Original Iterative Algorithm Research Articles

A novel stochastic home energy management system considering negawatt trading

The decarbonization and sustainability of smart cities is calling for new schemes and businesses capable of increasing the efficiency of energy systems. In this context, energy communities are emerged as a valuable framework to optimally integrate prosumers into electricity networks and take the maximum advantage of domestic resources such as controllable appliances or rooftop photovoltaic arrays. Within communities, prosumers can exchange energy through peer-to-peer platforms, but they can also exchange negawatts. This trading mechanism focuses on acquiring or selling consumption rights rather than actual energy, thus further incrementing the flexibility of the system. In this regard, this paper develops a novel stochastic-based home energy management model considering negawatt trading. An original iterative algorithm is presented by which the developed negawatt-focus problem is converted into a more useful and practical negawatt-aware mechanism, by which the scheduling plan is ready to exploit trading opportunities without notably incrementing the electricity bill, which is considered the primary objective of home energy management applications. Several results are presented to validate the new tool, illustrate the concept of negawatt trading and how it affects to power allocation in prosumer installations. Results show that negawatt trading supposes a monetary opportunity for prosumers if it is optimally exploited, enabling a reduction of the electricity bill by 45 % compared with the conventional case. Moreover, the role of batteries in negawatt trading is highlighted, providing energy backup when importable power must be reduced. Actually, total negawatts exported increase by 34 % when storage capacity increases from 3 to 5 kWh.

Numerical analysis for iterative filtering with new efficient implementations based on FFT

The development of methods able to extract hidden features from non-stationary and non-linear signals in a fast and reliable way is of high importance in many research fields. In this work we tackle the problem of further analyzing the convergence of the Iterative Filtering method both in a continuous and a discrete setting in order to provide a comprehensive analysis of its behavior. Based on these results we provide a new efficient implementation of Iterative Filtering algorithm, called Fast Iterative Filtering, which reduces the original iterative algorithm computational complexity by utilizing, in a nontrivial way, Fast Fourier Transform in the computations.

Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that combines DL with conjugate gradient algorithm is proposed for CE precoding. Specifically, the original iterative algorithm is unfolded and parameterized by trainable variables. With the proposed architecture, the variables can be learned efficiently from training data through unsupervised learning approach. Thus, the proposed network learns to obtain the search step size and adjust the search direction. Simulation results demonstrate the superiority of the proposed network in terms of multiuser interference suppression capability and computational overhead.

Weighted iterative algorithm for beam alignment in scanning beam interference lithography.

To obtain low phase errors and good interference fringe contrast, an automated beam alignment system is used in scanning beam interference lithography. In the original iterative algorithm, if the initial beam deviation is large or the optical parameters are inappropriate, the beam angle (or position) overshoot may exceed the detector's range. To solve this problem, a weighted iterative algorithm is proposed in which the beam angle and position overshoots can be suppressed by adjusting the weighting coefficients. The original iterative algorithm is introduced. The weighted iterative algorithm is then presented and its convergence is analyzed. Simulation and experimental results show that the proposed weighted iterative algorithm can reduce the beam angle and position overshoots at the expense of convergence speed, avoiding the alignment failure caused by exceeding the detector's range. Besides, the original and weighted iterative algorithms can be combined to optimize the iteration.

Convergence control of single loop approach for reliability-based design optimization

For solution of reliability-based design optimization (RBDO) problems, single loop approach (SLA) shows high efficiency. Thus SLA is extensively used in RBDO. However, the iteration solution procedure by SLA is often oscillatory and non-convergent for RBDO with nonlinear performance function. This prevents the application of SLA to engineering design problems. In this paper, the chaotic single loop approach (CLSA) is proposed to achieve the convergence control of original iterative algorithm in SLA. The modification involves automated selection of the chaos control factor by solving a novel one-dimensional optimization model. Additionally, a new oscillation-checking method is constructed to detect the oscillation of iterative process of design variables. The computational capability of CLSA is demonstrated through five benchmark examples and one stiffened shell application. The comparison of numerical results indicates that CSLA is more efficient than the double loop approach and the decoupled approach. CSLA also solves the RBDO problems with highly nonlinear performance function and non-normal random variables stably.

A new procedure to calculate the constitutive equation of nuclear fuel cladding from ring compression tests

The geometry of the nuclear fuel cladding (thin-walled tube) makes it difficult to obtain its hoop mechanical properties.A new procedure is devised to obtain the constitutive equation of nuclear fuel cladding along the hoop direction from ring compression tests. The method combines experimental results, finite element simulations and an original iterative algorithm to adjust the experimental data. The process is successfully applied to unirradiated pre-hydrided ZIRLO nuclear fuel cladding, tested at three temperatures (20, 135 and 300 °C) with hydrogen contents (0, 150, 250, 500, 1200 and 2000 ppm). The stress-strain curves were obtained for each configuration with an excellent agreement between the numerical results (based on back calculation of the obtained constitutive equation) and the experimental data.The stress-strain curves calculated show that the mechanical properties do not depend strongly on hydrogen concentration, only a small ductility decrease with the hydrogen concentration was observed. The cladding shows a light strain hardening which is similar for the samples tested at 20 and 135 °C and does not depend on the hydrogen concentration. However, at 300 °C, the samples with the highest hydrogen concentrations (1200 and 2000 ppm) present a behavior that is close to an elastic-perfectly plastic material.

Array Thinning of Coupled Antennas Based on the Orthogonal Matching Pursuit Method and a Spherical-Wave Expansion for Far-Field Synthesis

This work is focused on the array thinning problem in shaped beam far-field synthesis. The orthogonal matching pursuit algorithm, which allows finding sparse solutions in linear systems, is combined with a fast full-wave analysis method for antenna arrays based on a spherical wave expansion, thereby taking mutual coupling between real antennas into account in the thinning process. To this aim, the original iterative algorithm is modified so that the current residual is obtained for the selected real coupled antennas in each step of the algorithm. In this way, the remaining nonselected elements are effectively removed instead of turned off. Results for arrays made up of microstrip patch antennas and dielectric resonator antennas arranged in triangular and rectangular lattices are presented.

Superiorization: An optimization heuristic for medical physics

To describe and mathematically validate the superiorization methodology, which is a recently developed heuristic approach to optimization, and to discuss its applicability to medical physics problem formulations that specify the desired solution (of physically given or otherwise obtained constraints) by an optimization criterion. The superiorization methodology is presented as a heuristic solver for a large class of constrained optimization problems. The constraints come from the desire to produce a solution that is constraints-compatible, in the sense of meeting requirements provided by physically or otherwise obtained constraints. The underlying idea is that many iterative algorithms for finding such a solution are perturbation resilient in the sense that, even if certain kinds of changes are made at the end of each iterative step, the algorithm still produces a constraints-compatible solution. This property is exploited by using permitted changes to steer the algorithm to a solution that is not only constraints-compatible, but is also desirable according to a specified optimization criterion. The approach is very general, it is applicable to many iterative procedures and optimization criteria used in medical physics. The main practical contribution is a procedure for automatically producing from any given iterative algorithm its superiorized version, which will supply solutions that are superior according to a given optimization criterion. It is shown that if the original iterative algorithm satisfies certain mathematical conditions, then the output of its superiorized version is guaranteed to be as constraints-compatible as the output of the original algorithm, but it is superior to the latter according to the optimization criterion. This intuitive description is made precise in the paper and the stated claims are rigorously proved. Superiorization is illustrated on simulated computerized tomography data of a head cross section and, in spite of its generality, superiorization is shown to be competitive to an optimization algorithm that is specifically designed to minimize total variation. The range of applicability of superiorization to constrained optimization problems is very large. Its major utility is in the automatic nature of producing a superiorization algorithm from an algorithm aimed at only constraints-compatibility; while nonheuristic (exact) approaches need to be redesigned for a new optimization criterion. Thus superiorization provides a quick route to algorithms for the practical solution of constrained optimization problems.

On quasi-minimal residual approach of iterative algorithms for solving nonsymmetric linear systems

Many iterative algorithms, for example the BCG algorithm, the CGS algorithm etc., for solving nonsymmetric linear systems have the erratic convergence behavior. Recently, Freund[6] proposed a BCG-like approach, the Quasi-minimal residual (QMR) method, that remedies this problem for BCG and produces smooth convergence curves.The QMR approach is also applied to CGS and BI-CGSTAB to obtain smoothly convergent variants of these algorithms [7,5,12].In this paper, we propose a simple but universal QMR approach which can be applied with unified manner to any iterative algorithm to construct smoothly convergent variants, provided this algorithm includes two-term recurrence for the approximate solutions. The resulting QMR algorithms can be implemented very easily by changing only a few lines in the original iterative algorithm. We compare the performance of our QMR approach with that of other QMR methods presented in [5],[7] and [12]. Finally, numerical examples are given.