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With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected outcome for each individual. Motivated by complex individualized decision making procedures and the popular conditional value at risk (CVaR) measure, we propose a new robust criterion to estimate optimal IDRs in order to control the average lower tail of the individuals' outcomes. In addition to improving the individualized expected outcome, our proposed criterion takes risks into consideration, and thus the resulting IDRs can prevent adverse events. The optimal IDR under our criterion can be interpreted as the decision rule that maximizes the "worst-case" scenario of the individualized outcome when the underlying distribution is perturbed within a constrained set. An efficient non-convex optimization algorithm is proposed with convergence guarantees. We investigate theoretical properties for our estimated optimal IDRs under the proposed criterion such as consistency and finite sample error bounds. Simulation studies and a real data application are used to further demonstrate the robust performance of our methods. Several extensions of the proposed method are also discussed. for this article are available online. [ABSTRACT FROM AUTHOR]

In combination with Bayesian estimates based on a population pharmacokinetic model, limited sampling strategies (LSS) may reduce the number of samples required for individual pharmacokinetic parameter estimations. Such strategies reduce the burden when assessing the area under the concentration versus time curves (AUC) in therapeutic drug monitoring. However, it is not uncommon for the actual sample time to deviate from the optimal one. In this work, we evaluate the robustness of parameter estimations to such deviations in an LSS. A previously developed 4-point LSS for estimation of serum iohexol clearance (i.e., dose/AUC) was used to exemplify the effect of sample time deviations. Two parallel strategies were used: (a) shifting the exact sampling time by an empirical amount of time for each of the four individual sample points, and (b) introducing a random error across all sample points. The investigated iohexol LSS appeared robust to deviations from optimal sample times, both across individual and multiple sample points. The proportion of individuals with a relative error greater than 15% (P15) was 5.3% in the reference run with optimally timed sampling, which increased to a maximum of 8.3% following the introduction of random error in sample time across all four time points. We propose to apply the present method for the validation of LSS developed for clinical use. [ABSTRACT FROM AUTHOR]

Fuzzy extractor (FE) extracts and reproduces a uniform string from a fuzzy source. Robustly reusable fuzzy extractor (rrFE) considers reusability and robustness simultaneously. Reusability of rrFE allows multiple extractions of pseudorandom strings from the same source and robustness detects active attacks. To achieve reusability and robustness, the existing constructions of rrFE make heavy use of perfect random coins (which are uniformly distributed and independent of each other), besides the fuzzy source. However, efficiently sampling unbiased random bits only exists in the ideal world. In this paper, we show how to construct rrFE resorting to imperfect randomness (non-uniform but of high entropy), which is easy to sample in practice. We propose two generic constructions of rrFE in the CRS model, with one construction dealing with perfect randomness and the other dealing with imperfect randomness. We also present two instantiations of rrFE from the DDH and LPN assumptions working with perfect randomness, and another two instantiations of rrFE from DDH and LPN working with imperfect randomness. All instantiations support linear fraction of errors between samples of the fuzzy source. Our DDH-based rrFE (both rrFE with perfect randomness and rrFE with imperfect randomness) are the first tightly secure rrFEs in the standard model, i.e., the reusability and robustness are tightly reduced to the DDH assumption. Compared with the DDH-based rrFE scheme in PKC2019 by Wen et al., our rrFE enjoys tighter security, better efficiency, and support of usage of imperfect randomness. Our LPN-based rrFE (both rrFE with perfect randomness and rrFE with imperfect randomness) are the first rrFEs from the LPN assumption in the standard model. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. [ABSTRACT FROM AUTHOR]

The article focuses on sensitivity analysis particularly global sensitivity analysis (GSA) which is considered a key part of good modeling practice for understanding how model results depend on parameters and assumptions. Topics include the importance of robustness in sensitivity analysis, factors that can impact the robustness of a GSA exercise, and methods for achieving computational efficiency in GSA exercises.

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model. [ABSTRACT FROM AUTHOR]

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed federated Tobit Kalman filter fusion problem for a class of discrete-time systems subject to measurement censoring and packet delays. The censored measurement is characterized by the Tobit measurement model and the packet delay obeys the Poisson distribution. At the input terminal of each local estimator, a censoring detection device and a buffer with finite length are exploited to tackle the censored/delayed measurements. The filtering fusion is carried out in two steps: in the first step, every sensor in the network collects and sends its measurements to the corresponding local estimator via an unreliable network with probabilistic packet delays and, in the second step, the estimates from local estimators are further collected by the fusion center to form a fused estimate. The local estimator runs a modified Tobit Kalman filtering algorithm, which is designed based on the modified Tobit regression model, while the fusion center performs a distributed federated modified Tobit Kalman filtering algorithm, which is devised on the basis of the federated Kalman fusion rule. Given the buffer length, the prescribed bound on the estimation error covariance and the probability distribution of the packet delay, the stability performance of the proposed filtering fusion algorithm is evaluated via a probabilistic approach. Simulation results are provided to show the feasibility of the proposed filter. [ABSTRACT FROM AUTHOR]

The control of automobile electronic throttle (AET) systems is a challenging task owing to multiple nonlinearities, i.e., transmission friction, gear backlash, limp-home spring set, and external load disturbance. In this paper, a practical tracking control scheme of an AET system is developed using a continuous fast nonsingular terminal sliding mode (CFNTSM) technique based on uncertainty observer. By using the prescribed CFNTSM surface and fast terminal sliding mode-based reaching element, the proposed control implementation guarantees the fast error convergence characteristic and high tracking accuracy under parameter uncertainties and perturbations. Furthermore, due to the adoption of the finite-time exact observer (FEO) for the lumped uncertainty estimation in the controller, the ease of the selection of the control gains is well-achieved since they only depend on the uncertainty estimation error. The closed-loop stability and finite-time convergence are presented based on the Lyapunov stability theory. Experimental verifications are conducted to validate the remarkable performance of the proposed control, in terms of the step and sinusoidal tracking as well as anti-disturbance ability. [ABSTRACT FROM PUBLISHER]

Sign information is the key for overcoming the inevitable saturation error in compressive sensing system, which causes loss of information and may result in great bias. For sparse signal recovery from saturation, we propose to use linear loss to improve the effectiveness from the existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, analytical solution in the update progress is obtained and some nonconvex penalties are applicable, e.g., minimax concave penalty, $\ell _0$ norm, and sorted $\ell _1$ norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance can be significantly improved and the computational time is also largely saved, which is verified by the numerical experiments. [ABSTRACT FROM AUTHOR]

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This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise). [ABSTRACT FROM AUTHOR]

We provide a new sequential predictors approach for the exponential stabilization of linear time-varying systems. Our method circumvents the problem of constructing and estimating distributed terms in the control laws, and allows arbitrarily large input delay bounds, pointwise time-varying input delays, and uncertainties. Instead of using distributed terms, our approach to handling longer delays is to increase the number of predictors. We obtain explicit formulas to find lower bounds for the number of required predictors. The formulas involve bounds on the delays and on the derivatives of the delays. We illustrate our method in three examples. [ABSTRACT FROM AUTHOR]

Deadbeat control (DBC) is developed based on the system model, which is commonly used to achieve good control performance for a three-phase grid connected converter (GCC). Unbalanced grid voltage is a common phenomenon in power systems. In this study, an improved DBC based on current predictive correction (CPC_DBC) is used to control GCC under an unbalanced grid voltage. The main purposes of CPC_DBC are to reduce the adverse effects due to random sampling error to improve current control performance, and to enhance the interference rejection capability of the control system. Then, the characteristics of CPC_DBC and the conventional DBC (Con_DBC) are analyzed by transfer functions in the discrete time domain. Several common control objectives under an unbalanced grid voltage are used to demonstrate the superiority of CPC_DBC. Finally, the experimental results indicate that CPC_DBC can effectively reduce the adverse effects of random sampling error; the robustness of system due to the uncertain value of the filter inductance is improved as well. [ABSTRACT FROM PUBLISHER]

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery's kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries. [ABSTRACT FROM AUTHOR]

The $$ \overline{X} $$ type charts are not robust against estimation errors or changes in process standard deviation. Thus, the t type charts, like the t and exponentially weighted moving average (EWMA) t charts, are introduced to overcome this weakness. In this paper, a run sum t chart is proposed, and its optimal scores and parameters are determined. The Markov chain method is used to characterize the run length distribution of the run sum t chart. The statistical design for minimizing the out-of-control average run length (ARL) and the economic statistical design for minimizing the cost function are studied. Numerical results show that the t type charts are more robust than the $$ \overline{X} $$ type charts for small shifts, in terms of ARL and cost criteria, with respect to changes in the standard deviation. Among the t type charts, the run sum t chart outperforms the EWMA t chart for medium to large shifts by having smaller ARL and lower minimum cost. The run sum t chart surpasses the $$ \overline{X} $$ type charts by having lower ARL when the charts are optimally designed for large shifts but the run sum $$ \overline{X} $$ and EWMA $$ \overline{X} $$ prevail for small shifts. In terms of minimum cost, the $$ \overline{X} $$ type charts are superior to the t type charts. As occurrence of estimation errors is unpredictable in real process monitoring situations, the run sum t chart is an important and useful tool for practitioners to handle such situations. [ABSTRACT FROM AUTHOR]

Modern gasoline internal combustion engines use a variety of technologies to enhance the efficiency of fresh air induction. These technologies, which include variable valve timing and variable intake geometry systems, also make it more difficult to predict the mass of fresh air that is trapped during the induction stroke of the engine because they not only affect the residual gas fraction of the trapped air charge, but also the wave dynamics of the system. As the number of controllable actuators increases, this estimation problem becomes even more difficult. As these technologies continue to develop, the importance of robustness in air-to-fuel ratio control continues to grow. This paper presents an air-to-fuel ratio control algorithm based on a switching frequency regulator that has favorable robust stability properties in the presence of both input and model errors. Instead of modeling the air path system with a simplified model, this control architecture considers the air estimate as a control input. As a result, air estimation errors behave like input errors, not modeling errors. By using the rich-to-lean and lean-to-rich air-to-fuel ratio switching frequencies of the pre-catalyst exhaust gas oxygen sensor as the primary feedback signal, the control laws are completely independent of the parameters of the plant model. The performance of this controller is demonstrated both with a robust stability analysis and through a vehicle-based experimental validation. [ABSTRACT FROM PUBLISHER]

The accurate determination of the steering vector of a sensor array that corresponds to a desired signal is often hindered by uncertainties due to array imperfections, such as the presence of a direction-of-arrival (DOA) estimation error, mutual coupling, array sensor gain/phase uncertainties, and senor position perturbations. Consequently, the performance of conventional beamforming algorithms that use the nominal steering vector may be significantly degraded. A new method for recursively correcting possible deterministic errors in the estimated steering vector is proposed here. It employs the subspace principle and estimates the desired steering vector by using a convex optimization approach. We show that the solution can be obtained in closed form by using the Lagrange multiplier method. As the proposed method is based on an extended version of the conventional orthonormal PAST (OPAST) algorithm, it has low implementation complexity, and moving sources can be handled. In addition, a robust beamformer with a new error bound that uses the proposed steering vector estimate is derived by optimizing the worst case performance of the array after taking the uncertainties of the array covariance matrix into account. This gives a diagonally loaded Capon beamformer, where the loading level is related to the bound of the uncertainty in the array covariance matrix. Numerical results show that the proposed algorithm performs well, especially at high signal-to-noise ratio (SNR) and in the presence of deterministic sensor uncertainties. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel method for interest region description which pools local features based on their intensity orders in multiple support regions. Pooling by intensity orders is not only invariant to rotation and monotonic intensity changes, but also encodes ordinal information into a descriptor. Two kinds of local features are used in this paper, one based on gradients and the other on intensities; hence, two descriptors are obtained: the Multisupport Region Order-Based Gradient Histogram (MROGH) and the Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor (MRRID). Thanks to the intensity order pooling scheme, the two descriptors are rotation invariant without estimating a reference orientation, which appears to be a major error source for most of the existing methods, such as Scale Invariant Feature Transform (SIFT), SURF, and DAISY. Promising experimental results on image matching and object recognition demonstrate the effectiveness of the proposed descriptors compared to state-of-the-art descriptors. [ABSTRACT FROM AUTHOR]

In this paper, we focus on adaptive time-variant channel estimation for vehicle-to-vehicle (V2V) communications in intelligent transportation systems (ITS) using the IEEE 802.11p physical layer. The IEEE 802.11p pilot pattern is identical to that in the well-known IEEE 802.11a/g (WiFi) standard, which was initially designed for indoor environments with little or no mobility. However, in a V2V drive-by situation, the channel impulse response rapidly changes due to the high relative velocity between transmitter and receiver, as well as the changes in the scattering environment. Hence, for such V2V channels, advanced decision directed channel estimation methods are needed to reach a frame error rate (FER) smaller than \10^-1. Even more importantly, the channels are nonstationary, which implies that the Doppler power spectral density (DSD) and the power delay profile (PDP) change on a timescale comparable with the frame length, which complicates the estimator design. In this paper, we develop an adaptation method for the channel estimation filter that is suitable for the following: 1) the short frame length in IEEE 802.11p; 2) the given pilot structure; and 3) the requirement of only a single received short frame for filter adaptation. We define a set of hypotheses on the support of the DSD and a second set of hypotheses on the support of the PDP. Each hypothesis is represented by a specific subspace spanned by orthogonal basis vectors. For basis vector calculation, we develop a numerically stable algorithm utilizing generalized discrete prolate spheroidal sequences. The adaptation algorithm chooses a hypothesis from both sets such that a probabilistic bound on the channel estimation error is minimized. We implement the hypothesis test by means of a novel subspace selection algorithm that allows utilizing correlated observations of a time- and frequency-selective (2-D) fading process. We validate the adaptive channel estimation scheme in an IEEE 802.11p compliant link level simulation for a relative velocity range from 0 to 111 m/s \approx 400 km/h \approx 249 mi/h. Adaptive filtering enables an up to fourfold reduction in the number of required iterations to reach an FER below \10^-1 for an Eb/N0 = \12 \ \dB. [ABSTRACT FROM PUBLISHER]

This paper considers linear minimum mean-square-error (MMSE) transceiver design problems for downlink multiuser multiple-input multiple-output (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum mean-square-error (MSE) minimization problems. The problems are examined for the generalized scenario where the power constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zero-mean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average mean-square-error (AMSE) duality. Second, we formulate the power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS power problems, we have established novel downlink-uplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol power problems, we have established novel downlink-interference duality. For the total BS power constrained robust sum MSE minimization problem, the current duality is established by modifying the constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) power constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively. We also show that our sum AMSE duality are able to solve other sum MSE-based robust design problems. Computer simulations verify the robustness of the proposed robust designs compared to the nonrobust/naive designs. [ABSTRACT FROM PUBLISHER]