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In this paper, we consider finite-horizon predictive control of linear stochastic systems with chance constraints where the admissible region is a convex polytope. For this problem, we present a novel solution approach based on box approximations. The key notion of our approach consists of two steps. First, we apply a linear operation to the joint state probability density function such that its covariance...
Techniques from multi-objective optimization are incorporated into the stochastic multi-armed bandit (MAB) problem to improve performance when the rewards obtained from pulling an arm are random vectors instead of random variables. We call this problem the stochastic multi-objective MAB (or MOMAB) problem. In this paper, we study the analytical and empirical proprieties of MOMABs with the goal of...
Recently, taxonomy has attracted much attention. Both automatic construction solutions and human-based computation approaches have been proposed. The automatic methods suffer from the problem of either low precision or low recall and human computation, on the other hand, is not suitable for large scale tasks. Motivated by the shortcomings of both approaches, we present a hybrid framework, which combines...
Uncertain graphs have been widely used to represent graph data with inherent uncertainty in structures. Reliability search is a fundamental problem in uncertain graph analytics. This paper studies a new problem, the top-k reliability search problem on uncertain graphs, that is, finding k vertices v with the highest reliabilities of connections from a source vertex s to v. Note that the existing algorithm...
A Random Finite Set (RFS) based multi-target filter using a mixture of multi-object Dirac Delta and Poisson RFSs is proposed. The resulting distribution is closed under the Chapman-Kolmogorov equation while also being a conjugate prior to the “natural” multi-target likelihood function. A filtering algorithm is presented which efficiently extracts the highest weight components of the complete mixture...
The cardinality balanced multi-target multi- Bernoulli (CB-MeMBer) filter has received a lot of attention as a good approximation to the ideal multi-target filter. Consequently, several schemes have been proposed in order to perform sensor management when a CB-MeMBer filter is used. Most of these criteria are computationally very expensive due to the nature of the CB-MeMBer filter and the integration...
Crowdsourcing provides a cheap but efficient approach for large-scale data and information collection. However, human judgments are inherently noisy, ambiguous and sometimes biased, and should be calibrated by additional (usually much more expensive) expert or true labels. In this work, we study the optimal allocation of the true labels to best calibrate the crowdsourced labels. We frame the problem...
A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method...
We consider closed-loop feedback (CLF) stochastic model predictive control of nonlinear time-invariant systems with imperfect state information. In this class of control problems, future information feedback is considered in the decision making process, and thus, the effect of the control influencing the state uncertainty is taken into account. The main challenge in the solution is to find a good...
The execution of long-horizon tasks under uncertainty is a fundamental challenge in robotics. Recent approaches have made headway on these tasks with an integration of task and motion planning. In this paper, we present Interfaced Belief Space Planning (IBSP): a modular approach to task and motion planning in belief space. We use a task-independent interface layer to combine an off-the-shelf classical...
We discuss an adjoint approach for error control adjusted for a stochastic collocation method. The method is used for uncertainty quantification of low-frequency electromagnetic problems governed by the magnetoquasistatic equations. The error can be estimated by applying a collocation method of the same polynomial degree to both the primal and the adjoint field problem. The performance of the error...
This paper deals with analysis of suboptimal dual controller design for linear discrete time stochastic system with uncertain parameters. The design of the dual controller is based on augmentation of the criterion with suitable measure of the parameters uncertainty in order to explicitly ensure the probing property of the control law. As the uncertainty measure the weighted prediction error of the...
In this paper, we will present design methods for Single input IT2-FLCs (SIT2-FLCs) and we will introduce an online tuning mechanism to enhance their control system performance. The most important feature of the SIT2-FLC is the closed form output presentation which is defined in a two dimensional domain. Based on this structural information, we will present design methods for SIT2-FLCs composed of...
This paper presents a newly created significance measure based on a variation of Bayes' theorem, one which quantifies the significance of any value contained within an R-fuzzy set. An R-fuzzy set is a relatively new concept and an extension to fuzzy sets. By utilising the lower and upper approximations from rough set theory, an R-fuzzy approach allows for uncertain fuzzy membership values to be encapsulated...
Centroid of a general type-2 (GT2) fuzzy set (FS) has been a critical concept. With the introduction of the α plane representation / the z slice representation for a GT2 FS, the centroid of a GT2 FS can be computed by calculating the centroid endpoints of its α planes using the Karnik-Mendel (KM) iterative algorithm. However, there lack closed form formulas for the centroid endpoints of the α planes...
This paper proposes a set-based parameter identification method for biochemical systems. The developed method identifies not a single parameter but a set of parameters that all explain time-series experimental data, enabling the systematic characterization of the uncertainty of identified parameters. Our key idea is to use a state-space realization that has the same input-output behavior as experimental...
Dynamic optimization techniques for nonlinear systems can provide the process industry with sustainable and efficient operating regimes. However, these regimes often lie close to the operating limits. It is therefore critical that these model based operating conditions are robust with respect to process noise, i.e, unmodeled time-varying random disturbances. Besides the effect of uncertainty in the...
A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of...
Aggregations of electric loads, like heating and cooling systems, can be controlled to help the power grid balance supply and demand, but the amount of balancing reserves available from these resources is uncertain. In this paper, we investigate data-driven optimization methods that are suited to dispatching power systems with uncertain balancing reserves provided by load control. Specifically, we...
A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the...
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