Development of multivariable PID controller gains in presence of measurement noise

This paper examines the ability of a multivariable PID controller rejecting measurement noise without the use of any external filter. The work first provides a framework for the design of the PID gains comprising of necessary and sufficient conditions for boundedness of trajectories and zero-error convergence in presence of measurement noise. It turns out that such convergence requires time-varying gains. Subsequently, novel recursive algorithms providing optimal and sub-optimal time-varying PID gains are proposed for discrete-time varying linear multiple-input multiple-output (MIMO) systems. The development of the proposed optimal algorithm is based on minimising a stochastic performance index in presence of erroneous initial conditions, white measurement noise, and white process noise. The proposed algorithms are shown to reject measurement noise provided that the system is asymptotically stable and the product of the input–output coupling matrices is full-column rank. In addition, convergence results are presented for discretised continuous-time plants. Simulation results are included to illustrate the performance capabilities of the proposed algorithms.     

State space identification of stable systems

This paper studies a two stage approach to state space identification with certain optimality properties. The first stage is essentially a linear correlation stage via orthonormal filtering to reduce the problem to a setup with small equivalent noise. The second stage is an optimal Hankel norm model reduction step. This two stage algorithm is shown to be consistent for a large class of stable, possibly infinite dimensional, linear time-invariant systems under a very mild condition on the additive noise. The consistency analysis is based on a non-probabilistic framework for noise which allows the inclusion of large classes of non-ergodic and non-stationary noise, possibly having deterministic components, with maximum transparency. Furthermore, l and H convergence results are derived for a popular class of ortho1 normal basis expansions based on shift operators. These bases include, e.g. Laguerre bases.     

The calculation of the distribution of the Sun glitter radiance on the ocean surface by observing from a geostationary orbit

This paper presents a method for calculating the distribution of Sun glitter radiances from the ocean surface and a definition of the contour of the Sun disc image on the smooth ocean surface. Remotely sensed data were recorded from geostationary orbit and the spherical form of the Earth and shadowing effect on the rough ocean surface were taken into account. The reflected Sun radiance was calculated using the stochastically distributed facets method. The results of the calculations showed that the size of the Sun glint-affected region of ocean and also the value of the Sun glitter radiance in this region depend on both the geometry of observation and the degree of ocean surface roughness (i.e. over ocean wind speed v     

Globally exponentially stable attitude observer with Earth velocity estimation

This paper presents a novel observer for attitude estimation based on a triad of high‐grade rate gyros aided by a body‐fixed vector measurement of a constant inertial vector, departing from the majority of solutions that consider at least two of these vectors. A cascade approach is proposed, where the first block estimates a vector that is related to the angular velocity of the Earth and a second block estimates the attitude. While the topological characteristics are relaxed in the attitude observer so that global exponential stability is achieved, an additional stage is also devised that yields estimates directly on the special orthogonal group, preserving global convergence of the estimation error. Simulation results with realistic sensor noise were performed, including extensive Monte Carlo runs. These results illustrate the convergence of the proposed solution, as well as the achievable performance and robustness to sensor noise.     

Boundary stabilisation to non-stationary solutions for deterministic and stochastic parabolic-type equations

In this work, we design explicit, finite-dimensional boundary feedback laws for stabilisation to trajectories for parabolic-type equations. The simple form of the feedback allows to write the solution of the corresponding closed-loop equation in a mild formulation via a kernel; then, taking advantage of this, the stability is shown. As an application, null stabilisation for stochastic parabolic-type equations is deduced as well. As far as we know, the present work is the first result on boundary feedback stabilisation to trajectories and for stochastic parabolic-type equations, with stability guaranteed independent of how large the level of the noise is.     

A Direct Link Minimal Resource Allocation Network for Adaptive Noise Cancellation

This paper presents a modification to the Minimal Resource Allocation Network (MRAN) of Yingwei et al. by introducing direct links from inputs to output and investigates its performance for noise cancellation problems. MRAN has the same structure as a Radial Basis Function network but uses a sequential learning algorithm that adds and prunes hidden neurons as input data is received sequentially so as to produce a parsimonious network. Earlier work by Sun Yonghong et al. has demonstrated the capability of MRAN to produce a compact network with excellent noise reduction properties. In this paper the capability of the direct link Minimal Resource Allocation Network (DMRAN) is evaluated by comparing it with MRAN on several nonlinear adaptive noise cancellation problems. The direct link MRAN uses the same learning algorithm as MRAN but with the introduction of direct links we are able to realise even smaller networks than MRAN with better noise reduction properties.     

Thwarting intercept-and-resend attack on Zhang’s quantum secret sharing using collective rotation noises

This work deliberately introduces collective-rotation noise into quantum states to prevent an intercept-resend attack on Zhang’s quantum secret sharing scheme over a collective-noise quantum channel (Zhang in Phys A 361:233–238, 2006). The noise recovering capability of the scheme remains intact. With this design, the quantum bit efficiency of the protocol is doubled when compared to Sun et al.’s improvement on Zhang’s scheme (Sun et al. in Opt Commun 283:181–183, 2010).     

Analysis of large aeolian (wind-blown) bedforms using the Shuttle Radar Topography Mission (SRTM) digital elevation data

Dry land areas cover large parts of the land masses of Earth. Of these a large portion is mantled by aeolian (wind-blown) deposits and subjected to aeolian process, i.e., the transport of sand or dust by wind. One of the most prominent morphologies created by wind-driven deposits are dunes and in their larger form draas or mega dunes. These mega dunes tend to be widely spaced; very large; often forming a base on which smaller dunes form, producing a compound dune pattern.In February 2000 the Shuttle Radar Topography Mission, onboard the space shuttle Endeavour flew an interferometric radar system whose objective was to create a DEM of nearly all Earth land masses. In this paper DEM data from the C-band 90 m data set and the X-band 30 m data set were used to map and characterize the height and spacing of the large sand seas (Ergs) on Earth. Only the larger forms of dunes could be mapped reliably. The dune spacing and heights extracted from the SRTM DEMs tended to be in overall agreement with those reported in the literature. In a comparison between the X-band and the C-band data it was found that the X-band data are more sensitive to the smaller scale undulations on the compound dunes and better revealed the full height of the dunes.     

An evaluation of satellite dust-detection algorithms in the Middle East region

In the last 15 years, the frequency, spatial extent, and intensity of dust storms have increased and it is one of the main continuously occurring environmental hazard in the Middle East region. Since dust storms generally cover a large spatial extent and are highly dynamic, satellite Earth Observation (EO) is a key tool for detecting their occurrence, identifying their origin, and monitoring their transport and state. A variety of satellite dust detection algorithms have been developed to identify dust emissions sources and dust plumes once entrained in the atmosphere. This paper evaluates the performance of five widely applied dust detection algorithms: the Brightness Temperature Difference (BTD), D-parameter, Normalized Difference Dust Index (NDDI), Thermal-Infrared Dust Index (TDI) and the Middle East Dust Index (MEDI). These algorithms are applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data to detect dust-contaminated pixels during three significant dust events in 2007 in the Middle East region that originated from sources in Iraq, Syria and Saudi Arabia. The results indicate that all methods have a comparable performance in detecting dust-contaminated pixels during the three dust events with an average detection rate (between all algorithms) of 85%. However, substantial differences exist in their ability to distinguish dust from clouds and the land surface, which resulted in large errors of commission. Direct validation of these algorithms with observations from seven Aerosol Robotic Network (AERONET) stations in the region found an average false detection rate (between all algorithms) of 89.6%. Although the algorithms performed well in detecting the dust-contaminated pixels their high false detection rate means it is challenging to apply these algorithms in operational context.     

Noisy dynamic simulations in the presence of symmetry: Data alignment and model reduction

We process snapshots of trajectories of evolution equations with intrinsic symmetries, and demonstrate the use of recently developed eigenvector-based techniques to successfully quotient out the degrees of freedom associated with the symmetries in the presence of noise. Our illustrative examples include a one-dimensional evolutionary partial differential (the Kuramoto–Sivashinsky) equation with periodic boundary conditions, as well as a stochastic simulation of nematic liquid crystals which can be effectively modeled through a nonlinear Smoluchowski equation on the surface of a sphere. This is a useful first step towards data mining the symmetry-adjusted ensemble of snapshots in search of an accurate low-dimensional parametrization and the associated reduction of the original dynamical system. We also demonstrate a technique (Vector Diffusion Maps) that combines, in a single formulation, the symmetry removal step and the dimensionality reduction step.     

基于群组与密度的轨迹聚类算法

现有基于密度的聚类方法主要用于点数据的聚类,不适用于大规模轨迹数据。针对该问题,提出一种利用群组和密度的轨迹聚类算法。根据最小描述长度原则对轨迹进行分段预处理找出具有相似特征的子轨迹段,通过两次遍历轨迹数据集获取基于子轨迹段的群组集合,并采用群组搜索代替距离计算减少聚类过程中邻域对象集合搜索的计算量,最终结合群组和密度完成对轨迹数据集的聚类。在大西洋飓风轨迹数据集上的实验结果表明,与基于密度的TRACLUS轨迹聚类算法相比,该算法运行时间更短,聚类结果更准确,在小数据集和大数据集上的运行时间分别减少73.79%和84.19%,且运行时间的减幅随轨迹数据集规模的扩大而增加。     

配套新型布袋除尘控制系统软件设计

大型火电厂中粉尘污染已经成为最主要的污染源之一,所以研究高除尘率的布袋除尘器具有十分重要的意义.介绍了一种基于Profibus总线的新型布袋除尘器控制系统软件设计,该系统软件设计分成四个子系统进行.为提高系统的可靠性和实用性,以及满足工艺要求,设计又采用了先进的控制策略.通过实际运行证明,该系统达到了节约成本、降低粉尘排放浓度的控制目的.     

基于信息补偿的自适应联邦滤波算法

针对采用标准卡尔曼滤波器必须知道系统噪声统计特性的局限性,研究了一类系统噪声未知情况下的自适应联邦滤波方法,指出了自适应滤波方法应用于联邦结构时应当注意的问题,提出了一种基于信息补偿的自适应联邦滤波算法。SINS／BDS／GPS组合导航系统的仿真结果表明,该方法可以有效抑制系统噪声未知情况下的滤波发散现象,提高了滤波的稳定性和估计性能。     

A Contraction Theory Approach to Stochastic Incremental Stability

We investigate the incremental stability properties of Ito stochastic dynamical systems. Specifically, we derive a stochastic version of nonlinear contraction theory that provides a bound on the mean square distance between any two trajectories of a stochastically contracting system. This bound can be expressed as a function of the noise intensity and the contraction rate of the noise-free system. We illustrate these results in the contexts of nonlinear observers design and stochastic synchronization.     

Formant tracking linear prediction model using HMMs and Kalman filters for noisy speech processing

This paper presents a formant tracking linear prediction (LP) model for speech processing in noise. The main focus of this work is on the utilization of the correlation of the energy contours of speech, along the formant tracks, for improved formant and LP model estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of the inter-frame correlation of speech parameters across successive speech frames; the within frame correlations are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning spectral amplitude estimation stage where an initial estimate of the LP model of speech for each frame is obtained, (2) a formant classification and estimation stage using probability models of formants and Viterbi-decoders and (3) an inter-frame formant de-noising and smoothing stage where Kalman filters are used to model the formant trajectories and reduce the effect of residue noise on formants. The adverse effects of car and train noise on estimates of formant tracks and LP models are investigated. The evaluation results for the estimation of the formant tracking LP model demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and Kalman smoothing stages, results in a significant reduction in errors and distortions.     

基于极大后验估计的自适应容积卡尔曼滤波器

针对标准的容积卡尔曼滤波器(CKF) 设计需要精确已知噪声先验统计知识的问题, 提出一种自适应CKF 算法. 该算法在滤波过程中, 利用Sage-Husa 极大后验估值器对噪声的统计特性进行在线估计和修正, 有效地提高了CKF 的估计精度和数值稳定性. 在某些情况下, 噪声协方差估计会出现异常现象使得滤波发散, 进而提出了相应的改进方法. 仿真结果表明了自适应CKF 算法的可行性和有效性, 且明显改善了标准CKF 算法的滤波效果.     

Hyperspectral change detection: an experimental comparative study

ABSTRACT

The Earth’s surface is constantly changing due to variations originating from the increasing human population. In the last decade, numerous methods were presented in the literature for change detection using multispectral image data. Owing to the increasing availability of hyperspectral images, these methods are now being applied to hyperspectral images. The main objective of this study is to present different change detection methods in hyperspectral imagery. Numerous algorithms (more than 43 algorithms) have been proposed for change detection in hyperspectral imagery over the last decade. In this work, we provide a comparative review of these algorithms through experimental results. We place the algorithms in five major groups: (1) match-based, (2) transformation-based, (3) direct classification-based, (4) post-classification-based, and (5) hybrid-based. We evaluate and compare the performances of all five groups using two real-world data sets of multi-temporal hyperspectral imagery. This comparative study investigates the advantages and disadvantages of the effects of preprocessing steps in the efficiency of the hyperspectral change detection (HSCD) methods. These preprocessing steps are considered in four scenarios, including: (1) considering only spatial or geometric correction without noise reduction and spectral correction; (2) spatial, atmospheric, and radiometric corrections without noise reduction; (3) spatial correction and noise reduction without atmospheric and radiometric corrections; and (4) spatial, atmospheric, and radiometric correction with noise reduction. The empirical results, followed by a summary of the pros and cons of each algorithm, aim to help researchers select the procedures with the best characteristics for HSCD applications.     

Edge preservation of impulse noise filtered images by improved anisotropic diffusion

This paper provides a robust scheme for random valued impulsive noise reduction along with edge preservation by anisotropic diffusion with improved diffusivity. The defective impulse noisy pixels are detected by Laplacian based second order pixel difference operation where these defective pixels are replaced by appropriate values with regard of the gray level of their four directional neighbors. This de-noised image undergoes the diffusion operation where diffusion coefficient function is modified to make it adaptive by incorporating local gray level variance information. The proposed modified diffusion scheme effectively restore the edges and fine details destroyed during impulse noise reduction process. The effect of proposed diffusion scheme has been studied on various images and the results are compared with some existing diffusion methods which are independently used for impulse noise reduction and edge preservation. The results shows that the prior removal of impulsive noise before the application of diffusion process is advantageous over the direct application of diffusion for removing the impulsive noise. In addition, the results of the proposed diffusion scheme are compared with some of the median filter based methods which are effectively used for impulse noise reduction without caring of edge preservation. The proposed diffusion scheme sufficiently preserves the edges without boosting of impulsive noise components on images corrupted up to 50 % of the impulsive noise density.     

非因果先验信噪比估计的LSA算法改进

对于大多数的语音增强算法,先验信噪比及背景噪音频谱估计的准确与否,对语音增强的效果影响至关重要.为此,在传统MMSE-LSA算法的基础上,提出一种基于非因果先验信噪比估计的LSA 改进算法,较好地弥补了传统 LSA 算法在先验信噪比上估计的不足,同时采用平滑系数动态更新噪音频谱值,使估计值能更好地跟踪噪音的变化.实验结...     

定量配料实践教学的远程监控系统设计

针对定量配料加工过程粉尘大、噪声大不适合长期监看加工,定量配料实践教学的远程监控系统设计采用了远程计算机MCGS组态软件来监控多台皮带秤配料加工过程。网络控制网关解析MCGS组态软件通过RS485发出的命令,分别监控各台皮带秤稳定工作。网络控制网关采用的是嵌入式ARMLPC2136芯片开发,LPC2136微处理器是高性能、低功耗、高集成度并具有工业级温度范围和性能的微处理器。