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Dynamic Thermal Line Rating (DTLR) incorporates weather conditions prevailing on line ampacity to calculate actual current-carrying capacity of transmission lines. These weather variables include ambient temperature, wind speed, wind direction, and solar intensity. In this paper, probability and fuzzy techniques are adopted to model the existing uncertainties in weather variables and their performances...
Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep learning since it allows converting complex relation between data into knowledge. In this paper, we present a novel Deep-Neuro-Fuzzy strategy for unsupervised estimation of the interaction...
Probabilistic fuzzy systems (PFS) are shown to be valuable methods for conditional density estimation that combine fuzziness or linguistic uncertainty and probabilistic uncertainty. Several PFS applications have shown the added value of the different reasoning mechanisms of PFS and gains from incorporating two types of uncertainty. The effects of parametrization and parameter estimation on the function...
We estimated inter-annual growth of broad-leaved trees planted in the urban park, by using multi-temporal airborne LiDAR datasets acquired in 2004, 2008, and 2010. The annual changes in the LiDAR-estimated growth of average canopy heights were significantly correlated with one another over the study periods at the plot and individual-tree levels. A moderate and significant relation was shown with...
When the exact mathematical model is not known or too difficult to handle, fuzzy signatures are useful tools in modeling and analysis of complex systems. In these cases the input values naturally have uncertainties, due to lack of knowledge or human activities. These built-in uncertainties influence the final decision about the system. In this paper we deal with the issue when the input parameters...
Airborne lidar has emerged as the most powerful remote sensing technology for mapping vegetation structure, but its application has been limited to landscape level due to the sheer data volume and daunting demand for computation. This study is to break this stereotype and conduct a statewide mapping study of aboveground biomass (AGB) by integrating airborne lidar data and FIA (Forest Inventory and...
A design method for controlling the attitude of a fixed wing aircraft in the presence of uncertainties is proposed in the Multiple Sliding Surface framework. Inertial Delay Controller is integrated with Multiple Sliding surface Controller to increase the performance of system by estimating the uncertainties that are present in the system.
This paper is concerned about the wheel slip measurement of the anti-lock braking system (ABS). The wheel slip must follow the desired wheel slip; for this purpose multiple sliding surface controller (MSSC) based on disturbance observer (DO) is used. DO is integrated with sliding mode controller (SMC) to strengthen the overall performance of the system by estimating the lumped uncertainties that are...
This paper considers the problem of estimating the parameters of a signal using time-varying thresholded noisy one-bit measurements. The problem is shown to be deterministically identifiable under reasonable conditions on the signal and thresholds. A spectral sensing application is considered, and two sparse methods are presented. In addition to the standard ℓ1 norm based approach, a “zero-norm” approximation...
Due to the growing popularity in wireless services as well as wide applications in recent years, the concerns about efficient utilization of frequency spectrum have raised. In this paper, we propose a blind source separation based energy detection method using second-order blind identification algorithm, even though the noise uncertainty concern is considered, it can be a solution of more effectively...
Usually in game theoretic formulations for robust motion planning, the model as well as the capabilities (input set) of all dynamic obstacles are assumed to be known. This paper aims to relax the assumption of known input set by proposing a unified framework for motion planning and admissible input set estimation. The proposed approach models every dynamic obstacle as an uncertain-constrained system...
In many control problems, not all states can be measured and the system is subject to parametric uncertainties, measurement noise, and hard input constraints. To tackle such problems for linear systems, we propose to combine a recursive parameter and state estimator based on Bayes' theorem with a stochastic model predictive control approach. To efficiently obtain the probability density functions...
Direction of arrival (DoA) estimation has wideranging applications and is particularly challenging in complex propagation environments. Virtually all current approaches estimate the DoA based on the phase of the impinging signal on a sensor array. This approach has several challenges: for example, it requires tight synchronization amongst array elements, and the estimated DoA is very sensitive to...
Piezoelectric Actuators (PEAs) are the key devices in micro/nano positioning systems. However, the PEAs performance is significantly degraded by the inherent non-linear behaviour. This behaviour is a consequence of the hysteresis properties contained within PEAs. Therefore, with micro/nano application a robust control system has to be adopted for such actuators. This paper proposes a systematic control...
This manuscript contributes to the development of a neighboring optimal control law coupled with estimation for solving the trajectory tracking and/or regulator problem of a twin-rotor multi-input multi-output system (TRMS). The proposed control law is able to provide necessary control inputs to the TRMS's main and tail rotors so that its actual azimuth and elevation angles follow a pre-defined ones...
The emphasis in this paper is mainly on the point estimation of unknown parameters for uncertainty distribution. Firstly, principle of least squares and the least squares estimation are introduced. Secondly, by the k-th empirical moments of the expert's experimental data, we establish a method of moments to estimate the unknown parameters for uncertainty distribution. Finally, a moment estimation...
We address the problem of scheduling jobs with utilities that depend solely upon their completion-times in a shared cloud that imposes considerable uncertainty on the jobs' runtime. However, it is very hard to estimate the jobs' runtime in a shared cloud where jobs are often delayed due to reasons such as slow I/O performance and variations in memory availability. Unlike prior works, we acknowledge...
In this paper a new unknown input estimation method is proposed for a class of nonlinear stochastic systems in the presence of time dependent unknown inputs, when the system states and process noises are unknown but bounded. In this study, a new augmented state vector is constructed by augmenting unknown inputs as a new state to the original state vector. Then a recursive algorithm based on unknown...
The control of the hypersonic glide vehicle (HGV) during reentry is confronted with the unknown dynamical disturbance and parameter uncertainty problem. In this paper, we use the HGV mathematical model with an assumption that the earth is spherical first, and then a new self-organizing functional link network (SOFLN) is presented to approximate the dynamical disturbances/uncertainties. Finally, the...
The research is relevant since there is a necessity to solve identification problems of non-stationary signals of control and communication systems in conditions of uncertainty. Research objective is the development of models and algorithms of non-stationary signal identification with variables, time-dependent parameters and with account of additional a-priory information. Integrated system of mathematical...
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