Decomposition technique for composite signals

A new method for decomposition of compo,.~itsei gnals is presented. It is shown that high freyuency portion of composite signal spectrum possesses information on echo structure. The proposed technique does not assume the shape of basic wavelet and does not place any restrictions on the amplitudes and arrival times of echoes inm the composite signal. In the absence of noise any desirrd resolution can he obtained The effect of sampling rate and jFequency window function on echo resolutio.~ are di.wussed. Voiced speech segment is considered as an example of conzpxite sigrnl to demonstrate the application of the decomposition technique.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

ε-bounded state estimation for time-delay systems with bounded disturbances

A new problem on ε-bounded functional state estimation for time-delay systems with unknown bounded disturbances is studied in this paper. In the presence of unknown bounded disturbances, the common assumption regarding the observers matching condition is no longer required. In this regard, instead of achieving asymptotic convergence for the observer error, the error is now required to converge exponentially within a ball with a small radius ε > 0. This means that the estimate converges exponentially within an ε-bound of the true value. A general observer that utilises multiple-delayed output and input information is proposed. Sufficient conditions for the existence of the proposed observer are first given. We then employ an extended Lyapunov-Krasovskii functional which combines the delay-decomposition technique with a triple-integral term to study the ε-convergence problem of the observer error system. Moreover, the obtained results are shown to be more effective than the existing results for the cases with no disturbances and/or no time delay. Three numerical examples are given to illustrate the obtained results.

Mathematical decomposition technique applied to the probabilistic power flow problem

In this paper a framework based on the decomposition of the first-order optimality conditions is described and applied to solve the Probabilistic Power Flow (PPF) problem in a coordinated but decentralized way in the context of multi-area power systems. The purpose of the decomposition framework is to solve the problem through a process of solving smaller subproblems, associated with each area of the power system, iteratively. This strategy allows the probabilistic analysis of the variables of interest, in a particular area, without explicit knowledge of network data of the other interconnected areas, being only necessary to exchange border information related to the tie-lines between areas. An efficient method for probabilistic analysis, considering uncertainty in n system loads, is applied. The proposal is to use a particular case of the point estimate method, known as Two-Point Estimate Method (TPM), rather than the traditional approach based on Monte Carlo simulation. The main feature of the TPM is that it only requires resolve 2n power flows for to obtain the behavior of any random variable. An iterative coordination algorithm between areas is also presented. This algorithm solves the Multi-Area PPF problem in a decentralized way, ensures the independent operation of each area and integrates the decomposition framework and the TPM appropriately. The IEEE RTS-96 system is used in order to show the operation and effectiveness of the proposed approach and the Monte Carlo simulations are used to validation of the results. © 2011 IEEE.

Application of the Input-Output Decomposition Technique to China's Regional Economies

Structural decomposition techniques based on input-output table have become a widely used tool for analyzing long term economic growth. However, due to limitations of data, such techniques have never been applied to China's regional economies. Fortunately, in 2003, China's Interregional Input-Output Table for 1987 and Multi-regional Input-Output Table for 1997 were published, making decomposition analysis of China's regional economies possible. This paper first estimates the interregional input-output table in constant price by using an alternative approach: the Grid-Search method, and then applies the standard input-output decomposition technique to China's regional economies for 1987-97. Based on the decomposition results, the contributions to output growth of different factors are summarized at the regional and industrial level. Furthermore, interdependence between China's regional economies is measured and explained by aggregating the decomposition factors into the intraregional multiplier-related effect, the feedback-related effect, and the spillover-related effect. Finally, the performance of China's industrial and regional development policies implemented in the 1990s is briefly discussed based on the analytical results of the paper.

Turbulence measurements in a shallow tidal estuary : analysis based on triple decomposition

In an estuary, mixing and dispersion are the result of the combination of large scale advection and small scale turbulence which are both complex to estimate. A field study was conducted in a small sub-tropical estuary in which high frequency (50 Hz) turbulent data were recorded continuously for about 48 hours. A triple decomposition technique was introduced to isolate the contributions of tides, resonance and turbulence in the flow field. A striking feature of the data set was the slow fluctuations which exhibited large amplitudes up to 50% the tidal amplitude under neap tide conditions. The triple decomposition technique allowed a characterisation of broader temporal scales of high frequency fluctuation data sampled during a number of full tidal cycles.

Kinetics and mechanism of thermal decomposition of tetramethylammonium nitrate

The mechanism of thermal decomposition of tetramethylammonium nitrate has been investigated by thermogravimetry and mass spectrometry. The activation energy for the decomposition has been determined by isothermal decomposition technique using thermogravimetry and by monitoring mass spectrometrically the formation of trimethylamine. The activation energies determined in both the cases compare well, suggesting that the decomposition proceeds via dissociation of tetramethylammonium nitrate into trimethylamine and methylnitrate.

HYDROGENATED AMORPHOUS SILICON THIN-FILM DEPOSITION BY DIRECT PHOTO-ENHANCED DECOMPOSITION OF SILANE USING AN INTERNAL HYDROGEN DISCHARGE LAMP.

Hydrogenated amorphous silicon (a-Si:H) thin films have been deposited from silane using a novel photo-enhanced decomposition technique. The system comprises a hydrogen discharge lamp contained within the reaction vessel; this unified approach allows high energy photon excitation of the silane molecules without absorption by window materials or the need for mercury sensitisation. The film growth rates (exceeding 4 Angstrom/s) and material properties obtained are comparable to those of films produced by plasma-enhanced CVD techniques. The reduction of energetic charged particles in the film growth region should enable the fabrication of cleaner semiconductor/insulator interfaces in thin-film transistors.

An Improved Design of Combinational Digital Circuits with Multiplexers using Genetic Algorithm

This paper presents a new approach to the design of combinational digital circuits with multiplexers using Evolutionary techniques. Genetic Algorithm (GA) is used as the optimization tool. Several circuits are synthesized with this method and compared with two design techniques such as standard implementation of logic functions using multiplexers and implementation using Shannon’s decomposition technique using GA. With the proposed method complexity of the circuit and the associated delay can be reduced significantly

Evaluation of empirical mode decomposition for event-related potential analysis

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

A decentralized approach for optimal reactive power dispatch using a Lagrangian decomposition method

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TWO DECOMPOSITION APPROACHES APPLIED TO THE MULTI-AREA OPTIMAL REACTIVE POWER FLOW PROBLEM

This paper applies two methods of mathematical decomposition to carry out an optimal reactive power flow (ORPF) in a coordinated decentralized way in the context of an interconnected multi-area power system. The first method is based on an augmented Lagrangian approach using the auxiliary problem principle (APP). The second method uses a decomposition technique based on the Karush-Kuhn-Tucker (KKT) first-order optimality conditions. The viability of each method to be used in the decomposition of multi-area ORPF is studied and the corresponding mathematical models are presented. The IEEE RTS-96, the IEEE 118-bus test systems and a 9-bus didactic system are used in order to show the operation and effectiveness of the decomposition methods.

The Blinder-Oaxaca decomposition for linear regression models

The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

Standard Errors for the Blinder-Oaxaca Decomposition

The decomposition technique introduced by Blinder (1973) and Oaxaca (1973) is widely used to study outcome differences between groups. For example, the technique is commonly applied to the analysis of the gender wage gap. However, despite the procedure's frequent use, very little attention has been paid to the issue of estimating the sampling variances of the decomposition components. We therefore suggest an approach that introduces consistent variance estimators for several variants of the decomposition. The accuracy of the new estimators under ideal conditions is illustrated with the results of a Monte Carlo simulation. As a second check, the estimators are compared to bootstrap results obtained using real data. In contrast to previously proposed statistics, the new method takes into account the extra variation imposed by stochastic regressors.