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Geospatial Intelligence analysis involves the combination of multi-source information expressed in logical form (as sentences or statements), computational form (as numerical models of physics or other processes), and sensor data (as measurements from transducers). Each of these forms has its own way to describe uncertainty or error: e.g., frequency models, algorithmic truncation, floating point roundoff,...
We investigate methods to define a probabilistic logic and their application to multi-source fusion problems in geospatial decision support systems1. We begin with a discussion of augmenting propositional calculus with probabilities. Given a set of sentences, S, each with a known probability, the problem is to determine the probability of a query sentence that is a disjunction of literals appearing...
Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To that end, post-hoc confidence measures have been introduced to assess the per-pixel reliability of the flow. We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly...
This paper investigates the uncertainty of the day-ahead distribution system scheduling considering the random variations of both Photovoltaic-based distributed generator (PV-DG) output power and load. Instead of Monte-Carlo simulation (MCS), a two-point estimation method (2PEM) is applied to obtain accurate and computation-efficient analysis results. Based on the two-year real-world hourly weather...
The paper presents a probabilistic approach (PA) to quantify the impacts of increased PV connections in bidirectional feeders within a distribution network. The aim is to establish a tool that can serve distribution network operators (DNOs) in seizing the maximum allowable photovoltaic (PV) connections, and ultimately with their responsibility on providing a reliable and secure power. An uncertainty...
A Probabilistic Conditional Preference network (PCP-net) provides a compact representation of preferences characterized with uncertainty. We propose to enrich the expressive power of the PCP-net by adding constraints between some of the variables. We call this new model, the Constrained PCP-net (CPCP-net). We study the key preference reasoning task with the proposed CPCP-net which consists in finding...
Power quality (PQ) analysis requires to calculate a lot of different parameters (e.g. unbalance, THD, harmonic powers, harmonic impedances, …) based on measured voltages and currents (fundamental and harmonics). Respective standards and data sheets of measurement equipment define the measurement accuracies only for these measured quantities. The resulting uncertainty of parameters derived from these...
Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion...
The procedure of the aggregation of experts' individual probability estimates for solving probability inference problems in probability trees are proposed. The suggested methodology allows to consider specific forms of uncertainty arising from the process of interaction between the expert judgments. The structure of such interactions may be different in nature — they can be consistent, compatible,...
Besides environmental benefits of using electric vehicles (EVs), the presence of EVs in the parking lots, acting as the power consumer or generator, have created widespread changes in the operation of power systems. The Electric Vehicle Parking Operator (EVPO) can strategically purchase and sell power in both the day-ahead and the real-time markets. Here, the effect of bidding strategy of an EVPO...
In this paper, we introduce the main concepts of a new maximum livelihood evidential reasoning (MAKER) framework for data-driven inferential modelling and decision making under different types of uncertainty. It consists of two types of model: state space model (SSM) and evidence space model (ESM), driven by the data that reflects the relationships between system inputs and output. SSM is constructed...
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...
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...
We consider a setting where a system has to interact, and hence create distinct outputs (observables), but subject to such operational constraints wants to minimize the leakage that such observables reveal about its secret input. It has been previously demonstrated that under some (highly symmetrical) constraints on the observables, it is possible to design systems that are universally optimal in...
Future self-driving cars and current ones with advanced driver assistance systems are expected to interact with other traffic participants, which often are multiple other vehicles. To facilitate the motion planning of the autonomously controlled vehicle in collision avoidance, individual object vehicles with closeness in positions and velocities can be grouped as a single extended moving object. However,...
Possibility theory is briefly presented and proposed for the evaluation of epistemic uncertainty in electromagnetic-compatibility models. In particular, fuzzy sets are exploited to describe some unknown/uncontrolled model parameters which cannot be rigorously treated through probability theory, since their statistical distribution is unknown. A hybrid approach is used to combine such possibilistic...
Probabilistic selling is a marketing strategy in which a multi-item seller provides buyer with an option to purchase for less by accepting an uncertainty risk in getting a random item from a set of multiple distinct items. However, previous studies on this strategy assume that no return is allowed, partly because return policy will shift part of the mentioned uncertainty risk back to the seller. Since...
This paper attempts to thoroughly evaluate the long-term performance of the most well-established voltage control strategies applied in active medium-voltage (MV) networks. This is attained by performing an exhaustive probabilistic analysis to consider and assess the impact of generation and demand uncertainties on the network operation. More specifically, Monte Carlo-based time-series simulations...
In this article, after introducing fuzzy-dual numbers, functions and functionals, the optimization of a fuzy-dual functional is considered through an extension of Euler's condition. Then, once uncertainty is imbedded in a fuzzy-dual dynamical system, the optimization of such systems is considered, leading to an extended Hamilton-Jacobi-Bellman equation to characterize optimal fuzzy-dual solutions.
Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the...
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