Optical axis jitter rejection for double overlapped adaptive optics systems

Optical axis jitters, or vibrations, which arise from wind shaking and structural oscillations of optical platforms, etc., cause a deleterious impact on the performance of adaptive optics systems. When conventional integrators are utilized to reject such high frequency and narrow-band disturbance, the benefits are quite small despite their acceptable capabilities to reject atmospheric turbulence. In our case, two suits of complete adaptive optics systems called double overlapped adaptive optics systems (DOAOS) are used to counteract both optical jitters and atmospheric turbulence. A novel algorithm aiming to remove vibrations is proposed by resorting to combine the Smith predictor and notch filer. With the help of loop shaping method, the algorithm will lead to an effective and stable controller, which makes the characteristics of error transfer function close to notch filters. On the basis of the spectral analysis of observed data, the peak frequency and bandwidth of vibrations can be identified in advance. Afterwards, the number of notch filters and their parameters will be determined using coordination descending method. The relationship between controller parameters and filtering features is discussed, and the robustness of the controller against varying parameters of the control object is investigated. Preliminary experiments are carried out to validate the proposed algorithms. The overall control performance of DOAOS is simulated. Results show that time delays are a limit of the performance, but the algorithm can be successfully implemented on our systems, which indicate that it has a great potential to reject jitters.     

A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement

In this paper, we address the problem of noise reduction and speech enhancement by adaptive filtering algorithm. Recently, the well known forward blind source separation (FBSS) structure has been largely studied and intensively used to reduce acoustic noise components and to enhance speech signal. The FBSS structure is often combined with adaptive algorithms to accelerate the adaptation of the cross-filters, and to improve noise suppression at the output. In this paper, we propose to use a wavelet transform decomposition in the FBSS structure by using a two-channel forward wavelet symmetric adaptive decorrelating (WFSAD) algorithm. The proposed WFSAD algorithm provides a better compromise between time and frequency resolution and improves robustness of the noise reduction process when compared with the classical two-channel forward symmetric adaptive decorrelating (FSAD) algorithm. Simulation results prove the efficiency of the proposed WFBSS algorithm in comparison with conventional ones in terms of several objective and subjective criteria.     

基于简化模型与自适应滤波的车载SINS静基座快速对准

为了实现捷联惯性导航系统(Strap-down Inertial Navigation System,SINS)快速初始对准,根据已有可观测性分析结果,通过理论分析和计算得到了扩展观测量时初始对准系统最优可观测状态量组合,在此基础上简化了对准模型,建立了新的系统方程。针对载车发动机启动或其他情况导致系统噪声无法精确统计,提出了运用基于强跟踪滤波原理的自适应卡尔曼滤波(Kalman Filter,KF)算法抑制滤波发散,加快收敛速度。仿真结果表明运用简化模型和自适应滤波在系统噪声不匹配时具有更快的收敛速度和更高的对准精度,车载实验结果也表明运用简化模型和自适应滤波可以实现快速对准。     

应用于声反馈抑制的变步长归一化子带自适应滤波算法*

针对自适应滤波算法中稳态失调量和收敛速度之间的矛盾,提出了一种新的变步长归一化子带自适应滤波算法。该算法在系统噪声抵消原理的基础上,用迭代收缩的方法估计得到无噪先验子带误差的功率,对每个子带步长进行更新。对所提出的算法进行数学分析,可以得出该算法是稳定的和收敛的。在长回声路径和短回声路径两种情况下,将该算法应用于助听器声反馈抑制系统中。相对于其他归一化子带自适应滤波算法,仿真实验表明,所提算法实现了更快的收敛速度,获得了更低的失调量。     

FEEDFORWARD ADAPTIVE CONTROL OF FLEXURAL VIBRATION IN A BEAM USING WAVE AMPLITUDES

This paper concerns the real-time estimation of wave amplitudes and their subsequent use as a cost function in adaptive active control of bending vibrations in a beam. The amplitude of the wave propagating downstream from the control location is estimated by filtering the outputs of an array of sensors. Minimizing this wave amplitude has significant advantages over the more conventional approach in which velocity at some point is minimized. Expressions for the ideal frequency responses of the wave filters are found for the case of an array comprising two sensors in the far field. These filters are non-causal. FIR implementations designed using direct and time-delay methods are described, the latter offering some substantial advantages. Practical performance considerations are discussed, including filter length, frequency range, effects of near fields, group delays, accuracy and cross-sensitivity. Simulations and experimental measurements are performed and compared.     

基于自适应粒子滤波的红外目标跟踪

为有效解决非线性环境中的红外目标跟踪问题,提出一种自适应粒子滤波目标跟踪算法.建立了目标加权概率模型.在滤波过程中,提出双过程粒子重抽样方法,形成对抽样粒子集的自适应调节,有效地解决了粒子退化问题.用实际红外图像序列做了实验.结果表明,在非线性环境下用该方法得到的红外目标跟踪结果优于用传统粒子滤波和扩展卡尔曼滤波算法获得的结果.     

基于小波变换的图像混合噪声自适应滤除算法

为同时滤除图像中的椒盐噪声和高斯噪声,提出了一种基于小波变换的混合噪声自适应滤除算法,该算法首先采用中值滤波去除椒盐噪声,然后借助边缘检测算子区将图像为分边缘与非边缘区域,进一步对非边缘区域引入改进的均值滤波器,有效削弱高斯噪声的同时保护图像边缘细节,既初步削弱高斯噪声又保护了边缘,最后采用改进的小波阈值滤波算法,对不同的小波系数采用不同的阈值函数,通过线性回归得到各最优阈值关系式。实验结果表明,该混合噪声自适应滤除算法能有效滤除椒盐噪声和高斯噪声,在图像主观质量和客观质量上均取得了较好的效果,能提高去噪图像峰值信噪比0.5~2.0 dB。     

Cooperative impulsive formation control for networked uncertain Euler–Lagrange systems with communication delays

This paper investigates the cooperative formation problem via impulsive control for a class of networked Euler–Lagrange systems. To reduce the energy consumption and communication frequency, the impulsive control method and cooperative formation control approach are combined. With the consideration of system uncertainties and communication delays among agents, neural networks-based adaptive technique is used for the controller design. Firstly, under the constraint that each agent interacts with its neighbors only at some sampling moments, an adaptive neural-networks impulsive formation control algorithm is proposed for the networked uncertain Euler–Lagrange systems without communication delays. Using Lyapunov stability theory and Laplacian potential function in the graph theory, we conclude that the formation can be achieved by properly choosing the constant control gains. Further, when considering communication delays,a modified impulsive formation control algorithm is proposed, in which the extended Halanay differential inequality is used to analyze the stability of the impulsive delayed dynamical systems. Finally, numerical examples and performance comparisons with continuous algorithm are provided to illustrate the effectiveness of the proposed methods.     

A sliding Goertzel algorithm for adaptive passive neutralizers

A common method used in tuning adaptive–passive tuned vibration neutralizers is to adjust its resonance frequency to match the excitation frequency, which has the characteristic that the phase angle between its vibrating mass and its support is ?90°. A sliding-Goertzel algorithm is presented and demonstrated for extracting the vibration signals at the frequency of interest. The benefit of using the sliding Goertzel algorithm compared to other methods when used in vibration environments with multiple tones is that additional band-pass notch filtering is not required. This algorithm could also be used for adaptive tuned mass dampers, adaptive Helmholtz resonators, and adaptive quarter-wave tubes.     

A digital filtering scheme for SQUID based magnetocardiography

Considering the properties of slow change and quasi-periodicity of magnetocardiography (MCG) signal, we use an integrated technique of adaptive and low-pass filtering in dealing with two-channel MCG data measured by high $T_{\rm c}$ SQUIDs. The adaptive filter in the time domain is based on a noise feedback normalized least-mean-square (NLMS) algorithm, and the low-pass filter with a cutoff at 100Hz in the frequency domain characterized by Gaussian functions is combined with a notch at the power line frequency. In this way, both relevant and irrelevant noises in original MCG data are largely eliminated. The method may also be useful for other slowly varying quasi-periodical signals.     

时域递归滤波对微光成像视距影响的研究

 针对微光成像中噪声较大及视距较短的问题,提出了一种自适应时域递归滤波算法。该算法通过自动调节时域递归的滤波系数来提高信噪比和延伸视距。实验显示当滤波系数分别为0.50、0.75和0.875时,视距分别能延伸至原始的1.73倍、2.64倍和3.87倍,所得到的视距延伸与理论值基本保持一致。实验结果表明该算法在保持图像运动部分清晰的同时,还能有效地消除噪声并延伸微光成像系统的视距。     

A multi-input multi-output adaptive feed-forward controller for vibration alleviation on a large blended wing body airliner

Turbulent atmosphere, gusts, and manoeuvres significantly excite aircraft rigid body motions and structural vibrations, which leads to reduced ride comfort and increased structural loads. In particular BWB (Blended Wing Body) aircraft configurations, while promising a significant fuel efficiency improvement compared to wing-tube configurations, exhibit severe sensitivity to gusts. In general, a flexible aircraft represents a lightly damped structure involving a large variety of uncertainties due to fuel mass variations during flight, control system nonlinearities, aerodynamic nonlinearities, and structural nonlinearities, to name just a few. Especially at the beginning of flight testing of a newly developed aircraft type, plant models generally require a lot of verification and adjustment based on obtained flight test data, before they can be used reliably for control law design. Adaptive control already is a well-established method for many active noise and vibration control problems, and thus is proposed here for application to the problem of gust load alleviation. However, safety requirements are significantly higher for gust load alleviation systems than for most noise and vibration control systems. This paper proposes a MIMO (Multi-Input Multi-Output) adaptive feed-forward controller for the alleviation of turbulence-induced rigid body motions and structural vibrations on aircraft. The major contribution to the research field of active noise and vibration control is the presentation of a detailed stability analysis of the MIMO adaptive algorithm in order to support potential certification of this method for a safety-critical application. Finally, the proposed MIMO adaptive feed-forward vibration controller is applied to a longitudinal flight dynamics model of a large flexible BWB airliner in order to verify the derived vibration controller on a challenging control problem.     

中点箝位型逆变器在EAST低次谐波平台中的应用

针对EAST磁体电源等非线性负载产生低次谐波(75~125 Hz)难以治理的问题,提出注入式混合有源滤波的方案。基于EAST低次谐波实验平台,对注入式混合有源电力滤波装置的拓扑结构与滤波原理进行了研究; 同时采用一种降低直流侧中点电位波动的三电平空间电压矢量调制算法,并给出基本矢量作用时间的求取方法; 对该方案及算法进行了仿真与实验验证,仿真与实验结果具有很高的一致性,证明该方案能有效抑制EAST磁体电源运行中产生的低次谐波及其谐振放大。     

Sinusoidal reference strategy for adaptive feedforward vibration control: numerical simulation and experimental study

A new control approach named Sinusoidal Reference Strategy is developed for adaptive feedforward structural control. In this approach, the recursive-least-squares algorithm is used and a higher frequency sinusoidal signal is utilized as the reference signal. The present approach is able to overcome some of the shortcomings of the conventional adaptive feedforward control. Numerical simulations are then conducted on reducing wind-induced vibrations of the JIN MAO Building in Shanghai, China, with a height of 420 m. A multiple-degree-of-freedom (m.d.o.f.) aeroelastic model of a general super-tall building and an active mass damper (AMD) actuator are designed and manufactured. Wind tunnel tests are carried out to investigate further the control efficiency and robustness of the present approach. Both the simulation and experimental results show that the approach can reduce vibration of super-tall buildings remarkably, and can adapt to dynamic uncertainties and modelling errors of the buildings.     

一种公共网络攻击数据挖掘智能算法研究

公共网络的开放性和自组织特性导致网络容易受到病毒干扰和入侵攻击,对攻击数据的准确高效挖掘能确保网络安全。传统方法采用时频指向性波束特征聚类方法实现攻击数据挖掘,在信噪比较低时攻击数据准确挖掘概率较低。提出一种基于自适应滤波检测和时频特征提取的公共网络攻击数据挖掘智能算法。首先进行公共网络攻击数据的信号拟合和时间序列分析,对含噪的攻击数据拟合信号进行自适应滤波检测,提高信号纯度,对滤波输出数据进行时频特征提取,实现攻击数据的准确挖掘。仿真结果表明,采用该算法进行网络攻击数据挖掘,对攻击数据特征的准确检测性能较高,对干扰的抑制性能较强,能有效实现网络安全防御。     

An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles

This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances.     

云计算环境下资源调度系统设计与实现

在云计算环境下,对开放的网络大数据库信息系统中的数据进行优化调度,提高数据资源的利用效率和配置优化能力。传统的资源调度算法采用资源信息的自相关匹配方法进行资源调度,当数据传输信道中的干扰较大及资源信息流的先验数据缺乏时,资源调度的均衡性不好,准确配准度不高。提出一种基于云计算资源负载均衡控制和信道自适应均衡的资源调度算法,并进行调度系统的软件开发和设计。首先构建了云计算环境下开放网络大数据库信息资源流的时间序列分析模型,采用自适应级联滤波算法对拟合的资源信息流进行滤波降噪预处理,提取滤波输出的资源信息流的关联维特征,通过资源负载均衡控制和信道自适应均衡算法实现资源调度改进。仿真结果表明,采用资源调度算法进行资源调度系统的软件设计,提高了资源调度的信息配准能力和抗干扰能力,计算开销较小,技术指标具有优越性。     

Variable step-size subband backward BSS algorithms for speech quality enhancement

This paper addresses the problem of the speech quality improvement using adaptive filtering algorithms. Recently in Djendi and Bendoumia (2014) [1], we have proposed a new two-channel backward algorithm for noise reduction and speech intelligibility enhancement. The main drawback of proposed two-channel subband algorithm is its poor performance when the number of subband is high. This inconvenience is well seen in the steady state regime values. The source of this problem is the fixed step-sizes of the cross-adaptive filtering algorithms that distort the speech signal when they are selected high and degrade the convergence speed behaviours when they are selected small. In this paper, we propose four modifications of this algorithm which allow improving both the convergence speed and the steady state values even in very noisy condition and a high number of subbands. To confirm the good performance of the four proposed variable-step-size SBBSS algorithms, we have carried out several simulations in various noisy environments. In these simulations, we have evaluated objective and subjective criteria as the system mismatch, the cepstral distance, the output signal-to-noise-ratio, and the mean opinion score (MOS) method to compare the four proposed variables step-size versions of the SBBSS algorithm with their original versions and with the two-channel fullband backward (2CFB) least mean square algorithm.     

Modal control of beam flexural vibration

An active control system was developed to control the flexural vibrations of a beam with a modal filtering with only one secondary actuator. Segmented piezoelectric actuators and sensors were used for driving and sensing the bending beam vibrations. The primary actuator was fed by a broadband random disturbance signal in order to excite the first five modes of the structure. However, only the second to fifth modes were controlled. The control algorithm was implemented on a DSP board and the input and output signals were filtered using high order low pass filters. These filters, implemented on the DSP board avoid the degrading effect on the control performances of the higher order modes and which are not controlled. The modal filtering was achieved by computing. To this end, it is based on a previous identification procedure. This latter models, in one step, the dynamics of the structure and also the transfer function of the electronic circuits of the controller. The identified filtered modes were then used to compute the gain matrix using a LQR technique (linear quadratic regulator). Simulations of the active control were carried out and practical implementation of the control algorithms was performed. Experimental and simulation results were then compared and discussed.     

MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering北大核心CSCD

针对多像素光子计数器(MPPC)进行微光成像时,图像受光照不足和噪声影响出现的图像亮度低、对比度差、边缘模糊等问题,提出一种基于子窗口盒式滤波的自适应微光图像处理算法。为了减少算法运行时间的同时突出图像的边缘细节信息,利用子窗口盒式滤波器对图像进行分层得到基础层和细节层;对基础层图像采用自适应阈值直方图均衡化拉伸对比度,细节层图像采用自适应增益控制方式进行增强;根据基础层图像中有效灰度值个数占总灰度的比值自适应确定融合系数,将基础层图像与细节层图像融合得到增强后图像。通过微光实验平台设置3组不同照度的微光环境进行实验仿真,验证了本文算法在保持边缘信息和增强细节方面获得了更好的效果。实验结果表明本文算法在标准差、信息熵、平均梯度等客观评价方面优于改进前算法,提升了微光图像的成像效果。