Gain a second melee charge for Storm Fist and Sun Strike. (It doesn't apply to Void melee yet—this is a bug I believe we have fixed in 2.0.1.). Download the Filmakr.zip from below and unzip the plist file. Open Winrar and navigate. Download the app here. 10/10/16-16:46: SigPro 2.0.1 (Mac OS X).
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Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time.
Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.
Studying transient phenomena is important and useful in many fields such as biomedical research for the analysis of heart rate variability , the extraction of detailed information of muscle behavior , and the detection and classification of epileptic spikes ; mechanics for the study of the susceptibility of structures to vibration issues ; and for seismic events detection and temporal localization ,. Monitoring electrical loads and systems is particularly one of the areas where transients play a central role. We cite as applications the analysis of disturbances affecting the quality of the electric power system , , fault detection in rotary machines , , and non-intrusive load monitoring (NILM) –, a field concerned with extracting individual energy consumption (e.g., of different appliances) from measured total energy consumption (e.g., at main breaker panel).Transients embed a decay or damping characteristic as they exist for short periods, and therefore, the superimposed damped sinusoids (SDS) model was extensively used to model transients in many fields. For example, it was used for modeling electric disturbances , transient audio signals , and the free induction decay observed in nuclear resonance spectroscopy. Along with the model, different algorithms were proposed for its parameter estimation. The most known methods are Prony’s , Pisarenko’s , matrix pencil , Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) , and MUltiple SIgnal Classification (MUSIC). Despite its success, the SDS model is inadequate for turn-on transient current signals.
In fact, a lot of turn-on transient current signals are characterized by a quasi-stationary harmonic content (Fig. a) whereas the SDS model is best suited for modeling vanishing non-stationary content (Fig. b) because having different damping factors for each frequency produces a signal with non-stationary frequency content. Moreover, the turn-on transient current decays to a steady state that is different from the steady state preceding the turn-on of the appliance, whereas in the SDS the transient model starts from one steady state and decays to the same one afterwards. The electrical current “turn-on' transient is the current that appears with the switching-on of an electrical appliance. This corresponds to a transition from one steady state to another.
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For example, if we consider a single appliance on the network, then the first steady state is the state of no consumption, the second steady state is the state of steady consumption, and the transient is the part in between. Note that the transient we are interested in modeling is the one related to the electrical consumption. This transient is different from the very high frequency transient (appearing as a short pulse preceding the turn-on transient) generated by the switching devices because of the closure of the circuit.
Turn-on transients are appliance-dependent and last usually from few power system cycles to few seconds. A turn-on transient is typically characterized by a high current amplitude (surge) at the beginning of consumption followed by a decrease (or damping) in the amplitude of the consumed energy until reaching a stable state (Fig. a). Data modelingIn this section, we propose and discuss a mathematical model for turn-on transient current signals. Strictly speaking, we model the turn-on transient including a small part of the following steady state regime; mainly because the transient end is not well defined and because estimating the harmonic content is easier on the steady state part.The shape of the turn-on transient and the related amplitudes vary from one electrical appliance to another. To take into account these variations, we propose to model the noiseless electrical current turn-on transient s( t) as the product of two signals. Estimation performance assessment Assessment on simulated dataIn this section, we present the results of the estimation performance evaluation on simulated data.
Hereafter, we present (i) the simulated signal and its parameters, (ii) the bias of the estimated parameters, (iii) the estimated parameters variance and its comparison to the CRB, (iv) the CRB variation with respect to the sampling frequency, and (v) the convergence of the TCE algorithm. Simulated signal and its parametersTaking the considered setup ( n=3 and d=5) in this section, we end up with 14 parameters for the simulated signal. So, with such large number of degrees of freedom, we decided to choose the set of parameters such that the simulated signal will resemble as much as possible real signals, and without a priori knowledge on what parameter values are appropriate, we decided to tweak the model parameters and choose the ones that gave a simulated signal “resembling” (similar waveform) typical real current waveforms from our dataset. The noiseless signal model is.
This paper deals with the problem of delay-dependent robust H ∞ filter for T-S fuzzy time-delay systems with exponential stability. The purpose is to design filter parameters such that the filtering error system is exponentially stable and satisfies a prescribed H ∞ performance. In terms of linear matrix inequalities (LMIS), some sufficient conditions for the solvability of this problem are presented. Thanks to the new filter, the obtained stability criterion is less conservative than the existing ones. Finally, three examples are provided to demonstrate the effectiveness and the superiority of the proposed design methods.
In the past several decades, robust filtering problem has received extensive attention of people. The current study of robust filtering mainly concentrated on two aspects: Kalman filter and H ∞ filter. Among them, the research of H ∞ filter is wider, and many important and interesting results have been proposed in terms of all kinds of approaches (see, for example, –). Actual industrial system such as the power grid, chemical processes, nuclear reactor and others often contain time-delay, and time-delay is the main factor that leads to system performance degradation and instability.
Therefore, the research of the filtering problem for time-delay systems has important theoretical significance and application value.In recent years, the research of the filtering for time-delay systems has made abundant achievements. Delay-dependent robust H ∞ and L 2 − L ∞ filtering for a class of uncertain nonlinear time-delay systems was studied in. H ∞ filtering of time-delay T-S fuzzy systems based on piecewise Lyapunov-Krasovskii functional was investigated in. A new fuzzy H ∞ filter design for nonlinear continuous-time dynamic systems with time-varying delays was reported in.
Robust H ∞ filtering for a class of uncertain Lurie time-delay singular systems was studied in. Delay-dependent H ∞ filtering for singular Markovian jump time-delay systems was studied in.T-S fuzzy system has wide application in the network, economy, environment and other fields, it has attracted more and more concern of the scholars (see, for example, –).
H ∞ filter has come to play an important role in fuzzy model during the past years, so the filtering of fuzzy system is especially important. Delay-dependent nonfragile robust H ∞ filtering of T-S fuzzy time-delay systems was investigated in. An improved H ∞ filter design for nonlinear system with time-delay via T-S fuzzy models was studied in. Exponential H ∞ filter design for uncertain Takagi-Sugeno fuzzy systems with time-delay was reported in. New results on H ∞ filtering for fuzzy systems with interval time-varying delays was studied in.
Delay-dependent non-fragile H ∞ filtering for uncertain fuzzy systems based on switching fuzzy model and piecewise Lyapunov function was studied in. But at present, the problem of delay-dependent robust H ∞ filter for T-S fuzzy time-delay systems with exponential stability has rarely been reported.For T-S fuzzy time-delay systems with exponential stability, this paper discusses the design methods of delay-dependent robust H ∞ filter. First of all, it gave a criterion of exponential stability, and then discussed the conditions and design methods of delay-dependent robust H ∞ filter. The result of designed filter is exponential stability for the augmented system via LMI. Thanks to the new filter, the obtained criterion is less conservative than the existing ones.
Finally, some numerical examples are given to show the effectiveness and the superiority of the proposed design methods.
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