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Degradation reliability prediction under stochastic failure threshold is studied. The explicit expression of reliability is derived, by charactering the uncertainty of failure threshold with probability distribution. Then, a possibilistic approach for reliability modeling and prediction for degrading components is presented, by use of possibility distribution.
This paper applies the CVIU (Control Variation Increases Uncertainty) approach to a discrete time scenario. Important solution characteristics are indicated, which conceptually preserves the findings in the continuous time case presented in [1]. Mainly, the arising of three control regions in the state space and the association of the value function with a discrete Lyapunov and a modified Riccati...
Generally, investment decision is an evaluation of the proposed alternatives for the investor using a set of indicators. Evident, that to the start of the investment, the project should be finished and valid, however, search of investment resources and other difficulties can delay significantly start of the investment stage, which would increase risks. Therefore, it is important in addition to evaluating...
Distributed energy resources (DER) systems introduce uncertainties in the electrical grid that cannot be addressed by classical deterministic methods. Power system analytic tools, such as Load Flow (LF), should be revisited to address such uncertainties. Probabilistic Load Flow (PLF) provides a solution to this problem by handling these uncertainties as random variables. Among the existing sampling...
Network flow records are the foundational and key data of network flow technology. As network flow technology is widely applied, the correctness of network flow record becomes more and more important. For UDP flow identification, timeout strategy is accepted. However, there is no research to analyze the principle and reasonableness of timeout strategy, so we propose a concept of attribute recognition...
The focus of this paper is on the design of input shapers for systems with uncertainties in the parameters of the vibratory modes which need to be attenuated. A probabilistic framework is proposed for the design of the robust input shaper, when the uncertain modal parameters are characterized by probability density functions. A convex chance constrained optimization problem is posed to determine the...
An portfolio optimization problem with fuzzy random variables is discussed. Risk measures for fuzzy random variables are introduced by perception-based approach. Randomness is estimated stochastically, and fuzziness are evaluated by the mean values with evaluation weights and λ-mean functions. Using coherent risk measures, we discuss a portfolio optimization problem under randomness and fuzziness...
Situation information and sensor information are differentiated and a method for computing the situation information expected value (SIEV) is presented for use in Information Based Sensor Management (IBSM). Nine case pairs are evaluated in which the sensor capabilities vary among poor, average, and good sensors, and the goal lattice values vary among attack, defend, and stealth modes showing that...
Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving...
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage...
In this paper, we propose an integrated system to detect and track a single operator that can switch off and on when it leaves and (re-)enters the scene. Our method is based on a set-valued Bayes-optimal state estimator that integrates RGB-D detections and image-based classification to improve tracking results in severe clutter and under long-term occlusion. The classifier is trained in two stages:...
In this paper we consider the problem of cooperative control of a swarm of autonomous heterogeneous mobile agents that are required to intercept a group of moving targets while avoiding contacts with dynamic obstacles. Traditionally these type of problems are solved by decomposing the solution into several sub problems: targets assignments, coordinated interception control, motion planning and motion...
The distributed fusion filtering problem is addressed for stochastic uncertain systems with correlated noises, multi-step transmission delays and packet dropouts. Stochastic uncertainties in the state equation and measurement equation, one-step auto-correlated and cross-correlated noises as well as multi-step delays described by some Bernoulli distributed random variables are simultaneously considered...
This paper questions the sizing standardization of small scale energy storage systems in a context of high penetration of renewable energies and non-deterministic load within the power grid. The future electrical grid is more precarious than the classic one by many reasons, inter alia, abrupt meteorological variations are hard to predict. Therefore, the geographic expansion beside high penetration...
Different belief sources often provide conflicting evidence, due to e.g. varying source reliability or deliberate deception. Source trust expresses the source reliability as seen by the analyst. In case of conflicting sources the analyst needs a strategy for managing and revising source trust. Intuitively, trust should be reduced for sources that produce advice which is in conflict with the ground...
We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework. These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of new random variables that are obtained by power level partitioning of the original...
The uncertain nature of electric energy production from distributed generation based on renewable resources has to be considered when managing and operating distribution grids. In several cases, this uncertainty can be described using non-Gaussian random variables, requiring appropriate probabilistic load flow techniques. The present paper proposes a method that, exploiting Polynomial Chaos Expansion...
In this paper, we formulate a two-stage distributionally robust (DR) model for the optimal power flow (OPF) problem in the presence of uncertainties from wind power generation and load-based reserves. Assuming ambiguous distributions of the random variables, we minimize the costs of generation, reserves, and the worst-case expected value of the penalty cost of violating constraints. We consider a...
This paper proposes a new approach to solve Chance Constrained Optimization Problems (CCOPs). The stochastic objective and constraint values in CCOP are evaluated efficiently by using an approximation of Cumulative Distribution Function (CDF) instead of the primitive Monte Carlo simulation. In order to approximate CDF from samples, a technique of the computational statistics called Empirical CDF (ECDF)...
Construction sites may not be supported by near-future highly-automated driving systems. Therefore, the online detection of such situations well in advance is necessary to hand over control to the driver in time. This paper introduces an approach for online construction site detection on motorways by combining a set of uncertain cues in a probabilistic way. This allows to reason about the existence...
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