A new and practical influence measure for subsets of covariance matrix sample principal components with applications to high dimensional datasets

Principal Component Analysis (PCA) is an important tool in multivariate analysis, in particular when faced with high dimensional data. There has been much done with regard to sensitivity analysis and the development of influence diagnostics for the eigenvector estimators that define the sample principal components. However, little, if any, has been done in this setting with regard to the sample principal components themselves. In this paper we develop a sensitivity measure for principal components associated with the covariance matrix that is very much related to the influence function (Hampel, 1974). This influence measure is based on the average squared canonical correlation and differs from the existing measures in that it assesses the influence of certain observational types on the sample principal components. We use this measure to derive an influence diagnostic that satisfies two key criteria being (i) it detects influential observations with respect to subsets of sample principal components and (ii) is efficient to calculate even in high dimensions. We use several microarray datasets to show that our measure satisfies both criteria.     

Dynamics of the urban lightscape

The manifest importance of cities and the advent of novel data about them are stimulating interest in both basic and applied “urban science” (Bettencourt et al., 2007 [4]; Bettencourt, 2013 [3]). A central task in this emerging field is to document and understand the “pulse of the city” in its diverse manifestations (e.g., in mobility, energy use, communications, economics) both to define the normal state against which anomalies can be judged and to understand how macroscopic city observables emerge from the aggregate behavior of many individuals (Louail, 2013 [9]; Ferreira et al., 2013 [6]). Here we quantify the dynamics of an urban lightscape through the novel modality of persistent synoptic observations from an urban vantage point. Established astronomical techniques are applied to visible light images captured at 0.1 Hz to extract and analyze the light curves of 4147 sources in an urban scene over a period of 3 weeks. We find that both residential and commercial sources in our scene exhibit recurring aggregate patterns, while the individual sources decorrelate by an average of one hour after only one night. These highly granular, stand-off observations of aggregate human behavior – which do not require surveys, in situ monitors, or other intrusive methodologies – have a direct relationship to average and dynamic energy usage, lighting technology, and the impacts of light pollution. They may also be used indirectly to address questions in urban operations as well as behavioral and health science. Our methodology can be extended to other remote sensing modalities and, when combined with correlative data, can yield new insights into cities and their inhabitants.     

观测永久丢失下随机离散事件系统故障预测的验证算法

针对随机离散事件系统在故障预测时可能出现系统观测永久丢失,导致预测不准确的问题,提出一种观测永久丢失下故障预测验证的算法。首先对观测永久丢失的随机离散事件系统的U-可预测性进行了形式化。其次使用随机预测器构造了一个随机离散事件系统的U-预测器,实现了系统的故障预测。基于U-预测器,提出了随机离散事件系统U-可预测性的充分必要条件及验证算法,并且引入成对的方式,明显地改进了该验证算法的复杂度。仿真结果表明,该验证算法使得观测永久丢失下系统故障预测准确。最后,实例说明观测永久丢失下故障预测验证算法的应用。结果表明,该验证算法相比现有同类验证算法应用范围更广,验证结果更精确。     

Indirect density estimation using the iterative Bayes algorithm

Many practical problems involve density estimation from indirect observations and they are classified as indirect density estimation problems. For example, image deblurring and image reconstruction in emission tomography belong to this class. In this paper we propose an iterative approach to solve these problems. This approach has been successfully applied to emission tomography (Ma, 2008). The popular EM algorithm can also be used for indirect density estimation, but it requires that observations follow Poisson distributions. Our method does not involve such assumptions; rather, it is established simply from the Bayes conditional probability model and is termed the Iterative Bayes (IB) algorithm. Under certain regularity conditions, this algorithm converges to the positively constrained solution minimizing the Kullback-Leibler distance, an asymmetric measure involving both logarithmic and linear scales of dissimilarities between two probability distributions.     

不确定观测下离散事件系统的可诊断性

从系统诊断的角度来看,可诊断性是离散事件系统的一个重要性质.其要求系统发生故障后经过有限步的观测可以检测并隔离故障.为简单起见,对离散事件系统可诊断性的研究大都假定观测是确定的,即观测到的事件序列与系统实际发生的可观测事件序列一致.而在实际应用中,由于感知器的精度、信息传输通道的噪声等原因,获取的观测往往是不确定的.本文重点研究观测不确定条件下离散事件系统的可诊断性问题.首先,扩展了传统可诊断性的定义,定义了观测不确定条件下的可诊断性.接着,分别给出各类观测不确定条件下的可诊断性判定方法.而在更一般的情况下,各类观测不确定可能共同存在.因此,最后给出一般情况下的可诊断性判定方法.     

Consensus Control With a Constant Gain for Discrete-time Binary-valued Multi-agent Systems Based on a Projected Empirical Measure Method

This paper studies the consensus control of multi-agent systems with binary-valued observations. An algorithm alternating estimation and control is proposed. Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time. Based on the estimates, each agent designs the consensus control with a constant gain at some skipping time. The states of the system are updated by the designed control, and the estimation and control design will be repeated. For the estimation, the projected empirical measure method is proposed for the binary-valued observations. The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time (the same order as that in the case of accurate outputs). For the consensus control, a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations. And, there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’ states. Finally, the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature. Simulations are given to demonstrate the theoretical results.      

Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment

Summarizing a set of sequences is an old topic that has been revived in the last decade, due to the increasing availability of sequential datasets. The definition of a consensus object is on the center of data analysis issues, since it crystallizes the underlying organization of the data.Dynamic Time Warping (DTW) is currently the most relevant similarity measure between sequences for a large panel of applications, since it makes it possible to capture temporal distortions. In this context, averaging a set of sequences is not a trivial task, since the average sequence has to be consistent with this similarity measure.The Steiner theory and several works in computational biology have pointed out the connection between multiple alignments and average sequences. Taking inspiration from these works, we introduce the notion of compact multiple alignment, which allows us to link these theories to the problem of summarizing under time warping. Having defined the link between the multiple alignment and the average sequence, the second part of this article focuses on the scan of the space of compact multiple alignments in order to provide an average sequence of a set of sequences. We propose to use a genetic algorithm based on a specific representation of the genotype inspired by genes. This representation of the genotype makes it possible to consistently paint the fitness landscape.Experiments carried out on standard datasets show that the proposed approach outperforms existing methods.     

Concordance between VDU-users' ratings of comfort and perceived exertion with experts' observations of workplace layout and working postures

The aim of the present study was to evaluate the concordance (agreement) between VDU-users' ratings of comfort and ergonomists' observations of workplace layout, and the concordance between VDU-users' ratings of perceived exertion and ergonomists' observations of working postures during VDU-work. The study population consisted of 853 symptom free subjects. Data on perceived comfort in different dimensions and data regarding perceived exertion in different body locations were collected by means of a questionnaire. Data concerning workplace layout and working postures were collected with an observation protocol, by an ergonomist. Concordance between ratings of comfort and observations of workplace layout was reasonably good for the chair and the keyboard (0.60, 0.58) and good regarding the screen and the input device (0.72, 0.61). Concordance between ratings of perceived exertion and observations of working postures indicated good agreement (0.63-0.77) for all measured body locations (neck, shoulder, wrist and trunk). In conclusion ratings of comfort and perceived exertion could be used as cost-efficient and user-friendly methods for practitioners to identify high exposure to poor workplace layout and poor working postures.     

基于不确定观测下离散事件系统可诊断性的研究

在实际应用系统中,由于传感器故障、传感器限制和网络中的数据包丢失等原因,事件的可观测值变得不确定,使得观测系统行为变得尤为复杂。针对离散事件系统中,同个事件串可能有多个观测值以及不同状态下同个事件观测值也可能不同的问题,提出一种不确定观测下故障诊断验证的方法。首先对不确定观测的离散事件系统的可诊断性进行形式化,然后构建出用于上述故障诊断验证的验证器;基于验证器提出了系统基于不确定观测下可诊断的充要条件及验证算法;最后,实例说明不确定观测下故障诊断验证算法的应用。与现有研究相比,提出的方法对故障事件的观测值没有约束,可以为0个或多个观测值,使此方法应用的场景更为广泛。     

基于无人机观测的水稻冠层样方多角度反射特点分析

无人机平台成本低和灵活性高的优点可弥补传统遥感平台的缺陷,为农业遥感近地表数据获取提供有效途径。任何型号无人机搭载传感器进行数据采集时均有一定的观测几何,但无人机观测几何诱发的方向反射差异及其在后续应用中的潜在误差仍需深入分析。利用无人机采集典型扬花期水稻田样方多角度观测,探讨其样方级别的方向反射及可见光植被指数的方向特点。结果表明：红、绿、蓝三波段的方向反射率差异分别可高达30.17%、22.03%和27.31%,传递到后续可见光植被指数其相对误差可达62.08%,尤其是对归一化绿红差异指数（NGRDI）、可见光波段差异指数（VDVI）影响较大。研究发现,角度影响是基于无人机观测开展定量研究时不可忽视的重要因素。     

油石比光电测量仪

以比尔定律为理论依据，采用８０３１单片机构成油石比光电测量仪，实现沥青混合料油石比的测量，具有测量准确、速度快、经济、安全可靠、卫生等优点。     

Blind system identification using precise and quantized observations

This paper studies the blind identification of multi-channel FIR systems using precise and quantized observations. First, a new deterministic blind identification (DBI) algorithm is presented for multi-channel FIR systems using precise observations, in which the system parameters can be consistently estimated and the common source signal can be stably recovered. When the observed samples are quantized by a static finite-level quantizer, an iterative deterministic blind identification (IDBI) method is then provided. The asymptotic characters of the proposed IDBI method are discussed and the quantization effect on the identification performance is analyzed. Numerical simulations are given to support the developed DBI method and IDBI method.     

全球变化空间观测研究的文献计量分析

全球变化研究是国际科学界跨世纪的重大研究主题。随着全球环境问题的日益突出,全球变化研究日益得到国际科学界和国际社会的高度关注。空间观测是全球变化研究的一种不可或缺的重要方法,在全球变化研究中发挥着不可替代的重要作用。科技文献能够反映科学研究的发展动态,利用Thomson Data Anlyzer(TDA)和Ucinet对SCIE文献数据库中收录的1980~2009年间的全球变化空间观测研究论文进行数据挖掘和分析。结果表明,该研究涉及多个学科领域,大量研究始于1990年,气候变化、碳循环、土地利用变化等是该领域的主要研究内容,遥感、建模等是主要的研究方法,美国在该研究领域具有很大优势。     

A transformation method for stochastic control problems with partial observations

Two stochastic control problems with partial observations are studied, one where the policy or control law depends only on the latest observation (the controller does not have recall of observations), and the other with the standard partial observations model. The equivalent full observation problems are formulated, and the equivalence is proven using a new method. The results are illustrated with a simple example.     

Identification for Wiener‐Hammerstein systems under quantized inputs and quantized output observations

This paper investigates the Wiener‐Hammerstein system identification with quantized inputs and quantized output observations. By parameterizing the static nonlinear function, system identifiability is discussed first. Then, for the identifiable system a three‐step algorithm is proposed to estimate the unknown parameters by employing the empirical measure‐based method and the quasi‐convex combination technique. Finally, the algorithm is proved to be strongly convergent, the mean‐square convergence rate is presented, and the asymptotic efficiency is given by selecting a suitable transformation matrix. A numerical simulation is included to demonstrate the main results obtained.     

A genetic algorithm approach to the spectral estimation of time series with noise and missed observations

This study considers the problem of estimating the autoregressive moving average (ARMA) power spectral density when measurements are corrupted by noise and by missed observations. The missed observations model is based on a probabilistic structure. Unlike conventional cases of missed observation in parameter estimation problems, the variance of noise is unavailable, that is the time points of missed observations are unknown, and the probability of missing data needs to be estimated. In this situation, spectral estimation is more difficult to solve and becomes a highly nonlinear optimization problem with many local minima. In this paper, we use the genetic algorithm (GA) method to achieve a global optimal solution with a fast convergence rate for this spectral estimation problem. From the simulation results, we have determined that the performance is significantly improved if the probability of data loss is considered in the spectral estimation problem.     

Polynomial fixed-point smoothing of uncertainly observed signals based on covariances

In this article, the least-squares νth-order polynomial fixed-point smoothing problem of uncertainly observed signals is considered, when only some information about the moments of the processes involved is available. For this purpose, a suitable augmented observation equation is defined such that the optimal polynomial estimator of the original signal is obtained from the optimal linear estimator of the augmented signal based on the augmented observations and, hence, a recursive algorithm for this linear estimator is deduced. The proposed estimator does not require the knowledge of the state-space model of the signal, but only the moments (up to the 2νth one) of the signal and observation noise, as well as the probability that the signal exists in the observations.     

The specification of rank reducing observation sets in experimental design

If observations are lost from an experiment involving one or more forms of blocking, it can happen that the resultant design is treatment disconnected which has serious implications for the experiment. A method is described in this paper for specifying each set of observations which has the property that a treatment disconnected design will result if this observation set is missing. Some implications for two-replicate designs are described and four applications that portray the value of the method are discussed.     

Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm (τ₀₅₅). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60-70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.     

Stepwise local influence analysis

A new method called stepwise local influence analysis is proposed to detect influential observations and to identify masking effects in a dataset. Influential observations are detected step-by-step such that any highly influential observations identified in a previous step are removed from the perturbation in the next step. The process iterates until no further influential observations can be found. It is shown that this new method is very effective to identify the influential observations and has the power to uncover the masking effects. Additionally, the issues of constraints on perturbation vectors and bench-mark determination are discussed. Several examples with regression models and linear mixed models are illustrated for the proposed methodology.