Search results for "Linea"
showing 10 items of 7724 documents
The use of cryptograms with arithmetic operations in school teaching
2013
The text presents a method of solving cryptograms with arithmetic operations. This method is based on systems of equations. In the case of cryptograms with adding and subtracting operations we have to solve systems of linear equations while in the case of cryptograms with multiplying and dividing operations we have to solve systems of non-linear equations. The method of deciphering cryptograms will be illustrated by examples.
О проблеме Гарднера для систем управления фазовой автоподстройкой частоты
2019
This report shows the possibilities of solving the Gardner problem of determining the lock-in range for multidimensional phase-locked loops systems. The development of analogs of classical stability criteria for the cylindrical phase space made it possible to obtain analytical estimates of the lock-in range for third-order system. peerReviewed
FRAME MODELIZATION OF DISCOURSE PRESSURES ON INFORMATION TRANSFER AND TEXTUAL ORGANIZATION OF A TEXT GENRE.
2023
The present article aims at investigating the discursive pressures at the phrasal and textual level for a text genre, here wine advertisements from Austrian supermarket leaflets. After having developed the theoretical framework of Germanic inspiration, a case study is conducted to highlight these discursive pressures.
A new approach for estimating a nonlinear growth component in multilevel modeling
2011
This study presents a new approach to estimation of a nonlinear growth curve component with fixed and random effects in multilevel modeling. This approach can be used to estimate change in longitudinal data, such as day-of-the-week fluctuation. The motivation of the new approach is to avoid spurious estimates in a random coefficient regression model due to the synchronized periodical effect (e.g., day-of-the-week fluctuation) appearing both in independent and dependent variables. First, the new approach is introduced. Second, a Monte Carlo simulation study is carried out to examine the functioning of the proposed new approach in the case of small sample sizes. Third, the use of the approac…
Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system
2017
We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. Fr…
Acoustic detection and classification of river boats
2011
We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces fal…
Exact extension of the DIRECT algorithm to multiple objectives
2019
The direct algorithm has been recognized as an efficient global optimization method which has few requirements of regularity and has proven to be globally convergent in general cases. direct has been an inspiration or has been used as a component for many multiobjective optimization algorithms. We propose an exact and as genuine as possible extension of the direct method for multiple objectives, providing a proof of global convergence (i.e., a guarantee that in an infinite time the algorithm becomes everywhere dense). We test the efficiency of the algorithm on a nonlinear and nonconvex vector function. peerReviewed
Twister Tries
2015
Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms’ scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n 2 ), these algorithms have a space complexity of O(n 2 ), which can be reduced to O(n) if the time complexity is allowed to rise to O(n 2 log2 n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only lin…
Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models
2017
Abstract European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like the Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference discretization leads to a so-called full order model (FOM). Reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD). The early exercise constraint of American options is enforced by a penalty on subset of grid points. The presented numerical experiments demonstrate that pricing with ROMs can be orders of magnitude faster within a give…
Reduced Order Models for Pricing American Options under Stochastic Volatility and Jump-diffusion Models
2016
American options can be priced by solving linear complementary problems (LCPs) with parabolic partial(-integro) differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model (FOM). Here reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD) and non negative matrix factorization (NNMF) in order to make pricing much faster within a given model parameter variation range. The numerical experiments demonstrate orders of magnitude faster pricing with ROMs. peerReviewed