately 28%of AVH after active tDCS and 10%after sham tDCS.The tDCS protocols targeting the sensorimotor frontal-parietal network showed greater treatment effects compared with the protocols targeting other regions.In this regard,cathodal tDCS over the left temporoparietal area showed inhibitory effects on AVHs.The most effective tDCS protocol on AVHs was twice-daily sessions(2 mA,20-minute duration)over 5 consecutive days(10 sessions)with the anode over the left dorsolateral prefrontal cortex and the cathode over the left temporal area.Some patient-specific and diseasespecific factors such as young age,nonsmoking status,and higher frequencies of AVHs seemed to be the predictors of treatment response.Taken together,the results of tDCS as an alternative treatment option for AVH show controversy among current literatures,since not all studies were positive.However,the studies targeting the same site of the brain showed that the tDCS could be a promising treatment option to reduce AVH.Further RCTs,with larger sample sizes,should be conducted to reach a conclusion on the efficacy of tDCS for AVH and to develop an effective therapeutic protocol for clinical setting.

针灸联合经颅直流电刺激治疗脑卒中伴认知障碍的随机对照试验

目的:观察针灸联合经颅直流电刺激(tDCS)治疗脑卒中后血管性认知障碍的疗效及对同型半胱氨酸(Hcy)、神经元特异性烯醇化酶(NSE)和脑源性神经营养因子(BDNF)水平的影响。方法:选取2021年11月至2022年11月陕西省人民医院康复医学科收治的缺血性脑卒中伴认知障碍患者102例作为研究对象,按照随机数字表法随机分为针刺联合组(34例,脱落2例)、tDCS联合组(34例,脱落1例)和西药组(34例),进行为期4周的治疗。结果:治疗后,针灸联合组的蒙特利尔认知评分、洛文斯坦因作业疗法认知评定(LOTCA)评分、巴塞尔(Barthel)指数评分和血清BDNF均明显升高(P<0.05),且高于其他2组;针灸联合组和tDCS组的行为记忆测验评分均显著提高(P<0.05),且优于西药组;治疗后,3组的血清Hcy和NSE水平均明显降低,且针灸联合组明显低于其他2组(P<0.05)。结论:针灸联合tDCS治疗能显著改善脑卒中伴认知障碍患者的认知功能,提高日常活动能力和生命质量。

经颅电刺激镇痛研究的现状及展望

经颅电刺激技术是一种非侵入性神经调控方法,因其具有卓越的安全性、良好的患者依从性以及高度便携性等特点,被视为一种潜在的非药物镇痛手段。然而,目前对于经颅电刺激镇痛效果的研究结果不一致且镇痛机制尚未完全阐明。本文通过系统归纳总结3种主要的经颅电刺激技术——经颅直流电刺激、经颅交流电刺激和经颅随机噪声刺激——在镇痛领域的研究进展,评估了这些技术对短时、急性和慢性疼痛的镇痛效果,并深入剖析了其潜在的镇痛机制。同时,本文系统讨论了既往研究的局限性,并对未来研究提出了一系列切实可行的建议,如借助电场模拟技术实现个性化刺激以克服不同个体头部解剖结构差异的影响、应用多位点刺激和深部脑刺激技术来拓展刺激脑区、搭建经颅电刺激技术同步神经影像平台以制定个体特异性的刺激方案并深入揭示其镇痛机制、探索与其他治疗技术的联合应用以提高疗效等。这些建议的实施将有助于解决当前研究中存在的问题,充分发挥经颅电刺激在疼痛治疗中的临床价值,最终实现患者疼痛的缓解。

在线和离线高精度经颅直流电刺激对健康成年人手指运动技能学习效果的随机对照试验

目的观察高精度经颅直流电刺激(HD-tDCS)不同刺激时机(在线刺激、离线刺激)对健康成年人手指运动技能学习效果的影响。方法2022年3月至11月,在沈阳体育学院招募右利手男性受试者31例,随机分为3组。分别在HD-tDCS在线刺激、离线刺激以及假刺激下,完成顺序视觉等距捏力任务学习,共3 d。观察每天运动技能学习量、运动技能学习总量和速度-精度函数(SAF)。结果各组运动技能学习量均增加,在线刺激组增加最多(F=24.692,P<0.001),且第3天时,显著高于离线刺激组(t=4.732,P<0.001)和假刺激组(t=3.839,P=0.004)。运动技能学习总量:在线刺激组=离线刺激组>假刺激组。在线刺激组SAF高于离线刺激组和假刺激组。结论HD-tDCS刺激可提高健康受试者手指运动技能学习效果,在线刺激效果最佳。

基于主轴电机电流信号的表面粗糙度检测

针对表面粗糙度不能及时检测造成的工件浪费问题,首次提出根据主轴电机电流信号进行表面粗糙度检测分类。通过实验采集不同表面粗糙度加工时的主轴电机电流信号,采用小波包分解将电流信号分解成不同频段,借助能量特征和裕度因子对不同频段电流信号进行评估,过滤低相关性频段,再通过随机森林筛选特征,降低特征的冗余性。总谐波失真特征实现了积屑瘤检测,仅依赖构建的电流信号特征工程表面粗糙度检测准确率高达95%以上,并且检测时间在2 s以内,基本实现了工件表面粗糙度的快速准确检测。

考虑多扰动因子的含光伏电源低压台区漏电故障检测

针对含光伏电源的低压配电台区剩余电流检测易受多重因素干扰,难以实现漏电故障精准检测的问题,提出了一种基于随机森林算法的计及剩余电流扰动因子含光伏电源的低压配电台区漏电故障检测方法。通过多角度挖掘分析剩余电流扰动因子,利用剩余电流偏差法定量分析剩余电流扰动因子对剩余电流的影响,分析计及剩余电流扰动因子的漏电故障频域特性,构建了多维故障特征向量与特征数据集,建立了基于随机森林算法的漏电故障检测模型。通过仿真模型进行仿真分析与验证,结果表明所提方法可高精度检测漏电故障,与常用方法相比,所提方法的故障检测准确率和稳定性更高、抗干扰能力更强。

基于28 nm RRAM的可重构真随机数发生器

基于阻变存储器(RRAM)电流饥饿型环形振荡器的真随机数发生器(TRNG)方案,提出交替读写的操作模式,优化熵源重构机制.针对现有RRAM在多操作周期下跨导线性化问题,提出熵可配置电阻窗口钳位电路.通过减小电阻窗口获得RRAM非线性的最大化,所提电路能够有效避免RRAM出现过度置位、过度复位的现象,保证熵源稳定性.基于UMC 28 nm HKMG工艺对TRNG进行流片.输出数据统计性测试结果通过了NIST SP800-22所有测试集的真随机数标准测试.检测结果表明,在高斯分布的95%置信区间,所有统计数据的自相关函数值均落在-0.003~0.003,输出序列具有良好的随机性.

基于随机森林算法的低压系统漏电检测技术研究

随着低压配电系统规模及用户需求量迅速增加,用户线路与家庭电气设备漏电故障频发,极易发生人体触电及电气火灾事故。剩余电流保护器作为检测低压漏电故障的常用手段,近年来因线路(或设备)存在对地泄漏电流而频频误动,极大降低了保护设备投运率与可靠性。为此,本文提出一种基于随机森林(random forest,RF)算法的低压配电系统漏电检测技术,为最大程度贴近真实漏电故障场景,充分考虑实际故障场景存在的正常泄漏电流过大、故障邻近支路负荷投切频繁等干扰因素以获得贴近真实故障场景的原始剩余电流数据;通过对原始剩余电流数据进行数据预处理,分析掌握剩余电流的频域与时域特性并利用傅里叶变换算法提取时频域特征,完成低压系统漏电检测模型的建立与训练;在施加多种干扰因素情况下对漏电检测模型进行深度测试,其漏电故障的检测准确率可达99.97%,实现了多种干扰因素情况下的低压配电系统漏电故障检测;最后将支持向量机(support vector machine,SVM)算法、K最近邻(K-nearest neighbor,KNN)算法与本文基于RF算法的漏电故障检测模型的准确性进行对比,以此验证了基于RF算法的低压系统漏电检测模型的准确性与可行性。

音乐治疗基础上联合经颅直流电刺激对失眠效果的随机对照试验

目的探讨经颅直流电刺激(tDCS)联合音乐治疗对失眠患者的疗效。方法2023年7月至2024年4月,苏州大学附属第四医院失眠患者70例,随机分为对照组(n=35)和观察组(n=35)。两组均予音乐治疗,观察组予tDCS,对照组予假tDCS,共4周。治疗前后,采用匹兹堡睡眠质量指数量表(PSQI)总分和亚分、17项汉密尔顿抑郁量表(HAMD-17)、汉密尔顿焦虑量表(HAMA)、中文版压力知觉量表(CPSS)进行评定,测量静息态脑电相对功率,采用经颅多普勒测量脑各支动脉平均血流速度(Vm)。结果对照组脱落5例,观察组脱落4例。治疗后,观察组PSQI总分和亚分、HAMD-17、HAMA和CPSS评分均明显下降(|t|>3.503,P<0.01),除睡眠时间、睡眠效率和CPSS评分外,其他指标均优于对照组(|t|>2.304,P<0.05);观察组δ、θ频段相对功率增加,α、β和γ频段相对功率显著降低(|t|>6.468,P<0.001),且均优于对照组(|t|>2.395,P<0.05);观察组各支动脉Vm均显著提高(|t|>4.624,P<0.001),且均优于对照组(|t|>2.147,P<0.05)。结论在音乐治疗基础上联合tDCS有助于进一步改善失眠患者的睡眠质量和脑电活动,提高脑血流速度,减少不良情绪。

基于小波包分解多信息融合的配电网故障选线保护方法

为了解决配电网混合线路频发单相接地故障导致选线困难的问题,提出了一种新的保护方法。该方法通过db10小波包对配电网每条出线的零序电流进行第5层分解得到零序电流小波低频重构系数,运用Hausdorff距离算法将每条出线的低频重构系数进行计算得到不匹配度特征值;再结合小波包能量特点得到每条出线归一化后的综合小波能量特征值,将两种故障特征量作为输入量,结合随机森林算法具有数据融合和无需设定阀值的特性,建立配电网故障选线判别模型。通过将不同工况下得到的256组训练样本数据对模型进行训练获得判别模型的最优参数,再将剩余不同的48组测试数据进行模型验证,结果表明该方法在不同的故障条件下故障判别准确率高,具有较强的适用性。同时将所提出的方法与BP神经网络、ELM信息融合选线方法进行了对比,仿真结果表明,在加入信噪比为40 dB和20 dB的高斯白噪声的工况下,所提出的故障选线方法无论是分类准确率还是收敛时间都具有显著的优势。

基于随机森林法的GIS内隔离开关机械故障诊断研究

提出了基于驱动电机绕组电流与随机森林法的GIS内隔离开关机械故障诊断方法。将隔离开关驱动电机在不同操作阶段的极大电流值及其出现时刻作为特征值,通过力矩加载工装对隔离开关故障率较高的三类四种机械故障进行了模拟实验,并建立基于随机森林法的隔离开关机械故障诊断模型。最后,基于上述分类模型对模拟故障类型进行分类。分类结果表明,基于随机森林的隔离开关机械故障诊断的准确率高达90%,可有效实现隔离开关机械故障的诊断。