81 resultados para Uncertainty in generation
Resumo:
Impacts of climate change on hydrology are assessed by downscaling large scale general circulation model (GCM) outputs of climate variables to local scale hydrologic variables. This modelling approach is characterized by uncertainties resulting from the use of different models, different scenarios, etc. Modelling uncertainty in climate change impact assessment includes assigning weights to GCMs and scenarios, based on their performances, and providing weighted mean projection for the future. This projection is further used for water resources planning and adaptation to combat the adverse impacts of climate change. The present article summarizes the recent published work of the authors on uncertainty modelling and development of adaptation strategies to climate change for the Mahanadi river in India.
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We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for both classification and regression problems. Experiments show that the proposed formulations outperform imputation.
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In voiced speech analysis epochal information is useful in accurate estimation of pitch periods and the frequency response of the vocal tract system. Ideally, linear prediction (LP) residual should give impulses at epochs. However, there are often ambiguities in the direct use of LP residual since samples of either polarity occur around epochs. Further, since the digital inverse filter does not compensate the phase response of the vocal tract system exactly, there is an uncertainty in the estimated epoch position. In this paper we present an interpretation of LP residual by considering the effect of the following factors: 1) the shape of glottal pulses, 2) inaccurate estimation of formants and bandwidths, 3) phase angles of formants at the instants of excitation, and 4) zeros in the vocal tract system. A method for the unambiguous identification of epochs from LP residual is then presented. The accuracy of the method is tested by comparing the results with the epochs obtained from the estimated glottal pulse shapes for several vowel segments. The method is used to identify the closed glottis interval for the estimation of the true frequency response of the vocal tract system.
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A comprehensive scheme for analysing uniaxial deformation data, taking into account the finite stiffness of the testing machine is presented. Equations relevant to tension and stress relaxation tests carried out under cross head speed control, and to creep testing under constant load, are described. For the first two cases, the implications of not using gauge length extensometry but relying upon cross head displacement for inferring specimen extension, and the role of uncertainty in machine stiffness are also examined. The final section touches upon the extension of the present scheme to account for specimen anelasticity.
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This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user employs Orthogonal Frequency Division Multiplexing (OFDM). We specifically consider the scenario when the channel between the primary and a secondary user is frequency selective. We develop cooperative sequential detection algorithms based on energy detectors. We modify the detectors to mitigate the effects of some common model uncertainties such as timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power. The performance of the proposed algorithms are studied via simulations. We show that the performance of the energy detector is not affected by the frequency selective channel. We also provide a theoretical analysis for some of our algorithms.
Resumo:
The problem of determining optimal power spectral density models for earthquake excitation which satisfy constraints on total average power, zero crossing rate and which produce the highest response variance in a given linear system is considered. The solution to this problem is obtained using linear programming methods. The resulting solutions are shown to display a highly deterministic structure and, therefore, fail to capture the stochastic nature of the input. A modification to the definition of critical excitation is proposed which takes into account the entropy rate as a measure of uncertainty in the earthquake loads. The resulting problem is solved using calculus of variations and also within linear programming framework. Illustrative examples on specifying seismic inputs for a nuclear power plant and a tall earth dam are considered and the resulting solutions are shown to be realistic.
Resumo:
An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50-80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects-the standard reference source for a casting process-constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.
Resumo:
The effect of uncertainty in composite material properties on the aeroelastic response, vibratory loads, and stability of a hingeless helicopter rotor is investigated. The uncertainty impact on rotating natural frequencies of the blade is studied with Monte Carlo simulations and first-order reliability methods. The stochastic aeroelastic analyses in hover and forward flight are carried out with Monte Carlo simulations. The flap, lag, and torsion responses show considerable scatter from their baseline values, and the uncertainty impact varies with the azimuth angle. Furthermore, the blade response shows finite probability of resonance-type conditions caused by modal frequencies approaching multiples of the rotor speed. The 4/rev vibratory forces show large deviations from their baseline values. The lag mode damping shows considerable scatter due to uncertain material properties with an almost 40% probability of instability in hover.
Resumo:
This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user is using Orthogonal Frequency Division Multiplexing (OFDM). For this we develop cooperative sequential detection algorithms that use the autocorrelation property of cyclic prefix (CP) used in OFDM systems. We study the effect of timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power. We also modify the detector to mitigate the effects of these impairments. The performance of the proposed algorithms is studied via simulations. We show that sequential detection can significantly improve the performance over a fixed sample size detector.
Resumo:
The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.
Resumo:
A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.
Resumo:
The enthalpy increments and the standard molar Gibbs energies of formation-of DyFeO3(s) and Dy3Fe5O12(s) have been measured using a Calvet micro-calorimeter and a solid oxide galvanic cell, respectively. A co-operative phase transition, related to anti-ferromagnetic to paramagnetic transformation, is apparent. from the heat capacity data for DyFeO3 at similar to 648 K. A similar type of phase transition has been observed for Dy3Fe5O12 at similar to 560 K which is related to ferrimagnetic to paramagnetic transformation. Enthalpy increment data for DyFeO3(s) and Dy3Fe5O12(s), except in the vicinity of the second-order transition, can be represented by the following polynomial expressions:{H(0)m(T) - H(0)m(298.15 K)) (Jmol(-1)) (+/-1.1%) = -52754 + 142.9 x (T (K)) + 2.48 x 10(-3) x (T (K))(2) + 2.951 x 10(6) x (T (K))(-1); (298.15 less than or equal to T (K) less than or equal to 1000) for DyFeO3(s), and {H(0)m(T) - H(0)m(298.15 K)} (Jmol(-1)) (+/-1.2%) = -191048 + 545.0 x (T - (K)) + 2.0 x 10(-5) x (T (K))(2) + 8.513 x 10(6) x (T (K))(-1); (208.15 less than or equal to T (K) less than or equal to 1000)for Dy3Fe5O12(s). The reversible emfs of the solid-state electrochemical cells: (-)Pt/{DyFeO3(s) + Dy2O3(s) + Fe(s)}/YDT/CSZ//{Fe(s) + Fe0.95O(s)}/Pt(+) and (-)Pt/{Fe(s) + Fe0.95O(s)}//CSZ//{DyFeO3(s) + Dy3Fe5O12(s) + Fe3O4(s)}/Pt(+), were measured in the temperature range from 1021 to 1250 K and 1035 to 1250 K, respectively. The standard Gibbs energies of formation of solid DyFeO3 and Dy3Fe5O12 calculated by the least squares regression analysis of the data obtained in the present study, and data for Fe0.95O and Dy2O3 from the literature, are given by Delta(f)G(0)m(DyFeO3,s)(kJmol(-1))(+/-3.2)= -1339.9 + 0.2473 x (T(K)); (1021 less than or equal to T (K) less than or equal to 1548)and D(f)G(0)m(Dy3Fe5O12,s) (kJmol(-1)) (+/-3.5) = -4850.4 + 0.9846 x (T (K)); (1035 less than or equal to T (K) less than or equal to 1250) The uncertainty estimates for Delta(f)G(0)m include the standard deviation in the emf and uncertainty in the data taken from the literature. Based on the thermodynamic information, oxygen potential diagram and chemical potential diagrams for the system Dy-Fe-O were developed at 1250 K. (C) 2002 Editions scientifiques et medicales Elsevier SAS. All rights reserved.
Resumo:
The enthalpy increments and the standard molar Gibbs energy of formation of NdFeO3(s) have been measured using a hightemperature Calvet microcalorimeter and a solid oxide galvanic cell, respectively. A lambda-type transition, related to magnetic order-disorder transformation (antiferromagnetic to paramagnetic), is apparent from the heat capacity data at similar to 687 K. Enthalpy increments, except in the vicinity of transition, can be represented by a polynomial expression: {Hdegrees(m)(T)-Hdegrees(m) (298.15 K)} /J(.)mol(-1) (+/- 0.7%)=-53625.6+146.0(T/K) +1.150 X 10(-4)(T/K)(2) +3.007 x 10(6)(T/K)(-1); (298.15 less than or equal to T/K less than or equal to 1000). The heat capacity, the first differential of {Hdegrees(m)(T)-Hdegrees(m)(298.15 K)}with respect to temperature, is given by Cdegrees(pm)/J(.)K(-1.)mol(-1)=146.0+ 2.30x10(-4) (T/K) - 3.007 X 10(6)(T/K)(-2). The reversible emf's of the cell, (-) Pt/{NdFeO3(s) +Nd2O3(s)+Fe(s)}//YDT/CSZ// Fe(s)+'FeO'(s)}/Pt(+), were measured in the temperature range from 1004 to 1208 K. It can be represented within experimental error by a linear equation: E/V=(0.1418 +/- 0.0003)-(3.890 +/- 0.023) x 10(-5)(T/K). The Gibbs energy of formation of solid NdFeO, calculated by the least-squares regression analysis of the data obtained in the present study, and data for Fe0.95O and Nd2O3 from the literature, is given by Delta(f)Gdegrees(m)(NdFeO3 s)/kJ (.) mol(-1)( +/- 2.0)=1345.9+0.2542(T/K); (1000 less than or equal to T/K less than or equal to 1650). The error in Delta(f)Gdegrees(m)(NdFeO3, s, T) includes the standard deviation in emf and the uncertainty in the data taken from the literature. Values of Delta(f)Hdegrees(m)(NdFeO3, s, 298.15 K) and Sdegrees(m) (NdFeO3 s, 298.15 K) calculated by the second law method are - 1362.5 (+/-6) kJ (.) mol(-1) and 123.9 (+/-2.5) J (.) K-1 (.) mol(-1), respectively. Based on the thermodynamic information, an oxygen potential diagram for the system Nd-Fe-O was developed at 1350 K. (C) 2002 Elsevier Science (USA).
Resumo:
The enthalpy increments and the standard molar Gibbs energy (G) of formation of SmFeO3(S) and SM3Fe5O12(s) have been measured using a Calvet micro-calorimeter and a solid oxide galvanic cell, respectively. A X-type transition, related to magnetic order-disorder transformation (antiferromagnetic to paramagnetic), is apparent from the heat capacity data at similar to673 K for SmFeO3(s) and at similar to560 K for Sm3Fe5O12(S). Enthalpy increment data for SmFeO3(s) and SM3Fe5O12(s), except in the vicinity of X-transition, can be represented by the following polynomial expressions:
{H-m(0)(T) - H-m(0)(298.15 K){/J mol-(1)(+/-1.2%) = -54 532.8 + 147.4 . (T/K) + 1.2 . 10(-4) . (T/K)(2) +3.154 . 10(6) . (T/K)(-1); (298.15 less than or equal to T/K less than or equal to 1000)
for SmFeO3(s), and
{H-m(0)(T) - H-m(0)(298.15 K)}/J mol(-1) (+/-1.4%) = -192 763 + 554.7 . (T/K) + 2.0 . 10(-6) . (T/K)(2) + 8.161 . 10(6) - (T/K)(-1); (298.15 less than or equal to T/K less than or equal to 1000) for Sm3Fe5O12(s).
The reversible emf of the solid-state electrochemical cells, (-)Pt/{SmFeO3(s) + Sm2O3(S) + Fe(s)) // YDT / CSZ // {Fe(s) + Fe0.95O(s)} / Pt(+) and (-)Pt/{Fe(s) + Fe0.95O(S)} // CSZ // {SmFeO3(s) + Sm3Fe5O12(s) + Fe3O4(s) / Pt(+), were measured in the temperature ranges of 1005-1259 K and 1030-1252 K, respectively. The standard molar G of formation of solid SmFeO3 and Sm3Fe5O12 calculated by the least squares regression analysis of the data obtained in the current study, and data for Fe0.95O and Sm2O3 from the literature, are given by:
Delta(f)G(m)(0)(SmFeO3, s)/kj . mol(-1)(+/-2.0) = -1355.2 + 0.2643 .
Resumo:
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