传感器弱信号的放大应用电路

介绍了几种弱信号放大的基本方法和一些典型的应用电路。     

一种改进的应用于噪声数据中的KNN算法

基于实例的KNN算法不可避免地要依赖于数据的质量,但原始数据含有噪声,因而KNN算法的结果势必会因为数据中的噪声而受到严重的影响。事实上,大多噪声都服从一定的模型,而且模型一般是已知的。充分利用数据中的噪声模型,以减小噪声对KNN算法结果的影响。通过实验结果表明该方法是有效的。     

闪烁噪声的自相关函数及其表征

闪烁噪声是一种非平稳随机过程,其功率谱密度函数在频率低端（ｆ＝０）发散,无法直接利用Ｗｉｅｎｅｒ－Ｋｈｉｎｔｃｈｉｎｅ关系得到它的自相关函数,本文采用“降阶－积分”方法,得到了频率源中闪烁调频噪声的自相关函数的解析表达式。根据这一结果,分析了频率稳定度的表征原理,指出了频率稳定度表征的实质是将非平稳过程转化为平稳过程并求取时间平均。最后,通过时域分析得到了闪烁噪声的Ａｌｌａｎ方差表达式。     

基于标签噪声鲁棒学习的疾病风险预测

疾病风险预测能够筛查易患人群, 并在早期进行预防干预措施以降低疾病的发生率及死亡率. 随着机器学习技术的快速发展, 基于机器学习的疾病风险预测得到了广泛应用. 然而, 机器学习十分依赖于高质量的标注信息, 医疗数据中存在的标签噪声会给构建高性能的疾病风险预测算法带来严峻挑战. 针对这一问题, 本文提出了一种基于深度神经网络和动态截断损失函数的噪声鲁棒学习方法用于疾病风险预测. 该方法引入动态截断损失函数, 融合了传统交叉熵函数的隐式加权特性和均方差损失函数的标签噪声鲁棒性; 通过构造训练损失下界, 并引入样本动态加权机制减小可疑样本的梯度, 限制可能的带噪样本在训练过程中的权重, 进一步增强模型的鲁棒性. 以脑卒中筛查数据集为例进行实验, 结果表明本文算法在各个标签噪声比例下均能取得良好的预测性能, 可降低疾病风险预测中标签噪声的负面影响, 实现了带有标签噪声数据的鲁棒学习.     

Riemann-Liouville型分数阶导数的非线性估计（英文）

本文主要研究任意有界连续信号的Riemann-Liouville分数阶导数估计问题.当分数阶α属于0到1时,首先利用滑模技术提出一种有界连续信号分数阶导数的非线性估计方法;然后将其结果推广至分数阶α∈R⁺的情况,并给出相应的非线性估计方案.借助Riemann-Liouville分数阶微积分频率分布模型,本文详细分析讨论了所给分数阶导数非线性估计的收敛性问题,并得到相应闭环系统是渐近稳定的结论.文中所提方法的主要优点是在事先未知给定信号分数阶导数上界的情况下,不仅能自适应地估计其Riemann-Liouville分数阶导数,而且当信号中含有随机噪声和不确定扰动时依然能正常工作.数值仿真实例验证了本文所给估计方法的可行性和有效性.     

具有加性和乘性噪声的线性离散时间随机系统的无模型最优跟踪控制

本文研究一类同时受加性和乘性噪声影响的离散时间随机系统的最优跟踪控制问题.通过构造由原始系统和参考轨迹组成的增广系统,将随机线性二次跟踪控制(SLQT)的成本函数转化为与增广状态相关的二次型函数,由此推导出用于求解SLQT的贝尔曼方程和增广随机代数黎卡提方程(SARE),而后进一步针对系统和参考轨迹动力学信息完全未知的情形,提出一种Q-学习算法来在线求解增广SARE,证明了该算法的收敛性,并采用批处理最小二乘法(BLS)解决该在线无模型控制算法的实现问题.通过对单相电压源UPS逆变器的仿真,验证了所提出控制方案的有效性.     

Displacement field denoising for high-temperature digital image correlation using principal component analysis

Principal component analysis (PCA) was extended to minimize the noise effect in digital image correlation (DIC) measurement under a high-temperature atmosphere environment. First, the principle of PCA was introduced, and the singular vectors and singular values for each component of the displacement fields from DIC were obtained. Then, the simulated high-temperature speckle images were developed to investigate the influences of noise on the DIC method under a high-temperature environment. Finally, the displacement fields of several special conditions were extracted from the simulated speckle images and experimental images; the effects of noise on the PCA were also analyzed.     

Path integral solution for nonlinear systems under parametric Poissonian white noise input

In this paper the problem of the response evaluation in terms of probability density function of nonlinear systems under parametric Poisson white noise is addressed. Specifically, extension of the Path Integral method to this kind of systems is introduced. Such systems exhibit a jump at each impulse occurrence, whose value is obtained in closed form considering two general classes of nonlinear multiplicative functions. Relying on the obtained closed form relation liking the impulses amplitude distribution and the corresponding jump response of the system, the Path Integral method is extended to deal with systems driven by parametric Poissonian white noise. Several numerical applications are performed to show the accuracy of the results and comparison with pertinent Monte Carlo simulation data assesses the reliability of the proposed procedure.     

A Modified Region Approach for Multivariate Measurement System Capability Analysis

Measurement system capability analysis is to determine whether the measurement system is capable for use in quality control. The existing research has been extended from univariate to multivariate cases. Two approaches, the multivariate analysis of variance (MANOVA) and the weighted principal components (WPC), were advocated in literature. The MANOVA method is constructed based on the volume ratio that treats the volume of constant‐density contours as the variability estimations. However, it ignores the fact that the relative position change of multivariate measurement errors could affect the measurement system capability. The WPC method uses dimension reduction to reduce the complexity but is unable to build the precision‐to‐tolerance ratio because it does not include tolerance. In this paper, we propose a modified‐region‐based method to compute the precision‐to‐tolerance ratio, the percent of repeatability and reproducibility, and the signal‐to‐noise ratio. This method also incorporates the variance–covariance structure of the measurement errors when dealing with the constant‐density contours of tolerances, total variation, and process variation. The performance of the modified‐region‐based method is evaluated based on a dataset from the literature and a set of relevant simulation. The proposed method proves to be effective compared with other methods.Copyright © 2014 John Wiley & Sons, Ltd.     

Photo Noise

Like past avant-garde movements, glitch and noise in the twenty-first century are used to reveal the materiality of the medium. But instead of providing insight or clarity into the politics or social conditions of a technology, they demarcate broader conditions of opacity and blockage, contributing to what Jodi Dean has called ‘communicative capitalism’. The work of German photographer Thomas Ruff alongside the Düsseldorf School, New Photography and JPEG technology are used in this article to illustrate this tendency as a move away from meaning and toward visual noise and communicative breakdown. I use three core analytic registers: the deliberate failure to adhere to visual norms, communicative failure and machine failure and breakdown. Ruff’s work uses visual noise as art to at once reaffirm pervasive conditions of breakdown, failure and decline in high-tech global capitalism, while simultaneously providing a temporary reprieve from these conditions through an emergent form of visual beauty.