Search

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets., Accepted in TPAMI 2021

Due to the simultaneous existence of model uncertainties and external disturbances, designing automated ground vehicle path-following controllers is recognized as a challenging task. The $H_{∞}$ robust control methodology, as one of the accomplished strategies for controller robustification, has been commonly adopted by researchers to address the vehicle path-tracking problems. Nevertheless, despite its advantages, the $H_{∞}$ controller is only capable of limiting the total ``energy'' of the tracking errors. On the other hand, from a safety standpoint, constraining the ``peak'' of the tracking errors may carry an equal or more importance. To establish a guaranteed upper bound on the path-tracking errors, this paper proposes a novel methodology to synthesis the ground vehicle path-following controller in light of the energy-to-peak robust control theory. Additionally, to address the time-varying uncertainties presented in the tire dynamics, robust stabilization constraints based upon the small-gain theorem are also formulated into the overall controller design problem. Comparative study regarding the disturbance rejection performance between the proposed controller and the conventional $H_{∞}$ approach is conducted via CarSim-Simulink joint simulations. Furthermore, the robustness and disturbance attenuation ability of the energy-to-peak path-tracking controller is experimentally verified on a scaled car.

High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical methods usually assume independent observations and sub-Gaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently [Sun, Zhou, Fan, 2019, J. Amer. Stat. Assoc., in press] proposed Adaptive Huber Regression (AHR) to address the issue of heavy-tailed errors. They discover that the robustification parameter of the Huber loss should adapt to the sample size, the dimensionality, and the moments of the heavy-tailed errors. We progress in a vertical direction and justify AHR on dependent observations. Specifically, we consider an important dependence structure -- Markov dependence. Our results show that the Markov dependence impacts on the adaption of the robustification parameter and the estimation of regression coefficients in the way that the sample size should be discounted by a factor depending on the spectral gap of the underlying Markov chain.

In this letter, a robust quantized sampled–data controller is provided for a class of nonlinear systems affected by time–varying uncertainties, actuation disturbances and measurement noises. Sufficient conditions based on linear matrix inequalities and ensuring the existence of the proposed robust quantized sampled–data controller are given. Quantization of both state measurements and input signals is simultaneously considered. Input–to–state stability redesign technique is used in order to attenuate the effects of bounded actuation disturbances and of bounded observation errors. It is proved that, under suitably fast sampling and accurate quantization of the input/output channels, the proposed controller achieves the semi–global practical stability, with arbitrarily small final target ball, of the related quantized sampled–data closed–loop system provided that the observation errors do not affect (or affect marginally) the robustification term added in the controller and, that the bounds of the actuation disturbances as well as of the observation errors are a priori known. The theory here developed includes also the cases of time–varying sampling intervals and of non–uniform quantization of the input/output channels as well as the stability analysis of the inter–sampling system behavior. The provided results are validated through an example of one–link manipulator.

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, standard methods based on the (quasi-)likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the indirect inference procedure to take account of the parametrization of the variance-covariance matrix of the disturbances. Finite sample performance of the new estimator is assessed in a Monte Carlo study and found to offer advantages over existing methods. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

A successful index helps policy decision makers identify benchmark performances and trends and set policy priorities. Selecting representative variables from a large number of potential candidates in the system is crucial to the success of index construction. A robust and sparse method reflects an urgent need to effectively select useful variables for index construction. Although the least absolute shrinkage and selection operator (Lasso) is a popular technique for variable selection, it suffers from the influence of outlier or noise. In this paper, we propose a robust Lasso with a generic insensitive and adaptive loss function (GIA-Lasso) for variable selection. The generic loss function can achieve great robustness against outliers by adjusting an insensitive parameter, an elastic interval parameter, and an adaptive robustification parameter. The $$L_1$$ -norm regularization term and the supervised selection process in GIA-Lasso ensure that the most representative variables are selected. The variable selection results of the Financial Conditions Index and Innovation and Entrepreneurship Index confirm that GIA-Lasso is not only robust against outliers, but also selects representative variables. The Granger causality test further proves the reasonability of the selected variables.

Basic learning control law designs are summarized, and conditions for convergence of the learning process are developed when several different choices of filter frequency cutoffs are used for robustification. This paper presents a cliff harmonic-frequency filter with a sharp frequency cutoff and a weighted harmonic-frequency filter with frequency weighting, that are applied each iteration in iterative learning control. Filter matrices based on the state-space model of a finite-difference digital filter are derived for Matlab’s and Gustafsson’s forward and backward filtering which are commonly called filtfilt methods. Furthermore, filter matrices for Matlab’s and Gustafsson’s filtfilts are revised to make the input convergence matrix to be monotonically stable. Numerical examples are used to demonstrate the effectiveness of the harmonic-frequency filters comparing with Matlab’s and Gustafsson’s filtfilt methods based on Butterworth filters of different orders and cutoff frequencies. It is found that the singular values of filter matrices are related to the squared amplitude of the Butterworth filter at harmonic frequencies.

We investigate competitive online algorithms for online convex optimization (OCO) problems with linear in-stage costs, switching costs and ramp constraints. While OCO problems have been extensively studied in the literature, there are limited results on the corresponding online solutions that can attain small competitive ratios. We first develop a powerful computational framework that can compute an optimized competitive ratio based on the class of affine policies. Our computational framework can handle a fairly general class of costs and constraints. Compared with other competitive results in the literature, a key feature of our proposed approach is that it can handle scenarios where infeasibility may arise due to hard feasibility constraints. Second, we design a robustification procedure to produce an online algorithm that can attain good performance for both average-case and worst-case inputs. We conduct a case study on Network Functions Virtualization (NFV) orchestration and scaling to demonstrate the effectiveness of our proposed methods.

This article proposes a design approach, based on the integral sliding mode control paradigm, devoted to give robustness to multiagent systems executing arbitrary distributed optimization and consensus protocols which do not take this feature into account. Robustness is understood as the capability of rejecting the effect of exogenous disturbances, parameter uncertainties, and uncertain couplings between the agents dynamics, by achieving the same emerging behavior as that corresponding to a reference multiagent system (MAS) designed to achieve a given coordination objective in the nominal case. The proposed approach yields a distributed state feedback which can seamlessly be integrated into existing distributed optimization and cooperative control protocols which are usually prone to disturbances and uncertainties corrupting the MAS dynamics. Nonsmooth Lyapunov analysis supports the claimed properties. Numerical simulations, showing how popular distributed optimization and consensus protocols can effectively be robustified are discussed.

The connection between regularization and min–max robustification in the presence of unobservable covariate measurement errors in linear mixed models is addressed. We prove that regularized model parameter estimation is equivalent to robust loss minimization under a min–max approach. On the example of the LASSO, Ridge regression, and the Elastic Net, we derive uncertainty sets that characterize the feasible noise that can be added to a given estimation problem. These sets allow us to determine measurement error bounds without distribution assumptions. A conservative Jackknife estimator of the mean squared error in this setting is proposed. We further derive conditions under which min-max robust estimation of model parameters is consistent. The theoretical findings are supported by a Monte Carlo simulation study under multiple measurement error scenarios.

Given the effect that outliers can have on regression and specification testing, a vastly used robustification strategy by practitioners consists in: (i) starting the empirical analysis with an outlier detection procedure to deselect atypical data values; then (ii) continuing the analysis with the selected non-outlying observations. The repercussions of such robustifying procedure on the asymptotic properties of subsequent inferential procedures are, however, underexplored. We study the effects of such a strategy on testing for heteroscedasticity. Specifically, using weighted and marked empirical processes of residuals theory, we show that the White test implemented after the outlier detection and removal is asymptotically chi-square if the underlying errors are symmetric. In a simulation study, we show that-depending on the type of outliers-the standard White test can be either severely undersized or oversized, as well as have trivial power. The statistic applied after deselecting outliers has good finite sample properties under symmetry but can suffer from size distortions under asymmetric errors. Given these results, we devise an empirical modeling strategy to guide practitioners whose preferred approach is to remove outliers from the sample.

In this paper, we address a class of distributed optimization problems with non-strictly convex cost functions in the presence of communication delays between an agent and a coordinator. To this en...

International audience; In this letter, we consider the robustification of port-Hamiltonian systems with respect to matched time-varying disturbances generated by an exo-system, which is assumed to be known. This letter is an extension, to the case of time-varying disturbances, of recent integral action techniques, which are able to reject constant disturbances only. The approach is then extended to the special case of sinusoidal disturbances with unknown frequency. The main results of this letter are demonstrated on a 2 degree-of-freedom robotic manipulator.

This article addresses a convex optimization problem that suffers from communication delays between a central collector and subsystems. First, we introduce continuous-time optimization dynamics that mirror the alternating direction method of multipliers architecture. Furthermore, we reveal that the proposed optimization dynamics have an architecture similar to that of bilateral teleoperation systems. By virtue of this passivity-based perspective, we apply a scattering transformation, which compensates for communication delays. Finally, we demonstrate the effectiveness of the proposed dynamics through numerical simulations of a grasp force optimization problem.

In this paper, we propose a robust support vector regression with a novel generic nonconvex quadratic e-insensitive loss function. The proposed method is robust to outliers or noise since it can adaptively control the loss value and decrease the negative influence of outliers or noise on the decision function by adjusting the elastic interval parameter and adaptive robustification parameter. Given the nature of the nonconvexity of the optimization problem, a concave-convex programming procedure is employed to solve the proposed problem. Experimental results on two artificial data sets and three real-world data sets indicate that the proposed method outperforms support vector regression, L1-norm support vector regression, least squares support vector regression, robust least squares support vector regression, and support vector regression with the Huber loss function on both robustness and generalization ability.

Recently, the robustification of principal component analysis (PCA) has attracted much research attention in numerous areas of science and engineering. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix recovery problem in the presence of sparse corruption. With this model, the nuclear norm and $\ell _{1}$ -norm penalties are usually used for low-rank and sparsity promotion. Although the nuclear norm and $\ell _{1}$ -norm are favorable due to their convexity, they have a bias problem. In comparison, nonconvex penalties can be expected to yield better recovery performance. In this paper, we consider a formulation for robust PCA using generalized nonconvex penalties for low-rank and sparsity inducing. This nonconvex formulation is efficiently solved by a multi-block alternative direction method of multipliers (ADMM) algorithm. A sufficient condition for the convergence of this new ADMM algorithm has been derived. Furthermore, to address the important issue of nonconvex penalty selection, we have evaluated the new algorithm via numerical experiments in various low-rank and sparsity conditions. The results indicate that, “exact” recovery of the low-rank principle component can be achieved only by nonconvex regularization. MATLAB code is available at https://github.com/FWen/RPCA.git .

This work focuses on the robust optimization of a permanent magnet (PM) synchronous machine while considering a driving cycle. The robustification is obtained by considering uncertainties of different origins. Firstly, there are geometrical uncertainties caused by manufacturing inaccuracies. Secondly, there are uncertainties linked to different driving styles. The final set of uncertainties is linked to ambient parameters such as traffic and weather conditions. The optimization goal is to minimize the PM's volume while maintaining a desired machine performance measured by the energy efficiency over the driving cycle and the machine's maximal torque. The magnetic behavior of the machine is described by a partial differential equation (PDE) and is simulated by the finite-element method employing an affine decomposition to avoid reassembling of the system of equations due to the changing PM geometry. The Sequential Quadratic Programming algorithm is used for the optimization. Stochastic collocation is applied to compute moments of stochastic quantities. The robustness of the optimized configurations is validated by a Monte Carlo sampling. It is found that the uncertainties in driving style and road conditions have significant influence on the optimal PM configuration., 5 pages, 4 figures, 2 tables, Compumag 2019 conference

The global horizontal irradiance data recorded at the earth's horizontal surface is a mixture of deterministic (extra-terrestrially) and stochastic components due to the ever-changing atmospheric conditions. The Box-Jenkins short memory stochastic models and their hybrid versions have been used successfully to forecast solar irradiance data. However, these models lack robustness and faulter for distant horizon forecasting such as more than 2 hours in the hourly case. Using a quantile regression model as both a robust benchmark and robustification model, we remedy this drawback by using long memory models and their hybrid versions in 24 hour ahead irradiance forecasting at three sites in South Africa (RVD, UPR and SUN). Results show that the inherent long memory is anti-persistent in all models but one that shows persistence. The forecasts from these long memory-based models are generally within the prediction interval of the benchmark model forecasts for two sites. Based on the RMSE, rRMSE, MAE, rMAE and the pinball loss function at the central quantile level and two extreme quantile levels, forecasts obtained through robustification via forecasts combinations with the quantile regression yield the best performance. The predictive ability of the best model before forecasts combination against the quantile regression model combined (robustified) model based Diebold-Mariano test rejected the null hypothesis of the same accuracy. As a follow up to distinguish which has a superior predictive accuracy, “murphy diagrams” were used. For the RVD and UPR sites, the model based on forecast combination has the best predictive accuracy while the quantile regression based benchmark model has the best accuracy for the SUN data.

To increase the contribution of offshore wind energy to the global energy mix in an economically sustainable manner, it is required to reduce the costs associated with the production and operation of offshore wind turbines (OWTs). One of the largest uncertainties and sources of conservatism in design and lifetime prediction for OWTs is the determination of the global damping level of the OWT. Estimation of OWT damping based on field measurement data has hence been subject to considerable research attention and is based on the use of (preferably operational) vibration data obtained from sensors mounted on the structure. As such, it is an output-only problem and can be addressed using state-of-the-art operational modal analysis (OMA) techniques, reviewed in this paper. The evolution of classical time- and frequency-domain OMA techniques has been reviewed; however, the literature shows that the OWT vibration data are often contaminated by rotor speed harmonics of significantly high energy located close to structural modes, which impede classical damping identification. Recent advances in OMA algorithms for known or unknown harmonic frequencies can be used to improve identification in such cases. Further, the transmissibility family of OMA algorithms is purported to be insensitive to harmonics. Based on this review, a classification of OMA algorithms is made according to a set of novel suitability criteria, such that the OMA technique appropriate to the specific OWT vibration measurement setup may be selected. Finally, based on this literature review, it has been identified that the most attractive future path for OWT damping estimation lies in the combination of uncertain non-stationary harmonic frequency measurements with statistical harmonic isolation to enhance classical OMA techniques, orthogonal removal of harmonics from measured vibration signals, and in the robustification of transmissibility-based techniques.

Rad će prvotno predstaviti bihevioralni pristup teoriji upravljanja, kao polazišnu točku „data-based“ sinteze na temelju tzv. fundamentalne leme. Kao jedan od modernijih primjena bit će naglašeno optimalno prediktivno upravljanje na temelju podataka, kao alternativa klasičnom Modelskom prediktivnom upravljanju (eng. Model Predictive Control - MPC). Na većem broju primjera će se pokazati sažetost identifikacije sustava na temelju fundamentalne leme, te će se na bazi simulacija usporediti s istom temeljenom na modelu. Sekundarno, razvit će se algoritam koji za proizvoljni diskretni linearni vremenski-invarijantni sustav u prostoru stanja, za proizvoljnu referentnu trajektoriju, korak diskretizacije i težinske parametre, na temelju offline podataka generira vektorski prostor svih mogućih trajektorija sustava, te online identificira inicijalno stanja poznavanjem prethodnih izlaza, nakon kojeg slijedi generiranje optimalne trajektorije na pomičnom horizontu - DeePC (eng. Data-enabled predictive control) algoritam. Dodatno, u navedeni algoritam bit će integrirano i robusno djelovanje, na temelju regularizacijskih varijabli u funkciji cilja (robustifikacija), s namjerom da se obuhvate i nesigurnosti u identificiranom modelu, primjerice zbog utjecaja šuma. Pokazat će se i uvjeti na garanciju performansi takvog robusnog djelovanja. The thesis will firstly present behavioural approach to control theory, as a beginning setpoint for data-based synthesis based on fundamental lemma. As one of the more modern approaches, the emphasis will be on predictive optimal control based on data, as an alternative to Model predictive control (MPC). The simplicity of system identification based on fundamental lemma will be shown on more examples, and the comparison with model-based identification will be made. Secondly, the thesis shall develop an algorithm that, for a given arbitrary controllable linear time-invariant (LTI) discrete system in state space, reference trajectory and tuning parameters, generates the span of vector space of all possible system trajectories of a system based on offline data, identifies an initial state based on online measurements, and produces an optimal trajectory on finite horizon – DeePC (Data-enabled predictive control). Additionally, a robust control based on regularization variables in cost function (robustification) will be integrated into the algorithm, with a goal of covering the uncertainty in identified model, for example caused by noise. The performance guarantees for such robust control will be given.

Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.

This paper addresses the robust gait control for planar and passive biped robots using approaches based on energy properties. Energy pumping-and-damping passivity-based control is used to increase the robustness against uncertainties on the initial conditions of the passive gait exhibited by planar biped robots. The stability analysis is carried out by exploiting the system’s passivity and the hybrid zero dynamics method. Besides, the proposed approach is applied to new gaits that are generated using interconnection and damping assignment passivity-based control. The performance of the proposed design is evaluated through numerical simulations and compared with an existing technique.

Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of items' images. Recently, human-imperceptible image perturbations, defined adversarial samples, have been shown capable of altering the VRSs performance, for example, by pushing (promoting) or nuking (demoting) specific categories of products. One of the most effective adversarial defense methods is adversarial training (AT), which enhances the robustness of the model by incorporating adversarial samples into the training process and minimizing an adversarial risk. The AT effectiveness has been verified on defending DNNs in supervised learning tasks such as image classification. However, the extent to which AT can protect deep VRSs, against adversarial perturbation of images remains mostly under-investigated. This work focuses on the defensive side of VRSs and provides general insights that could be further exploited to broaden the frontier in the field. First, we introduce a suite of adversarial attacks against DNNs on top of VRSs, and defense strategies to counteract them. Next, we present an evaluation framework, named Visual Adversarial Recommender (VAR), to empirically investigate the performance of defended or undefended DNNs in various visually-aware item recommendation tasks. The results of large-scale experiments indicate alarming risks in protecting a VRS through the DNN robustification. Source code and data are available at https://github.com/sisinflab/Visual-Adversarial-Recommendation.

Model-based design of pharmaceutical manufacturing processes has received much interest in academia and industry. Model parameter uncertainties, however, might deteriorate the predicted process performance. Probability-based robust process design concepts as a countermeasure against uncertainties might be implemented. Here, parameter uncertainties are typically limited to Gaussian parameter distributions. However, parameter uncertainties derived with experimental data can be correlated and arbitrarily distributed. In our previous work, transformation techniques were combined with the point estimate method (PEM) to address non-Gaussian and correlated parameter distributions, but at the cost of additional nonlinearities and approximation errors. In this work, we take advantage of Gaussian mixture distributions (GMD) and decompose the parameter distribution into a finite set of Gaussian distributions using the Expectation-Maximization approach. Combining the GMD with the PEM ensures a proper and effective uncertainty quantification. The improved PEM algorithm is applied to a freeze-drying process (lyophilization) aiming for high-quality products with minimum processing time. Results obtained suggest that the novel GMD-PEM algorithm has the potential to outperform conventional robustification concepts regarding credibility and efficiency.

Robust generation of self-oscillation for mechanical systems in the presence of matched uncertainties/perturbations based on a two-relay controller, which naturally has a switched structure, is presented. Two scenarios are considered: the case when all the system states are available and the case when only output information is measured. In both scenarios, a continuous switched integral sliding modes controller is implemented to guarantee theoretically exact compensation of matched uncertainties/perturbations before the first switching. The chattering attenuation in the presence of fast parasitic dynamics can be realised by a switched gain strategy of the super-twisting algorithm. In the output-based scenario, the observer and the controller are initiated sequentially. A super twisting-based observer is used, guaranteeing theoretically exact reconstruction of the states before the controller is activated. The observer gains are also switched to diminish the chattering. Once the observer has converged, the reconstructed state is used in the controller to achieve the control objective. The observer and controller are designed to attain convergence before the first switching.

The goal of classification is to classify new objects into one of the several known populations. A common problem in most of the existing classifiers is that they are very much sensitive to outliers. To overcome this problem, several author’s attempt to robustify some classifiers including Gaussian Bayes classifiers based on robust estimation of mean vectors and covariance matrices. However, these type of robust classifiers work well when only training datasets are contaminated by outliers. They produce misleading results like the traditional classifiers when the test data vectors are contaminated by outliers as well. Most of them also show weak performance if we gradually increase the number of variables in the dataset by fixing the sample size. As the remedies of these problems, an attempt is made to propose a highly robust Gaussian Bayes classifiers by the minimum β-divergence method. The performance of the proposed method depends on the value of tuning parameter β, initialization of Gaussian parameters, detection of outlying test vectors, and detection of their variable-wise outlying components. We have discussed some techniques in this paper to improve the performance of the proposed method by tackling these issues. The proposed classifier reduces to the MLE-based Gaussian Bayes classifier when β → 0. The performance of the proposed method is investigated using both synthetic and real datasets. It is observed that the proposed method improves the performance over the traditional and other robust linear classifiers in presence of outliers. Otherwise, it keeps equal performance.

In this paper, we point out a common feature of zonoid and empirical depths that gives rise to a novel class of data depths, and provide a possible robustification of their deepest points. This general approach may be useful as it allows the robustification of the zonoid depth and other depths that are centered at the usual mean. Illustrations are also provided to demonstrate the depth contours induced from these depths based on a real data set.

This paper proposes a new robustification strategy of a primary H∞ controller based on a systematic selection of adequate fractional weights. Its robust performance (RP) margin is well enhanced wit...

Contamination detection in acoustic signals is crucial for their applicability in industrial surroundings and for robustification of state estimation algorithms which use them. This paper presents an adaptive solution which estimates nominal signal statistics and detects contamination by measuring the deviation of the obtained signal distribution from the expected one without requiring a state space model that is usually used in existing algorithms. Detecting a change in nominal working condition, this algorithm adapts to it by repeating the training procedure. Statistical deviation detection is conducted using Quantile-Quantile plot (QQ-plot) based decision scheme which calculates metrics in the form of mean squared error of deviation of the recorded signal from the expected distribution. This algorithm has been tested and verified on real acoustic signals obtained in noisy industrial surroundings. The results are compared to a well known T 2 multivariate control chart procedure.

In this paper, two active fault-tolerant control (AFTC) structures are proposed for multiplicative and additive actuator faults respectively. Considering that the traditional generalized internal model control (GIMC) changes the dynamic input-output relationship of the original system, more exact fault information is employed to design the robustification controller of GIMC with the help of fault diagnosis observers. Further, a feedforward compensation unit and a feedback compensation unit are respectively designed for multiplicative and additive faults, striving to approximately recover the system performance to the normal one. Finally, an air handling unit (AHU) system is provided to verify the effectiveness and superiority of the proposed structures.

Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programing (SDP) controller with minimax dynamic programing (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a tradeoff of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof-of-concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta-analytic-predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.

We consider uncertain robust electricity market equilibrium problems including transmission and generation investments. Electricity market equilibrium modeling has a long tradition but is, in most of the cases, applied in a deterministic setting in which all data of the model are known. Whereas there exist some literature on stochastic equilibrium problems, the field of robust equilibrium models is still in its infancy. We contribute to this new field of research by considering $$\Gamma $$ Γ -robust electricity market equilibrium models on lossless DC networks with transmission and generation investments. We state the nominal market equilibrium problem as a mixed complementarity problem as well as its variational inequality and welfare optimization counterparts. For the latter, we then derive a $$\Gamma $$ Γ -robust formulation and show that it is indeed the counterpart of a market equilibrium problem with robustified player problems. Finally, we present two case studies to gain insights into the general effects of robustification on electricity market models. In particular, our case studies reveal that the transmission system operator tends to act more risk-neutral in the robust setting, whereas generating firms clearly behave more risk-averse.