An iterative algorithm has been successfully used to process data from the three-flat test. On the basis of the iterative algorithm proposed by Vannoni, which is much faster and more effective than the Zernike polynom...
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An iterative algorithm has been successfully used to process data from the three-flat test. On the basis of the iterative algorithm proposed by Vannoni, which is much faster and more effective than the Zernike polynomial fitting method, an improved algorithm is presented. By optimizing the iterative steps and removing the scaling factors, the surface shape can be easily computed in a few iterations. The validity of the method is proved by computer simulation, and the interpolation error and principle error are analyzed. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
This work considers the multi-parameter identification problem in initial-boundary values of variable coefficient coupled partial differential equations. Based on ultrasonic echo time measurements, we identify the sur...
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This work considers the multi-parameter identification problem in initial-boundary values of variable coefficient coupled partial differential equations. Based on ultrasonic echo time measurements, we identify the surface heat flux and thickness simultaneously, and then achieve the reconstruction of the internal transient temperature field. Through rigorous theoretical analysis, we show the uniqueness recovery result for the multiparameter identification problem. By reformulating the inverse problem as an optimization problem, we propose an efficient alternating iteration algorithm in solving the related optimization problem and provide a rigorous convergence analysis of the algorithm. Finally, the reliability and feasibility of this algorithm are verified by some numerical examples.(c) 2022 Elsevier Inc. All rights reserved.
One of the problems faced by electric power distribution system operators is to know with certainty the actual location of all their assets in order to manage properly the grid and provide the best service to their cu...
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ISBN:
(纸本)9781665432993
One of the problems faced by electric power distribution system operators is to know with certainty the actual location of all their assets in order to manage properly the grid and provide the best service to their customers. In this work, we present a procedure for the identification of low voltage feeders or distribution lines in smart grids that is based on the mathematical formulation of the problem as an optimization model. In particular, we define the model with 0-1 variables (as many as meters to be identified in the different feeders) and with as many restrictions as the number of points in time that are considered. Given the large size of the problem in practice, the use of conventional optimization software becomes unfeasible. Based on this approach, and making use of the linear relaxation of the problem, some analytics over the coefficients (i.e., meter loads) and the special structure of the problem itself, we have developed an iterative procedure that allows us to recover the entire solution of the initial model in an efficient way. We have carried out a computational experience on a set of anonymized real data, obtaining results that support the efficiency of the proposed procedure.
In this paper, we investigate the split equality common fixed-point problem of firmly quasi-nonexpansive operators in Hilbert spaces. We introduce new iterative algorithms with a way of selecting the step-sizes such t...
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In this paper, we investigate the split equality common fixed-point problem of firmly quasi-nonexpansive operators in Hilbert spaces. We introduce new iterative algorithms with a way of selecting the step-sizes such that its implementation does not need any prior information about the operator norms. The new methods are extended from the method for solving the split common fixed-point problem. The range of the new step-sizes even can be enlarged two times. Under suitable conditions, we establish a weak convergence theorem of the proposed algorithm and a strong convergence theorem of its variant by the viscosity approximation method. Numerical results are reported to show the effectiveness of the proposed algorithm.
Thermal management and condition monitoring of power converters demand the accurate estimation of the junction temperature of insulated-gate bipolar transistor (IGBT). With existing iteration methods, the junction tem...
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ISBN:
(纸本)9781728163444
Thermal management and condition monitoring of power converters demand the accurate estimation of the junction temperature of insulated-gate bipolar transistor (IGBT). With existing iteration methods, the junction temperature is fed forward to calculate the power loss and derive the junction temperature of the next cycle, causing the problem of accumulated calculation error. This letter proposes a novel iterative algorithm to improve the calculation accuracy. Unlike the conventional solutions, utilizing the junction temperature of the previous moment to calculate the power losses, the proposed iteration method uses junction temperature and power losses of the same moment to tackle the accumulated error problem. Since the iterative equation has unique solution, the convergence value is the actual junction temperature and power losses. The initial power losses has no influence on the result, so it is not necessary to obtain the accurate power loss for the estimation of junction temperature. Power cycling experiment is performed to verify the advantages of the novel iterative algorithm. The novel method solves the accumulated error compared to the traditional method. The accuracy of the novel method is also verified by the result of 100mA current test during power cycling.
The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes...
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ISBN:
(纸本)9781728181042
The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical formulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameter...
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ISBN:
(纸本)9781728176055
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameters in the algorithms. However, these are forward methods and are indeed neither iterative nor convergent. Here, we present a novel DNN-based convergent iterative algorithm that accelerates conventional optimization algorithms. We train a DNN to yield parameters in scaled gradient projection method. So far, these parameters have been chosen heuristically, but have shown to be crucial for good empirical performance. In simulation results, the proposed method significantly improves the empirical convergence rate over conventional optimization methods for various large-scale inverse problems in image processing.
Image reconstruction can be formulated by the Fredholm equation of the first kind The method of projections onto convex sets (POCS) is an iterative algorithm for solving the equation. Multiplicative algebraic reconstr...
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ISBN:
(数字)9781510647206
ISBN:
(纸本)9781510647206;9781510647190
Image reconstruction can be formulated by the Fredholm equation of the first kind The method of projections onto convex sets (POCS) is an iterative algorithm for solving the equation. Multiplicative algebraic reconstruction techniques (MART) is one of POCS for solving a system of simultaneous equation. By discretizing the image reconstruction problem, we applied the MART to the problems and evaluate the image quality in computer simulations. We also investigated the normalized mean square error of reconstructed images with respect to the variations of the number of detectors and views, the relaxation parameters.
In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario. Instead of sending data directly to the server, th...
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ISBN:
(纸本)9781728176055
In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario. Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the optimal privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping.
This paper explores the source localization problem using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when sensor location information suffers from random uncertaintie...
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ISBN:
(纸本)9789881563804
This paper explores the source localization problem using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when sensor location information suffers from random uncertainties. The analysis of the Cramer-Rao lower bound (CRLB) illustrates that the sensor uncertainties can considerably deteriorate the localization accuracy, so we fully incorporate the sensor location information uncertainties into the problem formulation. An iterative source localization algorithm is proposed to recursively solve the formulated problem, where the sensor information uncertainties is incorporated in each iteration to improve the localization performance.
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