The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper we present a new and improved formulation for the Multimode Equivalent Network (MEN) representation of arbitrary waveguide junctions. In the new formulation the Kummer's transformation is used to separate the kernel into dynamic and static parts, by introducing higher order extraction terms. The main difference with respect to the old formulation is that the approximation of the kernel...
This paper develops a novel method for generating discrete equations of Lagrangian mechanics that are constructed to evolve near subsets or submanifolds of the configuration space in which empirical observations are concentrated. When the discrete observations are distributed by a measure ρ on the configuration space Ω, error bounds for the discrete Lagrangian formulation are derived in terms of the...
It is shown how differential invariance can be used to extract an underlying signal from its noisy measurement towards constructing a non-asymptotic state estimator for linear systems. While the model of the system is assumed known, the noise can have arbitrary characteristics. The differential invariance is rendered by the Cayley-Hamilton theorem and the system is represented in terms of a output...
We review some classic methods of incorporating noise into formal neural spiking models. In this way, we first introduce two most common single neuron models, in which neurons emit voltage spikes due to variations in their membrane potential. Then we investigate the effect of adding stochastic noise to the neural models. In the last part, we consider the behavior of population of neurons in the presence...
Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many...
Recent years have seen a growing interest in neural networks whose hidden-layer weights are randomly selected, such as Extreme Learning Machines (ELMs). These models are motivated by their ease of development, high computational learning speed and relatively good results. Alternatively, constructive models that select the hidden-layer weights as a subset of the data have shown superior performance...
This paper introduces a novel framework for the study of adaptive or online estimation problems for a common class of nonlinear systems governed by ordinary differential equations (ODEs) on ℝd. In contrast to most conventional strategies for ODEs, the approach here embeds the estimate of the unknown nonlinear function appearing in the plant in a reproducing kernel Hilbert space (RKHS), H. The nonlinear...
Safety verification of control systems is mostly discussed for full-state measurable systems and there are scant results on safety analysis of output-feedback control systems in which the states are partially measurable. This paper proposes a safety-preserving control scheme for output-feedback control systems. We specify a viable tube for the states of an observer (estimated states) based on constraints...
The capability of GPUs to accelerate general-purpose applications that can be parallelized into massive number of threads makes it promising to apply GPUs to real-time applications as well, where high throughput and intensive computation are also needed. However, due to the different architecture and programming model of GPUs, the worst-case execution time (WCET) analysis methods and techniques designed...
In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define...
We present a sampling-based stochastic optimal control (SOC) framework for systems with unknown dynamics based on the path integral formulation and probabilistic inference. This work is motivated by three major limitations of related SOC methods: first, full knowledge of the dynamics model is usually required. Second, model uncertainty is neglected. Third, convergence of the iterative scheme is quite...
Following a recent solution to the problem of boundary stabilization of linear coupled reaction-diffusion systems by means of the backstepping method, we present an observer for a coupled pair of reaction-diffusion partial differential equations (PDEs) with boundary measurements. We show that, as in the case of stabilization, the backstepping kernel PDEs are essentially equivalent to the PDEs governing...
This paper develops a data-driven method for control of partial differential equations (PDE) based on deep reinforcement learning (RL) techniques. We design a Deep Fitted Q-Iteration (DFQI) algorithm that works directly with a high-dimensional representation of the state of PDE, thus allowing us to avoid the model order reduction step common in the conventional PDE control design approaches. We apply...
We solve the problem of an underactuated coupled transport-wave system, which is physically motivated by instability phenomena seen in extreme ultraviolet (EUV) lithography. The system is transformed to a 2+1 system of first-order hyperbolic PDEs, where two transport PDEs (only one of which is actuated) convect leftward, and one rightward, unactuated. A controller is designed to be applied at the...
We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order...
Non-local means (NLM) filtering of fMRI can reduce noise while preserving spatial structure. We have developed a variant called temporal-NLM (tNLM) which uses similarity in time-series between voxels as the basis for computing the weights in the filter. Using tNLM, dynamic fMRI data can be denoised while spatial boundaries between functionally distinct areas in the brain tend to be preserved. The...
For achieve accurate estimate of State of Charge (SOC) in the management system of battery, a novel algorithm that combined the fuzzy information granulation (FIG) and support vector regression (SVR) was proposed in this paper to predict SOC. This algorithm use radial basis function as SVR kernel. To establish SOC prediction model of Lithium battery, the current, voltage and granulated time are regarded...
In the paper we propose a discrete singular convolution method to perform the steady-state analysis of relaxation oscillators in time domain. Approximation of derivatives in the ordinary differential equations representing the oscillators' mathematical model is provided in the matrix form with usage of the Shannon's series kernel. The examples are given to illustrate the application of the developed...
An advertising campaign is usually composed of a series of coordinated advertisements, with various formats and delivered through different media channels. Several existing studies have attempted to measure the individual contribution of related advertisements in a campaign, resulting in rule-based and data-driven multi-touch attribution models. However, most of these models ignored the interaction...
This paper presents a procedure for calculating efficiently the frequency perturbations for circular array of coupled cylindrical dipole antenna. This is commonly done through subsequent application of the algorithm at different values of frequency. A novel technique is developed where the coupled integral equations for the circular array is solved using the method of moments at a single frequency...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.