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The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is...
This paper compares the stochastic convergence of the Uniform Random number generators of two simulation software namely Matlab and Python and establishes the significance in choosing the right random number generator for error propagation studies. It further discusses about the application of Gaussian type of these random number generators to nonlinear cases of Error propagation using the Monte Carlo...
Predicting the High Intensity Radiated Field (HIRF) coupling in complex system is highly required in the aeronautic industry, which can be performed by electromagnetic simulation. But the validation of electromagnetic simulation is a difficult issue which needs proper flowchart and validation methods. In this paper, the numerical simulation by the parallel Finite-Difference Time-Domain (FDTD) method...
A multiobjective, reliability-based design optimization (RBDO) is performed considering manufacturing uncertainties in order to facilitate the performance and reliability of a centrifugal compressor. During the RBDO procedure, Monte Carlo Simulation (MCS) is utilized to calculate failure probability. Latin hypercube design (LHD) is adopted to sample and form simulation experiment scheme. Besides,...
For the problem of sail-assisted ship prone to track deviation influenced by wind and other disturbances, a fuzzy adaptive iterative sliding mode control method is presented. The evaluation function is introduced into fuzzy control scheme to evaluate and adjust the designed parameters online. Under the proposed method, the estimation of the uncertain parameters and disturbances can be avoided, moreover,...
This paper investigates the localization problem of a mobile source based on time difference of arrival (TDOA) measurements in the presence of random noises in both the TDOA and sensor location measurements. We develop an improved unscented Kalman filter (UKF), where the mobile model is augmented by incorporating the sensor positions into the state vector and the number of sigma points is enlarged...
Autonomic systems manage their own behaviour in accordance with high-level goals. This paper presents a brief outline of challenges related to Autonomic Computing due to uncertainty in the operational environments, and the role that models@run.time play in meeting them. We argue that the existing progress in Autonomic Computing can be further exploited with the support of runtime models. We briefly...
An uncertainty quantification approach to estimate the errors incurred by the Kanade Lucas Tomasi (KLT) feature tracking algorithm is presented. The covariance analysis is based on the linearized sensitivity calculations of the KLT algorithm. Track uncertainty thus computed is utilized to quantify the errors associated with feature based relative pose estimation algorithms. This paper also show that...
In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if...
Considering uncertainty in continuous production processes is key to compute short-time optimal schedules which can be trusted in practice. This paper proposes a two-step stochastic approach to the robust scheduling of several evaporation plants. This approach considers the possibility of reacting in the future once the uncertainty materializes. Each evaporator has different features (capacity, equipment,...
One of the most important realities and uncertainties in the deregulated electricity market is electricity demand. Electricity demand scenario generation in day-ahead markets using newly proposed Enhanced path-based scenario generation method based on autoregressive moving average is developed in this paper. A new enhanced path-based scenario generation method to generate scenarios of the random variable...
Prognostic deals with predicting the possible future failure of different types of engineering systems. For the purpose of prognostics, the failure of the system is defined to be the instant when the degradation (performance) level rises above (drops below) a defined threshold. It is impossible to make a precise prediction of the remaining useful life (RUL) of a system due to many sources of uncertainties...
An understanding of measurement uncertainties is a critical element in evaluating the performance of a device under test (DUT). For a vector network analyzer (VNA), the measurement process includes a calibration process that significantly impacts and also complicates the estimation of DUT measurement uncertainty. This lead to the creation of software to assist in the estimation of measurement accuracy...
Engineering volunteer services calls for novel self-adaptive approaches for dynamically managing the process of selecting volunteer services. As these services tend to be published and withdrawn without restrictions, uncertainties, dynamisms and 'dilution of control' related to the decisions of selection and composition are complex problems. These services tend to exhibit periodic performance patterns,...
The Brazilian power system is a hydrothermal system mainly composed of hydroelectric generation. In order to deal with the uncertainties related to future water inflows a hydroelectric power plant receives an assured energy certificate. In general, those assured energy certificates correspond to energy that each plant can produce considering a 5% risk of energy not supplied. Also, they represent the...
The electricity capacity market is designed to ensure the adequate availability of necessary resources in the long run. Demand resources (DR) could play an important role in the capacity market. However, since the capacity market is a long-term market, the uncertainties become a major problem for demand response providers' (DRPs) decision making. This paper first formulates the DRP decision problem...
Power system voltage stability is a very important aspect of power system study, and loads have a significant influence on it. With an increasing penetration level of renewable generation, it becomes more crucial. This paper applies Morris screening method to rank the power system load model parameters according to their influence on the voltage stability. The Monte Carlo simulation is used to generate...
The liberalization of electricity markets has significantly changed the perspective of the power generation business. Nowadays, generation companies pursue economic goals due their investment decisions are based on expectations of profitability and the risk of their alternatives. These expectations are difficult to predict because they depend upon various factors that are highly uncertain, including...
Although for the general public marine oil pollution happens when an oil tanker sinks in the ocean or an accident occurs in an oil platform, most of the oil entering the world ocean does it along less obvious paths. This research work focus on operational spills. It provides a first indication of the uncertainty associated to backtracking simulations using solutions from several operational models...
Hydrothermal systems should be characterized by a transmission-intensive nature in order to deal with climatic phenomena which, for example, can determine dry conditions in one region while there are large rainfalls in another one. Thus, the grid must be robust to deal with the different export/import patterns among regions and accommodate several economic dispatches. This paper describes a multiyear...
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