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Search results for: VECTOR AUTOREGRESSIVE MODELS
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Sparse vector autoregressive modeling of audio signals and its application to the elimination of impulsive disturbances
PublicationArchive audio files are often corrupted by impulsive disturbances, such as clicks, pops and record scratches. This paper presents a new method for elimination of impulsive disturbances from stereo audio signals. The proposed approach is based on a sparse vector autoregressive signal model, made up of two components: one taking care of short-term signal correlations, and the other one taking care of long-term correlations. The method...
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Low order autoregressive (AR) models for FDTD analysis of microwave filters.
PublicationArtykuł opisuje zastosowanie modeli AR w celu poprawy efektywności analizy struktur filtrujących metodą różnic skończonych w dziedzinie czasu (FD-TD). Opisanych jest szereg kryteriów pozwalających na automatyczne tworzenie modeli AR sygnałów czasowych, w tym wybór fragmentu odpowiedzi układu stanowiący podstawę ekstrakcji współczynników modelu, współczynnika decymacji oraz rzędu modelu. Skuteczność wprowadzonych kryteriów...
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Elimination of Impulsive Disturbances From Stereo Audio Recordings Using Vector Autoregressive Modeling and Variable-order Kalman Filtering
PublicationThis paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. Online tracking of signal model parameters is performed using the exponential ly weighted least squares algo- rithm. Detection of noise pulses an d model-based interpolation of the irrevocably distorted sampl es is realized using an adaptive, variable-order...
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Enhancing women’s engagement in economic activities through information and communication technology deployment: evidence from Central–Eastern European countries
PublicationThis study takes a macro perspective to examine the associations between the economic deployment of information and communication technology (ICT), women’s labor market participation, and economic growth in Central–Eastern European countries between 1990 and 2017. We use data extracted from World Bank Development Indicators, World Development Reports, and the World Telecommunication/ICT Indicators Database. Our methodological framework...
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STILL ‘FEW, SLOW AND LOW’? ON THE FEMALE DIMENSION OF TECHNOLOGY, LABOUR MARKETS AND ECONOMIC ACTIVITY: EVIDENCE FOR THE PERIOD OF 1990-2017
PublicationThe known in empirical economics question ‘Why so Few? Why so Slow? Why so Low?’ refers here to the persistently small number of women involved in innovative activities, the slowness of change in the inequalities between women and men in these fields, and women’s continuing lower rank in business and academic positions. In developing countries, women`s labour and entrepreneurial activity remains an ‘untapped resource’ for economic...
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Elimination of impulsive disturbances from stereo audio recordings
PublicationThis paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. On-line tracking of signal model parameters is performed using the stability-preserving Whittle-Wiggins-Robinson algorithm with exponential data weighting. Detection of noise pulses and model-based interpolation of the irrevocably distorted samples...
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On adaptive selection of estimation bandwidth for analysis of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one should choose the so-called estimation bandwidth, related to the effective width of the local analysis window. The choice should comply with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive estimation variance. The paper presents a novel method...
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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Prognozowanie ostrzegawcze w małej firmie
PublicationW artykule pokazano możliwości zastosowania prostych metod prognostycznych do ostrzegania w małej firmie przed niekorzystnymi zjawiskami. Przedmiotem badań jest sprzedaż, a ze względu na występowanie w szeregu czasowym sezonowości, w celu wyznaczenia sygnałów ostrzegawczych dokonano porównań trendów prognozowanych i rzeczywistych w okresach jednoimiennych za pomocą pierwszych różnic. Do prognozowania sprzedaży wykorzystano proste...
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On adaptive covariance and spectrum estimation of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one has to make two important decisions. First, one should choose the so-called estimation bandwidth, inversely proportional to the effective width of the local analysis window, in the way that complies with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive...
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Sparse autoregressive modeling
PublicationIn the paper the comparison of the popular pitch determination (PD) algorithms for thepurpose of elimination of clicks from archive audio signals using sparse autoregressive (SAR)modeling is presented. The SAR signal representation has been widely used in code-excitedlinear prediction (CELP) systems. The appropriate construction of the SAR model is requiredto guarantee model stability. For this reason the signal representation...
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On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes
PublicationAutoregressive modeling is a widespread parametricspectrum estimation method. It is well known that, in the caseof stationary processes with unknown order, its accuracy canbe improved by averaging models of different complexity usingsuitably chosen weights. The paper proposes an extension of thistechnique to the case of multivariate locally stationary processes.The proposed solution is based on local autoregressive...
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On autoregressive spectrum estimation using the model averaging technique
PublicationThe problem of estimating spectral density of a nonstationary process satisfying local stationarity conditions is considered. The proposed solution is a two step procedure based on local autoregressive (AR) modeling. In the first step Bayesian-like averaging of AR models, differing in order, is performed. The main contribution of the paper is development of a new final-prediction-error-like statistic, which can be used to select...
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On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublicationThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
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On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublicationWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
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Prognozowanie wpływu drgań komunikacyjnych na budynki mieszkalne za pomocą sztucznych sieci neuronowych i maszyn wektorów wspierających
PublicationDrgania komunikacyjne mogą stanowić duże obciążenie eksploatacyjne budynku, powodując zarysowania i spękania tynków, odpadanie wypraw, zarysowania konstrukcji, pękanie elementów konstrukcji lub nawet zawalenie się budynku. Pomiary drgań na rzeczywistych konstrukcjach są pracochłonne i kosztowne, a co ważne nie w każdym przypadku są one uzasadnione. Celem pracy jest analiza autorskiego algorytmu, dzięki któremu z dużym prawdopodobieństwem...
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Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in...
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RENOVATION OF ARCHIVE AUDIO RECORDINGS USING SPARSE AUTOREGRESSIVE MODELING AND BIDIRECTIONAL PROCESSING
PublicationThe paper presents a new approach to elimination of broadband noise and impulsive disturbances from archive audio recordings. The proposed adaptive Kalman-like algorithm, based on a sparse autoregressive model of the audio signal, simultaneously detects noise pulses, interpolates the irrevocably distorted samples and performs signal smoothing. It is shown that bidirectional (forward-backward) processing of the archive signal improves...
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Constructive Controllability for Incompressible Vector Fields
PublicationWe give a constructive proof of a global controllability result for an autonomous system of ODEs guided by bounded locally Lipschitz and divergence free (i.e. incompressible) vector field, when the phase space is the whole Euclidean space and the vector field satisfies so-called vanishing mean drift condition. For the case when the ODE is defined over some smooth compact connected Riemannian manifold, we significantly strengthen...
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Lattice filter based multivariate autoregressive spectral estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a multivariate nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running...
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Elimination of clicks from archive speech signals using sparse autoregressive modeling
PublicationThis paper presents a new approach to elimination of impulsivedisturbances from archive speech signals. The proposedsparse autoregressive (SAR) signal representation is given ina factorized form - the model is a cascade of the so-called formantfilter and pitch filter. Such a technique has been widelyused in code-excited linear prediction (CELP) systems, as itguarantees model stability. After detection of noise pulses usinglinear...
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Two-Stage Identification of Locally Stationary Autoregressive Processes and its Application to the Parametric Spectrum Estimation
PublicationThe problem of identification of a nonstationary autoregressive process with unknown, and possibly time-varying, rate of parameter changes, is considered and solved using the parallel estimation approach. The proposed two-stage estimation scheme, which combines the local estimation approach with the basis function one, offers both quantitative and qualitative improvements compared with the currently used single-stage methods.
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Tuning matrix-vector multiplication on GPU
PublicationA matrix times vector multiplication (matvec) is a cornerstone operation in iterative methods of solving large sparse systems of equations such as the conjugate gradients method (cg), the minimal residual method (minres), the generalized residual method (gmres) and exerts an influence on overall performance of those methods. An implementation of matvec is particularly demanding when one executes computations on a GPU (Graphics...
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Application of Barycentric Coordinates in Space Vector PWM Computations
PublicationThis paper proposes the use of barycentric coordinates in the development and implementationof space-vector pulse-width modulation (SVPWM) methods, especially for inverters with deformed space-vector diagrams. The proposed approach is capable of explicit calculation of vector duty cycles, independentof whether they assume ideal positions or are displaced due to the DC-link voltage imbalance. The use ofbarycentric coordinates also...
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The Hopf theorem for gradient local vector fields on manifolds
PublicationWe prove the Hopf theorem for gradient local vector fields on manifolds, i.e., we show that there is a natural bijection between the set of gradient otopy classes of gradient local vector fields and the integers if the manifold is connected Riemannian without boundary.
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Uncertainty estimation of loop impedance measurement determined by the vector method
PublicationThis article presents a detailed analysis of uncertainty estimation of loop impedance measurement determined by the vector method. The analysis includes the following estimates: resistance variance, voltage variance and time measurement variance. This paper presents a methodology for estimating the combined standard uncertainty of loop impedance by the vector method. The vector method allows to determine loop impedance based on...
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A remark on singular sets of vector bundle morphisms
PublicationIf characteristic classes for two vector bundles over the same base space do not coincide, then the bundles are not isomorphic. We give under rather common assumptions a lower bound on the topological dimension of the set of all points in the base over which a morphism between such bundles is not bijective. Moreover, we show that this set is topologically non-trivial.
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Concept of managing quality in baking industry, in vector representation
PublicationThe author introduced an innovative metrisable method of describing a manufacturing process. The idea of vector structure of a manufacturing process allows to formulate quantitative relations between the activity of input streams, elements of product quality, and measurable effects of losses. The structure was basis for the formulation of the concept of the process of managing product quality in the baking industry in a vector...
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Connected components of the space of proper gradient vector fields
PublicationWe show that there exist two proper gradient vector fields on Rn which are homotopic in the category of proper maps but not homotopic in the category of proper gradient maps.
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Formulation of Time-Fractional Electrodynamics Based on Riemann-Silberstein Vector
PublicationIn this paper, the formulation of time-fractional (TF) electrodynamics is derived based on the Riemann-Silberstein (RS) vector. With the use of this vector and fractional-order derivatives, one can write TF Maxwell’s equations in a compact form, which allows for modelling of energy dissipation and dynamics of electromagnetic systems with memory. Therefore, we formulate TF Maxwell’s equations using the RS vector and analyse their...
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The homotopy type of the space of gradient vector fields on the two-dimensional disc
PublicationWe prove that the inclusion of the space of gradient vector fields into the space of all vector fields on D^2 non-vanishing in S^1 is a homotopy equivalence
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Metrisable assessment of the course of stream‑systemic processes in vector form in industry 4.0
PublicationThe goal of this paper is to present an innovative conception how to use metrisable vector structure of a manufacturing process, based on quantitative relations between the activity of input streams, features of the product, and effect of losses; all of which are excellent practical solution for Industry 4.0, and in turn intelligent factories. This solution can be a usefull way in the process of building sustainable organization....
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Support Vector Machine Applied to Road Traffic Event Classification
PublicationThe aim of this paper is to present results of road traffic event signal recognition. First, several types of systems for road traffic monitoring, including Intelligent Transport System (ITS) are shortly described. Then, assumptions of creating a database of vehicle signals recorded in different weather and road conditions are outlined. Registered signals were edited as single vehicle pass by. Using the Matlab-based application...
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Determination of the Vehicles Speed Using Acoustic Vector Sensor
PublicationThe method for determining the speed of vehicles using acoustic vector sensor and sound intensity measurement technique was presented in the paper. First, the theoretical basis of the proposed method was explained. Next, the details of the developed algorithm of sound intensity processing both in time domain and in frequency domain were described. Optimization process of the method was also presented. Finally, the proposed measurement...
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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Measurement and visualization of sound intensity vector distribution in proximity of acoustic diffusers
PublicationIn this work, we would like to present analyses and visualizations of sound intensity distribution measured in proximity of an acoustic diffuser. Such distribution may be used for estimation of basic acoustic parameters of a diffuser. Measurement is performed with the use of a logarithmic sine sweep which allows for the analysis of waves scattered by the diffuser and rejecting the direct sound signal component. Pressure and sound...
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Counting and tracking vehicles using acoustic vector sensors
PublicationA method is presented for counting vehicles and for determining their movement direction by means of acoustic vector sensor application. The assumptions of the method employing spatial distribution of sound intensity determined with the help of an integrated 3D intensity probe are discussed. The intensity probe developed by the authors was used for the experiments. The mode of operation of the algorithm is presented in conjunction...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Detection of Water on Road Surface with Acoustic Vector Sensor
PublicationThis paper presents a new approach to detecting the presence of water on a road surface, employing an acoustic vector sensor. The proposed method is based on sound intensity analysis in the frequency domain. Acoustic events, representing road vehicles, are detected in the sound intensity signals. The direction of the incoming sound is calculated for the individual spectral components of the intensity signal, and the components...
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Localization of sound sources with dual acoustic vector sensor
PublicationThe aim of the work is to estimate the position of sound sources. The proposed method uses a setup of two acoustic vector sensors (AVS). The intersection of azimuth rays from each AVS should indicate the position of a source. In practice, the result of position estimation using this method is an area rather than a point. This is a result of inaccuracy of the individual sensors, but more importantly, of the influence of a source...
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Anisotropic Orlicz–Sobolev spaces of vector valued functions and Lagrange equations
PublicationIn this paper we study some properties of anisotropic Orlicz and Orlicz–Sobolev spaces of vector valued functions for a special class of G-functions. We introduce a variational setting for a class of Lagrangian Systems. We give conditions which ensure that the principal part of variational functional is finitely defined and continuously differentiable on Orlicz–Sobolev space.
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RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine
PublicationIn this letter, it is shown how direction-of-arrival (DoA) estimation for electronically steerable parasitic array radiator (ESPAR) antennas, which are designed to be integrated within wireless sensor network nodes, can be improved by applying support vector classification approach to received signal strength (RSS) values recorded at an antenna's output port. The proposed method relies on ESPAR antenna's radiation patterns measured...
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Dangerous sound event recognition using Support Vector Machine classifiers
PublicationA method of recognizing events connected to danger based on their acoustic representation through Support Vector Machine classification is presented. The method proposed is particularly useful in an automatic surveillance system. The set of 28 parameters used in the classifier consists of dedicated parameters and MPEG-7 features. Methods for parameter calculation are presented, as well as a design of SVM model used for classification....
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Neural Networks, Support Vector Machine and Genetic Algorithms for Autonomous Underwater Robot Support
PublicationIn this paper, artificial neural networks, a classification technique called support vector machine and meta-heuristics genetic algorithm have been considered for development in autonomous underwater robots. Artificial neural networks have been used for seabed modelling as well as support vector machine has been applied for the obstacles classification to avoid some collision problems. Moreover, genetic algorithm has been applied...
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Macromodeling of multiport systems using a fast implementation of the vector fitting method
PublicationMakromodelowanie układów wieloportowych przy użyciu vector fittingu jest czasochłonne oraz wymaga dużych zasobów obliczeniowych w przypadku gdy wszystkie elementy macierzy systemowej dzielą wspólne bieguny. Artykuł prezentuje stabilne rozwiązanie, które usuwa problem rzadkości macierzy poprzez zastosowanie bezpośrednie dekompozycje QR. Jako przykład przedstawiony został 60 portowy układ, który ilustruje oszczędność czasu potrzebnego...
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Parallelization of large vector similarity computations in a hybrid CPU+GPU environment
PublicationThe paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested for computation of all vector...
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Multiple sound sources localization in free field using acoustic vector sensor
PublicationMethod and preliminary results of multiple sound sources localization in free field using the acoustic vector sensor were presented in this study. Direction of arrival (DOA) for considered source was determined based on sound intensity method supported by Fourier analysis. Obtained spectrum components for considered signal allowed to determine the DOA value for the particular frequency independently. The accuracy of the developed...
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Multiple sound sources localization in real time using acoustic vector sensor
PublicationMethod and preliminary results of multiple sound sources localization in real time using the acoustic vector sensor were presented in this study. Direction of arrival (DOA) for considered source was determined based on sound intensity method supported by Fourier analysis. Obtained spectrum components for considered signal allowed to determine the DOA value for the particular frequency independently. The accuracy of the developed...
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Space vector modulation in multilevel inverters of the servo drives of the trajectory measurements telescopes
PublicationUsing the MatLab/Simulink mathematical model of a three-phase three-level voltage inverter, the influence of the space-vector modulation (SVM) algorithm on the pulsations of the current (torque) of an AC motor in the range of low rotation speeds is considered. It is shown that the SVM of the second kind does not provide a pulsations level comparable to the pulsations of a sinusoidal pulse-width modulation (SPWM), both in the static...
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Performance of Vector-valued Intensity Measures for Estimating Residual Drift of Steel MRFs with Viscous Dampers
PublicationViscous Dampers (VDs) are widely used as passive energy dissipation system for improving seismic performance levels especially in retrofitting of buildings. Residual Inter-story Drift Ratio (R-IDR) is another important factor that specifies the condition of building after earthquake. The values of R-IDR illustrates the possibility of retrofitting and repairing of a building. Therefore, this study aims to explore the vector-valued...