Stefania Tomasiello

Bio: Stefania Tomasiello is an academic researcher from University of Tartu. The author has contributed to research in topics: Fuzzy logic & Gauss–Kronrod quadrature formula. The author has an hindex of 19, co-authored 78 publications receiving 912 citations. Previous affiliations of Stefania Tomasiello include University of Salerno & University of Basilicata.

Numerical solutions of the Burgers-Huxley equation by the IDQ method

Abstract: In this paper, the Burgers-Huxley equation has been solved by a generalized version of the Iterative Differential Quadrature (IDQ) method for the first time. The IDQ method is a method based on the quadrature rules. It has been proposed by the author applying to a certain class of non-linear problems. Stability and error analysis are performed, showing the efficiency of the method. Besides, an error bound is tried. In the discussion about the numerical examples, the generalized Burgers-Huxley equation is involved too.

Stability and accuracy of the iterative differential quadrature method

Abstract: In this paper the stability and accuracy of an iterative method based on differential quadrature rules will be discussed. The method has already been proposed by the author in a previous work, where its good performance has been shown. Nevertheless, discussion about stability and accuracy remained open. An answer to this question will be provided in this paper, where the conditional stability of the method will be pointed out, in addition to an examination of the possible errors which arise under certain conditions. The discussion will be preceded by an overview of the method and its foundations, i.e. the differential quadrature rules, and followed by a numerical case which shows how the method behaves when applied to reduce continuous systems to two-degree-of-freedom systems in the non-linear range. In particular, here the case of oscillators coupled in non-linear terms will be treated. The numerical results, used to draw Poincare maps, will be compared with those obtained by using the Runge–Kutta method with a high precision goal. Copyright © 2003 John Wiley & Sons, Ltd.

Fuzzy Transform Based Compression of Electric Signal Waveforms for Smart Grids

Abstract: In this paper, a fuzzy-based paradigm for data compression aimed at reducing the computational burden of data analysis in smart grids (SGs) is proposed. In the SG context, it is challenging achieving an efficient use of the channel communication bandwidth and a reduced need of the storage space for operational data. Thus, we discuss a fuzzy-based mathematical kernel which transforms the data into a new domain where their cardinality can be sensibly reduced, consequently allowing the development of more efficient data analysis algorithms. Detailed numerical results obtained from several test systems are presented and discussed in order to demonstrate the effectiveness of the proposed approach to handle SG operation problems.

Multisignal 1-D compression by F-transform for wireless sensor networks applications

Abstract: Graphical abstractExample 1: MSE behaviour for ambient temperature (dotted, blocks with CR=1.33; dashed, LS with CR=1.33; dot-dashed, blocks with CR=1.83; continuous, LS with CR=1.83; thick, DWT). Display Omitted HighlightsIn WSNs data compression is a way for transferring a large amount of data to a sink.When data are not correlated popular methods such as DWT do not perform well.We propose two F-transform based techniques to address these issues.Publicly available environmental data were used for a comparative study.If compared with DWT our approaches allow higher data compression rates with lower distortions. In wireless sensor networks a large amount of data is collected for each node. The challenge of transferring these data to a sink, because of energy constraints, requires suitable techniques such as data compression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popular in this field. These methods behave well enough if there is a correlation in data. However, especially for environmental measurements, data may not be correlated. In this work, we propose two approaches based on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Discrete Wavelet Transform on publicly available real-world data sets. The comparative study shows the capabilities of our approaches, which allow a higher data compression rate with a lower distortion, even if data are not correlated.

Design And Control Of Concrete Mixtures

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Performance metrics in supply chain management

Abstract: This survey paper starts with a critical analysis of various performance metrics for supply chain management (SCM), used by a specific manufacturing company. Then it summarizes how economic theory treats multiple performance metrics. Actually, the paper proposes to deal with multiple metrics in SCM via the balanced scorecard — which measures customers, internal processes, innovations, and finance. To forecast how the values of these metrics will change — once a supply chain is redesigned — simulation may be used. This paper distinguishes four simulation types for SCM: (i) spreadsheet simulation, (ii) system dynamics, (iii) discrete-event simulation, and (iv) business games. These simulation types may explain the bullwhip effect, predict fill rate values, and educate and train users. Validation of simulation models requires sensitivity analysis; a statistical methodology is proposed. The paper concludes with suggestions for a possible research agenda in SCM. A list with 50 references for further study is included.

A new prediction model of battery and wind-solar output in hybrid power system

Abstract: In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.