The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform
H. Vincent Poor - One of the best experts on this subject based on the ideXlab platform.
-
Estimation in Poisson Noise: Properties of the Conditional Mean Estimator
IEEE Transactions on Information Theory, 2020Co-Authors: Alex Dytso, H. Vincent PoorAbstract:This paper considers estimation of a random variable in Poisson noise with signal Scaling Coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the Scaling Coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the Scaling Coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the Scaling Coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Levy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero.
-
ITW - Properties of the Conditional Mean Estimator in Poisson Noise
2019 IEEE Information Theory Workshop (ITW), 2019Co-Authors: Alex Dytso, H. Vincent PoorAbstract:This paper considers estimation of a random variable in Poisson noise. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the Scaling Coefficient, the dark current parameter, the distribution of the input random variable and channel realizations.With respect to the Scaling Coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the derivative of the conditional mean estimator with respect to the dark current parameter is proportional to the conditional variance. Moreover, a version of score function is proposed and a Tweedy-like formula for the conditional expectation is recovered.With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in the mean squared error, then the input distribution is approximately gamma in the Levy metric.
Alex Dytso - One of the best experts on this subject based on the ideXlab platform.
-
Estimation in Poisson Noise: Properties of the Conditional Mean Estimator
IEEE Transactions on Information Theory, 2020Co-Authors: Alex Dytso, H. Vincent PoorAbstract:This paper considers estimation of a random variable in Poisson noise with signal Scaling Coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the Scaling Coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the Scaling Coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the Scaling Coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Levy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero.
-
ITW - Properties of the Conditional Mean Estimator in Poisson Noise
2019 IEEE Information Theory Workshop (ITW), 2019Co-Authors: Alex Dytso, H. Vincent PoorAbstract:This paper considers estimation of a random variable in Poisson noise. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the Scaling Coefficient, the dark current parameter, the distribution of the input random variable and channel realizations.With respect to the Scaling Coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the derivative of the conditional mean estimator with respect to the dark current parameter is proportional to the conditional variance. Moreover, a version of score function is proposed and a Tweedy-like formula for the conditional expectation is recovered.With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in the mean squared error, then the input distribution is approximately gamma in the Levy metric.
Johan Driesen - One of the best experts on this subject based on the ideXlab platform.
-
assessment of voltage sag indices based on Scaling and wavelet Coefficient energy analysis
Power and Energy Society General Meeting, 2013Co-Authors: Flavio Murilo Pereira Da Costa, Johan DriesenAbstract:Summary form only given. The two main voltage sag indices are magnitude and duration, defined in terms of the well-known rms (root mean square) voltages. The spectral energy of the voltages provides the same voltage sag indices of the rms voltage analysis and less computational effort is required. However, neither of them provide point-on-wave of sag initiation and recovery. This paper presents a wavelet-based methodology for characterization of voltage sags, in which the spectral energy of a voltage is decomposed in terms of the Scaling and wavelet Coefficient energies. The Scaling Coefficient energies of the phase voltages are used for voltage sag characterization, providing sag indices (magnitude and duration) in agreement with the definition. However, the analysis of the wavelet Coefficient energies of such voltages provides additional information for identification of the point-on-wave of voltage sag initiation and recovery, important parameters for power system protection and voltage sag mitigation devices. The performance of the proposed wavelet-based methodology was assessed with actual data and it was scarcely affected by the choice of the mother wavelet. Therefore, a compact mother wavelet can be used for voltage sag analysis with computational effort equivalent to the rms method and in agreement with practical applications. The maximal overlap discrete wavelet transform (MODWT) presented better performance than the discrete wavelet transform (DWT). All the equations provided in this paper were developed for real-time analysis.
-
assessment of voltage sag indices based on Scaling and wavelet Coefficient energy analysis
IEEE Transactions on Power Delivery, 2013Co-Authors: Flavio Murilo Pereira Da Costa, Johan DriesenAbstract:The two main voltage sag indices are magnitude and duration, defined in terms of the well-known rms voltages. The spectral energy of the voltages provides the same voltage sag indices of the rms voltage analysis and less computational effort is required. However, neither of them provide point on wave of sag initiation and recovery. This paper presents a wavelet-based methodology for the characterization of voltage sags, where the spectral energy of a voltage is decomposed in terms of the Scaling and wavelet Coefficient energies. The Scaling Coefficient energies of the phase voltages are used for voltage sag characterization, providing sag indices (magnitude and duration) in agreement with the definition. However, the analysis of the wavelet Coefficient energies of such voltages provides additional information for the identification of the point on wave of voltage sag initiation and recovery as well as important parameters for power system protection and voltage sag mitigation devices. The performance of the proposed wavelet-based methodology was assessed with actual data and it was scarcely affected by the choice of the mother wavelet. Therefore, a compact mother wavelet can be used for voltage sag analysis with computational effort equivalent to the rms method and in agreement with practical applications. The maximal overlap discrete wavelet transform presented better performance than the discrete wavelet transform. All of the equations provided in this paper were developed for real-time analysis.
Thiago De Oliveira Alves Rocha - One of the best experts on this subject based on the ideXlab platform.
-
Wavelet-Based Power System Stabilizer
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Cecilio Martins De Sousa Neto, Flavio Bezerra Costa, Ricardo Lúcio De Araujo Ribeiro, Rodrigo Lopes Barreto, Thiago De Oliveira Alves RochaAbstract:This paper proposes a wavelet-based power system stabilizer (WPSS) by using both the Scaling Coefficient energy and the wavelet Coefficient energy with border distortions of a one-cycle sliding window. The boundary Scaling Coefficient energy is used for real-time extraction and compensation of electromechanical oscillations in the synchronous generator, whereas the boundary wavelet Coefficient energy is used for the real-time detection of electrical oscillations and for tripping the WPSS at the fault inception time. The performance of the proposed WPSS was assessed in an experimental power system laboratory setup and compared with a conventional power system stabilizer.
Weidong Zhao - One of the best experts on this subject based on the ideXlab platform.
-
characterizing the Scaling Coefficient ω between viscous and inertial permeability of fractures
Journal of Hydrology, 2021Co-Authors: Kun Xing, Jiazhong Qian, Weidong ZhaoAbstract:Abstract Quantitative analysis of flow in fractures is an important issue for hydrogeological and geological engineering. In this study, Forchheimer models involving viscous permeability (kv) and inertial permeability (ki) were developed for groundwater flow in different single fractures or fractured media. Based on experiments and numerical simulation, the relationship between viscous permeability and inertial permeability in a single fracture was disclosed and an important parameter ω (the Scaling Coefficient ω (ω = ki/kv3/2)) was characterized. The results showed: (1) the Forchheimer model fit the non-linear fluid flow in almost all types of artificial single fractures that investigated in this study; (2) inertial permeability could be predicted by viscous permeability based on the empirical quantitative model: k i = ω k v 3 / 2 \* MERGEFORMAT(ω ≈ 108 m−2); (3) the fluctuation range of ω was related to the number of medium types, more medium types would induce a larger fluctuation range; artificial single fractures produced a smaller ω than that in natural rock fractures; ω was inversely proportional to the magnitude of the inertial effect, roughness and average aperture; the roughness element shape that caused greater turbulence effects led to larger ω; and single fractures placed vertically produced larger ω than fractures placed horizontally. In addition, the ratio of viscous permeability to inertial permeability (kv/ki) in fractures could be used as the characteristic length of Forchheimer number (Fo). By characterizing Scaling factor ω, the accuracy of the quantitative model could be improved, and basis for further quantification of non-Darcy flow was established.
