A robust adaptive clustering analysis method for automatic identification of clusters

Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm.     

Data envelopment analysis and robust optimization: A review

This paper reviews the milestone approaches for handling uncertainty in data envelopment analysis (DEA). This paper presents the detailed classifications of robust data envelopment analysis (RDEA). RDEA is appropriate for measuring the efficiencies of decision-making units in the presence of the data and distributional uncertainties. This paper reviews scenario-based and uncertainty set of DEA models. It covers 73 studies from 2008 to 2019. The paper concludes with suggestions about the guidelines for future researches in the field of RDEA.     

Sharpening Wald-type inference in robust regression for small samples

The datasets used in statistical analyses are often small in the sense that the number of observations n is less than 5 times the number of parameters p to be estimated. In contrast, methods of robust regression are usually optimized in terms of asymptotics with an emphasis on efficiency and maximal bias of estimated coefficients. Inference, i.e., determination of confidence and prediction intervals, is proposed as complementary criteria. An analysis of MM-estimators leads to the development of a new scale estimate, the Design Adaptive Scale Estimate, and to an extension of the MM-estimate, the SMDM-estimate, as well as a suitable ψ-function. A simulation study shows and a real data example illustrates that the SMDM-estimate has better performance for small n/p and that the use the new scale estimate and of a slowly redescending ψ-function is crucial for adequate inference.     

Relations between uncertainty structures in identification for robust control

Various techniques of system identification exist that provide a nominal model and an uncertainty bound. An important question is what the implications are for the particular choice of the structure in which the uncertainty is described when dealing with robust stability/performance analysis of a given controller and when dealing with robust synthesis. It is shown that an amplitude-bounded (circular) uncertainty set can equivalently be described in terms of an additive, Youla parameter and ν-gap uncertainty. As a result, the choice of structure does not matter provided that the identification methods deliver optimal uncertainty sets rather than an uncertainty bound around a prefixed nominal model. Frequency-dependent closed-loop performance functions based on the uncertainty sets are again bounded by circles in the frequency domain, allowing for analytical expressions for worst-case performance and for the evaluation of the consequences of uncertainty for robust design. The results can be used to tune optimal experimental conditions in view of robust control design and in the further development of experiment-based robust control design methods.     

无线传感器网络中的数据传输精简算法

在部署无线传感器网络的相关应用中,由于无线带宽、计算能力、电池能源和意外干扰等限制,通讯环境十分严峻。为了能减少数据传输量并较为精确地由源端传感器向汇聚节点（sink）传输数据,已有方法提出只向sink节点传输无法预测的数据。然而,很少有算法研究在这种严峻的环境中,丢包对数据精简的影响。基于线性预测模型和Heartbeat机制提出LRPH算法来抵制丢包带来的影响,并且及时监测传感器是否故障。另外,提出LRSH算法来优化LRPH,减少冗余信息。实验结果表明LRPH方式可以在一定的误差阈值内,通过只传输4.15%的数据来预测所有的数据。而LRSH只需要3.63%的传输。同已有的一些方法相比,这两种算法都可以在条件严峻的通讯环境下,有效地抑制丢包带来的影响。     

Proteomic analysis of a podocyte vesicle-enriched fraction from human normal and pathological urine samples

Podocytes (glomerular visceral epithelial cells) release vesicles into urine. Podocyte vesicle-enriched fractions from normal and pathological human urine samples were prepared for proteomic analysis. An immunoadsorption method was applied and enrichment of podocyte vesicles was assessed. We identified 76 unique proteins. One protein, serum paraoxonase/arylesterase 1 (PON-1), was newly identified in normal human urine sample. We confirmed this result and showed PON-1 expression in normal human kidney. These results demonstrated the potential for using the urine samples enriched in podocyte vesicles as a starting material in studies aimed at discovery of biomarkers for diseases.     

A reinvestigation of robust scale estimation in finite samples

This paper reworks and expands on the results of existing simulation studies, investigating the performance of various robust estimators of scale for Tukey’s three corner distributions. We focus attention on the popular biweight A-estimator, but also propose a new estimator based on the Student’s t-distribution, which attains an efficiency close to that of the A-estimator. We investigate the use of more efficient auxiliary location and scale estimators in two-pass estimators such as the A- and t-estimators, and find overall efficiency can be improved. Using much larger simulation sizes than previous studies, significant departures from existing efficiencies are obtained, and these lead to different recommendations for estimation.     

Data flow analysis for anomaly detection and identification toward resiliency in extreme scale systems

The increased complexity and scale of high performance computing and future extreme-scale systems have made resilience a key issue, since it is expected that future systems will have various faults during critical operations. It is also expected that current solutions for resiliency, mainly counting on checkpointing in hardware and applications, will become infeasible because of unacceptable recovery time for checkpointing and restarting. In this paper, we present innovative concepts for anomaly detection and identification, analyzing the duration of pattern transition sequences of an execution window. We use a three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis system to immediately generate an alert and identify the source of faults when an abnormal behavior pattern is captured, indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios, and accuracy rate of 100% with short root cause analysis time.     

Necessary and sufficient conditions for robust identification of uncertain LTI systems

Robust identification of uncertain systems arises whenever a chosen family of models does not completely describe reality. In these situations the issue of unmodeled dynamics gains significance in addition to random measurement noise. To deal with such mixed stochastic/deterministic settings we introduce a novel notion for robust consistency, which requires that the expectation (with respect to noise) of the worst-case (with respect to unmodeled dynamics) identification error asymptotically approach zero. It turns out that this notion leads to transparent necessary and sufficient conditions. We show that robust consistency holds, if and only if there is an instrument-input-pair capable of annihilating the residual error as well as stochastic noise. An extension of this result to the well-known “bounded but unknown” noise model shows that if we were to remove a set of Lebesgue measure zero, the error bound asymptotically approaches zero.     

A comparative study of robust efficiency analysis and Data Envelopment Analysis with imprecise data

Data Envelopment Analysis gauges the performance of operating entities in the best scenario for input and output multipliers. Robust efficiency analysis is a conservative approach that is concerned with an assured level of performance for an entity across all possible multiplier scenarios. In this study, we extend the robust efficiency analysis procedure to the situation where precise information on some input and output data is unavailable. Perfect efficiency analysis and potential efficiency analysis methods are developed to determine, respectively, the lower and upper bounds of an entity’s robust efficiency rating. The concepts of robust efficiency are expanded to classify entities in consideration into three groups: perfectly robust efficient, potentially robust efficient and robust inefficient. Two approaches are presented to convert robust efficiency analysis models into linear programs. It is claimed that Data Envelopment Analysis and robust efficiency analysis together provide a comprehensive picture of an entity’s relative efficiency. A computational experiment is conducted to compare the traditional efficiency analysis method with robust efficiency analysis in the presence of imprecise data. The results illustrate that perfect efficiency analysis exhibits a superior power of discrimination than potential efficiency analysis and that an entity recommended by perfect efficiency analysis has a satisfactory average performance.     

电动助力转向系统多领域鲁棒控制模型的降阶方法

随着人们对汽车操纵舒适性、安全性和节能等方面要求的提高,鲁棒控制算法越来越广泛的应用于电动助力转向（EPS）控制系统中．针对高阶鲁棒控制器实时性差、成本高等问题,提出最优平方积分误差（ISE）模型降阶方法．应用该方法对EPS系统鲁棒控制器进行降阶,并与最优Hankel范数降阶法进行比较．仿真结果表明,最优ISE降阶方法具有更好的降阶效果,降阶系统具有很好的鲁棒性．     

Stability analysis and robust control for fixed-scale teleoperation

In this article we present a new theorem that reduces the stability problem of teleoperators from a structured singular value problem to a maximum singular value problem. A control scheme realizing an ideal response for fixed-scale teleoperation is proposed and its stability is proven analytically. Next, the ideal control scheme is extended to control schemes with a higher stability robustness. Also, for these more complicated schemes, stability analyses are performed making use of the new theorem and stability conditions are derived. Experiments on a 1-d.o.f. setup are conducted to confirm the validity of the proposed control schemes.     

Siamese multi-layer perceptrons for dimensionality reduction and face identification

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.     

Algorithms for robust identification in ℋ∞ with nonuniformly spaced frequency response data

In this paper, first a two-stage robustly covergent identification algorithm in ℋ_∞ for nonuniformly spaced data is proposed. The worst-case error of the algorithm converges to zero faster than polynomial rates in the noise-free case when the identified system is an exponentially stable discrete-time system. The algorithm is characterized by a rational interpolation step with fixed poles at zero and infinity. Next, a minimax algorithm with better convergence properties is introduced. Sensitivity of the algorithms to small variations in the frequency values is also studied.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

Structure identification for gene regulatory networks via linearization and robust state estimation

Inferring causal relationships among cellular components is one of the fundamental problems in understanding biological behaviours. The well known extended Kalman filter (EKF) has been proved to be a useful tool in simultaneously estimating both structure and actual gene expression levels of a gene regulatory network (GRN). First-order approximations, however, unavoidably result in modelling errors, but the EKF based method does not take either unmodelled dynamics or parametric uncertainties into account, which makes its estimation performances not very satisfactory. To overcome these problems, a sensitivity penalization based robust state estimator is adopted in this paper for revealing the structure of a GRN. Based on the specific structure of the estimation problem, it has been proved that under some weak conditions, both the EKF based method and the method suggested in this paper provide a consistent estimate, but the suggested method has a faster convergence speed. Compared with both the EKF and the unscented Kalman filter (UKF) based methods, simulation results and real data based estimations consistently show that both convergence speed and parametric estimation accuracy can be appreciably improved. These lead to significant reductions in both false positive errors and false negative errors, and may imply helpfulness of the suggested method in better understanding the structure and dynamics of actual GRNs.     

Adaptive identification and control algorithms for nonlinear bacterial growth systems

This paper suggests how nonlinear adaptive control of nonlinear bacterial growth systems could be performed. The process is described by a time-varying nonlinear model obtained from material balance equations. Two different control problems are considered: substrate concentration control and production rate control. For each of these cases, an adaptive minimum variance control algorithm is proposed and its effectiveness is shown by simulation experiments. A theoretical proof of convergence of the substrate control algorithm is given. A further advantage of the nonlinear approach of this paper is that the identified parameters (namely the growth rate and a yield coefficient) have a clear physical meaning and can give, in real time, a useful information on the state of the biomass.     

A robust algorithm for identification of the frequency of a sinusoidal signal

The problem of identification of an unknown frequency of a sinusoidal signal is considered. A new approach to estimating the frequency of a sinusoidal signal that is robust relative to unaccounted perturbations in the measurement of the useful signal is proposed.     

Guaranteed cost inequalities for robust stability and performance analysis

In this paper, we formulate robust stability and performance bounds in terms of guaranteed cost inequalities. We derive new guaranteed cost bounds for plants with real structured uncertainty, and we reformulate them as linear matrix inequalities (LMIs). In particular, we obtain a shifted linear bound and a shifted inverse bound, and give LMI forms for a shifted bounded real bound, a shifted Popov bound, a shifted linear bound and a shifted inverse bound. Several examples are used to compare the shifted bounds with their unshifted counterparts and to make comparisons among these new bounds and vertex LMI bounds. Copyright © 2002 John Wiley & Sons, Ltd.