In this paper, a new modeling and learning approach is presented which is based on two assumptions from the field of psychology: 1. The number of Tweets mainly depends on previous dynamics of the discussion, i.e. a state-space modeling approach is used for the first time. 2. Humans mainly react to emotional stimuli, i.e. Tweets are automatically characterized by their emotional content. Therefo...

This paper presents a work of utilizing multi-space random mapping (MRM) to formulate a dual-factor identification system, which combines speaker biometric and personal token. Personal token will be assigned to the client to constitute a unique random subspace during enrollment and the speaker template will be generated within the random subspace. Test features will be mapped to the random subs...

The modes of linear time invariant mechanical systems can be estimated from output-only vibration measurements under ambient excitation conditions with subspace-based system identification methods. In the presence additional unmeasured periodic excitation, for example due to rotating machinery, described by a state-space model where input dynamics appear as subsystem in addition structural inte...

Introduction, Let © be the class of all countable and connected perfectly separable Hausdorff spaces containing more than one point. I t is known that an ©-space cannot be regular or compact. Urysohn, using a complicated identification of points, has constructed the first example of an ©-space. Two ©-spaces, X and X*, more simply constructed and not involving identifications, are presented here...

The aim of the paper is to first investigate some properties of the hyperspace $theta(X)$, and then in the next article it deals with some detailed study of a special type of subspace $downarrowtheta C(X)$ of the space $theta (Xtimes mathbb I)$.

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite inputoutput sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output mod...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

this article presents a new subspace-based technique for reducing the noise ofsignals in time-series. in the proposed approach, the signal is initially representedas a data matrix. then using singular value decomposition (svd), noisy datamatrix is divided into signal subspace and noise subspace. in this subspace division,each derivative of the singular values with respect to rank order is used ...

This paper is concerned with the construction of reduced–order models for high–order linear systems in such a way that the L2 norm of the impulse–response error is minimized. Two convergent algorithms that draw on previous procedures presented by the same authors, are suggested: one refers to s–domain representations, the other to time–domain state–space representations. The algorithms are base...

The iterative rational Krylov algorithm (IRKA) of Gugercin et al. (2008) [8] is an interpolatory model reduction approach to the optimal H2 approximation problem. Even though the method has been illustrated to show rapid convergence in various examples, a proof of convergence has not been provided yet. In this note, we show that in the case of state-space-symmetric systems, IRKA is a locally co...