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Journal articles on the topic 'Estimation de terme source'
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Xiao, Yingchun, Yang Yang, and Feng Zhu. "A Separation Method for Electromagnetic Radiation Sources of the Same Frequency." Journal of Electromagnetic Engineering and Science 23, no. 6 (November 30, 2023): 521–29. http://dx.doi.org/10.26866/jees.2023.6.r.197.
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To separate electromagnetic interference sources with an unknown source number, a new separation method is proposed, which includes five key steps: spatial spectrum estimation, source number and direction-of-arrival estimation, mixed matrix estimation, separation matrix estimation, and source signal recovery. A pseudospatial spectrum estimation network based on a convolutional neural network is proposed to estimate the number of electromagnetic radiation sources, their direction of arrival, and the mixing matrix. A new loss function is designed as an optimization criterion for estimating the separation matrix. To ensure generalization, both simulated and measured datasets are used to train the proposed network. Experimental results demonstrate that the proposed separation method outperforms existing source separation techniques in terms of correlation coefficient, root mean square error, and running time. Importantly, it exhibits strong performance in underdetermined cases, as well as in overdetermined or determined cases.
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Ilyas, Muhammad, Agah D. Garnadi, and Sri Nurdiati. "Adaptive Mixed Finite Element Method for Elliptic Problems with Concentrated Source Terms." Indonesian Journal of Science and Technology 4, no. 2 (July 9, 2019): 263–69. http://dx.doi.org/10.17509/ijost.v4i2.18183.
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An adaptive mixed finite element method using the Lagrange multiplier technique is used to solve elliptic problems with delta Dirac source terms. The problem arises in the use of Chow-Anderssen linear functional methodology to recover coefficients locally in parameter estimation of an elliptic equation from a point-wise measurement. In this article, we used a posterior error estimator based on averaging technique as refinement indicators to produce a cycle of mesh adaptation, which is experimentally shown to capture singularity phenomena. Our numerical results showed that the adaptive refinement process successfully refines elements around the center of the source terms. The results also showed that the global error estimation is better than uniform refinement process in terms of computation time.
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Wu, Tao, Yiwen Li, Zhenghong Deng, Bo Feng, and Xinping Ma. "Parameter Estimation for Two-Dimensional Incoherently Distributed Source with Double Cross Arrays." Sensors 20, no. 16 (August 14, 2020): 4562. http://dx.doi.org/10.3390/s20164562.
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A direction of arrival (DOA) estimator for two-dimensional (2D) incoherently distributed (ID) sources is presented under proposed double cross arrays, satisfying both the small interval of parallel linear arrays and the aperture equalization in the elevation and azimuth dimensions. First, by virtue of a first-order Taylor expansion for array manifold vectors of parallel linear arrays, the received signal of arrays can be reconstructed by the products of generalized manifold matrices and extended signal vectors. Then, the rotating invariant relations concerning the nominal elevation and azimuth are derived. According to the rotating invariant relationships, the rotating operators are obtained through the subspace of the covariance matrix of the received vectors. Last, the angle matching approach and angular spreads are explored based on the Capon principle. The proposed method for estimating the DOA of 2D ID sources does not require a spectral search and prior knowledge of the angular power density function. The proposed DOA estimation has a significant advantage in terms of computational cost. Investigating the influence of experimental conditions and angular spreads on estimation, numerical simulations are carried out to validate the effectiveness of the proposed method. The experimental results show that the algorithm proposed in this paper has advantages in terms of estimation accuracy, with a similar number of sensors and the same experimental conditions when compared with existing methods, and that it shows a robustness in cases of model mismatch.
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Ryanto, Theo Alvin, Jupiter Sitorus Pane, Muhammad Budi Setiawan, Ihda Husnayani, Anik Purwaningsih, and Hendro Tjahjono. "THE PRELIMINARY STUDY ON IMPLEMENTING A SIMPLIFIED SOURCE TERMS ESTIMATION PROGRAM FOR EARLY RADIOLOGICAL CONSEQUENCES ANALYSIS." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 25, no. 2 (July 28, 2023): 61. http://dx.doi.org/10.55981/tdm.2023.6869.
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Indonesia possesses numerous potential sites for nuclear power plant development. A fast and comprehensive radiological consequences analysis is required to conduct a preliminary analysis of radionuclide release into the atmosphere, including source terms estimation. One simplified method for such estimation is the use of the Relative Volatility approach by Kess and Booth, published in IAEA TECDOC 1127. The objective of this study was to evaluate the use of a simple and comprehensive tool for estimating the source terms of planned nuclear power plants to facilitate the analysis of radiological consequences during site evaluation. Input parameters for the estimation include fuel burn-up, blow-down time, specific heat transfer of fuel to cladding, and coolant debit, using 100 MWe PWR as a case study. The results indicate a slight difference in the calculated release fraction compared to previous calculations, indicating a need to modify Keywords: Source terms, Relative volatility, Release fraction, PWR, SMART
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Oliveira, André José Pereira de, Luiz Alberto da Silva Abreu, and Diego Campos Knupp. "Explicit scheme based on integral transforms for estimation of source terms in diffusion problems in heterogeneous media." Journal of Engineering and Exact Sciences 9, no. 10 (December 29, 2023): 17811. http://dx.doi.org/10.18540/jcecvl9iss10pp17811.
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The estimation of source terms present in differential equations has various applications, ranging from structural assessment, industrial process monitoring, equipment failure detection, environmental pollution source detection to identification applications in medicine. Significant progress has been made in recent years in methodologies capable of estimating this parameter. This work employs a methodology based on an explicit formulation of the integral transformation to characterize the unknown source term, reconstructing it through the expansion in known eigenfunctions of the Sturm-Liouville eigenvalue problem. To achieve this, a linear model is considered in a heterogeneous medium with known and spatially varying physical properties and two heat sources, with both temporal and spatial dependencies, and only spatial dependence. The eigenvalue problem contains information about the heterogeneous properties and is solved using the generalized integral transformation technique. Additionally, an initial interpolation of the sensor data is proposed for each observation time, making the inverse problem computationally lighter. The solutions of the inverse problem exhibit optimal performance, even with noisy input data and sources with abrupt discontinuities. The temperatures recovered by the direct problem considering the recovered source closely match synthetic experimental data, showing errors less than 1%, ensuring the robustness and reliability of the technique for the proposed application.
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Fang, Qingyuan, Mengzhe Jin, Weidong Liu, and Yong Han. "DOA Estimation for Sources with Large Power Differences." International Journal of Antennas and Propagation 2021 (March 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/8862789.
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Sources with large power differences are very common, especially in complex electromagnetic environments. Classical DOA estimation methods suffer from performance degradation in terms of resolution when dealing with sources that have large power differences. In this paper, we propose an improved DOA algorithm to increase the resolution performance in resolving such sources. The proposed method takes advantage of diagonal loading and demonstrates that the invariant property of noise subspace still holds after diagonal loading is performed. We also find that the Cramer–Rao bound of the weak source can be affected by the power of the strong source, and this has not been noted before. The Cramer–Rao bound of the weak source deteriorates as the power of the strong source increases. Numerical results indicate that the improved algorithm increases the probability of resolution while maintaining the estimation accuracy and computational complexity.
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Herranz, D., F. Argüeso, L. Toffolatti, A. Manjón-García, and M. López-Caniego. "A Bayesian method for point source polarisation estimation." Astronomy & Astrophysics 651 (July 2021): A24. http://dx.doi.org/10.1051/0004-6361/202039741.
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The estimation of the polarisation P of extragalactic compact sources in cosmic microwave background (CMB) images is a very important task in order to clean these images for cosmological purposes –for example, to constrain the tensor-to-scalar ratio of primordial fluctuations during inflation– and also to obtain relevant astrophysical information about the compact sources themselves in a frequency range, ν ∼ 10–200 GHz, where observations have only very recently started to become available. In this paper, we propose a Bayesian maximum a posteriori approach estimation scheme which incorporates prior information about the distribution of the polarisation fraction of extragalactic compact sources between 1 and 100 GHz. We apply this Bayesian scheme to white noise simulations and to more realistic simulations that include CMB intensity, Galactic foregrounds, and instrumental noise with the characteristics of the QUIJOTE (Q U I JOint TEnerife) experiment wide survey at 11 GHz. Using these simulations, we also compare our Bayesian method with the frequentist filtered fusion method that has been already used in the Wilkinson Microwave Anisotropy Probe data and in the Planck mission. We find that the Bayesian method allows us to decrease the threshold for a feasible estimation of P to levels below ∼100 mJy (as compared to ∼500 mJy which was the equivalent threshold for the frequentist filtered fusion). We compare the bias introduced by the Bayesian method and find it to be small in absolute terms. Finally, we test the robustness of the Bayesian estimator against uncertainties in the prior and in the flux density of the sources. We find that the Bayesian estimator is robust against moderate changes in the parameters of the prior and almost insensitive to realistic errors in the estimated photometry of the sources.
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Ma, Qian, Wen Xu, and Yue Zhou. "Statistically robust estimation of source bearing via minimizing the Bhattacharyya distance." Journal of the Acoustical Society of America 151, no. 3 (March 2022): 1695–709. http://dx.doi.org/10.1121/10.0009677.
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Source bearing estimation is a common technique in acoustic array processing. Many methods have been developed and most of them exploit some underlying statistical model. When applied to a practical system, the robustness to model mismatch is of major concern. Traditional adaptive methods, such as the minimum power distortionless response processor, are notoriously known for their sensitivity to model mismatch. In this paper, a parameter estimator is developed via the minimum Bhattacharyya distance estimator (MBDE), which provides a measure of the divergence between the assumed and true probability distributions and is, thus, capable of statistically matching. Under a Gaussian random signal model typical of source bearing estimation, the MBDE is derived in terms of the data-based and modeled covariance matrices without involving matrix inversion. The performance of the MBDE, regarding the robustness and resolution, is analyzed in comparison with some of the existing methods. A connection with the Weiss-Weinstein bound is also discussed, which gives the MBDE an interpretation of closely approaching a large-error performance bound. Theoretical analysis and simulations of bearing estimation using a uniform linear array show that the proposed method owns a considerable resolution comparable to an adaptive method while being robust against statistical mismatch, including covariance mismatch caused by snapshot deficiency and/or noise model mismatch.
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Lu, Jinshu, Mengqing Huang, Wenfeng Wu, Yonghui Wei, and Chong Liu. "Application and Improvement of the Particle Swarm Optimization Algorithm in Source-Term Estimations for Hazardous Release." Atmosphere 14, no. 7 (July 19, 2023): 1168. http://dx.doi.org/10.3390/atmos14071168.
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Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model; however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.
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Amir Mohd Nor, Muhammad Izzat, Mohd Azri Mohd Izhar, Norulhusna Ahmad, and Hazilah Md Kaidi. "Exploiting 2-Dimensional Source Correlation in Channel Decoding with Parameter Estimation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2633. http://dx.doi.org/10.11591/ijece.v8i4.pp2633-2642.
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<span>Traditionally, it is assumed that source coding is perfect and therefore, the redundancy of the source encoded bit-stream is zero. However, in reality, this is not the case as the existing source encoders are imperfect and yield residual redundancy at the output. The residual redundancy can be exploited by using Joint Source Channel Coding (JSCC) with Markov chain as the source. In several studies, the statistical knowledge of the sources has been assumed to be perfectly available at the receiver. Although the result was better in terms of the BER performance, practically, the source correlation knowledge were not always available at the receiver and thus, this could affect the reliability of the outcome. The source correlation on all rows and columns of the 2D sources were well exploited by using a modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm in the decoder. A parameter estimation technique was used jointly with the decoder to estimate the source correlation knowledge. Hence, this research aims to investigate the parameter estimation for 2D JSCC system which reflects a practical scenario where the source correlation knowledge are not always available. We compare the performance of the proposed joint decoding and estimation technique with the ideal 2D JSCC system with perfect knowledge of the source correlation knowledge. Simulation results reveal that our proposed coding scheme performs very close to the ideal 2D JSCC system.</span>
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Prasad Gundu, Ram, P. Pardhasaradhi, S. Koteswara Rao, and V. Gopi Tilak. "TOA-based source localization using ML estimation." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 742. http://dx.doi.org/10.14419/ijet.v7i2.7.10936.
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This paper proposes the Time of arrival (TOA) measurement model for finding the position of a stationary emitting source for Line-of-Sight (LOS) scenario. Here Maximum Likelihood Estimation (MLE) is used as the positioning algorithm. For approximation of the roots of the solution, which directly corresponds to the source location, the optimization techniques used are Gauss-Newton, Gradient descent and Newton-Raphson methods. Two different cases are considered for investigation in this paper. The first case compares the three different optimization techniques in terms of convergence rate. In the second case the error values obtained from two different scenarios are compared, one involving a single trial only, while the second scenario uses Monte Carlo method of simulations. Firstly, the error values, for both the coordinates (two-dimensional), obtained by getting the difference between the measured source positions and the initially guessed source position are obtained for a single trial. Later using Monte Carlo simulation method, the Root-Mean-Square (RMS) error values, for both the coordinates (two-dimensional), for the optimization techniques are obtained. To improve the performance of the algorithm, Monte Carlo simulation has been used for multiple trials.
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Meschini, Samuele, Raffaella Testoni, and Giorgio Maddaluno. "Radiological source terms estimation for the Divertor Tokamak Test (DTT) facility." Fusion Engineering and Design 180 (July 2022): 113198. http://dx.doi.org/10.1016/j.fusengdes.2022.113198.
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Ollivier, Etienne, Richard X. Touret, Matthew McKinley, Jihui Jin, Annalisa Bracco, and Karim G. Sabra. "Performance study of ray-based ocean acoustic tomography methods for estimating submesoscale variability in the upper ocean." Journal of the Acoustical Society of America 155, no. 2 (February 1, 2024): 1315–35. http://dx.doi.org/10.1121/10.0024819.
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Ocean acoustic tomography (OAT) methods aim at estimating variations of sound speed profiles (SSP) based on acoustic measurements between multiple source-receiver pairs (e.g., eigenray travel times). This study investigates the estimation of range-dependent SSPs in the upper ocean over short ranges (<5 km) using the classical ray-based OAT formulation as well as iterative or adaptive OAT formulations (i.e., when the sources and receivers configuration can evolve across successive iterations of this inverse problem). A regional ocean circulation model for the DeSoto Canyon in the Gulf of Mexico is used to simulate three-dimensional sound speed variations spanning a month-long period, which exhibits significant submesoscale variability of variable intensity. OAT performance is investigated in this simulated environment in terms of (1) the selected source-receivers configuration and effective ray coverage, (2) the selected OAT estimator formulations, linearized forward model accuracy, and the parameterization of the expected SSP variability in terms of empirical orthogonal functions, and (3) the duration over which the OAT inversion is performed. Practical implications for the design of future OAT experiments for monitoring submesoscale variability in the upper ocean with moving autonomous platforms are discussed.
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Biao, Wang, and He Cheng. "Underwater Target Direction of Arrival Estimation by Small Acoustic Sensor Array Based on Sparse Bayesian Learning." Polish Maritime Research 24, s2 (August 28, 2017): 95–102. http://dx.doi.org/10.1515/pomr-2017-0070.
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Abstract Assuming independently but identically distributed sources, the traditional DOA (direction of arrival) estimation method of underwater acoustic target normally has poor estimation performance and provides inaccurate estimation results. To solve this problem, a new high-accuracy DOA algorithm based on sparse Bayesian learning algorithm is proposed in terms of temporally correlated source vectors. In novel method, we regarded underwater acoustic source as a first-order auto-regressive process. And then we used the new algorithm of multi-vector SBL to reconstruct the signal spatial spectrum. Then we used the CS-MMV model to estimate the DOA. The experiment results have shown the novel algorithm has a higher spatial resolution and estimation accuracy than other DOA algorithms in the cases of less array element space and less snapshots.
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Wang, Hanchen, Qiang Guo, Tariq Alkhalifah, and Zedong Wu. "Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set." GEOPHYSICS 85, no. 6 (November 1, 2020): KS207—KS219. http://dx.doi.org/10.1190/geo2019-0738.1.
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One of the key goals of microseismic processing is accurate estimation of the source location. Using full-waveform information in passive-source data sets can potentially delineate microseismic sources. The accuracy of the compressional-wave and shear-wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We have adopted a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed of source images and source functions because it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions, and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative representation of the source mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We have thus used a source-independent objective function, based on convolving reference traces with modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically examined our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, our method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy because the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Although our method has a higher computational cost than traveltime- or migration-based methods, the estimated event locations have significantly improved accuracy.
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Ding, Litao, and Peter Mathé. "Minimax Rates for Statistical Inverse Problems Under General Source Conditions." Computational Methods in Applied Mathematics 18, no. 4 (October 1, 2018): 603–8. http://dx.doi.org/10.1515/cmam-2017-0055.
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AbstractWe describe the minimax reconstruction rates in linear ill-posed equations in Hilbert space when smoothness is given in terms of general source sets. The underlying fundamental result, the minimax rate on ellipsoids, is proved similarly to the seminal study by D. L. Donoho, R. C. Liu, and B. MacGibbon [4]. These authors highlighted the special role of the truncated series estimator, and for such estimators the risk can explicitly be given. We provide several examples, indicating results for statistical estimation in ill-posed problems in Hilbert space.
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Si, Weijian, Pinjiao Zhao, Zhiyu Qu, and Liwei Wang. "Computationally Efficient Angle and Polarization Estimation in the Presence of Multipath Propagation Using Dual-Polarization Vector Sensor Array." International Journal of Antennas and Propagation 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7537160.
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This paper presents a computationally efficient angle and polarization estimation method for a mixture of uncorrelated and coherent sources using a dual-polarization vector sensor array. The uncorrelated sources are separated from the coherent sources on the basis of the modulus property of eigenvalues. The angles of the uncorrelated sources are estimated by employing rotational invariance and the associated polarization is obtained from the estimate of the uncorrelated array response matrix through elementwise division. For the distinguished coherent sources, two Hankel matrices are constructed from the elements of the estimated coherent array response matrix of each coherent group, from which two rotational-invariant submatrix pairs are extracted for estimating the coherent angles with a high precision. The least-square solution to the coherent polarization equation is derived for estimating the coherent polarization parameters. For each uncorrelated source and coherent group, the proposed method estimates the associated angle and polarization parameters separately, which avoids the need of 3D spectral search. In comparison with the existing methods, the simulation results show that the proposed method yields favorable performance in terms of computational efficiency and estimation accuracy.
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Liu, Ke, Zhu Liang Yu, Wei Wu, Zhenghui Gu, and Yuanqing Li. "STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging." International Journal of Neural Systems 25, no. 04 (May 25, 2015): 1550016. http://dx.doi.org/10.1142/s0129065715500161.
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For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.
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Lee, Taewook, Puneet Singla, Tarunraj Singh, and Ajith Gunatilaka. "Sparse Approximation-Based Maximum Likelihood Approach for Estimation of Radiological Source Terms." IEEE Transactions on Nuclear Science 63, no. 2 (April 2016): 1169–87. http://dx.doi.org/10.1109/tns.2016.2520255.
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Martinez-Vargas, J. D., L. Duque-Muñoz, F. Vargas-Bonilla, J. D. Lopez, and G. Castellanos-Dominguez. "Enhanced Data Covariance Estimation Using Weighted Combination of Multiple Gaussian Kernels for Improved M/EEG Source Localization." International Journal of Neural Systems 29, no. 06 (July 29, 2019): 1950001. http://dx.doi.org/10.1142/s0129065719500011.
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In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback–Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity.
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Kim, S. G., Y. T. Chen, Z. L. Wu, and G. F. Panza. "A mathematical theorem useful for the direct estimation of seismic source spectra." Bulletin of the Seismological Society of America 87, no. 5 (October 1, 1997): 1281–87. http://dx.doi.org/10.1785/bssa0870051281.
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Abstract In this note, we prove a mathematical theorem for the direct estimation of the source spectra of earthquakes using a single seismogram. Considering a combination of Fourier spectra, the effects of the “source term” and the “attenuation term” from a single seismogram can be separated. Using signal-processing techniques, such an approach may be expressed in terms of the estimation of the Wigner distribution.
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Wu, Yichen, Junwei Qi, Ying-Zhen Wang, and Yingsong Li. "Symmetric Double-Supplemented Nested Array for Passive Localization of Mixed Near-Field and Far-Field Sources." Remote Sensing 16, no. 6 (March 14, 2024): 1027. http://dx.doi.org/10.3390/rs16061027.
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In mixed-field source localization, the physical properties of a sensor array, such as the degrees of freedom (DOFs), aperture, and coupling leakage, directly affect the accuracy of estimating the direction of arrival (DOA). Compared to conventional symmetric uniform linear arrays, symmetric non-uniform linear arrays (SNLAs) have a greater advantage in mixed-field source localization due to their larger aperture and higher DOF. However, current SNLAs require improvements in their physical properties through modifications to the array structure in order to achieve more accurate source localization estimates. Therefore, this study proposes a symmetric double-supplemented nested array (SDSNA), which translates nested subarrays based on symmetric nested arrays to increase the aperture and inserts two symmetric supplemented subarrays to fill the holes created by the translation. This method results in longer consecutive difference coarray lags and larger apertures. The SDSNA is compared to existing advanced SNLAs in terms of their physical properties and DOA estimation. The results show that, with the same number of sensors, the SDSNA has a higher DOF, a larger aperture, and smaller coupling, indicating the advantages of the SDSNA in terms of its physical properties. Under the same experimental conditions, the SDSNA has a lower root-mean-square error of source location, thus indicating better performance in terms of both DOA and distance estimation.
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Ulman, Paweł. "Equivalence Scale in Terms of Polish Households' Source of Income." Folia Oeconomica Stetinensia 10, no. 2 (January 1, 2012): 114–27. http://dx.doi.org/10.2478/v10031-011-0044-8.
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Equivalence Scale in Terms of Polish Households' Source of Income The paper presents a comparison of the costs of Polish households by their main source of income. In order to compare these costs the author estimated three kinds of equivalence scales using the Engel method, ELES (Extended Linear Expenditure System), the demand equation and the Bernoulli well-being function. Estimation for household groups determined on account of the main source of income produced by the scales confirmed differences in households' maintenance costs compared to the costs of the reference household. A comparison of the values of equivalence scales revealed a similarity of the results produced by the ELES and the Bernoulli methods which differed from the results obtained by means of the Engel method.
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Brandić, Ivan, Alan Antonović, Lato Pezo, Božidar Matin, Tajana Krička, Vanja Jurišić, Karlo Špelić, et al. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models." Energies 16, no. 2 (January 6, 2023): 690. http://dx.doi.org/10.3390/en16020690.
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Agricultural biomass is one of the most important renewable energy sources. As a byproduct of corn, soybean and sunflower production, large amounts of biomass are produced that can be used as an energy source through conversion. In order to assess the quality and the possibility of the use of biomass, its composition and calorific value must be determined. The use of nonlinear models allows for an easier estimation of the energy properties of biomass concerning certain input and output parameters. In this paper, RFR (Random Forest Regression) and SVM (Support Vector Machine) models were developed to determine their capabilities in estimating the HHV (higher heating value) of biomass based on input parameters of ultimate analysis. The developed models showed good performance in terms of HHV estimation, confirmed by the coefficient of determination for the RFR (R2 = 0.79) and SVM (R2 = 0.93) models. The developed models have shown promising results in accurately predicting the HHV of biomass from various sources. The use of these algorithms for biomass energy prediction has the potential for further development.
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Jun, K. S., J. W. Kang, and K. S. Lee. "Simultaneous estimation of model parameters and diffuse pollution sources for river water quality modeling." Water Science and Technology 56, no. 1 (July 1, 2007): 155–62. http://dx.doi.org/10.2166/wst.2007.447.
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Diffuse pollution sources along a stream reach are very difficult to both monitor and estimate. In this paper, a systematic method using an optimal estimation algorithm is presented for simultaneous estimation of diffuse pollution and model parameters in a stream water quality model. It was applied with the QUAL2E model to the South Han River in South Korea for optimal estimation of kinetic constants and diffuse loads along the river. Initial calibration results for kinetic constants selected from a sensitivity analysis reveal that diffuse source inputs for nitrogen and phosphorus are essential to satisfy the system mass balance. Diffuse loads for total nitrogen and total phosphorus were estimated by solving the expanded inverse problem. Comparison of kinetic constants estimated simultaneously with diffuse sources to those estimated without diffuse loads, suggests that diffuse sources must be included in the optimization not only for its own estimation but also for adequate estimation of the model parameters. Application of the optimization method to river water quality modeling is discussed in terms of the sensitivity coefficient matrix structure.
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Lai, Chung Kwan, Chung Kwan Lai, Jing Sheng Tey, Dongyuan Shi, and Woon-Seng Gan. "Robust estimation of open aperture active control systems using virtual sensing." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 4 (February 1, 2023): 3397–407. http://dx.doi.org/10.3397/in_2022_0483.
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For active noise control systems in an open aperture application, virtual sensing system is often needed to overcome design constraint in terms of microphone placement. The virtual sensing system, however, make assumptions to the acoustical field and thus is highly sensitive to any changes in the primary field. Taking the window aperture ANC application for example, incoming disturbance signals could impinge from multiple locations, altering the spatial correlation between the physical and virtual microphones. For instance, in the context of a high-rise apartment window, aircraft noise would propagate downwards from the sky, whereas traffic noise propagate upward from the ground. This paper considers the estimation performance of the remote microphone technique, an example of a virtual sensing method, in an open aperture application when faced with changes in the primary noise source. Additionally, this paper introduces a new method for estimating the spectral density due to multiple sources through the incoherent decomposition method. It has been shown experimentally that this algorithm is able to improve the nearfield estimation of the system.
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Xiang, Ning, and Thomas Metzger. "Prediction model formulations for detection, enumeration, and localization of multiple sound sources using spherical harmonics." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A231. http://dx.doi.org/10.1121/10.0011157.
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A spherical microphone array is used to detect and localize sound sources in terms of model-based machine learning (ML). In this application, it is crucial to establish parametric models to distinguish background sound environment from presence of sound sources. In the presence of sound sources, the parameter models are also used to localize an unknown number of potentially multiple sound sources. In this work, a model-based Bayesian learning framework is presented for localizing an unknown number of sound sources. Among them, a no-source scenario needs to be accounted for. The model-based machine learning applies the model comparison between the no-source model and the one-source model for sound source detection. After detecting sound sources, the machine learning needs to involve sound source enumeration and localization in order to correctly localize potential multiple sound sources. Specifically, sound environment is analyzed using Bayesian model comparison of two different models accounting for absence and presence of the sound sources for source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) for far-field and to localize sound sources for near-field including source distances, amplitudes, and DoAs using Bayesian model selection and parameter estimation.
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Ikelle, Luc T., Graham Roberts, and Arthur B. Weglein. "Source signature estimation based on the removal of first‐order multiples." GEOPHYSICS 62, no. 6 (November 1997): 1904–20. http://dx.doi.org/10.1190/1.1444291.
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The estimation of the source signature is often one of the necessary first steps in the processing of seismic reflection data, especially if the processing chain includes prestack multiple removal. However, most methods for source estimation are based on poststack data or assume that the earth is 1-D. In this work, a new source estimation method for prestack data is presented. It consists of finding the source signature that permits the removal of events attributable to the first‐order free‐surface reflections (i.e., first‐order multiples). The method exploits the formulation of the relationship between the free‐surface reflections and the source signature as a scattering Born series. In this formulation, the order of the scattering series coincides with that of the free‐surface reflections, and the series is constructed exclusively with seismic data and the source signature without any knowledge of the subsurface other than the velocity of sea water. By restricting the problem to first‐order free‐surface reflections, we have rendered the relationship between free‐surface reflections and the source signature linear, which also corresponds to a truncation of the scattering Born series to its first two terms. Thus, the source signature estimation can be formulated as a linear inverse problem. Assuming that the removal of first‐order free‐surface events produces a significant reduction in the energy of the data, we posed the inverse problem as finding the source signature that minimizes this energy. The optimization leads to an iterative solution. The iterations are needed to correct for the truncation effects. Synthetic and real data examples show the applicability and stability of the source estimation method as well as its use for attenuating free‐surface multiples.
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Xu, Jin, Lindi J. Quackenbush, Timothy A. Volk, and Jungho Im. "Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions." Remote Sensing 12, no. 18 (September 10, 2020): 2934. http://dx.doi.org/10.3390/rs12182934.
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Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.
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Biradar, Nagashettappa, M. L. Dewal, ManojKumar Rohit, Sanjaykumar Gowre, and Yogesh Gundge. "Blind Source Parameters for Performance Evaluation of Despeckling Filters." International Journal of Biomedical Imaging 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3636017.
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The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein’s unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.
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Koutlis, Christos, and Dimitris Kugiumtzis. "The Effect of a Hidden Source on the Estimation of Connectivity Networks from Multivariate Time Series." Entropy 23, no. 2 (February 8, 2021): 208. http://dx.doi.org/10.3390/e23020208.
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Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure has been assessed using simulated dynamical systems where the ground truth is known. Here, we consider the presence of an unobserved variable that acts as a hidden source for the observed high-dimensional dynamical system and study the effect of the hidden source on the estimation of the connectivity structure. In particular, the focus is on estimating the direct causality effects in high-dimensional time series (not including the hidden source) of relatively short length. We examine the performance of a linear and a nonlinear connectivity measure using dimension reduction and compare them to a linear measure designed for latent variables. For the simulations, four systems are considered, the coupled Hénon maps system, the coupled Mackey–Glass system, the neural mass model and the vector autoregressive (VAR) process, each comprising 25 subsystems (variables for VAR) at close chain coupling structure and another subsystem (variable for VAR) driving all others acting as the hidden source. The results show that the direct causality measures estimate, in general terms, correctly the existing connectivity in the absence of the source when its driving is zero or weak, yet fail to detect the actual relationships when the driving is strong, with the nonlinear measure of dimension reduction performing best. An example from finance including and excluding the USA index in the global market indices highlights the different performance of the connectivity measures in the presence of hidden source.
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Bokpin, Godfred A., Lord Mensah, and Michael E. Asamoah. "Legal source, institutional quality and FDI flows in Africa." International Journal of Law and Management 59, no. 5 (September 11, 2017): 687–98. http://dx.doi.org/10.1108/ijlma-03-2016-0028.
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Purpose This paper aims to find out how the legal system interacts with other institutions in attracting Foreign Direct Investment (FDI) into Africa. Design/methodology/approach The authors use annual panel data of 49 African countries over the period 1980 to 2011, and use the system generalized method of moments (GMM) estimation technique and pooled panel data regression. Findings The authors find that the source of a country’s legal system deters FDI inflow as institutions alone cannot bring in the needed quantum of FDI. In terms of trading blocs, it was found that there is negative significant relationship between institutional quality and FDI for South African Development Community (SADC) as well as Economic Community of West Africa States (ECOWAS) countries. Practical implications For policy implications, the results suggest that reliance on institutions alone cannot project the continent to attract the needed FDI. Originality/value Empiricists have devoted considerable effort to estimating the relationship between institutions and FDI on the African continent, but this paper seeks to ascertain the effect of legal systems and institutional quality within African specific trade and regional blocks.
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Nesme, Nicolas, Rodolphe Marion, Olivier Lezeaux, Stéphanie Doz, Claude Camy-Peyret, and Pierre-Yves Foucher. "Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site." Remote Sensing 13, no. 24 (December 8, 2021): 4992. http://dx.doi.org/10.3390/rs13244992.
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Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models.
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Zhu, Junhao, Yuren Mao, Lu Chen, Congcong Ge, Ziheng Wei, and Yunjun Gao. "FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data." Proceedings of the VLDB Endowment 17, no. 6 (February 2024): 1337–49. http://dx.doi.org/10.14778/3648160.3648174.
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Centralised data management systems (e.g., data lakes) support queries over multi-source heterogeneous data. However, the query results from multiple sources commonly involve between-source conflicts, which makes query results unreliable and confusing and degrades the usability of centralised data management systems. Therefore, resolving the between-sourced conflicts is one of the most important problems for centralised data management systems. To solve it, many batch data fusion-based methods have been proposed, which require traversing all the data in the centralised data management systems and cause scalability and flexibility issues. To address these issues, this paper explores the problem of on-demand fusion queries, where the between-sourced conflicts are solved with only the query-related data; moreover, we propose an efficient on-demand fusion query framework, FusionQuery, which consists of a query stage and a fusion stage. In the query stage, we frame the heterogeneous data query problem as a knowledge graph matching problem and present a line graph-based method to accelerate it. In the fusion stage, we develop an Expectation Maximization-style algorithm to iteratively updates data veracity and source trustworthiness. Furthermore, we design an incremental estimation method of source trustworthiness to address the lack of sufficient observations. Extensive experiments on two real-world datasets demonstrate that FusionQuery outperforms state-of-the-art data fusion methods in terms of both effectiveness and efficiency.
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Hanuš, Oto, Marcela Vyletělová Klimešová, Gustav Chládek, Petr Roubal, and Růžena Seydlová. "Metaanalysis of ketosis milk indicators in terms of their threshold estimation." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 6 (2013): 1681–92. http://dx.doi.org/10.11118/actaun201361061681.
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Real time analyses of main milk components are attended in milking parlours today. Regular day information without delay is advantageous. Farmers can know milk composition every day. They can calculate milk energy quotients, identified subclinical ketosis in early lactation of dairy cows and thus improve ketosis prevention and avoid economical losses. Aim was to improve the estimation reliability of thresholds of milk indicators of energy metabolism for subclinical ketosis detection and its prevention support by metaanalysis. This can have higher result reliability than individual studies. Results of similar papers were analysed. These were focused on ketosis indicators in milk (acetone (AC) and milk energy quotients (fat/crude protein, F/CP; fat/lactose, F/L)) and their thresholds for subclinical ketosis. Methods for threshold derivation were specified: – statistically to reference procedure; – calculation according to relevant data frequency distribution; – qualified estimation; – combinations of mentioned procedures. This was as weight source. Variability in AC subclinical ketosis cut–off values was high (78.5%) and in ketosis milk quotients was low (from 5 to 8%). The value 10.57 mg.l−1could be the validated estimation of milk AC cut–off limit for subclinical ketosis identification. Similarly the milk quotients F/CP and F/L 1.276 and 0.82. The F/CP F/L relationship is closer in 1stthird of lactation (0.89;P< 0.001) than in whole lactation (0.86;P< 0.001). This could be one of proofs of ability for subclinical ketosis identification because the majority of cases occurs in early lactation. The improved estimations of thresholds of milk indicators in early lactation for subclinical ketosis can be used at this technological innovation. Combined use of both quotients could bring an improvement of regular diagnosis of subclinical ketosis.
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Rahbar, Faezeh, Ali Marjovi, and Alcherio Martinoli. "Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization." Sensors 19, no. 3 (February 5, 2019): 656. http://dx.doi.org/10.3390/s19030656.
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Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.
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Celik, Nuri. "Robust Post-Hoc Multiple Comparisons: Skew t Distributed Error Terms." Revista Colombiana de Estadística 45, no. 2 (July 14, 2022): 363–72. http://dx.doi.org/10.15446/rce.v45n2.100837.
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The pairwise comparisons or post-hoc methods are used for determining the source of the difference of group means in one-way ANOVA. These methods are mostly depend on normality assumption. However, nonnormal distributions are more prevalent than normal distribution. Therefore, robust estimation methods become very important tools in statistical analysis. In this paper, we assume that the distribution of the error terms is Azzalini's skew $t$ and obtain the robust estimators in order to make post-hoc tests in one-way ANOVA. We use maximum likelihood (ML) methodology and compare this methodology with some of robust estimators like M estimator, Wave estimator, trimmed mean and modified maximum likelihood (MML) methodology with Monte Carlo simulation study. Simulation results show that the proposed methodology is more preferable. We also compare power values of the test statistics and conclude that the test statistics based on the ML estimators are more powerful than the test statistics based on other methods.
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Jatoi, Munsif Ali, Nidal Kamel, Sayed Hyder Abbas Musavi, and José David López. "Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 2 (January 10, 2019): 184–93. http://dx.doi.org/10.2174/1573405613666170629112918.
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Background: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. Methods: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. Results: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. Conclusion: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.
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Lubeigt, Corentin, Lorenzo Ortega, Jordi Vilà-Valls, Laurent Lestarquit, and Eric Chaumette. "Joint Delay-Doppler Estimation Performance in a Dual Source Context." Remote Sensing 12, no. 23 (November 27, 2020): 3894. http://dx.doi.org/10.3390/rs12233894.
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Evaluating the time-delay, Doppler effect and carrier phase of a received signal is a challenging estimation problem that was addressed in a large variety of remote sensing applications. This problem becomes more difficult and less understood when the signal is reflected off one or multiple surfaces and interferes with itself at the receiver stage. This phenomenon might deteriorate the overall system performance, as for the multipath effect in Global Navigation Satellite Systems (GNSS), and mitigation strategies must be accounted for. In other applications such as GNSS reflectometry (GNSS-R) it may be interesting to estimate the parameters of the reflected signal to deduce the geometry and the surface characteristics. In either case, a better understanding of this estimation problem is directly brought by the corresponding lower performance bounds. In the high signal-to-noise ratio regime of the Gaussian conditional signal model, the Cramér-Rao bound (CRB) provides an accurate lower bound in the mean square error sense. In this article, we derive a new compact CRB expression for the joint time-delay and Doppler estimation in a dual source context, considering a band-limited signal and its specular reflection. These compact CRBs are expressed in terms of the baseband signal samples, making them especially easy to use whatever the baseband signal considered, therefore being valid for a variety of remote sensors. This extends existing results in the single source context and opens the door to a plethora of usages to be discussed in the article. The proposed CRB expressions are validated in two representative navigation and radar examples.
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Wefky, A., F. Espinosa, L. de Santiago, P. Revenga, J. L. Lázaro, and M. Martínez. "Electrical Drive Radiated Emissions Estimation in Terms of Input Control Using Extreme Learning Machines." Mathematical Problems in Engineering 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/790526.
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With the increase of electrical/electronic equipment integration complexity, the electromagnetic compatibility (EMC) becomes one of the key points to be respected in order to meet the constructor standard conformity. Electrical drives are known sources of electromagnetic interferences due to the motor as well as the related power electronics. They are the principal radiated emissions source in automotive applications. This paper shows that there is a direct relationship between the input control voltage and the corresponding level of radiated emissions. It also introduces a novel model using artificial intelligence techniques for estimating the radiated emissions of a DC-motor-based electrical drive in terms of its input voltage. Details of the training and testing of the developed extreme learning machine (ELM) are described. Good agreement between the electrical drive behavior and the developed model is observed.
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Ling, Yongsheng, Chengfeng Liu, Qing Shan, Daqian Hei, Xiaojun Zhang, Chao Shi, Wenbao Jia, and Jing Wang. "Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model." Atmosphere 14, no. 1 (January 9, 2023): 148. http://dx.doi.org/10.3390/atmos14010148.
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During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios.
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Khan, Nabeel Ali, Sadiq Ali, and Kwonhue Choi. "An Efficient and Accurate Multi-Sensor IF Estimator Based on DOA Information and Order of Fractional Fourier Transform." Entropy 24, no. 4 (March 25, 2022): 452. http://dx.doi.org/10.3390/e24040452.
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Instantaneous frequency in multi-sensor recordings is an important parameter for estimation of direction of arrival estimation, source separation, and sparse reconstruction. The instantaneous frequency estimation problem becomes challenging when signal components have close or overlapping signatures and the number of sensors is less than the number of sources. In this study, we develop a computationally efficient method that exploits the direction of the IF curve in addition to the angle of arrival as additional features for the accurate tracking of IF curves. Experimental results show that the proposed scheme achieves better accuracy compared to the-state-of-art method in terms of mean square error (MSE) with a slight increase in the computational cost, i.e., the proposed method achieves MSE of −50 dB at the signal to noise ratio of 0 dB whereas the existing method achieves the MSE of −38 dB.
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Singh, Arun Prakash, and Nachiketa Tiwari. "An improved two-stage dereverberation method based on bayesian estimation of a speech source." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, no. 1 (June 24, 2022): 963–76. http://dx.doi.org/10.3397/nc-2022-848.
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It is not easy to extract speech emanating from a specific direction in a large reverberant room with additional sound sources. Many of the existing methods work well only if there is a single sound source, and their performance degrades if additional sound sources are present. In this work, we have addressed this problem by maximizing the posterior probability of speech signal as computed by use of Bayes' Theorem. Our de-reverb method is two-staged. We have evaluated the efficacy of our method against several popular methods in terms of five objective measures; Signal-to-Interference Ratio, Log-Likelihood Ratio, Cepstrum Distance, Frequency-Weighted Segment SNR, and Speechto-Reverberation Modulation Energy Ratio. We show that on each of these parameters, the method proposed in this work performs better than the one we have benchmarked it against.
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Cho, Yong Thung, M. J. Roan, and J. S. Bolton. "A comparison of near-field beamforming and acoustical holography for sound source visualization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 4 (January 16, 2009): 819–34. http://dx.doi.org/10.1243/09544062jmes1209.
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Acoustical holography procedures make high-resolution visualization possible via estimation of the sound intensity on surfaces closer to the sources than the near-field measurement surface. Another source localization technique, beamforming, has been used to estimate the direction of arrival of sound from sources that typically lie in the far-field. However, little work has been done using beamforming as a visualization technique based on near-field measurements. As a result, the performance of beamforming and acoustical holography in terms of source resolution capabilities has not been directly compared when using near-field measurements. In this work, point source beamforming was used to visualize sources based on near-field measurements. Acoustic intensity estimated from beamformed pressure measurements was compared with the absolute intensity estimated using acoustical holography techniques. In addition to noise-free, anechoic simulations, cases of measurement pressure with random noise were generated and used to compare source resolution accuracy of acoustical holography and beamforming techniques in the presence of measurement noise. It was found that intensity estimated using acoustical holography provided the clearest image of sources when the measurement surface was conformal with the source geometry. However, sources can be resolved more accurately using near-field beamforming than acoustical holography at high frequencies when the sources are not located perfectly on a surface conformal with the measurement geometry.
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Kumar, Manoj, Anuj Rani, and Sangeet Srivastava. "Image Forensics Based on Lighting Estimation." International Journal of Image and Graphics 19, no. 03 (July 2019): 1950014. http://dx.doi.org/10.1142/s0219467819500141.
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Computer generated images are assumed to be a key part in each person’s life in this era of information technology, where individuals effectively inhabit the advertisements, magazines, websites, televisions and many more. At the point when digital images played their role, the event of violations in terms of misrepresentation of information, use of their wrong doings winds up and also becomes easier with the help of image editing application programs. To be legitimate, if anyone does wrong anything then the proposed method can be used for a correct identification of the forgery and the imitations in the digital images. In existing techniques, researchers have suggested most well-known types of digital photographic manipulations based on source, meta-data, image copying, splicing and many more. The proposed approach is inspired by physics-based techniques and requires less human involvement. The presented approach works for images having any type of objects present in the scene, i.e. not only limited to human faces and selection of same intensity regions of the image. By assessing the lighting parameters, the proposed technique identifies the manipulated object and returns angle of incidence w.r.t light source direction. The demonstrated result produces forgery recognition rate of 92% on an image dataset comprising of various types of manipulated images.
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Cheng, Fengyun, Guanglong Ou, Meng Wang, and Chang Liu. "Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms." Forests 15, no. 4 (April 10, 2024): 681. http://dx.doi.org/10.3390/f15040681.
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Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.
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Ahn, Jaeyoon, Dongseop Lee, Shinin Han, Youngwook Jung, Sangwoo Park, and Hangseok Choi. "Experimental study on performance of sand filter layer to remove non-point source pollutants in rainwater." Water Supply 17, no. 6 (May 9, 2017): 1748–63. http://dx.doi.org/10.2166/ws.2017.056.
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Abstract Clogging characteristics of conventional sand filter layers with different grain-size distributions were experimentally studied to estimate their filtration capacity to capture non-point source pollutants in an artificial rainwater reservoir. A series of laboratory-scale chamber tests was conducted for artificial urban runoff synthesized with non-point source pollutants collected from a real road in Seoul, Korea. In addition, an analytical filtration model for estimating removal of non-point source pollutants was adopted considering the clogging characteristics. To evaluate the performance of three types of sand filter layers with different grain size characteristics, the pollutant concentration was measured in terms of total suspended solids and chemical oxygen demand. The lumped parameter (θ) related to the clogging property was estimated by comparing the accumulated weight of pollutant particles obtained from the laboratory chamber experiments and the theoretical estimation from the analytical filtration model. Based on the experimental study and theoretical consideration, a double-sand-filter layer consisting of two separate layers is proposed as the optimum system for removing non-point source pollutants in the pilot-scale rainwater reservoir.
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Turbelin, Grégory, Sarvesh Singh, Jean Pierre Issartel, Xavier Busch, and Pramod Kumar. "Computation of Optimal Weights for Solving the Atmospheric Source Term Estimation Problem." Journal of Atmospheric and Oceanic Technology 36, no. 6 (June 2019): 1053–61. http://dx.doi.org/10.1175/jtech-d-18-0145.1.
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AbstractIn case of a release of a hazardous material (e.g., a chemical or a biological agent) in the atmosphere, estimation of the source from concentration observations (provided by a network of sensors) is a challenging inverse problem known as the atmospheric source term estimation (STE) problem. This study emphasizes a method, known in the literature as the renormalization inversion technique, for addressing this problem. This method provides a solution that has been interpreted as a weighted minimal norm solution and can be computed in terms of a generalized inverse of the sensitivity matrix of the sensors. This inverse is constructed by using an appropriate diagonal weight matrix whose components fulfill the so-called renormalizing conditions. The main contribution of this paper is that it proposes a new compact algorithm (it requires less than 15 lines of MATLAB code) to obtain, in a fast and efficient way, those optimal weights. To show that the algorithm, based on the properties of the resolution matrix, matches the requirements of emergency situations, analysis of the computational complexity and memory requirements is included. Some numerical experiments are also reported to show the efficiency of the algorithm.
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Javed, Muhammad Yaqoob, Iqbal Ahmed Khurshid, Aamer Bilal Asghar, Syed Tahir Hussain Rizvi, Kamal Shahid, and Krzysztof Ejsmont. "An Efficient Estimation of Wind Turbine Output Power Using Neural Networks." Energies 15, no. 14 (July 18, 2022): 5210. http://dx.doi.org/10.3390/en15145210.
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Wind energy is a valuable source of electric power as its motion can be converted into mechanical energy, and ultimately electricity. The significant variability of wind speed calls for highly robust estimation methods. In this study, the mechanical power of wind turbines (WTs) is successfully estimated using input variables such as wind speed, angular speed of WT rotor, blade pitch, and power coefficient (Cp). The feed-forward backpropagation neural networks (FFBPNNs) and recurrent neural networks (RNNs) are incorporated to perform the estimations of wind turbine output power. The estimations are performed based on diverse parameters including the number of hidden layers, learning rates, and activation functions. The networks are trained using a scaled conjugate gradient (SCG) algorithm and evaluated in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) indices. FFBPNN shows better results in terms of RMSE (0.49%) and MAPE (1.33%) using two and three hidden layers, respectively. The study indicates the significance of optimal selection of input parameters and effects of changing several hidden layers, activation functions, and learning rates to achieve the best performance of FFBPNN and RNN.
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Zidikheri, Meelis J., Christopher Lucas, and Rodney J. Potts. "Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms." Journal of Geophysical Research: Atmospheres 122, no. 15 (August 10, 2017): 8187–206. http://dx.doi.org/10.1002/2017jd026679.
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