Application of S-transform threshold filtering in Anhui experiment airgun sounding data de-noising

As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.

Reduction of ultrasonic echo noise based on improved wavelet threshold de-noising algorithm for friction welding

In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.

Characteristics of unsteady flow in porous media while considering threshold pressure gradient with Green＇s function

The flow behavior in porous media with threshold pressure gradient(TPG) is more complex than Darcy flow and the equations of motion, and outer boundary and inner boundary with TPG are also different from Darcy flow for unsteady flow of a producing well in a reservoir. An analytic method to solve this kind of problem is in a need of reestablishment. The classical method of Green's function and Newman product principle in a new way are used to solve the unsteady state flow problems of various shapes of well and reservoir while considering the TPG. Four Green's functions of point, line, band and circle while considering the TPG are achieved. Then, two well models of vertical well and horizontal well are built and simultaneously the function to calculate the moving boundary of each well model is provided. The results show that when considering TPG the pressure field is much different, which has a sudden pressure change, with a moving boundary in it. And the moving boundary of each well model increases with time but slows down rapidly, especially when the TGP is large.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

On Functions of K-Balanced Matroids

In this paper, we prove an analogous to a result of Erdös and Rényi and of Kelly and Oxley. We also show that there are several properties of k-balanced matroids for which there exists a threshold function.

The Khuri-Jones Threshold Factor as an Automorphic Function

The Khuri-Jones correction to the partial wave scattering amplitude at threshold is an automorphic function for a dihedron. An expression for the partial wave amplitude is obtained at the pole which the upper half-plane maps on to the interior of semi-infinite strip. The Lehmann ellipse exists below threshold for bound states. As the system goes from below to above threshold, the discrete dihedral (elliptic) group of Type 1 transforms into a Type 3 group, whose loxodromic elements leave the fixed points 0 and ∞ invariant. The transformation of the indifferent fixed points from -1 and +1 to the source-sink fixed points 0 and ∞ is the result of a finite resonance width in the imaginary component of the angular momentum. The change in symmetry of the groups, and consequently their tessellations, can be used to distinguish bound states from resonances.

Optimization of Quantizer’s Segment Threshold Using Spline Approximations for Optimal Compressor Function

In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.

Fast First-Order Methods for Minimizing Convex Composite Functions

Two new versions of accelerated first-order methods for minimizing convex composite functions are proposed. In this paper, we first present an accelerated first-order method which chooses the step size 1/ Lk to be 1/ L0 at the beginning of each iteration and preserves the computational simplicity of the fast iterative shrinkage-thresholding algorithm. The first proposed algorithm is a non-monotone algorithm. To avoid this behavior, we present another accelerated monotone first-order method. The proposed two accelerated first-order methods are proved to have a better convergence rate for minimizing convex composite functions. Numerical results demonstrate the efficiency of the proposed two accelerated first-order methods.

区域土特征函数:收敛性

无论从一个地区内建立N-s_(v)关系曲线的工程应用角度还是作为区域土特征理论的必要组成部分,构建科学的N-v_(s)函数所需的数组量都是重要问题,但以往研究为空白。采用实测数据的随机分析,研究不同样本数量下N-v_(s)特征函数的稳定性和收敛性,并提出构建不同精度N-v_(s)特征函数的数组阈值。实测数据来源于4个国家9个地区,共11个工况,对此分别进行随机分析的结果表明,随样本数量增加,N-v_(s)特征函数具有稳定性与收敛性。研究表明,一个地区内取N-v_(s)数组超过50,100,200和800,则可分别得到变异系数小于0.2,0.15,0.10和0.05的N-s_(v)特征函数。

基于改进切尾均值的矿井图像去噪算法

现有矿井图像去噪算法对于复杂噪声的去除效果有限,且处理速度不能满足实时监控需求。针对该问题,提出一种基于改进切尾均值的矿井图像去噪算法。首先,采用切尾均值滤波器对图像噪声进行初步滤除,同时引入二次检验机制处理残留的噪声点,通过引入离散系数提升算法对不同像素的区分能力,增强去噪性能;其次,采用基于极值数量的分类处理及再次检验机制,有效减少残留噪声问题;然后,在小波函数中引入新的控制变量优化软阈值函数和硬阈值函数,构建双阈值函数,结合Radon变换增强对线性特征的处理,增强对矿井图像的检测能力;最后,采用均方误差(MSE)与峰值信噪比(PSNR)进行图像质量评价。实验结果表明:相较于切尾均值算法、硬阈值算法、软阈值算法,基于改进切尾均值的矿井图像去噪算法处理的图像的MSE增长相对缓慢,MSE最小,图像去噪效果最好;引入离散系数后,去噪图像的MSE相较于引入前低300 dB左右,PSNR相较于引入前高20 dB左右,引入离散系数能有效减少噪声点对算法的影响;相较于卡尔曼遗传优化算法、变换域图像去噪算法、交叉分支卷积去噪网络,基于改进切尾均值的矿井图像去噪算法处理的图像MSE分别降低了27,21,13 dB,PSNR分别提升了8,6,3 dB,去噪耗时分别缩短了0.20,0.16,0.14 s。

基于改进小波阈值函数和全尺度Retinex的红外图像融合增强算法

针对现有红外图像增强算法存在信噪比低、细节模糊、清晰度差等问题,本文提出基于改进小波阈值函数和全尺度Retinex的红外图像融合增强算法。首先,为克服尺度参数固定和光线散射导致红外图像退化的问题,利用大气透射率得到Retinex尺度参数的全尺度映射图,从而有效提高图像的清晰度,并将输入图像和使用全尺度Retinex处理后的输入图像作为算法的第一个输入和第二个输入。其次,为解决传统小波阈值函数在图像降噪过程中存在伪影、细节丢失等问题,设计改进小波阈值函数,通过引入尺度因子,在计算每层高频子图小波系数后,能根据该层数自适应调整尺度因子,并引入调节因子,结合指数函数,使该函数不仅能抑制高频子图噪声,还能极大程度保留细节信息。然后,使用小波图像融合的方式融合输入的高频子图和低频子图,进一步提高输出图像的纹理细节。主客观仿真结果表明,所提算法比其它对比算法具有更好的降噪和细节突出能力,并能提高红外图像的人眼视觉效果。最后,本文算法应用于红外成像模块采集的红外图像增强,效果良好,表明本文方法具有实用性。

基于能量阈值的双参数阈值函数在生理信号降噪中的应用

针对弱生理信号在采集过程中易被噪声淹没,传统小波去噪算法存在去噪效果差和信号提取失真的问题,根据小波系数的能量分布特点,提出一种改进的小波阈值去噪算法。通过计算各层小波系数的能量来确定阈值,避免阈值计算的不平衡性,同时提高自适应性和弱信号的保真度;采用一种改进的可调节的双参数阈值函数对小波系数进行处理,在小波系数压缩程度可控的同时可以自由调节阈值函数的变化趋势。实验结果表明:改进的小波阈值去噪算法相较于两种传统去噪算法(经验模态分解算法和滤波器算法)以及12种传统小波阈值和阈值函数组合算法,在信噪比、均方根百分比和均方根误差上都具有明显的优势,并且在实测生理信号中取得了最小的平均相对误差和最小的波动性。

基于参数优化VMD-小波阈值的轴承振动信号降噪方法

为了解决复杂工况下滚动轴承振动信号存在随机噪声的问题,提出了一种基于参数优化变分模态分解(VMD)-小波阈值的滚动轴承降噪方法。首先,利用以包络熵为适应度函数的天鹰算法对变分模态分解算法的模态分解数K和惩罚因子α进行了自适应选择,代入VMD分解中,得到若干本征模态函数(IMFs);然后,根据峭度-相关系数将IMF分量划分为纯净分量和含噪分量,对含噪分量进行了小波阈值降噪处理;最后,对处理后的分量进行了重构,并用重构信号进行了包络谱分析,实现了滚动轴承的信号降噪目的,并利用仿真信号和美国凯斯西储大学公开的轴承数据集对上述降噪方法的有效性进行了验证。研究结果表明:基于参数优化VMD-小波阈值的降噪方法减少了滚动轴承运行状态下的随机噪声,相对小波阈值降噪方法,所得仿真信号信噪比提升53%,均方误差降低13%;在故障特征频率为162 Hz时,所得实验降噪信号包络谱的前6倍频谱峰值更为明显,且受随机噪声影响较小。该研究方法在滚动轴承等旋转机械信号降噪方面具有一定的参考价值。

一种基于Schnorr签名的区块链预言机改进方案

区块链预言机是一种为上链数据提供可信保障的中间件,可以有效解决链上链下数据传输的信任问题。当前预言机方案大多存在成本高、容错率低、密钥泄露、数据泄漏、签名失效等问题,为此,提出一种基于Schnorr门限聚合签名的区块链预言机改进方案。通过引入可验证随机函数生成可验证随机数,解决签名过程中随机数重复或能够被预测而导致的密钥泄露问题;通过引入可验证秘密分享技术,解决签名过程中密钥分发者作恶而导致的签名失效问题。预言机通过验证密钥碎片检测密钥分发者是否作恶,验证结束后向预言机智能合约发送验证结果,当预言机智能合约收到t个验证失败的结果时向所有预言机发送消息,达成一致性共识后重启签名。在数据提交阶段,只有预言机身份验证通过时才允许其提交,以防止签名过程中的数据泄露。同时,设置预言机信誉与激励机制,保证信誉较好的预言机在有效时间内参与签名并获得奖励。实验结果表明,在预言机节点数量为50、密钥分发者作恶且恶意预言机占预言机群体总数近50%时,该方案消耗的总时间不超过200 ms,gas消耗量不超过5×10~5 wei,可以有效实现可信数据上链。

Maximizing Submodular+Supermodular Functions Subject to a Fairness Constraint

We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness constraint.This sum function is non-submodular in general.For an offline model,we introduce two approximation algorithms:A greedy algorithm and a threshold greedy algorithm.For a streaming model,we propose a one-pass streaming algorithm.We also analyze the approximation ratios of these algorithms,which all depend on the total curvature of the supermodular function.The total curvature is computable in polynomial time and widely utilized in the literature.

自适应新小波阈值函数中子图像去噪方法

中子射线照相技术是一种重要的无损检测技术,但在中子成像过程中会受到一些噪声因素的干扰,导致图像质量降低,不利于后期研究。本研究提出一种基于粒子群优化算法(particle swarm optimization,PSO)的新小波阈值函数去噪方法来降低噪声对中子图像的影响。其基本思想是将PSO算法与小波阈值函数去噪相结合。通过PSO算法寻找适合当前图像去噪的最优调节因子。Matlab软件实验的结果表明,新方法在去除高斯噪声、泊松噪声较其他四种对比方法可以明显提高噪声图像的峰值信噪比(PSNR)和降低噪声图像的均方误差(MSE),有效提高中子图像的质量。

FaultMonitoring Strategy for PV System Based on I-V Feature Library

Long-term use in challenging natural conditions is possible for photovoltaic modules,which are extremely prone to failure.Failure to diagnose and address faults in Photovoltaic(PV)power systems in a timely manner can cause permanent damage to PV modules and,in more serious cases,fires.Therefore,research into photovoltaic module defect detection techniques is crucial for the growth of the photovoltaic sector as well as for maintaining national economic prosperity and ensuring public safety.Considering the drawbacks of the current real-time and historical data-based methods for monitoring distributed PV systems,this paper proposes a method for monitoring PV systems at the module or string level that can be achieved by monitoring only electrical signals.The approach doesn’t need a lot of tests to get the operational data of PV modules beforehand and only requires theoretical feature libraries of PV modules through panel parameter calculations.The present operating conditions and the open-circuit and short-circuit faults can be precisely identified by comparing the observed open-circuit voltage and short-circuit current with the corresponding data in the theoretical feature library.After that,by comparing the measured maximum power point voltage and current with the corresponding data in the theoretical feature library through the threshold method,aging and shadowing faults can be accurately determined.Experimental testing was done to see whether the suggested method was effective.The results show that the proposed technique is able to diagnose open-circuit faults,short-circuit faults,aging faults,and shadowing faults with shadow occlusion above 20%.

颈椎光疗仪改善慢性颈痛患者颈部疼痛及相关功能的作用

背景:红光治疗具有无创、性价比高等特点,在临床上被广泛应用于治疗各种急慢性疼痛,但目前临床上使用的光疗设备成本昂贵、具有一定的场地限制,因此有必要探索更加便捷经济的光疗应用途径。目的:观察居家型颈椎光疗仪对于慢性颈痛的临床疗效。方法:于2022年11月至2023年2月在上海交通大学医学院附属第九人民医院康复医学科招募慢性颈痛患者24例,其中女18例,男6例;年龄(29.67±6.40)岁;体质量指数为(21.39±3.52)kg/m^(2)。每日使用颈椎光疗仪2次,每次20 min,持续4周。观察治疗前、治疗2周及治疗4周后的疼痛目测类比评分、肌肉压痛阈值、颈部主动活动度、颈部功能障碍指数及匹兹堡睡眠质量指数变化情况。结果与结论:①与治疗前相比,治疗4周后目测类比评分、压痛阈值、颈部功能障碍指数及匹兹堡睡眠质量指数均有显著改善(P<0.05),同时部分颈椎部分活动度(后伸及左右旋转)出现改善(P<0.05);②其中双侧目测类比评分、左斜方肌压痛阈值、C_(5)C_(6)压痛阈值及颈部功能障碍指数在治疗2周后即出现改善(P<0.05);③提示该颈椎光疗仪的应用在短期内可以改善慢性颈痛患者的疼痛评分、肌肉压痛、睡眠质量、功能水平及部分主动活动度,是一项便捷有效安全的治疗方法。

阈值压力负荷呼吸肌训练联合持续气道正压通气对脑卒中伴阻塞性睡眠呼吸暂停低通气综合征患者睡眠及呼吸功能的影响:一项随机对照试验

背景 阻塞性睡眠呼吸暂停低通气综合征(OSAHS)与脑卒中关系密切,两者并存不仅会影响患者的康复进程,且可能增加病死率。目前关于持续气道正压通气(CPAP)联合呼吸训练对脑卒中伴OSAHS患者的临床研究较为缺乏。目的 观察阈值压力负荷呼吸肌训练(TIMT)联合CPAP对脑卒中伴OSAHS患者睡眠状况、肺功能及呼吸肌功能的影响。方法 选取2022年4月—2023年8月于郑州大学第二附属医院就诊的符合入组标准的69例脑卒中伴OSAHS患者,通过随机数字表法将患者分为试验组(n=35)和对照组(n=34)。对照组行CPAP与常规治疗,试验组在此基础上行TIMT,疗程6周。比较2组患者干预前后的睡眠状况、肺功能及呼吸肌功能。结果 干预6周后,2组患者平均血氧饱和度(SpO_(2))、深睡眠比例(N3期睡眠占总睡眠时间比例)、最低动脉血氧饱和度(LSaO_(2))、第1秒用力呼气容积(FEV_(1))、用力肺活量(FVC)、一秒率(FEV1/FVC)、最大呼气压(MEP)、最大吸气压(MIP)均高于组内干预前(P<0.05);试验组干预6周后SpO_(2)、深睡眠比例(N3期睡眠占总睡眠时间比例)、LSaO_(2)、FEV_(1)、FVC、FEV_(1)/FVC、MEP、MIP高于对照组(P<0.05)。2组患者干预6周后睡眠呼吸暂停低通气指数(AHI)、氧减指数(ODI)、觉醒指数、Epworth嗜睡量表(ESS)评分均较组内干预前降低(P<0.05)。试验组干预6周后ODI、觉醒指数、ESS评分低于对照组(P<0.05)。结论 TIMT联合CPAP能明显改善脑卒中伴OSAHS患者的睡眠紊乱状况,提高动脉氧分压、呼吸肌肌力与肺功能。

基于改进VMD-WTFD算法的二次谐波光谱信号反演分析

采用变分模态分解和小波阈值调整函数(VMD-WTFD)算法在处理噪声时,信号波形恢复往往偏离标准的谐波信号,导致反演结果出现偏差。提出了一种改进VMD-WTFD算法,该算法采用新型阈值函数对VMD-WTFD算法进行改进,然后以此改进算法对二次谐波光谱信号进行反演分析。仿真与实验结果表明:与其它几种降噪算法相比,改进VMD-WTFD算法在不同调制度下反演出的原始信号的峰值、峰位和峰谷值更接近原始标准二次谐波光谱信号,去噪后的信噪比为17.545 dB,相关系数可达到0.9929;可以较清晰地反演出人体呼出气体中CO_(2)的二次谐波幅值。